laura wood diversity and distribution of amphibians in luxembourg phd thesis

Diversity and Distribution

of Amphibians in Luxembourg

Laura R. Wood

Thesis submitted for the degree of

Doctor of Philosophy

February 2011

The Durrell Institute of Conservation and Ecology

University of Kent, Canterbury, CT2 7NR, England

i

Research and printing funded by:

Project collaborators:

ii

Abstract

Amphibians are highly threatened on a global scale, with more species considered

to be in decline than in any other vertebrate class. There are still gaps in our

knowledge of amphibian ecology, distribution and status, even in developed

countries where investment in research and scientific output are high. In

Luxembourg there is an extensive protected area (PA) network, which has been

created to meet obligations under European and national law. Luxembourg has 12

amphibian species, five of which are present in more than a quarter of surveyed 1

km grid squares and seven that are only present in < 15%, including two in < 1%.

The national PA system was found to cater well for the common species – records

of their presence both inside and outside these areas indicated that they were

accommodated by, but not restricted to, PAs. Record centre data from pre-1985

and 1996-2005 indicated that common species‟ range sizes had increased or

remained stable, while four of the less common and rare species had decreased,

although Alytes obstetricans had more than doubled its range size. Since 2005 the

three very rare species (Hyla arborea, Bufo calamita and Bombina variegata)

have occupied only four sites between them, all of which are protected. The most

important component of local landscape variables in predicting species records

was „forest and elevation‟. Amphibian species richness in breeding ponds was

found to show potential as an indicator of aquatic plant species richness, but no

relationship with invertebrate species richness was found. Occupancy modelling

was used to compare the detectability of different species and to evaluate survey

methods. With sexes and life stages combined, netting was not found to be

reliable for any species; aural survey was good for anurans with extended

breeding periods; trapping was the most successful method for newts and visual

searches achieved moderate to high detection rates for all species. Occupancy

modelling has the potential to transform the design of survey protocols for all taxa

with imperfect detection rates. The greater investment required to collect higher

quality data for modelling was considered worthwhile, due to the time it could

save through improved survey protocols. Amphibians‟ cryptic and condition-

dependent behaviour often makes them difficult to survey for, thus necessitating

careful survey design. Models developed here are an important step towards

advancing ecological survey protocol design and maximising returns from survey

efforts.

Keywords: amphibian; landscape covariates; Luxembourg; macroinvertebrates;

occupancy modelling; plants; protected areas; range size.

iii

Acknowledgements

I have had so much help and support from so many colleagues, friends and family

in the last few years, which I can only begin to address here. Firstly, my

collaborators and funders in Luxembourg, most especially Edmée Engel (Musée

national d‟histoire naturelle) and Dr Laurent Schley (Administration de la Nature

et des Forêts), who conceived the project, acted as in-country guides and

fieldwork „fixers‟ and provided generous financial support, and of course the FNR

(Fonds National de la Recherche Luxembourg) for funding my studentship and

attendance at conferences. I am also very grateful to the Amphibian Conservation

Research Trust (ACRT) for a further grant to fund fieldwork equipment.

At the Durrell Institute of Conservation and Ecology (DICE) I‟ve had extensive

and very patient support from my supervisor Prof. Richard Griffiths and also Dr

David Sewell and Dr Bob Smith with the applied modelling and mapping. I am

also very grateful to Guru Guillera-Arroita, José Lahoz-Monfort and Rachel

McCrea at the National Centre for Statistical Ecology for their specialist help with

occupancy modelling, and for not making me feel too stupid! Other good friends

and colleagues at DICE, most notably Nina Cornish, Leida dos Santos, Steve

Green, Brett Lewis, Susy Paisley and Claire Raisin, have been a tremendous

source of encouragement, entertainment, blanket dances, pizza and gin. Mrs

Roche and the menagerie at Thanington Court (including some of the afore-

mentioned) provided the most wonderful home / retreat in Canterbury and a

plentiful supply of funny animal and river antics.

During fieldwork I was very lucky to have some fantastic assistants, namely Liza

Glesener, Jan Herr, Véronique Ludwig, Masse Sylla, Heléne Smuk-Matringe and

Lucia Cabete Rodrigues, who made the fieldwork not only possible, but

enjoyable, and made me feel at home in Luxembourg.

Last but not least, I would like to say a special thank you to Grannie Banks who

instilled in me a love of natural history and my whole family who‟ve shown an

un-wavering belief in me and my PhD quest. Especially my parents Phil and

Jennie, sisters Amy and Sally, and „little‟ cousin Alicia whose bespoke newt traps

and company and help with fieldwork in Luxembourg were invaluable.

iv

Contents

Abstract ............................................................................................................... ii

Acknowledgements ............................................................................................. iii

Contents.............................................................................................................. iv

Abbreviations and Acronyms ............................................................................ viii

List of Tables ....................................................................................................... x

List of Figures .................................................................................................... xii

1. General introduction ........................................................................................ 1

1.1 Amphibian global status ............................................................................. 1

1.2 Luxembourg ............................................................................................... 3

1.2.1 Location and geography....................................................................... 3

1.3 Conservation legislation ............................................................................. 4

1.3.1 Influence of political boundaries .......................................................... 6

1.3.2 IUCN .................................................................................................. 7

1.4 Pond ecology.............................................................................................. 8

1.4.1 Amphibian surveying .......................................................................... 8

1.5 Project objectives ....................................................................................... 9

2. General methodology ..................................................................................... 11

2.1 Historical data - MNHN ........................................................................... 11

2.1.1 Map data ........................................................................................... 12

2.2 Study pond selection ................................................................................ 13

2.3 Amphibian surveying ............................................................................... 14

2.3.1 Visual encounter survey (VES) .......................................................... 15

2.3.2 Funnel trapping ................................................................................. 15

2.3.3 Dip netting ........................................................................................ 15

2.3.4 Aural ................................................................................................. 16

2.4 Data analysis ............................................................................................ 16

3. Landscape features as predictors of amphibian presence................................. 17

3.1 Introduction .............................................................................................. 17

3.1.1 Recording centres .............................................................................. 17

v

3.1.2 Landscape and amphibians ................................................................ 18

3.2 Methods ................................................................................................... 19

3.3 Results ..................................................................................................... 20

3.3.1 Historical data descriptives ................................................................ 20

3.3.2 Landscape predictors of amphibian species‟ occurrence ..................... 23

3.3.3 Landscape predictors of amphibian diversity ..................................... 27

3.4 Discussion ................................................................................................ 28

4. Amphibian range size change and the effectiveness of Luxembourg‟s protected

areas .................................................................................................................. 32

4.1 Introduction .............................................................................................. 32

4.1.1 Protected areas................................................................................... 32

4.1.2 Range size ......................................................................................... 34

4.2 Methods ................................................................................................... 35

4.2.1 Historical data ................................................................................... 35

4.2.2 Protected areas................................................................................... 35

4.2.3 Protected area species richness analyses ............................................ 37

4.2.4 Range size ......................................................................................... 37

4.3 Results ..................................................................................................... 38

4.3.1 Amphibians and protected areas ........................................................ 38

4.3.2 Range size change ............................................................................. 40

4.4 Discussion ................................................................................................ 43

4.4.1 Protected areas and amphibian species richness ................................. 43

4.4.2 Range size changes ............................................................................ 46

5. Amphibians as indicators of plant species richness ......................................... 47

5.1 Introduction .............................................................................................. 47

5.2 Methods ................................................................................................... 50

5.2.1 Study area.......................................................................................... 50

5.2.2 Plant survey method .......................................................................... 50

5.2.3 Amphibian data ................................................................................. 51

5.2.4 Analysis ............................................................................................ 51

5.3 Results ..................................................................................................... 52

5.3.1 Plant community descriptives and pond size effect ............................ 52

5.3.2 Amphibians as indicators of plant richness and vegetation cover ....... 54

5.3.3 Wetland plant richness and amphibian occupancy.............................. 55

vi

5.4 Discussion ................................................................................................ 57

6. Amphibian species richness as an indicator of macroinvertebrate family

richness .............................................................................................................. 61

6.1 Introduction .............................................................................................. 61

6.2 Methodology ............................................................................................ 63

6.2.1 Study area.......................................................................................... 63

6.2.2 Macroinvertebrate survey method ...................................................... 64

6.2.3 Amphibian data ................................................................................. 65

6.2.4 Analysis ............................................................................................ 65

6.3 Results ..................................................................................................... 67

6.3.1 Macroinvertebrate descriptives .......................................................... 67

6.3.2 Pond size and macroinvertebrate numbers and family richness .......... 69

6.3.3 Macroinvertebrates and wetland plant richness and extent ................. 69

6.3.4 Amphibians as indicators of macroinvertebrate diversity ................... 70

6.3.5 Macroinvertebrate richness and amphibian species occupancy ........... 71

6.3.6 Specific relationships between macroinvertebrate and amphibian

species ....................................................................................................... 73

6.4 Discussion ................................................................................................ 74

7. Absent or not detected? Development of an occupancy modelling approach to

amphibian survey design .................................................................................... 77

7.1 Introduction .............................................................................................. 77

7.1.1 Comparing survey methodologies ...................................................... 77

7.1.2 Detectability ...................................................................................... 78

7.1.3 Occupancy modelling ........................................................................ 79

7.1.4 Covariates ......................................................................................... 80

7.2 Methods ................................................................................................... 81

7.2.1 Study area and amphibian data .......................................................... 81

7.2.2 Covariate measurements .................................................................... 81

7.2.3 Program PRESENCE analyses .......................................................... 84

7.2.4 Comparing survey methods ............................................................... 85

7.2.5 Predictors of occupancy and detection ............................................... 86

7.2.6 Model selection ................................................................................. 86

7.2.7 Number of survey visits required ....................................................... 88

7.3 Results ..................................................................................................... 89

vii

7.3.1 Detectability by different survey methods .......................................... 89

7.3.2 Predictors of occupancy and detection ............................................... 91

R. temporaria ......................................................................................... 92

Pelophylax spp. ...................................................................................... 96

A. obstetricans ........................................................................................ 98

I. alpestris .............................................................................................. 99

T. cristatus ........................................................................................... 101

L. helveticus ......................................................................................... 105

L. vulgaris ............................................................................................ 109

7.4 Discussion .............................................................................................. 113

7.4.1 Covariates of detection .................................................................... 114

7.4.2 Covariates of occupancy .................................................................. 114

7.4.3 Occupancy modelling ...................................................................... 115

8. General discussion ....................................................................................... 117

8.1.1 Future data collection ...................................................................... 120

9. References ................................................................................................... 122

10. Appendices ................................................................................................ 147

Appendix 1 – Amphibian nomenclature ....................................................... 148

Appendix 2 – Contributors to the MNHN database....................................... 150

Appendix 3 – Study ponds ........................................................................... 151

Appendix 4 – Species dates .......................................................................... 152

Appendix 5 – Species distribution maps ....................................................... 153

Appendix 6 – Plants species surveyed .......................................................... 155

Appendix 7 – Macroinvertebrate families surveyed ...................................... 159

Appendix 8 – Occupancy modelling covariates ............................................ 161

Appendix 9 – Publications ........................................................................... 163

Wood, L. R., Griffiths, R. A. Groh, K., Engel, E. & Schley, L. (2008)

Interactions between freshwater mussels and newts: a novel form of

parasitism? Amphibia-Reptilia, 29, 457-462. ............................................ 163

Wood, L. R., Griffiths, R. A. & Schley, L. (2009) Amphibian

chytridiomycosis in Luxembourg. Bulletin de la Société des Naturalistes

Luxembourgois, 110, 109-114. ................................................................. 170

viii

Abbreviations and Acronyms

AIC Delta AIC

ACT Administration du Cadastre et de la Topographie

AIC Akaike‟s Information Criterion

AICc Akaike‟s Information Criterion – adjusted for small sample size

Ao Alytes obstetricans (European midwife toad)

BAP Biodiversity Action Plan

Bb Bufo bufo (common toad)

Bc Bufo calamita (natterjack toad)

BMWP Biological Monitoring Working Party

BS Bray-Curtis Dissimilarity Index

Bv Bombina variegata (yellow-bellied toad)

ĉ C-hat, variance inflation factor (in occupancy models)

CBD Convention on Biological Diversity

EC European Commission

EU European Union

GAA Global Amphibian Assessment

Ha Hyla arborea (European tree frog)

Ia Ichthyosaura alpestris (Alpine newt)

IBEM Indice de Biodiversité des Etangs et Mares

IUCN International Union for Conservation of Nature

KARCH Koordinationsstelle für Amphibien- und Reptilienschutz in der

Schweiz (Coordination centre for the protection of amphibians and

reptiles in Switzerland)

km kilometres

km2 kilometres squared

Lh Lissotriton helveticus (palmate newt)

Lv Lissotriton vulgaris (smooth newt)

m metres

m.a.s.l. metres above sea level

m2 metres squared

MNHN(L) Musée national d‟histoire naturelle (Luxembourg)

NARRS National Amphibian and Reptile Recording Scheme

ix

NPS National Pond Survey

p Detectability (in occupancy models)

PA Protected Area

PC Principal Component

PCA Principal Component Analysis

PLOCH Plans d‟eau suisses

PNPN Plan national concernant la protection de la nature

P.spp Pelophylax species (green frogs: P. lessonae and P. kl. esculentus)

PSYM Predictive System for Multimetrics

QAIC Quasi Akaike‟s Information Criterion

QAICc Quasi Akaike‟s Information Criterion (adjusted for small sample

size)

RAVON Reptilien Amfibieën Vissen Onderzoek Nederland (Reptile,

Amphibian and Fish Conservation Netherlands)

RIVPACS River Invertebrate Prediction and Classification System

Rt Rana temporaria (common frog)

SAC Special Area of Conservation

s.e. Standard error

SPSS Statistical Package for the Social Sciences

Ss Salamandra salamandra (fire salamander)

Tc Triturus cristatus (great crested newt)

UNEP United Nations Environment Programme

VES Visual Encounter Survey

WDPA World Database of Protected Areas

ψ Occupancy

x

List of Tables

Table 1.1 Luxembourgish amphibians on the Bern Convention and Habitats

Directive appendices and their IUCN Red List status and global population trend.

............................................................................................................................ 5

Table 2.1 Landscape features drawn in ArcMap and extracted to quantify habitat

in each 1 km grid square. ................................................................................... 12

Table 2.2 Survey methods appropriate to each amphibian species by life stage and

sex. .................................................................................................................... 16

Table 3.1 Multivariate factor loadings of principal components with eigenvalues

> 1. .................................................................................................................... 23

Table 3.2 Final models from stepwise logistic regression for anurans. ............... 24

Table 3.3 Final models from stepwise logistic regression for newts. .................. 26

Table 3.4 Multiple logistic regression chi-square values for landscape features

and amphibian species richness. ......................................................................... 27

Table 4.1 Mann-Whitney U tests comparing amphibian species richness in PAs

and non-PAs. ..................................................................................................... 38

Table 4.2 Index of relative range size change: proportion of grid squares occupied

in the early and later survey periods. .................................................................. 43

Table 5.1 The five most commonly occurring wetland plants across all study

ponds (n = 43). ................................................................................................... 53

Table 5.2 Relationships between individual amphibian and plant species, before

and after sequential Bonferroni correction. ......................................................... 57

Table 6.1 The amphibian species present in ponds surveyed for

macroinvertebrates. ............................................................................................ 63

Table 6.2 The five most numerous macroinvertebrate families across all 11 ponds

surveyed. ........................................................................................................... 67

Table 6.3 The most commonly occurring macroinvertebrate families across all 11

ponds surveyed. ................................................................................................. 68

Table 6.4 Pearson's product-moment correlation results for wetland vegetation

and macroinvertebrates, including all ponds surveyed for invertebrates (n = 11). 70

Table 6.5 One-way ANOVA values comparing the invertebrate predator total

count and the number of families and species in ponds occupied / not occupied by

moderately common amphibian species. ............................................................ 72

Table 6.6 Moderately common macroinvertebrates exhibiting a significant

relationship with amphibian species‟ occupancy (Nponds = 11) before and after

sequential Bonferroni correction. ....................................................................... 74

Table 7.1 Site-specific covariates concerning the occupancy of other species and

the method of normalisation. .............................................................................. 82

Table 7.2 Site-specific habitat covariates and the method of normalisation. ....... 83

Table 7.3 Survey-specific covariates and the method of normalisation............... 84

xi

Table 7.4 Most parsimonious models for R. temporaria surveys in 2007 and 2008

by three survey methods..................................................................................... 93

Table 7.5 Most parsimonious models for Pelophylax spp. surveys in 2007 and

2008 by four survey methods. ............................................................................ 96

Table 7.6 Most parsimonious models for A. obstetricans 'year 1' data using aural

surveys............................................................................................................... 99

Table 7.7 Most parsimonious models for I. alpestris surveys in 2007 and 2008 by

three survey methods. ...................................................................................... 100

Table 7.8 Most parsimonious models for T. cristatus surveys in 2007 and 2008 by

three survey methods. ...................................................................................... 102

Table 7.9 Most parsimonious models for L. helveticus surveys in 2007 and 2008

by three survey methods................................................................................... 106

Table 7.10 Most parsimonious models for L. vulgaris surveys in 2007 and 2008

by three survey methods................................................................................... 110

Table 10.1 Nomenclature of study species (following Speybroeck et al., 2010).

........................................................................................................................ 149

Table 10.2 Study ponds in 25 m and 200 m groupings. .................................... 151

Table 10.3 Survey dates in 2007. ..................................................................... 152

Table 10.4 Survey dates in 2008. ..................................................................... 152

Table 10.5 Survey dates in 2009 – additional ponds used in „year 1‟ data for A.

obstetricans. ..................................................................................................... 152

Table 10.6 All wetland plants surveyed and the percentage of study ponds in

which each plant occurred (names following Stace, 1997). ............................... 155

Table 10.7 Non-wetland plants surveyed and the percentage of study ponds in

which each plant occurred (names following Stace, 1997). ............................... 157

Table 10.8 The macroinvertebrates found and their frequency of occurrence

(names following ZipcodeZoo.com, 2009). ...................................................... 159

Table 10.9 Summary of survey-specific covariate values for each study year... 161

Table 10.10 Summary of site-specific covariates for each study year. .............. 162

Table 10.11 Site-specific variables concerning the presence / non-detection of

other species. ................................................................................................... 162

xii

List of Figures

Figure 1.1 Luxembourg. ...................................................................................... 3

Figure 2.1 Study pond locations in Luxembourg. .............................................. 14

Figure 3.1 The number of amphibian species recorded in each 1 km grid square,

(a) 1955-2005, (b) 1996-2005. ........................................................................... 21

Figure 3.2 Comparison of the mean area or length of landscape features in

squares surveyed for amphibians between 1996 and 2005 and those not surveyed.

.......................................................................................................................... 22

Figure 3.3 The percentage of 1 km grid squares surveyed (n = 654) between 1996

and 2005 occupied by each amphibian species. .................................................. 22

Figure 3.4 Scree plot of the eigenvalues of all components. ............................... 24

Figure 3.5 Mean values of the significant principal components predicting anuran

species‟ occupancy in 1 km grid squares. ........................................................... 25

Figure 3.6 Mean values of the significant principal components predicting newt

species‟ occupancy in 1 km grid squares. ........................................................... 27

Figure 4.1 Map of Luxembourg: grid squares (1 km2) randomly selected and

retained for analysis. .......................................................................................... 36

Figure 4.2 Amphibian species richness in 1 km grid squares (all records 1996 –

2005) and all statutory protected areas in Luxembourg. ...................................... 39

Figure 4.3 The frequency distribution of amphibian species richness in non-PA

grid squares and (a) ≥ 50% PA, (b) ≥ 80% PA, (c) ≥ 90% PA and (d) ≥ 98% PA

grid squares. ....................................................................................................... 40

Figure 4.4 The percentage of 1 km grid squares at each PA coverage level

occupied by each amphibian species (all records 1996 – 2005). ......................... 41

Figure 4.5 Range sizes of pond-breeding amphibians recorded in the early and

later survey periods from 104 grid squares (a) untransformed 1 km squares and (b)

as logit-transformed proportions of the total. ...................................................... 42

Figure 4.6 Species percentage occupancy in grid squares surveyed in both

periods. .............................................................................................................. 42

Figure 5.3Figure 5.1 There was no correlation between the number of amphibian

species detected and the size of the study pond (r = 0.28, n = 34, p > 0.05). ....... 53

Figure 5.2 Diversity of wetland plant species in study ponds and the frequency of

occurrence (including plants not identified to species and plants at ≤ 5% and > 5%

cover). ............................................................................................................... 53

Figure 5.3 The number of wetland plant species (≤ 5% and > 5% cover) plotted

against pond size (logarithmic scales; y = 0.2675x + 0.8013). ............................ 54

Figure 5.4 The mean number (±s.e.) of wetland plant species and all plant species

(wetland and non-wetland) found in ponds according to amphibian species

diversity (plants of ≤ 5% and > 5% cover).......................................................... 54

Figure 5.5 The extent (%) of wetland plant cover (±s.e.) and the number of

amphibian species present. ................................................................................. 55

xiii

Figure 5.6 Mean number of wetland plant species in ponds occupied / not

occupied by moderately common amphibian species, plants of ≤ 5% and > 5%

cover included. A. obstetricans data include the additional ponds surveyed in

2009. .................................................................................................................. 55

Figure 5.7 Occupancy of Potamogetan natans in ponds by A. obstetricans

occupancy. ......................................................................................................... 56

Figure 6.1 The number of ponds that macroinvertebrate families occurred in. ... 68

Figure 6.2 The relationship between pond size and macroinvertebrate family

richness (y = 0.0033x + 3.1533) and the total count of macroinvertebrates (y =

0.1261x + 6.2569) (logarithmic scales; including pond 17). ............................... 69

Figure 6.3 The number of macroinvertebrate families in surveyed ponds by (a)

the number of wetland plant species present and (b) the extent of wetland plant

cover. ................................................................................................................. 70

Figure 6.4 (a) The mean count of macroinvertebrates and (b) the mean number of

macroinvertebrate families found in ponds according to the number of amphibian

species present. .................................................................................................. 71

Figure 6.5 Mean number of macroinvertebrate families in ponds occupied / not

occupied by moderately common amphibian species. ......................................... 72

Figure 6.6 The mean number of predatory invertebrate families with (a) the

number of amphibian species present and (b) amphibian species‟ occupancy. .... 73

Figure 7.1 „Detectability‟ (±s.e.) of anurans by survey method, ψ(.)

p(survey_method, max_temp). ............................................................................ 90

Figure 7.2 'Detectability' (±s.e.) of newts by survey method, ψ(.)

p(survey_method, max_temp). ............................................................................ 91

Figure 7.3 The mean number of adult and larval B. bufo seen or captured during

surveys, by day of year. ..................................................................................... 92

Figure 7.4 The mean number of adult and larval R. temporaria seen or captured

during surveys, by day of year. .......................................................................... 93

Figure 7.5 Detection rates (±s.e.) of R. temporaria by the most parsimonious

models, where water temperature was a covariate of detection. .......................... 94


models, where „clear shore‟ was a predictor of detection rate. ............................ 94

Figure 7.7 Detection rates (±s.e.) of R. temporaria by top models, where „day of

year‟ was a covariate of detection. ..................................................................... 95

Figure 7.8 Detection rates (±s.e.) of R. temporaria by trapping in 2008, where

time taken was a covariate of detection. ............................................................. 95

Figure 7.9 The mean number of adult and larval Pelophylax spp. seen or captured

during surveys by day of year. ........................................................................... 96

Figure 7.10 Detection rates (±s.e.) of Pelophylax spp. by the most parsimonious

models, where „day of year‟ was a covariate of detection rate. ........................... 97


models, where water temperature was a covariate of detection. .......................... 98

xiv

Figure 7.12 The mean number of adult and larval I. alpestris seen or captured

during surveys by day of year. ......................................................................... 100

Figure 7.13 The detection rate (±s.e.) of VES for I. alpestris in 2008, where

„pm_timetaken‟ was a covariate of detection. ................................................... 101

Figure 7.14 Detection rates (±s.e.) of I. alpestris by top models where the day of

year was a covariate of the detection rate. ........................................................ 101

Figure 7.15 The detection rate (±s.e.) of VES for T. cristatus in 2007, where the

percentage of clear shore was a covariate of detection. ..................................... 103

Figure 7.16 The detection rate (±s.e.) of T. cristatus in the top 2007 trapping

model, where „max_temp‟ was a covariate of detection. ................................... 103

Figure 7.17 Naïve and model-estimated occupancy (ψ) rates (±s.e.) of T. cristatus

by survey method and year. .............................................................................. 105

Figure 7.18 Detection rates (±s.e.) of L. helveticus by top models where water

temperature was a covariate of detection rate. .................................................. 107

Figure 7.19 Detection rates (±s.e.) of L. helveticus by top models, where „time

taken‟ was a covariate of detection. .................................................................. 108

Figure 7.20 Naïve and model-estimated occupancy (ψ) rates (±s.e.) of L.

helveticus by survey method and year. ............................................................. 109

Figure 7.21 Detection rate (±s.e.) of L. vulgaris by the top trapping model in

2008, where „pm_timetaken‟ was a covariate of detectability. .......................... 110

Figure 7.22 Detection rates (±s.e.) of L. vulgaris by top models, where „day of

year‟ was a covariate of detection. ................................................................... 111

Figure 7.23 Occupancy of 2007 study ponds by T. cristatus and L. vulgaris. ... 112

Figure 7.24 Naïve and model-estimated occupancy (ψ) rates (±s.e.) of L. vulgaris

by survey method and year. .............................................................................. 112

Figure 10.1 Distributions of amphibian species in Luxembourg in 1 km squares,

1996-2005, data from the MNHN database. ..................................................... 153

1. General introduction

1


General introduction

1.1 Amphibian global status

Amphibian populations globally have experienced alarming declines (Alford and

Richards, 1999). It has been estimated that up to a third of all known amphibian

species are facing decline or even extinction. They are more threatened than either

birds or mammals (Stuart et al., 2004). Since declines were first detected in the early

1980s and their extent and severity was collectively realised at the First World

Congress of Herpetology in 1989 (Barinaga, 1990; Skelly et al., 2003) the possible

causes have been a hot topic of research (Collins and Halliday, 2005). However the

threats facing amphibians are often complex (Daszak et al., 1999; Corser, 2001;

Kiesecker et al., 2001) and they are difficult to differentiate because they interact

with each other (Blaustein et al., 2003; Collins and Storfer, 2003).

Many of the challenges faced by amphibians are directly or indirectly caused by

humans and have occurred too rapidly for species to adapt. The extinction rate across

all living organisms is 100 to 1000 times greater now than before global colonisation

by humans (Pimm et al., 1995), but it soared in the 20th century largely due to habitat

destruction (Pimm et al., 2006). Amphibians often have specific habitat requirements

and, compared to other vertebrates, cannot – or do not – disperse over great distances

(Duellman and Trueb, 1986), either due to physical limitation or inflexible

behavioural patterns (Stebbins and Cohen, 1995). The key threats directly inflicted

by human activities include habitat loss and degradation, for example by clear-felling

of mature forests, draining of wetlands and urban expansion (e.g. Semlitsch, 2000;

Wood et al., 2003; Hamer and McDonnell, 2008), environmental pollution (e.g.

Beebee et al., 1990; Rouse et al., 1999) and over-harvesting for food (e.g. Warkentin

et al., 2009). Equally, indirect anthropogenic impacts include predation by

introduced species (e.g. Kats and Ferrer, 2003), disease (e.g. Carey et al., 1999; Lips

et al., 2006), increased ultra-violet radiation (e.g. Blaustein et al., 2003) and climate

change (e.g. Pounds et al., 1999). The threats interact in a variety of ways (Pounds,

2001; Green, 2003): for example, low pH might make amphibians more susceptible

to UV-B radiation (Long et al., 1995), while UV-B radiation increases mortality

from disease (Kiesecker and Blaustein, 1995; Blaustein et al., 2003). Equally,


2

amphibian harvesting and trade not only deplete the source population, but

potentially lead to non-native introductions and spread of disease (Picco and Collins,

2008).

Heightened awareness of amphibian declines has stimulated greater research activity

in all areas of amphibian biology, improving understanding of population and

behavioural ecology, examining direct and indirect threats and developing

management strategies to tackle the known underlying causes. Species richness and

threatened species are not evenly distributed around the world – areas such as

Central and South America have the highest species richness and the highest number

of endangered species (Myers et al., 2000). However, building a more complete

knowledge of amphibian ecology in all types of habitat and developing efficient

survey and analysis methodologies are important to advance understanding. Detailed

field surveys and long-term datasets are central to achieving this (Alford and

Richards, 1999; Houlahan et al., 2000).

While there is greater species diversity in the tropics, there are more ecologists in

temperate regions (Collen et al., 2008), especially North America and Europe. This

expertise bias has lead to a huge skew towards developed countries in conservation

research output (Memmott et al., 2010; Milner-Gulland et al., 2010) and in practical

conservation efforts such as captive breeding programmes (Griffiths and Pavajeau,

2008). Despite these biases, occasionally there are still surprising findings in well-

studied regions, such as the discovery in 1980 of live Mallorcan midwife toads

(Alytes muletensis), which were previously known only from the fossil record (Buley

and Garcia, 1997). Even in developed countries, there are still knowledge gaps in

ecosystem ecology and a lack of data on some taxa, which impede conservation.

Moreover, the relatively simple species assemblages in Europe represent an

opportunity to develop and improve field methods and analytical approaches to the

study and monitoring of herpetofauna.


3

1.2 Luxembourg

1.2.1 Location and geography

The present study was conducted in Luxembourg, a small landlocked country

bordered by Belgium, France and Germany (Figure 1.1) in the middle of the

temperate region of Europe (Martínez Rica, 1997). Despite its small size (2586 km2)

Luxembourg has very diverse geology and landscapes (Wolff, 2009), ranging from

the foothills of the Ardennes in the north to lowland agriculture and former wetlands

in the south. One of the major costs of the country‟s economic success has been the

loss of biodiversity during the progression from a largely rural economy to a major

European financial centre, via steel production (Chamber of Commerce of the Grand

Duchy of Luxembourg, 2009). The booming economy precipitated a rapid expansion

of housing and business developments and also a vastly expanded transport network

(Wolff, 2009), particularly in the centre and south. Although Luxembourg is in the

top ten highest human population densities in Europe (Gaston et al., 2008), it

maintains a high percentage of forest cover (37.0%, mostly in the north) and a good

protected area coverage (20.2%). The forested areas generally have a better

conservation status than farmed and aquatic ecosystems (Wolff, 2009).

Figure 1.1 Luxembourg.

According to local assessments approximately 55% of Luxembourgish mammals,

40% of birds, 30% of reptiles and 70% of amphibians are threatened (Wolff, 2009),

although biodiversity and nature conservation have recently attracted greater

France

Germany

Belgium

Luxembourg

N 300 km


4

political attention and consequently better funding and better informed actions. The

key threats to amphibians and other wildlife in Luxembourg are considered to be

agricultural chemicals, wetland drainage and hedgerow removal (Wolff, 2009).

There are 12 native species of amphibian extant in Luxembourg, for which the

nomenclature is detailed in Appendix 1, and possibly one additional species (Rana

dalmatina) that has not yet been confirmed as present. Eleven of the known species

usually breed in ponds (Rana temporaria, Pelophylax spp., Hyla arborea, Bufo bufo,

Alytes obstetricans, Bufo calamita, Bombina variegata, Ichthyosaura alpestris,

Triturus cristatus, Lissotriton helveticus and L. vulgaris), while Salamandra

salamandra favours streams (Kwet, 2009).

1.3 Conservation legislation

The legal protection and conservation of animals, plants and habitats in Luxembourg

derive from both European and national law. The key international legislation that

dictates the protection afforded to species and habitats in Europe are the Convention

on the Conservation of European Wildlife and Natural Habitats (the 'Bern

Convention'; Council of Europe, 1979) and the Council Directive 92/43/EEC on the

Conservation of natural habitats and of wild fauna and flora (the 'Habitats

Directive'; Council of Europe, 1992). Adopted in 1992, the Habitats Directive is the

European Union‟s (EU) practical interpretation of its conservation obligations under

the Bern Convention (Joint Nature Conservation Committee, 2010), both include

appendices listing the species and habitats in need of protection, which also dictate

the level of protection that must be provided. EU member states are required to

implement a number of practices, including to:

Maintain or restore „favourable conservation status‟ for protected habitats and

species.

Help to build a protected area (PA) network across Europe using statutory site

designations.

Implement suitable management and planning for existing and proposed PAs.

Monitor species and habitats.

Enforce „strict protection‟ of the most highly threatened species, which are

listed in annex IV of the Habitats Directive (Table 1.1).


5

Under European legislation all native amphibian species are protected from

exploitation that could endanger whole populations (Council of Europe, 1979). Six

species occurring in Luxembourg benefit from additional „strict protection‟ under

Annex IV of the Habitats Directive, and T. cristatus and Bombina variegata are both

further protected under Annex II (Table 1.1), which stipulates that the sites where

they occur be designated as Special Areas of Conservation (SACs). National

legislation affords a high level of protection to all amphibian species in Luxembourg

under a 2004 law „Loi du 19 janvier 2004 concernant la protection de la nature et

des ressources naturelles‟ (Grand-Duché de Luxembourg, 2004) and a more recent

amendment where species not previously covered were added „Règlement grand-

ducal du 9 janvier 2009 concernant la protection intégrale et partielle de certaines

espèces animales de la faune sauvage‟ (Conseil d‟Etat [Luxembourg], 2009).

Table 1.1 Luxembourgish amphibians on the Bern Convention and Habitats

Directive appendices and their IUCN Red List status and global population trend.

Species Bern

Convention appendices

Habitats Directive annexes

Red List

status

Global population

trend

R. temporaria III V LC stable

Pelophylax spp.: P. kl. esculentus III V LC decreasing

P. lessonae III IV LC decreasing

H. arborea II, III IV LC decreasing

Bufo bufo III LC stable

A. obstetricans II, III IV LC decreasing

Bombina variegata II, III II, IV LC decreasing

Bufo calamita II, III IV LC decreasing

T. cristatus II, III II, IV LC decreasing

I. alpestris III LC decreasing

L. vulgaris III LC stable

L. helveticus III LC stable

S. salamandra III LC decreasing

Bern Convention (Council of Europe, 1979): Appendix II: no deliberate capture, keeping,

killing, damage to / destruction of habitat, disturbance, destruction or taking of eggs, or

possession or trade; Appendix III: no exploitation endangering the population and trade must be regulated; Species and Habitats Directive (Council of Europe, 1992): Annex II: species

whose conservation requires SAC designations; Annex IV: species of community interest,

requiring strict protection; Annex V: species of community interest, may need management

measures; LC = „least concern‟ (IUCN et al., 2008, criteria version 3.1).


6

Luxembourg‟s own conservation targets, set out under the „Plan national concernant

la protection de la nature‟ (PNPN; Ministère de l'Environnement, 2007) echo those

of the Habitats Directive and the EU Convention on Biological Diversity (CBD;

which is discussed further in section 4.1.1) – firstly to stop biodiversity loss by 2010

and secondly to preserve and re-establish ecosystem services and processes. Progress

has been made on a number of targets, such as designation of more PAs and habitat

improvement for threatened species (Wolff, 2009). Conservation action plans have

recently been drawn up for many of the priority plant and animal species, including

three amphibians, Bufo calamita, H. arborea and T. cristatus, and work is already

underway to improve management of the sites they occupy, create new potential

breeding sites and a reintroduction programme has started for H. arborea (MNHNL -

Groupe herpétologique, 2009a, b; Proess, 2009). Monitoring and management of

acutely threatened species or habitats in Luxembourg are implemented via national

and regional government agencies or consultants directly contracted by the

government. This ensures that adequate data are available to inform management

decisions at specific locations, for example a population size estimate for the donor

site of H. arborea for reintroduction is essential to avoid depletion. It would be

useful to have such detailed information for all threatened species, including those

remaining at multiple sites but thought to be in decline (e.g. A. obstetricans and T.

cristatus).

1.3.1 Influence of political boundaries

EU member states each apply the European Directives according to their own

constitution (Jongman et al., 2008), which leads to slight variation between

countries. The system of PAs has promoted the conservation of patches of land that

are frequently isolated from other similar habitat (Harrop, 1999) or that become less

relevant as conservation needs shift (Bennett, 2003; Hannah et al., 2007). The next

phase for EU conservation is to increase connectivity between existing reserves

using habitat corridors, which add value to PAs by making it possible for species to

travel across landscapes (Haslett et al., 2008; Haslett et al., 2010; Samways et al.,

2010).


7

The drive for coherence and connectivity of PAs within and between EU countries

was pre-empted by several decades by an agreement between Belgium, the

Netherlands and Luxembourg: The Benelux Agreement concerning Nature

Conservation and Landscape Protection (Benelux Economic Union, 1982). Formal

coordination of conservation efforts between these small countries improves

efficiency by working in the context of a larger area.

1.3.2 IUCN

The IUCN Red List (International Union for Conservation of Nature) is an

assessment of species‟ population trends and risk of extinction, its aim is to

document every known species‟ conservation status. It classifies species into „threat

categories‟ by application of quantitative criteria that can be fitted across a broad

spectrum of taxa, informed by extensive consultation with experts. The system is

designed to produce an impartial evaluation of extinction risk, that is widely

understood and comparable across taxa and geographic areas (IUCN, 2001). The

degree of legal protection afforded to a species is often determined by its Red List

status, which can also prompt research and conservation action, and, as in Europe,

oblige countries to take measures to safeguard species and habitats.

For the Global Amphibian Assessment (GAA) in 2004 amphibians became one of

the first taxonomic groups to be fully assessed under the IUCN Red List criteria,

which was updated in 2006 and 2008, and covers all of the 6260 known species

worldwide (IUCN, 2009). Like 42% of amphibian species in the world (IUCN,

2009), most of the species occurring in Luxembourg currently have a downward

global population trend: 8 out of 12 species were assessed to be decreasing across

their global range, while the other four species are stable (Table 1.1). Despite the

negative population trends, in the global Red List all Luxembourgish amphibian

species are categorised as „Least Concern‟ in the 2008 assessment, meaning that they

are considered to be widespread and/or abundant, although „a taxon may require

conservation action even if it is not listed as threatened‟ (IUCN, 2001). However,

according to national assessments by IUCN criteria, the three amphibian species with

action plans are considered at high risk within Luxembourg: H. arborea is highly


8

threatened, B. calamita is at risk of extinction and T. cristatus is categorised as

threatened (MNHNL - Groupe herpétologique, 2009a, b; Proess, 2009).

1.4 Pond ecology

Assessments and monitoring of European amphibian species tend to be undertaken

during their aquatic phase, when they converge on breeding ponds and behave more

conspicuously, and therefore become relatively easy to survey. At a regional level

ponds are very important for biodiversity – on average ponds hold a greater number

of species than other waterbodies (Davies et al., 2008) and due to their relatively

small catchment areas ponds within the same area can host quite different species

assemblages (Nicolet et al., 2004). Amphibians need both terrestrial and aquatic

habitats to complete their life cycles, making them vulnerable to environmental

changes in two places (Pope et al., 2000; Marsh and Trenham, 2001), but this

sensitivity to change also makes them candidates for use as surrogate measures of

biodiversity (Oertli et al., 2005). When general information on the „value‟ of a site is

required, proven surrogates, or indicators, may be used instead of undertaking

surveys for every taxon, although even very well-fitting surrogates give only a broad

generalisation of what is actually present (Bilton et al., 2006). Amphibians are

regularly touted as potential surrogates for species richness of other taxa or for

general ecosystem health (e.g. Lips, 1998; Loyola et al., 2007). However studies

examining European pond-breeding species and other aquatic groups have generally

found that while there is potential for such relationships, further study is required

(Oertli et al., 2005; Sewell, 2006).

1.4.1 Amphibian surveying

Amphibians are often cryptic both in behaviour and colouration (Vitt and Caldwell,

2009), with activity dependent on environmental variables such as temperature and

rainfall (e.g. Heyer et al., 1994; Brooke et al., 2000; Grafe and Meuche, 2005). This

makes them difficult to survey because of the confounding effect of imperfect

detection (Altwegg et al., 2008). In long-term projects or databases it is particularly

important that the effectiveness of survey protocols is thoroughly examined, because

they may be used without further appraisal for many years (Crouch and Paton, 2002;

Kaiser, 2008). Selecting an inefficient monitoring strategy can be costly in terms of


9

both finances and data quality (Paszkowski et al., 2002; Field et al., 2005), so the

survey method costs and benefits should be carefully considered alongside project

aims (Quinn et al., 2007).

How a method is implemented is as important as the choice of method itself

(Paszkowski et al., 2002; de Solla et al., 2005; Gooch et al., 2006). For example, call

surveys have been used routinely for anurans in North America and Canada, but the

recommendations for survey length derived from them tend to vary widely, from

three minutes (Shirose et al., 1997) up to fifteen minutes of survey effort (Pierce and

Gutzwiller, 2004). Dip-netting is commonly used to survey for newts and all

amphibian larvae, but opinion differs on its usefulness. Sometimes netting is done

very rigorously covering 100% of the pond (e.g. Arntzen, 2002), while other surveys

are more restricted, limiting netting to a set number of sweeps to minimise damage

and disturbance to the vegetation (Griffiths and Raper, 1994). Although most

European amphibian species are well-studied, there is still room for improvement at

all stages of survey design and analysis. Use of appropriate methods is important to

ensure that the data collected are appropriate to study aims and that inaccuracies

such as false absences are minimised. Development of survey protocols and analysis

techniques is essential in all areas of biodiversity monitoring, even where methods

are already established and species are already well described.

1.5 Project objectives

The present study addresses some core questions in amphibian ecology and survey

methodology, and tests a new approach to statistical analysis of survey data:

Does the distribution of amphibians in Luxembourg – as assessed from

historical records - exhibit any relationships with local landscape variables?

(Chapter 3.)

Have the range sizes of amphibians changed in recent decades? (Chapter 4.)

How much of Luxembourg‟s amphibian diversity is captured within its

protected areas? (Chapter 4.)

Are amphibians useful indicators of plant or invertebrate species richness?

(Chapters 5 and 6.)


10

What combination of survey methods is needed to reliably detect the various

species as being present in a pond? (Chapter 7.)

The pros and cons of different data types, survey methods and analyses are discussed

in each chapter. Further to the studies included within the thesis chapters, two papers

published in the course of the work are included in Appendix 9. The first is not

directly relevant to the thesis subject, however was an interesting observation of the

phenomenon of freshwater mussels attached to newts‟ feet. The second paper reports

on the results of screening for the amphibian fungal disease chytridiomycosis in

Luxembourg – background levels of the disease-causing fungus Batrachochytrium

dendrobatidis were found, which has implications for amphibian conservation and

survey protocols, discussed in chapters 7 and 8.

2. General methodology

11


General methodology

The data in Chapters 3 and 4 were taken from a national database collated by the

Musée national d‟histoire naturelle (Luxembourg) (MNHN[L]), and data for

Chapters 5 – 7 were collected specifically for the present study on pond-breeding

amphibians in Luxembourg. The species are listed and their nomenclature is detailed

in Appendix 1.

2.1 Historical data - MNHN

Historical data on amphibian presence throughout Luxembourg were retrieved from

the database held by the MNHN. Sixty-five ambiguous, incomplete and duplicated

records were deleted, and the dataset was refined to a maximum of one record per

species / year / 1 km grid square; leaving a total of 1317 records between 1955 and

2005. Records from 2006 onwards had not all been submitted to the MNHN

database when these data were extracted, so are not used here. One kilometre grid

squares were the finest resolution possible with this dataset whilst maintaining its

explanatory power (Luoto et al., 2007). Six figure grid references (describing 1 km

grid squares) were found and checked for all records, most importantly for those

referred to by place names and less accurate grid references in the original database.

Green frogs, Pelophylax lessonae and P. kl. esculentus, were originally recorded

separately, however the names are likely to have been used interchangeably by many

recorders due to the difficulty of distinguishing them in the field (Appendix 1).

Records have therefore been merged for present use and they are collectively

referred to as Pelophylax spp. (or P.spp) here and throughout following chapters.

The MNHN data (Chapters 3 and 4) are presence-only records – i.e. there are also

unsurveyed, unrecorded grid squares where the study species might also be present.

False absences are a flaw in this dataset and they cannot be addressed due to the

nature of the data collection, however this issue is taken up in Chapter 7.


12

2.1.1 Map data

Base maps were drawn in ArcMap (v9.3.1.1850) with data from the Luxembourg

government‟s cartography department, the „Administration du Cadastre et de la

Topographie‟ (ACT). The landscape features and protected areas projected onto the

map are listed in Table 2.1. Most map files had been updated by ACT in 2006.

Similar layers were merged together using ArcMap‟s „geoprocessing wizard‟, for

example streams and rivers were merged to create „running water‟.

A grid of 1 x 1 km squares was applied to the map and each square partly or wholly

in Luxembourg was assigned a unique identification code and its within-country area

was measured. Landscape layers were converted to „shapefiles‟ and the area, or the

length of line features, within each grid square was extracted. The identification

codes were used to import extracted data into a spreadsheet detailing the contents of

each grid square. Measurements of features in squares not wholly within

Luxembourg were adjusted according to their within-country area (e.g. 20,000 m2

coniferous forest cover in a square only half in Luxembourg was reduced by a factor

of 0.5 to 10,000 m2).

Table 2.1 Landscape features drawn in ArcMap and extracted to quantify habitat in

each 1 km grid square.

Type Feature Measurement unit

Geographical & habitat

National & regional boundaries n/a

Waterbodies - running: streams & rivers - still: ponds, lakes & uncoded ‘basins’ - water treatment basins

Length (m) Area (m

2)

Forest - coniferous - broadleaved - mixed

Area (m2)

Area (m2)

Area (m2)

Roads – all widths Length (m)

Buildings – all types (including domestic, agricultural, industrial & undefined)

Area (m2)

Elevation Mean (m.a.s.l.)

Protected Areas (PAs)

Nature reserves Area (m2)

Natura 2000 sites - Habitats Directive - Birds Directive

Area (m2)

Area (m2)


13

Preliminary models in Chapter 3 also included a soil type covariate, however it

produced uninformative models which are not reported. This may have been because

it was a categorical variable, and using only the commonest soil type in each square

produced a very simplified, over-generalised soil map. Data on agricultural uses of

land surrounding ponds were unavailable.

2.2 Study pond selection

The present study was conducted in Luxembourg (see section 1.2). Study ponds

were selected from historical data collated by the Luxembourg Musée national

d‟histoire naturelle (MNHN; see Appendix 2) between 1997 and 2006, initially to

ensure inclusion of ponds where three species thought to be in steep local decline

had been recorded: Hyla arborea, Alytes obstetricans and Triturus cristatus. Other

ponds were found on topographical maps (1:20,000) to give a good spread across

central and southern Luxembourg (Figure 2.1) and to facilitate surveys at several

ponds in one evening.

The definition of a „pond‟ was taken from the National Pond Survey (NPS) (Biggs et

al., 1998, pp.6):

„a body of water, of man-made or natural origin, between 1 m2 and 2 ha,

which usually holds water for at least four months of the year‟.

Study ponds are listed in Appendix 3, pond area (m2) was estimated in the field and

confirmed by measurement on maps in ArcGIS. Thirty-one ponds were studied in

2007, and in 2008 26 of the same ponds were surveyed: access permission was

withdrawn at one pond (number 8) and four others were dropped to allow inclusion

of three extra ponds to boost data on ponds occupied by A. obstetricans. In 2009 a

short fieldwork season was undertaken to include nine ponds from the north of the

country, and only A. obstetricans data were collected in that final season.

In Chapters 5 and 6 ponds separated by less than 25 m were classified as a single

pond because this distance is readily traversed by all of the amphibian species found

in Luxembourg. For occupancy modelling, landscape features within 100 m of ponds


14

were examined, therefore ponds separated by less than 200 m were grouped for

Chapter 7.

Figure 2.1 Study pond locations in Luxembourg.

2.3 Amphibian surveying

Four standard methods were used to survey for amphibians: visual encounter surveys

(VES; for adults, larvae and eggs), funnel traps, dip-netting and aural surveys – these

are described in detail below. Where possible in 2007 and 2008 all survey methods

were employed at each pond on at least five repeat visits between late February and

early August (Appendix 4), but occasionally a survey method had to be dropped

when conditions were unsuitable (e.g. water too shallow or warm for trapping). In

2009 only aural surveys were conducted and five survey rounds were completed

between 12th May and 2

nd June.

The appropriate survey methods for each species are detailed in Table 2.2, where

possible, the species, sex and life stage of animals found were recorded. Only

definite identifications were retained in the final data. Larvae of the Lissotriton newt

species could not be distinguished, and females of the same species were only

10 km

N


15

included where they could be identified in the hand. A review of the advantages and

disadvantages of the following survey methods is available from Griffiths and Raper

(1994).

2.3.1 Visual encounter survey (VES)

After dark a powerful torch (Clulite© Clubman Standard CB1, 0.5 million

candlepower) was used to survey for amphibians in the water. All VESs were

conducted by one person (LW) walking slowly around accessible parts of the pond

perimeter, sweeping the torch beam from the bank to approximately 2 m into the

pond and counting amphibians seen in the light. Where identifiable, adults, larvae

and eggs were recorded (Table 2.2). Torching took up to 20 minutes for the biggest

ponds. Night-time torch survey data and visual sightings made during other daytime

surveys were included as VES.

2.3.2 Funnel trapping

Funnel traps were set after VES. Traps were made from 2 litre plastic drinks bottles

with the spout cut off and inverted (following Griffiths, 1985). Up to 25 traps were

set in each pond, or 30 traps between paired ponds, at 2 m intervals along the

accessible margin, in groups of 5 for larger ponds. As ponds dried up and decreased

in size the number of traps was reduced to maintain the 2 m spacing.

Traps were checked and emptied after dawn the following morning, an average of 10

hours 20 minutes after setting (maximum 14.5 hours, minimum 6.5 hours). The

length of time traps were in situ was generally shortened throughout the season as

hours of darkness decreased and water temperature increased. Trapping ceased

during June of both 2007 and 2008 when the water temperature became too high to

safely use traps in any pond.

2.3.3 Dip netting

Dip-netting was undertaken in the morning, after traps had been removed to the

bank. A full net sweep was made from the bank at each trap location (i.e. up to 25

sweeps per pond, or 30 sweeps for paired ponds). The net was swept in a continuous

movement across the pond substrate in an arc (radius approximately 1 m). Netting


16

was limited to 25 or 30 sweeps to minimise damage to the ponds. The net used had a

0.3 cm gauge mesh, 30 cm long net bag and a square frame 25 cm across.

2.3.4 Aural

Adult anurans were monitored by call surveys. This was conducted simultaneously

with other night time surveys (VES and trap setting). The seven anuran species

present in Luxembourg have distinctive calls (Rana temporaria, Pelophylax spp.,

Hyla arborea, Bufo bufo, Alytes obstetricans, Bufo calamita, and Bombina

variegata). Where A. obstetricans were not already calling, their distinctive call was

mimicked over short periods during the survey visit to elicit response calls from

females and competitive calls from males.

Table 2.2 Survey methods appropriate to each amphibian species by life stage and

sex.

Species Eggs Larvae

Adults Males Females

VES Trap Net VES Trap Net VES Trap Net Aural VES Trap Net Aural

Rt

P.spp

Ha

Bb

Bc

Ao

Bv

Ia

Tc

Lh

Lv

See Appendix 1 for amphibian species name abbreviations; „Adults‟ includes recently

metamorphosed and juvenile individuals; VES = visual encounter survey; newt eggs were not differentiated between and therefore were excluded from analysis; L. helveticus and L.

vulgaris larvae are indistinguishable in the field, and females can only be distinguished in

the hand and are therefore excluded from VES.

2.4 Data analysis

Data analysis is explained in each chapter. Unless otherwise stated, analyses were

performed in SPSS v.16.

3. Amphibians and landscape features

17

3. Landscape features as predictors of amphibian presence

Amphibians and landscape features

Summary: The data collated by biological record centres are an

important resource for examining broad scale relationships and trends.

An understanding of species‟ relationships with their wider habitat helps

to inform management and conservation decisions. Between 1996 and

2005 amphibians were surveyed in 23.5% of 1 km grid squares in

Luxembourg: surveyed squares had a greater average forest cover than

unsurveyed squares, but there was no difference in markers of human

development. Principal component analysis grouped habitat variables

into four components (PCs): 1. forest and elevation, 2. water, 3. human

developments, 4. protected areas. Overall PC1 was the most useful in

predicting amphibian presence records. Bufo bufo and Alytes obstetricans

were positively associated with PC1, while Pelophylax spp., Triturus

cristatus and Lissotriton vulgaris were negatively associated with the

same variable. B. bufo and A. obstetricans records were also positively

related to human development, whereas Ichthyosaura alpestris and L.

helveticus were negatively associated with markers of development.

Suitable terrestrial habitat surrounding amphibian breeding ponds is, of

course, crucial to their persistence in an area, and knowing how habitat

features influence amphibians is key to their conservation. Here broad

categories of habitat are examined.

3.1 Introduction

3.1.1 Recording centres

Recent technological advances have vastly increased the availability of fine scale

data from ecological surveys, along with the facility to store, share and map them

(Phillips et al., 2006; Gaston et al., 2008). Large databases are essential in the

production of atlases showing species‟ ranges, for example the „Atlas of Amphibians

and Reptiles in Europe‟ by Gasc et al. (1997) gives broad distribution details of all

herpetofauna species occurring in Europe. Compiling such an extensive atlas is a big

task, requiring collation of existing datasets and commissioning of additional

research to fill the gaps.

Some interest groups have exploited modern computing power more than others. For

example, a concerted effort by the ornithological community in many parts of the

world has led to the development of „atlassing‟, whereby volunteers systematically

survey map squares and submit their data to a central database. Atlassing has


18

immensely expanded knowledge of bird distributions (Donald and Fuller, 1998), but

unfortunately no other taxa have been subjected to the same thorough and sustained

recording effort. Consequently atlas data are usually presence-only (i.e. empty grid

squares could indicate a species‟ absence or a lack of surveys, and abundance is not

routinely recorded) and therefore cannot be used for population monitoring (Donald

and Fuller, 1998).

Many countries have regional and national recording centres that attempt to collate

biological records voluntarily submitted and to make the data available for research

and conservation decision-making. Sometimes these records are collected following

a standardised scheme, but more often the databases contain incidental records. Data

collected unsystematically are inherently biased and should therefore be used

carefully and critically (Elith et al., 2006). Presence-only data collected by ad-hoc

and variable methods, such as those in record centre databases, tend to be rather

patchy, both in geographical location and across time. The data may be subject to

multiple biases, with recorders perhaps more likely to report rare species than

common ones and sampling effort being concentrated in species-rich areas close to

recorders‟ homes (Dennis et al., 1999; Dennis and Thomas, 2000).

The Musée national d‟histoire naturelle (MNHN) is the national biological recording

centre in Luxembourg and holds a large database of animal and plant records

submitted by professional and amateur naturalists (all contributors are listed in

Appendix 2). Amphibian records date back to 1955, but increase greatly in number

after 1970 and again during the 1990s. The data have previously been summarised in

an atlas of Luxembourgish amphibians (Proess, 2003b), which is a useful overview

of species‟ distributions, but they have not been analysed in any detail. The atlas by

Proess (2003b) maps species records before and after 1996 at a fairly broad scale (5

km grid squares) and includes some general information on amphibian natural

history and the geography of Luxembourg.

3.1.2 Landscape and amphibians

Examination of the relationships between organisms and their habitats allows

generalisation about the places they are likely to occur – record centre data can be


19

very useful for looking at general trends. Surrounding landscape features may be just

as important in determining amphibian presence and abundance as the more

immediate variations in habitat conditions (Van Buskirk, 2005). The influence of

landscape features on amphibian species richness and abundance is strongest near

breeding habitat: features such as forest cover and wetland area have been found to

be positively correlated with both measures, while markers of human development

and activity, such as road density and soil nitrogen levels, are negatively correlated

(Houlahan and Findlay, 2003). All of the amphibian species occurring in

Luxembourg spend much of their time away from water; many forage and over-

winter on land and immature animals often do not return to water until they are ready

to breed.

In the current chapter the influence of landscape features on amphibian occurrence is

considered, with data collated by the MNHN. Habitat features are also examined in

Chapter 7 as covariates in occupancy models using the more detailed data collected

specifically for this purpose. The present chapter aims to address the following

questions:

Are all areas of Luxembourg represented in the MNHN amphibian record

database?

Which broad landscape variables are useful in predicting each species‟

occurrence?

Which landscape variables are indicative of high amphibian species richness?

3.2 Methods

The maps and methods of data extraction are given in the General Methods chapter

(Chapter 2). The habitat features extracted from maps (Table 2.1) were organised

into eleven continuous scale variables (coniferous forest, broadleaved forest, mixed

forest, nature reserves, Natura 2000 sites, buildings, roads, water treatment basins,

still water, running water and mean elevation). Prior to any analyses the variables

were transformed by log10(x+1) to reduce the disproportionate influence of very high

or zero values.


20

Only amphibian data collected between 1996 and 2005 were used here, to ensure the

relevance of the investigation. Firstly the landscape features in surveyed 1 km grid

squares were compared to unsurveyed grid squares using MANOVA, followed by

one-way ANOVA to explore where the significant differences lay.

A number of the habitat variables exhibited some degree of correlation with each

other, therefore to avoid multicollinearity confounding the results, principal

component analysis (PCA) was applied. Components with eigenvalues > 1 (Field,

2005) were retained for logistic regression.

Forward stepwise (likelihood ratio) logistic regression was used to explore the

derived principle components as predictors of the presence / absence (non-records)

of each amphibian species and the recorded amphibian species richness in surveyed

grid squares only. A record for one species was assumed to indicate that general

amphibian surveys had been conducted; Salamandra salamandra was excluded

because it requires an entirely different set of survey methods. The Wald test was

chosen to test the significance of regression coefficients and the Hosmer and

Lemeshow test to check goodness of fit. To maintain sufficient statistical power in

the logistic regression analyses, only species recorded in ≥ 5% of surveyed squares

were used.

3.3 Results

3.3.1 Historical data descriptives

Surprisingly, 76.6% of the 1 km grid squares wholly or partly in Luxembourg (n =

2789) have no amphibian records in the database between 1955 and 2005. This could

indicate either that amphibians have never been found there, or simply that no

surveys have been conducted. A further 19.4% of grid squares had only been

surveyed in one year (out of 51), while very few (0.14%) had records from ten or

more years. Over the 51 years, between one and nine amphibian species were

recorded in each of the surveyed grid squares (Figure 3.1a). Coverage in the most

recent complete decade from the database (1996-2005) is similar to that in the entire

database, with 76.5% of grid squares having no amphibian records, while the


21

surveyed squares held between one and eight amphibian species in this period

(Figure 3.1b).

(a) 1955-2005

(b) 1996-2005

Figure 3.1 The number of amphibian species recorded in each 1 km grid square, (a)

1955-2005, (b) 1996-2005.

Multivariate analysis (MANOVA) revealed that there is a significant difference in

the landscape features between the 1 km grid squares that were surveyed between

1996 and 2005 and those that were not (F12,2774 = 3.75, p < 0.001). Squares where

amphibian surveys were carried out have, on average, more broadleaved forest cover

(F1,2786 = 19.98, p < 0.001) and more „total‟ forest cover (F1,2786 = 9.49, p < 0.005),

greater Natura2000 coverage (F1,2786 = 4.52, p < 0.05) and more water treatment

works (F1,2786 = 7.06, p < 0.01; Figure 3.2). However, there was no difference in the

measures of development between surveyed and unsurveyed squares (buildings:

F1,2786 = 3.28, p > 0.05; roads: F1,2786 = 0.19, p > 0.05).

Unsurveyed squares had a significantly smaller area within the boundaries of

Luxembourg (F1,2785 = 25.41, p < 0.001), indicating that more were on the border:

14.8% of unsurveyed squares were on the border, compared to 9.8% of the surveyed

squares.

N

Number of amphibian species recorded:

10 km


22

Figure 3.2 Comparison of the mean area or length of landscape features in squares

surveyed for amphibians between 1996 and 2005 and those not surveyed.

= not surveyed, = surveyed; * p < 0.05, ** p < 0.01, *** p < 0.005; measurements adjusted for grid square area within-country; dotted bars are linear features measured in

metres (rather than m2).

Figure 3.3 The percentage of 1 km grid squares surveyed (n = 654) between 1996

and 2005 occupied by each amphibian species.

= not occupied, = occupied; see Appendix 1 for species abbreviations.

All of the amphibian species present in Luxembourg were recorded in at least two 1

km grid squares between 1996 and 2005. However the three rarest species (Hyla

arborea, Bufo calamita and Bombina variegata) were only recorded in less than 3%

of squares (Figure 3.3) and were therefore excluded from further analysis.

***

***

*

**

0

500

1000

1500

2000

2500

3000

3500M

ean m

2(±

s.e

.)

0

10

20

30

40

50

60

70

80

90

100

Rt P.spp Ha Bb Ao Bc Bv Ia Tc Lh Lv Ss

% o

f surv

eyed s

quare

s


23

3.3.2 Landscape predictors of amphibian species’ occurrence

PCA reduced the habitat variables to four principal components (PCs) with

eigenvalues > 1 (Table 3.1). This cut-off point is supported by the scree plot which

has an „elbow‟ after four components (Figure 3.4; Everitt and Dunn, 2001). Overall

the four extracted PCs explain 61.41% of the total variance. Grouped by highest

loading the variables fit tidily into the four components, which can be interpreted as

(1) Forest and elevation, (2) Water, (3) Human development and (4) Protected areas

(PAs).

Water treatment basins were included in the principal component analysis based on

personal observation of amphibians reproducing in them. Many of the water

treatment works are „natural‟, a series of ponds planted with reeds (Phragmites), and

they account for 40% of all still water basins in Luxembourg. Although they

associate slightly more with human developments (PC3) than other waterbodies,

water treatment works did not load heavily onto any component.

Table 3.1 Multivariate factor loadings of principal components with eigenvalues > 1.

Variable Component number

1 2 3 4

Coniferous forest 0.829

Mixed forest 0.761

Broadleaved forest 0.695 0.169 -0.173

Mean elevation 0.515 -0.465 0.399

Still water 0.820 0.140 0.140

Running water 0.114 0.754

Roads 0.823

Buildings -0.319 0.711

Water treatment 0.259 0.331

Nature reserves 0.864

Natura 2000 sites 0.818

Eigenvalue 2.439 1.745 1.332 1.240

% of variance 22.170 15.859 12.111 11.269

Cumulative % 22.170 38.029 50.140 61.409

Very low factor loadings (< 0.1) are suppressed; loadings > 5 are shown in bold typeface; 4 of 11 components had an eigenvalue > 1; rotation method: Oblimin with Kaiser

Normalization.


24

Figure 3.4 Scree plot of the eigenvalues of all components.

Table 3.2 Final models from stepwise logistic regression for anurans.

Species Predictor Step

Regression coeff.

Wald statistic (squared)

Odds ratio

e s.e. 2 df p

R. temporaria Constant - 0.19 0.08 5.87 1 - -

PC2: water 1 -0.25 0.09 7.39 1 0.007 0.78

Notes: H&L 2

8 = 8.42, p = 0.394; model 2

1 = 7.54, p < 0.01

Pelophylax spp. Constant - -0.66 0.09 57.43 1 - -

PC1: forest & elev. 1 -0.79 0.10 56.61 1 < 0.001 0.46

Notes: H&L 2

8 = 15.63, p = 0.048; model 2

1 = 62.29, p < 0.005

Bufo bufo Constant - -1.02 0.09 119.46 1 - -

PC1: forest & elev. 1 0.36 0.11 9.77 1 0.002 1.43

PC3: human dev. 2 0.32 0.11 8.98 1 0.003 1.38

Notes: H&L 2

8 = 7.29, p = 0.506; model 2

2 = 16.12, p < 0.001

A. obstetricans Constant - -2.15 0.14 238.53 1 - -

PC1: forest & elev. 1 0.55 0.17 10.03 1 0.002 1.72

PC3: human dev. 2 0.32 0.14 5.02 1 0.025 1.38

Notes: H&L 2

8 = 6.75, p = 0.563; model 2

2 = 13.85, p < 0.005

Step = logistic regression step that added covariate; Goodness of fit statistic: H&L = Hosmer and Lemeshow; Odds ratio: values < 1 indicate that the PC and the species are less likely to

occur together, a value of 1 would indicate no effect and a value > 1 indicates a positive

association.

One landscape PC was added by stepwise logistic regression to explain the

distribution of Rana temporaria: the water principal component (PC2) was

significantly negatively associated with R. temporaria presence, although the final

model explains only 56.7% of the variation (Table 3.2 and Figure 3.5a).

Pelophylax spp. records were best explained by the inclusion of the forest and

elevation PC (Table 3.2), but the model has a high Hosmer and Lemeshow score and

is only a borderline good fit even though the final model explains 70.3% of the

0.0

0.5

1.0

1.5

2.0

2.5

1 2 3 4 5 6 7 8 9 10 11

Eig

envalu

e

Component number


25

variation. The species‟ negative association with forest and elevation (PC1) is very

clear (Figure 3.5b).

Two principal components made a well-fitted model for Bufo bufo (Table 3.2),

which accounted for 71.7% of the variation: PC1 representing forest and elevation

and PC3 representing human development were both positively associated with Bufo

bufo (Figure 3.5c).

Occupying just 79 of the 654 grid squares surveyed, Alytes obstetricans can be

described as rare in the study area (along with Triturus cristatus, Lissotriton vulgaris

and Salamandra salamandra; Figure 3.3). Like Bufo bufo, the presence of A.

obstetricans was best predicted by PCs 1 and 3 (Figure 3.5d), with the final model

explaining 87.9% of the variation.

PC

valu

e (

±s.e

.)

(a) R. temporaria

(b) Pelophylax spp.

PC

valu

e (

±s.e

.)

(c) Bufo bufo

(d) A. obstetricans

Figure 3.5 Mean values of the significant principal components predicting anuran

species‟ occupancy in 1 km grid squares.

* p < 0.05, *** p < 0.005, indicating the step significance (Wald statistic); = species

absent / unrecorded, = species present.

*

-0.2

0.0

0.2

PC2: water

***

-0.4

-0.2

0.0

0.2

0.4

PC1: forest & elevation

***

***

-0.2

0.0

0.2

0.4


PC3: human development

***

*

-0.2

0.0

0.2

0.4

0.6




26

Table 3.3 Final models from stepwise logistic regression for newts.

Species Predictor Step

Regression coeff.

Wald statistic (squared)

Odds ratio

e s.e. 2 df p

I. alpestris Constant - -0.74 0.08 77.76 1 - -

PC3: human dev. 1 -0.22 0.10 5.43 1 0.020 0.80

Notes: H&L 2

8 = 4.92, p = 0.766; model 2

1 = 5.50, p < 0.019

T. cristatus Constant - -3.08 0.21 220.73 1 - -

PC1: forest & elev. 1 -0.77 0.18 18.16 1 < 0.001 0.46

PC4: PAs 2 0.37 0.16 5.19 1 0.023 1.45

Notes: H&L 2

8 = 10.02, p = 0.264; model 2

2 = 22.91, p < 0.001

L. helveticus Constant - -0.46 0.08 32.16 1 - -

PC3: human dev. 1 -0.22 0.09 5.84 1 0.016 0.80

Notes: H&L 2

8 = 8.00, p = 0.434; model 2

1 = 5.96, p < 0.05

L. vulgaris Constant - -2.31 0.14 260.02 1 - -

PC1: forest & elev. 1 -0.72 0.14 26.17 1 < 0.001 0.49

Notes: H&L 2

8 = 7.05, p = 0.531; model 2

1 = 25.62, p < 0.001

Step = logistic regression step that added covariate; Goodness of fit statistic: H&L = Hosmer

and Lemeshow.

PC3 was negatively related to Ichthyosaura alpestris presence, resulting in a model

accounting for 67.9% of the variance. Every unit increase in human development

markers decreased the odds of this species‟ presence by a factor of 0.80 (Table 3.3

and Figure 3.6a).

The presence of T. cristatus in grid squares was best modelled by PCs 1 and 4

(Figure 3.6b), explaining 94.6% of the variance: it has a negative relationship with

PC1 representing forest and elevation, while the likelihood of T. cristatus presence

increases by a factor of 1.45 in association with PC4, protected areas.

Like I. alpestris, presence of Lissotriton helveticus was also best predicted by a

negative relationship with PC3 (Table 3.3 and Figure 3.6c), the final model

explained 61.2% of the variance.

PC1 showed a negative association with L. vulgaris: there is a highly significant

difference in forest cover and elevation between grid squares where L. vulgaris was

recorded and where it was not recorded (Table 3.3 and Figure 3.6d). The final model

accounted for 90.4% of the variance.


27

PC

valu

e (

±s.e

.)

(a) I. alpestris

(b) T. cristatus

PC

valu

e (

±s.e

.)

(c) L. helveticus

(d) L. vulgaris

Figure 3.6 Mean values of the significant principal components predicting newt

species‟ occupancy in 1 km grid squares.

* p < 0.05, *** p < 0.005, indicating the step significance (Wald statistic); = species

absent / unrecorded, = species present.

3.3.3 Landscape predictors of amphibian diversity

None of the four principal components contributed significantly to a logistic

regression model of the influence of landscape features on amphibian species

richness (Table 3.4).

Table 3.4 Multiple logistic regression chi-square values for landscape features and

amphibian species richness.

Predictor -2 Log

Likelihood

Likelihood Ratio Tests

2 df p

Constant 2665.00 - - -

PC1: Forest & elev. 2026.00 6.19 7 0.518

PC2: Water 2025.00 5.48 7 0.602

PC3: Human dev. 2024.00 4.85 7 0.678

PC4: PAs 2026.00 6.16 7 0.521

*

-0.2

0.0

0.2


***

*

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8


PC4: PAs

*

-0.2

0.0

0.2


***

-0.6

-0.4

-0.2

0.0

0.2

0.4



28

3.4 Discussion

Many previous studies examining the effect of landscape variables on amphibian

occupancy have looked at breeding ponds and the surrounding terrestrial habitat (e.g.

Beebee, 1977, 1979, 1980, 1981, 1985; Pavignano et al., 1990; Marnell, 1998; Van

Buskirk, 2005). The current chapter uses a large set of presence-only data and is

lacking the detail included by others with small sets of presence-absence (or

detection–non-detection) data, but while both data types are susceptible to errors

caused by non-detection (Gu and Swihart, 2004), here detail is forfeited for sample

size.

The records held by the MNHN indicate comprehensive coverage of the whole

country at the 5 km grid square level, as used in Luxembourg‟s 2003 atlas of

amphibian distributions (Proess, 2003b). However it was surprising that at a higher

magnification (1x1 km grid) many squares (76.6%) have never been surveyed or had

amphibians reported. In a small wealthy country with a strong network of

professional and amateur naturalists it could perhaps have been expected that a

greater part of the country would be surveyed, or that the unsurveyed squares would

mostly be urban, but this was not the case. A scarcity of still waterbodies may

explain the high number of unsurveyed squares: the maps show still water in

relatively few grid squares (14.9%), although 84.6% of squares have running and/or

still water. However, the maps were not ground-truthed and might be missing some

of the very small or temporary pools and ditches used by amphibians.

The 5 km scale used in the Luxembourg atlas (Proess, 2003b) is similar to that

recently used by other European countries with active herpetological research and

monitoring groups. For example, on their herpetology group websites the

Netherlands (RAVON, 2008) and Switzerland (KARCH, 2002) report 5 km grid

square data for all of their amphibian species, while larger countries Spain

(Ministerio de Medio Ambiente y Medio Rural y Marino, 2008) and Italy (Societas

Herpetologica Italica, 2009) report 10 km square data. In the UK the National

Amphibian and Reptile Recording Scheme aims to collect data at a 1 km scale by

allocating map squares to surveyors (NARRS, 2011). However, so far

comprehensive data at a 5 km scale are publically available only from a few counties


29

(e.g. Clemons, 1998; Guest and Harmer, 2006). Each country‟s most common

species show records from almost every grid square countrywide; the scales selected

probably reflect practical constraints such as display space and clarity more than

limitations of the data. Distribution maps for each amphibian species found in

Luxembourg are given in Appendix 5.

The principal component representing mostly the three forest types and elevation

(PC1) was selected by logistic regression as predicting records of five of the eight

amphibian species modelled. There is extensive forest coverage (38.6%) in

Luxembourg, predominantly in the hills and valleys of the north and in patches

between the towns and farms that dominate the central and southern areas.

Pelophylax spp. were found to be negatively predicted by PC1, which is consistent

for a lowland species (IUCN, 2006) with a preference for open landscapes (Van

Buskirk, 2005) and open waterbodies where they can bask in the sun (Sinsch, 1984).

Pelophylax spp. are very widespread in Luxembourg (Figure 10.1b, Appendix 5) and

their conspicuous behaviour makes them unlikely to be missed by surveyors

(Beebee, 1980; but see 'detectability rates' given in Chapter 7). The broad habitat

categories created by principal component analysis mean that the selected variables

are not always what would be expected by experienced field surveyors. For example

Pelophylax spp. are generally considered to be strongly linked to waterbodies, but

the PCA did not identify this with the current dataset.

T. cristatus and L. vulgaris were also negatively related to PC1 (forest and elevation)

– the similarity of their habitat preferences have often been noted (e.g. Dolmen,

1980; Denton, 1991), although usually researchers conclude that some scrub or

woodland in the terrestrial habitat near to breeding ponds is essential for both species

(Beebee, 1977, 1981; Denton, 1991; Van Buskirk, 2005). The negative relationship

here may be due to the scale of the comparison between occupied and unoccupied

squares – many grid squares had very high (≈ 100%) forest cover and were on hills

(hence forests and elevation loading onto the same component), which are unsuitable

for T. cristatus (Schoorl and Zuiderwijk, 1980) and make the relationship appear

biased towards a preference for less forest.


30

Bufo bufo and A. obstetricans were significantly positively associated with

forest/elevation (PC1) and human development (PC3). Forests provide ideal

terrestrial habitat for both of these species, which are aquatic only for relatively short

periods (Bousbouras and Ioannidis, 1997; AmphibiaWeb, 2010) and as adults spend

most or all of the year foraging or sheltering terrestrially. A. obstetricans in

particular require many small holes, cracks and fissures (Marquez, 1992) which are

abundant under tree roots and leaf litter, although they may also inhabit grassland.

Previous work has also suggested a strong association between Bufo bufo and urban-

suburban ponds in England, Switzerland and Jersey (Beebee, 1979, 1981; Van

Buskirk, 2005; Wilkinson, 2007). However, human developments generally have a

negative impact on amphibian dispersal (Holenweg Peter, 2001; Schmidt and

Zumbach, 2008) and Bufo bufo suffer particularly high mortality on roads as they

migrate to and from breeding ponds (Hels and Buchwald, 2001; Orłowski, 2007),

indicating that the positive relationship exists despite hazards encountered during

migration and dispersal. In the course of amphibian surveys during 2007 and 2008,

Bufo bufo were regularly seen on the cycleways beside major roads, where there are

also deep pools to collect water runoff. These artificial habitats might contribute to

the association between Bufo bufo and human development in Luxembourg. A.

obstetricans is tolerant of a wide range of habitats, including urban areas (IUCN,

2006), but a significant association with human-influenced landscapes was not

expected. This may be due to bias in the data, whereby animals are more likely to be

seen or heard near to human habitations and records of rare species are more likely

to be reported (Dennis and Thomas, 2000).

I. alpestris and L. helveticus showed a negative association with human

developments. The preference of newts for open habitat, without buildings and roads

has been noted before (e.g. Beebee, 1979; Van Buskirk, 2005). Although the forest

PC was not selected by these species‟ regression models, both I. alpestris and L.

helveticus are common in open deciduous forest (Kwet, 2009), being more tolerant

of the low pH caused by leaf litter than T. cristatus and L. vulgaris (Marnell, 1998).

Two PCs only occurred once in the final models: PC2 (water) was negatively

associated with R. temporaria, although this model only predicts square occupancy


31

at slightly above chance levels and therefore is not very informative. The poor model

performance may simply indicate that none of the variables measured or principal

components were good predictors of R. temporaria, but water was a marginally

better predictor than chance alone. It is counter-intuitive that a pond-breeding

amphibian should occur less often in grid squares with more water, however both the

original running water and still water variables that loaded heavily on to the water

PC had lower average values in grid squares where R. temporaria was recorded. The

protected area PC was added at the second regression step for T. cristatus, showing a

positive relationship; the relationship between amphibian species‟ distributions and

PAs is examined in the next chapter (Chapter 4).

Suitable terrestrial and aquatic habitats are necessary for amphibian populations to

thrive. Most species, including those in Luxembourg, are on land for the majority of

the year. Terrestrial habitat also influences conditions in breeding ponds, for

example rain falling through pine trees is more acidic than rain falling through birch

woodlands (Beebee et al., 1990) and road run-off contains different pollutants to

agricultural run-off. In this chapter detail was compromised to use a large dataset:

the exact locations of records in the MNHN database are not known and therefore

the landscape features measured here share the same map square as the record rather

than being within an evenly measured distance from it. Also, here it is unknown

whether the MNHN records are breeding sites or just incidental records, because the

life-stage columns in the original database were often incomplete. Despite the coarse

nature of the MNHN dataset, the findings presented here are broadly in agreement

with previous studies, where forest cover and markers of human development were

found to be important predictors of amphibian diversity.

Characteristics of amphibian breeding ponds are explored in a smaller, more detailed

dataset in later chapters (Chapters 5 and 6) and local terrestrial habitat covariates are

examined as predictors of species‟ occupancy in ponds during the breeding season

(Chapter 7).

4. Range sizes and protected areas

32

4. Amphibian range size change and the effectiveness of

Luxembourg’s protected areas

Range sizes and protected areas

Summary: Based on the principle of providing protection from the

threats imposed by human activities, protected areas (PAs) have been

promoted and enforced by national and international legislation as a key

strategy in biodiversity conservation. A total of 20.2% of Luxembourg‟s

area has a statutory PA designation. The five common amphibian species

in Luxembourg (Rana temporaria, Pelophylax spp., Bufo bufo,

Ichthyosaura alpestris and Lissotriton helveticus) were found to be

equally distributed inside and outside PAs. Controlling for differences in

survey effort, their range sizes either increased or remained the same

between pre-1985 and 1996-2005. Over the same period the less

common Alytes obstetricans records more than doubled, while Triturus

cristatus and L. vulgaris were underrepresented in PAs and their range

sizes shrank. The rarest species to be assessed, Hyla arborea and

Bombina variegata, showed alarming range size reductions, but all

remaining sites are within PAs. PAs form a significant part of most

amphibian species‟ ranges in Luxembourg and therefore serve an

important function in their conservation.

4.1 Introduction

4.1.1 Protected areas

Protected areas (PAs) ideally perform two key functions: to hold a sample of

biodiversity and to protect this sample from external pressures (Margules and

Pressey, 2000). However they tend to occur on land that is of little commercial value

(e.g. at high elevation or too water-logged for agriculture), which can create a bias in

the types of habitat, and therefore species, that are conserved (Jackson and Gaston,

2008). Another bias in the creation of PAs arises from the focus on rare or threatened

species or remarkable habitats, paying little attention to the common species that

may also be in sharp decline and not well accommodated within them (Devictor et

al., 2007), although Devictor et al. (2007) also noted that PAs could act as important

„spatial refuges‟ for common species in decline. As the spaces between PAs continue

to undergo development and to support less biodiversity, so PAs are increasingly

isolated from similar habitat patches (Gaston et al., 2008).


33

There is surprisingly little information available on the degree to which species are

restricted to PAs (Gaston et al., 2006) – while PAs may be valuable as population

strongholds, isolation threatens long-term survival. In a meta analysis of „BAP‟

(Biodiversity Action Plan) species in Britain, Jackson and Gaston (2008) found that

more than half were mostly or completely restricted to PAs.

Signatories – including Luxembourg – to the Convention on Biological Diversity

(CBD), which followed the United Nations 1992 Conference on Environment and

Development, pledged to aim to significantly reduce or halt the rate of biodiversity

loss by 2010, at national, regional and global scales. The CBD aims to “set the

global biodiversity agenda” through several key principles: communication,

education, raising public awareness and engaging key players and stakeholders

(Convention on Biological Diversity, 2002).

In the same year as the CBD the European Union (EU) launched the Natura 2000

network, a combination of the existing „Birds Directive‟ (Council Directive

2009/147/EC) and a new complementary „Habitats Directive‟ (Council Directive

92/43/EEC). Under the Natura 2000 directives, EU member states are “obliged to

maintain or re-establish in a favourable conservation status” (Grand-Duché de

Luxembourg, 2004) a long list of species and habitats at sites approved by the

European Commission (EC). Countries are required to protect 18% of their land area

under Natura 2000, an arbitrary figure representing the political agenda rather than

useful or realistic goals (Gaston et al., 2008), with sanctions imposed by the EC for

failure to comply (Joint Nature Conservation Committee, 2010).

National law in Luxembourg defines all PAs under three titles: Natura 2000 sites,

National Protected Areas and Local Protected Areas (Grand-Duché de Luxembourg,

2004), although currently none are designated under the latter. The Natura 2000

coverage comes close to EC targets, at 17.5% of the country‟s area, and statutory

PAs of all designations cover 20.2% of the country. Luxembourg has the third

highest percentage of PA cover out of the 48 countries in geographical Europe,

which is remarkable for the country with the seventh highest human population

density (Gaston et al., 2008). Thirty-eight sites are designated under the Habitats


34

Directive and thirteen under the Birds Directive, covering 352.0 km2 and 160.2 km

2

respectively, with the bird zones almost entirely inside the Habitats areas (Ministère

de l'Environnement, 2003).

4.1.2 Range size

Contracting range sizes of species under threat have regularly been documented and

occur in various patterns according to the types and scale of the threat. A species‟

„niche breadth‟, or its degree of habitat specialisation, determines its sensitivity to

habitat alteration. The way in which range size is measured affects if or how changes

are detected (Gaston, 1996): for example measuring the „extent‟ of a species‟ range

(i.e. the area within a polygon drawn around all populations) will not detect losses

from the centre of its range.

Range sizes are often thought to reduce from the edges, where populations might be

smaller, with less immigration and less access to other suitable habitat. At a very

broad scale a threat occurring across a gradient can shift a species‟ range; for

example agricultural intensification spreading across a continent (Thomas et al.,

2008) or climate change pushing species‟ entire ranges towards the poles (e.g.

Parmesan and Yohe, 2003; Root et al., 2003; Thomas et al., 2004a; Anderson et al.,

2009). However, local habitat loss and fragmentation are better represented by the

more fine scale approach of counting occupied grid squares, which is more sensitive

to local changes (Swihart et al., 2003).

Previous studies have used atlas data to roughly quantify changes in species‟

distributions. Record centre (or museum) data are generally more available than

detailed long-term datasets (Skelly et al., 2003) and are a useful source for

identifying trends across taxa and for individual species. Amphibian range sizes

cannot be examined over a short period because of inter-year variations in their site

occupancy (Alford and Richards, 1999; Skelly et al., 2003). Telfer et al. (2002)

described a method to calculate a relative change index for groups of species

surveyed simultaneously – for example all birds or all carabid beetles. Using this

method on British census data, it has been demonstrated that the median range

change of butterflies was a 13% loss of 10 km squares, including some species


35

increasing by 164%, while others declined and two species became extinct (Thomas

et al., 2004b).

Luxembourg has a large historical dataset available, as used in the previous chapter.

Here I explore the MNHN data in relation to PAs and species range sizes to address

the following questions:

Is there greater amphibian species richness inside PAs than outside?

Are amphibians in Luxembourg restricted to PAs?

Have amphibian species‟ range sizes changed between pre-1985 and 1996-

2005?

4.2 Methods

4.2.1 Historical data

See General Methods (section 2.1.1) for description of how the large dataset collated

by the MNHN was prepared for use here.

The ten most contemporary complete survey years from the MNHN database were

included in the present chapter (1996-2005 inclusive), with data up to 1985 included

for comparison of range sizes. Amphibian species were given a single occupancy

score for every 1 km grid square: „1‟ for recorded and „0‟ for not-recorded, and

species richness was given as the total number of amphibian species recorded in that

period.

4.2.2 Protected areas

Amphibian data from protected and non-protected areas (PAs) in Luxembourg were

compared. Grid squares on the country borders contain less than 1 km2 of land

within country, but are retained as long as they fit the PA percentage criteria. The

percentage of Luxembourgish land within each grid square covered by a PA was

extracted from maps using ArcMap (v9.3.1.1850; see section 2.1.1).

Grid squares containing no PA, ≥ 50%, ≥ 80%, ≥ 90% and ≥ 98% PA were grouped.

Sections of road or river are sometimes not included in a PA even if they run through

it, therefore ≥ 98% PA was taken to mean that a square was completely protected.


36

(a) Non-PA and ≥ 50% PA

(b) Non-PA and ≥ 80% PA

(c) Non-PA and ≥ 90% PA

(d) Non-PA and ≥ 98% PA

Figure 4.1 Map of Luxembourg: grid squares (1 km2) randomly selected and

retained for analysis.

= protected areas; = non-PA squares, = PA squares.

Spatial autocorrelation of mapped data violates the independence and equal variance

assumptions of standard statistical methods (Dormann et al., 2007). In this case

neighbouring grid squares cannot be considered independent sampling units because

of the local dispersal of amphibians (Proess, 2003a). Therefore random selection was

applied to each percentage group to determine which squares would be retained for

analysis: a list of random numbers was generated (Microsoft Excel „randbetween‟

10 km

N


37

function) and the corresponding grid squares were drawn in that order; squares

whose neighbour had already been drawn were excluded. The non-PA set varies

slightly between comparisons because the PA squares were drawn first each time.

The grid squares retained for analysis are shown in Figure 4.1.

The exact positions of amphibian records within grid squares are not known, due to

the low resolution of the MNHN dataset, therefore here it is assumed that amphibian

records originate either from within the PA or are at least close enough to benefit

from the protection it offers (e.g. terrestrial habitat or a source population).

4.2.3 Protected area species richness analyses

The amphibian species richness in non-PA grid squares was compared to that in ≥

50%, ≥ 80%, ≥ 90% and ≥ 98% PA squares with Mann-Whitney U tests. Individual

species‟ occupancy of the sets of non-PA and PA grid squares were compared with

Pearson‟s chi-square test in 2x2 contingency tables, and acceptable alpha levels were

adjusted with a sequential Bonferroni correction.

4.2.4 Range size

Following Telfer et al. (2002), the dataset was examined for changes in species‟

range size between early and later survey periods, up to 1985 and 1996-2005

respectively. Telfer et al.‟s method was developed specifically for use with

biological atlas data where similar (but not identical) surveys were conducted across

both time periods. It accounts for variable geographic coverage and recording effort,

so is ideal to use with data held by recording centres. This method examines only

grid squares that were surveyed in both periods and compares their species

assemblages between early and late.

A key assumption is that recorders tried to record all species in the taxonomic group

on any given survey, therefore species-specific surveys in the original dataset were

excluded from range size analyses. Telfer et al. (2002) acknowledge that

detectability differs between species within a taxonomic group, but these differences

remain the same over time. Most of the amphibian species occurring in Luxembourg

have overlapping habitat and survey requirements and therefore it is reasonable to


38

assume that they would have been recorded simultaneously. However Salamandra

salamandra use different habitats and require different, separate, surveys and

therefore cannot be assumed to have been surveyed alongside the other species and

were excluded from range size analyses.

There are 2789 1 km grid squares wholly or partially in Luxembourg; only 104 of

these (3.73%) had amphibian surveys recorded in both the early and later time

periods compared here – these squares were retained for analyses. All amphibian

species occurring locally are represented in both periods. The proportion of grid

squares occupied in each survey period was used to calculate an index of relative

change, by squaring the difference between periods. Range size change is also

presented as a percentage increase or decrease.

4.3 Results

4.3.1 Amphibians and protected areas

The mean size of the 186 PAs in Luxembourg is just 5.4 km2 (±3.1 km

2 s.e.), ranging

from less than 0.01 km2 to 360 km

2 (calculated from data on the World Database of

Protected Areas (WDPA) website, IUCN and UNEP, 2009).

Table 4.1 Mann-Whitney U tests comparing amphibian species richness in PAs and

non-PAs.

% PA PA squares non-PA squares

U p n median n median

≥ 50 81 2.0 144 2.0 5545.00 0.520

≥ 80 46 1.5 151 2.0 3297.00 0.583

≥ 90 36 1.5 151 2.0 2571.50 0.597

≥ 98 20 2.0 152 2.0 1431.50 0.661

There is no difference in amphibian species richness between non-PA squares and

squares with ≥ 50%, ≥ 80%, ≥ 90% and ≥ 98% PA (Table 4.1, Figure 4.2 and Figure

4.3). Some amphibian species were recorded in too few grid squares to statistically

compare the non-PA and PA squares. For example, between 1996 and 2005

Bombina variegata was recorded in only two squares, both of which contained a

high percentage of PA (Figure 4.4e) but give insufficient data for comparison. Some

species were never recorded in high percentage PA squares and therefore were not


39

compared with non-PA squares (e.g. there are no Triturus cristatus or Lissotriton

vulgaris in the ≥ 98% PA grid square sample).

Figure 4.2 Amphibian species richness in 1 km grid squares (all records 1996 –

2005) and all statutory protected areas in Luxembourg.

= protected areas.

Chi-square was applied to test the difference between each species‟ occupancy in

non-PA squares and the four PA categories: most species were unaffected by PAs,

either negatively or positively. Figure 4.4 appears to show small differences between

non-PA and PA squares for several species, however most were non-significant – a

larger sample size in each of the PA categories may have improved the statistical

power. Hyla arborea, Bufo calamita and Bombina variegata were too rare to show

any differences.

Pre-Bonferroni correction, significantly more S. salamandra records were from grid

squares with a high percentage (≥ 80%) cover of PA than those with no PA at all, but

there was no difference between grid squares of medium PA (≥ 50%) cover and no

PA (Figure 4.4l; ≥ 98% PA: 2 = 3.94, p < 0.05, n = 172; ≥ 90% PA:

2 = 5.90, p <

N

1 to 8 amphibian species

10 km


40

0.05, n =187; ≥ 80% PA: 2 = 4.44, p < 0.05, n =197; ≥ 50% PA:

2 = 2.18, p > 0.05,

n = 225).

G

rid s

quare

s (

%)

Grid s

quare

s (

%)

Number of amphibian species

Figure 4.3 The frequency distribution of amphibian species richness in non-PA grid

squares and (a) ≥ 50% PA, (b) ≥ 80% PA, (c) ≥ 90% PA and (d) ≥ 98% PA grid

squares.

= non-PA squares, = PA squares.

Lissotriton helveticus was recorded in a greater proportion of grid squares with some

PA coverage than those with no PA, but this trend was only significant in the

comparison of non-PA squares with ≥ 50% PA squares (2 = 3.92, p < 0.05, n = 225;

Figure 4.4j), and none of the differences reported here stand up to sequential

Bonferroni correction.

4.3.2 Range size change

104 squares surveyed in both the early and later survey periods (≤ 1985 and 1996-

2005) were retained for range change analysis. In Figure 4.5 data points are quite

evenly spread about the intercept line, indicating that about half were recorded in a

higher number of squares in the later survey period, while others‟ range size had

shrunk or remained the same, and this is confirmed in the index of range size change

(Table 4.2). Bufo calamita was excluded from further analysis with Telfer‟s method

because it was present in too few grid squares (< 5) in the earlier period. A linear

relationship in range size between the survey periods indicates stability – i.e. species

0

10

20

30

40

50

1 2 3 4 5 6 7

(a) ≥ 50% PA

0

10

20

30

40

50

1 2 3 4 5 6 7

(b) ≥ 80% PA

0

10

20

30

40

50

1 2 3 4 5 6 7

(c) ≥ 90% PA

0

10

20

30

40

50

1 2 3 4 5 6 7

(d) ≥ 98% PA


41

that were rare initially stayed rare and common species stayed common (Figure 4.5b;

y = 0.47 + 1.28x, R2 = 0.712).

The greatest range size changes between the early and later survey periods were in

the relatively common species, Pelophylax spp. and L. helveticus, which both

increased their range sizes. Increases were also seen in Rana temporaria, A.

obstetricans and Ichthyosaura alpestris, while Bufo bufo did not change and H.

arborea, Bombina variegata, T. cristatus and L. vulgaris all decreased in range size

(Table 4.2 and Figure 4.6).

Square

s o

ccupie

d (

%)

Protected Area (%)

Figure 4.4 The percentage of 1 km grid squares at each PA coverage level occupied

by each amphibian species (all records 1996 – 2005).

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

R. temporaria

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

Pelophylax spp.

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

H. arborea

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

Bufo bufo

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

A. obstetricans

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

Bufo calamita

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

Bombina variegata

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

I. alpestris

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

T. cristatus

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

L. helveticus

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

L. vulgaris

0

20

40

60

0 ≥50 ≥80 ≥90 ≥98

S. salamandra


42

(a)

(b)

Figure 4.5 Range sizes of pond-breeding amphibians recorded in the early and later

survey periods from 104 grid squares (a) untransformed 1 km squares and (b) as

logit-transformed proportions of the total.

Dashed lines = intercept, for comparison of both periods on the same scale; solid line =

linear regression; see Appendix 1 for species name abbreviations.

Figure 4.6 Species percentage occupancy in grid squares surveyed in both periods.

≤ 1985, = 1996-2005; see Appendix 1 for species name abbreviations.

Ao

Bb

Bc Bv

Ha

P.spp

Rt

Ia

Tc

Lh

Lv

0

10

20

30

40

50

60

0 20 40 60

Num

ber

of r

ecord

ed 1

km

square

s i

n

late

r peri

od (

1996-2

005)

Number of recorded 1km squares in early period

(≤1985)

Ao

Bb

Bv

Ha

P.sppRt

Ia

Tc

Lh

Lv

-5

-4

-3

-2

-1

0

-4 -3 -2 -1 0

Logit-t

ransfo

rmed range s

ize

pro

port

ions in late

r peri

od (

1995-2

005)

Logit-transformed range size proportions in early period

(≤1985)

0

5

10

15

20

25

30

35

40

45

Rt P.spp Ha Bb Ao Bv Ia Tc Lh Lv

Surv

eyed g

rid s

quare

s o

ccupie

d (

%)

Amphibian species


43

Table 4.2 Index of relative range size change: proportion of grid squares occupied in

the early and later survey periods.

Species

Percentage of surveyed squares occupied Direction and

% change

Index of relative change

(squared residual) ≤ 1985 1996-2005

R. temporaria 35.5 41.3 + 16.33 0.0034

Pelophylax spp. 18.1 38.4 + 112.00 0.0412

H. arborea 6.5 2.9 - 55.56 0.0013

Bufo bufo 23.2 23.2

0.00 0.0000

A. obstetricans 6.5 15.9 + 144.44 0.0089

Bombina variegata 4.3 1.4 - 66.67 0.0008

I. alpestris 27.5 37.0 + 34.21 0.0089

T. cristatus 23.9 13.8 - 42.42 0.0103

L. helveticus 29.0 42.8 + 47.50 0.0190

L. vulgaris 26.8 19.6 - 27.03 0.0053

4.4 Discussion

4.4.1 Protected areas and amphibian species richness

The small average size of protected areas in Luxembourg may limit their potential to

provide refuge to viable populations of threatened species due to the greater „edge

effects‟ they are subject to (Janzen, 1983, in Jackson and Gaston, 2008) and the

fragmentation caused by development of intervening land. On the other hand,

multiple small reserves can perform better than a single large one, as long as the

nearby unprotected land still provides some habitat (Hansen and DeFries, 2007).

There is greater variation in environmental pressures between multiple small sites

than within one large PA, which reduces the likelihood of a single catastrophic event

simultaneously affecting all populations (Goodman, 1987).

In Luxembourg there is no difference in species richness in squares with and without

statutory PA designation. All amphibian species in Luxembourg occur in both PA

and non-PA grid squares, except the rarest, which cannot be assessed with the

current coarse dataset, but are known to only occur in PAs. It is very encouraging

that while some populations of amphibians are safeguarded by PAs, they are

generally not restricted to them – although PAs are an essential conservation tool,

most of Europe is not protected (Papageorgiou and Vogiatzakis, 2006) and therefore

it is ideal that species thrive both inside and outside. Recent EU conservation policy


44

to increase connectivity between PAs (Samways et al., 2010) will benefit both the

common and rare species. As shown on the maps in Figure 4.1 and Figure 4.2 the

PAs in Luxembourg form some large areas where many are adjacent to one another,

so the average size could be much larger than figures from the WDPA suggest

(IUCN and UNEP, 2009). Their listing as separate PAs reflects local circumstances,

such as land ownership and natural boundaries more than whether or not it borders

another PA.

The two least common newt species, T. cristatus and L. vulgaris, are not well

represented in the high PA cover squares, although in the previous chapter (Chapter

3) the principal component representing PAs was significantly positively related to

T. cristatus records in 1 km grid squares. This under-representation may be due to

the small sample size, but could also indicate that few PAs cater for the habitat

requirements of these species. The rarest anurans, H. arborea, Bombina variegata

and Bufo calamita, occupied very few grid squares in the first place and therefore are

not well represented by the dataset excluding neighbouring squares; their recent

history in Luxembourg is described below.

In the late 19th and early 20

th centuries, Bufo calamita is thought to have been present

throughout Luxembourg, even abundant in some places (Junck et al., 2003). Since

MNHN records began in 1955, it has only been recorded in ten 1 km grid squares at

five different locations, but between 2001 and 2005 it was known from only two

sites (a total of five 1 km grid squares) in central Luxembourg, both are former

quarries within PAs and are separated by approximately 25 km in a straight line. The

remaining populations are both small and are geographically and genetically isolated

from each other (Frantz et al., 2009).

In the five years to 2000 the MNHN database shows diminishing numbers of H.

arborea in fourteen 1 km grid squares at several sites in central Luxembourg and a

more stable population at one south-east site. However, since 2000 there are only

two records entered and the species is thought to have gone extinct at all of the

central sites, remaining only at a single pond in the south east. This last breeding

pond supports a large population and is surrounded by statutory protected areas, but


45

it is isolated with the next nearest standing waterbodies both more than 1.5 km away,

separated by agricultural land and roads. H. arborea are reported to be declining

throughout their range, especially in western Europe, and the main cause is thought

to be intensive land use (Stumpel, 1997).

The only population of Bombina variegata remaining in Luxembourg today has been

regularly recorded since the early 1970s in a large nature reserve in the very south of

the country. Historically Luxembourg was on the north-western edge of the species‟

European distribution (AmphibiaWeb, 2010), but it is threatened at its range margins

by low precipitation and rising temperatures (Gollmann et al., 1997). The MNHN

database includes records from up to ten 1 km grid squares in the centre and south of

the country, however these are mostly from the early 1980s. Luxembourg‟s only

extant population is rumoured to have been unofficially re-introduced by a member

of the public. There are thriving populations several kilometres away in France (R.

Proess, pers. comm., 2007), but major roads and a large town in the intervening

space make natural recolonisation unlikely.

The approach taken here to compare grid squares with some level of statutory

protection to un-protected squares is useful for looking at large datasets.

Unfortunately, more specific grid references were not available for most records, and

therefore it was often not possible to identify exactly whether they were from within

PAs or nearby. Jackson and Gaston (2008) applied similar comparisons to look at the

level of restriction of „biodiversity features‟ to PAs, but with datasets from a larger

geographic area and taxonomic groups containing many more species than the

Amphibia of Luxembourg. This method is a more realistic analysis of the efficiency

of PAs than the computerised random re-distributions of PAs used in other work

(e.g. Araújo, 1999), which do not account for the fact that PAs are based on real

landscape features and many other practical and logistical constraints. However a

major weakness of all these approaches is the use of presence-only data (Altwegg et

al., 2008), a problem that cannot be addressed in the historical data, but that could be

built into recording schemes to improve the data quality in the future. A further

parameter of interest would be the age of each PA, however these data were not

available for the current study.


46

4.4.2 Range size changes

Comparing the range sizes of Luxembourg‟s amphibians between the early and later

survey periods demonstrates, on average, no change in range size between the two

periods. However, some species exhibit a sharp decline: H. arborea and Bombina

variegata both declined between the early and later periods and as described above

are thought to have declined even further since. On the other hand, A. obstetricans,

which is also one of the rarer species, shows more than a doubling in range size

between the two periods.

A weakness of using atlas data for calculating changes in range size is the unknown

degree of bias within the data – for example rare species may be more likely to be

reported as „one off‟ sightings than common species, and records tend to be clustered

near the homes of the most active recorders (Dennis and Thomas, 2000). Telfer et

al.‟s (2002) method employed here addresses these potential biases by only

comparing grid squares that were surveyed in both periods, however this also

drastically reduces the sample size. The comparison could be extended to more than

two survey periods, but in the current study that would leave too few grid squares for

meaningful analysis and therefore also exclude more species. A larger dataset from a

wider area (e.g. Western Europe) could have increased the statistical power and

reduced the possible bias from relatively few recorders in a small area.

In this chapter and the previous one a sweeping generalisation is made about the

survey effort: that recorders tried to record all species on each survey. This does not

assume equal detectability between species (Boulinier et al., 1998), but it does

assume that suitable methods were used and that species‟ detectability rates remain

similar across time (see Chapter 7).

5. Wetland plants and amphibians

47

5. Amphibians as indicators of plant species richness

Wetland plants and amphibians

Summary: Plants are an important feature of amphibian breeding ponds,

providing shelter, food and egg-laying substrates. The strong reliance of

amphibians on aquatic plants means that it is often suggested that they

may be used as a surrogate measure of plant diversity in ponds. The

species richness of wetland plants and amphibians were assessed in 43

ponds. The plant species richness was found to increase with pond size

(following the „species – area‟ relationship), and there was also a positive

relationship between the number of plant species and the number of

amphibian species recorded. However, relationships between plant

species and individual amphibian species were weaker. Although

amphibian assemblages showed potential as indicators of plant species

richness, their role as surrogate measures of wider biodiversity is limited.

5.1 Introduction

Waterbodies are an important feature in any landscape, harbouring different fauna

and flora to the surrounding terrestrial areas and acting as corridors for dispersal of

organisms. At a regional level ponds harbour a higher proportion of the total aquatic

biodiversity than other aquatic habitats (Williams et al., 2003; Davies et al., 2008)

and should therefore receive a greater amount of attention in monitoring schemes.

A greater variety of resources, both trophic (i.e. food) and non-trophic (i.e. habitat),

is available to consumers when there is more variety among primary producers

(Tilman, 1986; Auderset Joye et al., 2002; Zhao et al., 2006). Amphibians are

dependent on plants in many ways, such that the plant assemblage at a site could be

expected to influence amphibian presence. Plants provide an egg-laying substrate,

particularly important for newts that wrap eggs individually in leaves (e.g. Duellman

and Trueb, 1986; Norris and Hosie, 2005), but also for amphibian species that attach

egg masses or strings to underwater stems or whose spawn floats in large clumps

protected from drifting apart by plants (Stebbins and Cohen, 1995; Egan and Paton,

2004). Newt eggs folded in leaves also benefit from protection from ultraviolet

radiation, mechanical damage and predation (Miaud, 1993, 1994; Marco et al., 2001;

Orizaola and Brana, 2003). Greater plant variety increases the availability of suitable

oviposition sites (Dvorak and Gvozdik, 2009) and newts show selectivity for certain

plants and leaves for egg-wrapping (Hosie and Potter unpublished data cited by


48

Norris and Hosie, 2005). In addition to their role in protecting amphibian eggs,

plants provide shelter to larvae and adults from predation (Babbitt and Jordan, 1996;

Babbitt and Tanner, 1997; Tarr and Babbitt, 2002) and also habitat for their

invertebrate prey.

The use of easy-to-survey taxa as a metric for those that are more difficult or costly

to survey is a popular and useful idea in ecology (Landres et al., 1988), but one that

must be applied only to well tested relationships between species and / or habitats

(Caro and O'Doherty, 1999). Caro and O‟Doherty (1999) classified so-called

surrogate species into three categories: indicators, umbrellas and flagships. Where

indicators are a species chosen to represent trends among biota sharing a habitat,

umbrellas are species whose distribution determines the extent of protected areas and

flagships are species promoted to garner public interest and support for conservation.

Indicators can be a very useful tool in improving the efficiency with which surveys

are done and maximising the scope of conclusions drawn from data collected in the

field (Balmford et al., 1996). In temperate zones amphibians have rarely been

considered as indicators because there are too few species (Sewell, 2006) but they

have shown potential for this role (Lebboroni et al., 2006). In the current study

amphibians could be considered as an indicator for wetland plants, or vice versa.

Amphibian surveys require greater time investment than plant surveys. However, in

Europe plant surveys require greater identification expertise due to the relatively

high number of species. Here amphibians are examined as indicators of plant

diversity, since the literature favours the use of higher taxonomic groups to indicate

biodiversity in lower groups (e.g. Balmford et al., 1996; Balmford and Gaston, 1999;

Gaston, 2000; Grelle, 2002). Selection of an indicator species or group should be

objectively determined by what it is intended for (Dale and Beyeler, 2001), but in the

current project it is clearly determined by the focus of the overall study.

An indicator species or group must be carefully selected and robust across the whole

geographic area to which it will be applied (Qian, 2007) – since different metrics

may apply at, for example, different altitudes (Menetrey et al., 2004) – and also

reflect changes in condition in the same way as the group or species it is


49

representing. The intended purpose of an indicator should be considered carefully

when it is selected and tested to avoid „erroneous assumptions‟ (Sahlén and

Ekestubbe, 2001) about its relationship to the organisms or conditions it is

indicating. A well-selected indicator would meet these criteria set out by Pearson

(1994): taxonomy fully known and described, easy to survey for and present in a

broad range of habitats. The criteria are elaborated by Sewell and Griffiths (2009) in

their review of the indicator literature, they found eight criteria for good indicators

that were repeatedly identified by other authors: ease and cost effectiveness,

correlation with other groups, sensitivity, susceptibility to a range of stressors,

distinguishable responses to manmade and natural stress, wide distribution, a group

of species rather than a single species and ecological relevance. Hereafter the term

„indicator‟ is used to describe a taxon or taxonomic group used as a proxy measure

of others.

Some studies have found a positive correlation between plant and amphibian species

richness in waterbodies. In very large scale studies amphibians have been found to

be strongly positively correlated with plant species richness, for example in reserves

across China (Zhao et al., 2006; Qian, 2007). These findings have been replicated at

both regional and local scales: amphibian species richness increases with vegetation

cover within study sites confined to a single valley or estate (Pavignano et al., 1990;

Hartel et al., 2007). Triturus cristatus presence has been identified as a potential

indicator of good aquatic plant richness in small waterbodies in both Sweden

(Gustafson et al., 2006) and England (Sewell, 2006).

Luxembourg covers a fairly small area (2585 km2) (see General methodology, Figure

2.1), therefore this study can be considered to be on a local scale. The objective of

surveying plants at ponds where the amphibian assemblage is known was to explore

the following relationships:

Is there a correlation between pond size and wetland plant species richness?

Are amphibians a good indicator of plant species richness?

Does any amphibian species live only in high or low plant diversity ponds?

Are there any strong associations between particular plant taxa and

amphibians?


50

5.2 Methods

5.2.1 Study area

The study area is described in the General Methodology (section 2.2). Most

vegetation surveys were conducted in 2008 (ponds 1-34, between 3-20 June 2008),

except pond 8 which was surveyed on 17 June 2007 and not resurveyed in 2008 due

to access permission being withdrawn, and the additional nine ponds (35-43) that

were surveyed 23-25 May 2009 and used only in analyses specifically about Alytes

obstetricans (Appendix 3).

5.2.2 Plant survey method

The vegetation in each study pond was surveyed from the bank and by wading to

produce an exhaustive list of the plant species present and an estimate of each

species‟ percentage cover within the high water level of the pond.

Vascular plants were mostly identified to species level using standard field guides

(Haslam et al., 1982; Hubbard and Hubbard, 1984; Rose, 1989, 2006), however

some were only identified to family or genus due to difficulty distinguishing similar

species (e.g. some grasses). Non-vascular plants were simply categorised into broad

groups (e.g. algae, mosses).

Plant species abundance was estimated as a percentage of the total pond surface area.

Where a plant covered less than 5% of the pond it was recorded as „< 5‟: these

species were only included in species richness analyses. The total „percentage‟ cover

of plants at any one pond can exceed 100% due to the overlapping of submerged,

floating and emergent plants. As all areas of each pond were surveyed, data were not

adjusted for sampling effort.

The plants surveyed were divided into wetland and non-wetland species according to

whether Stace (1997) described their normal habitat as wetland of any type (e.g.

“wetland”, “marsh”, “lake”). Plants normally only associated with dry ground were

assumed to be remnants from recent changes at the site or opportunist colonisers left

over from recent years when many ponds shrunk during hot, dry periods. Non-


51

wetland plant species were excluded from analyses. Common and scientific names

follow Stace (1997). Plant diversity data were also incorporated as a covariate in

occupancy modelling (Chapter 7).

5.2.3 Amphibian data

Amphibian survey methods are described in the General Methodology (Chapter 2).

For the present analyses amphibian species were recorded as a binary variable for

present / absent (1 / 0 respectively). Multiple visits to each site minimise the

possibility of non-detection of species (Zanini et al., 2009); nevertheless, as

illustrated by discrepancies in the naïve and model-estimated occupancy rates and

low detectability modelled in Chapter 7, there could be errors in ponds identified as

not occupied by any species. However, according to the final occupancy models in

Chapter 7 an adequate number of visits were made for 95% confidence that absences

were true, and for all survey methods cumulative detection probabilities were high

across multiple surveys and (in some cases) more than one season (Pellet and

Schmidt, 2005).

5.2.4 Analysis

Ponds within 25 m of each other were grouped for the current analyses. Plant data

were described by the number of species present in study ponds and their

commonness. Due to many wetland plant species being present only at low

percentage covers (< 5%) the diversity data would be significantly altered by

excluding low prevalence plants and those not identified to species level (t42 = 5.27,

p < 0.001), therefore they were retained for richness analyses but removed for cover

analyses.

Partial correlations were used to test the relationship between wetland plant species

richness and pond size, and between amphibians and plant richness and vegetation

cover. Correlation analyses between amphibians and plants were controlled for pond

size, with the log pond size as a covariate.

For the seven commonest amphibian species an ANCOVA was computed to

compare the wetland plant species richness between occupied and unoccupied ponds,


52

with pond size as a covariate. An a priori power analysis was calculated in

„G*POWER‟ (Erdfelder et al., 1996) to check that there was sufficient power ( >

0.8, with a medium effect size specified [w = 0.3]) for the test.

Pearson‟s chi-square in 2x2 contingency tables was applied to test specific

associations of moderately common amphibian species (Bufo bufo, T. cristatus, L.

vulgaris and A. obstetricans, present at 38%, 38%, 65% and 33% of ponds

respectively) with individual wetland plant species. The other seven amphibian

species (of 11 encountered) occurred at too many (> 91%) or too few (< 3%) ponds

to be tested for associations. A sequential Bonferroni correction was applied to

reduce the likelihood of Type I errors resulting from repeated tests (Rice, 1989).

5.3 Results

5.3.1 Plant community descriptives and pond size effect

A total of 113 species of wetland plant and 45 non-wetland plant species were

recorded in the ponds surveyed (Appendix 6). Fifty-two of the wetland plant species

recorded were only observed at very low coverage (i.e. less than 5% of a pond; n =

47) and / or could not be identified to species level (n = 12); most of the non-wetland

plants recorded did not exceed 5% cover at any pond (n = 42, of 45 species). Beyond

site descriptions, all non-wetland plant species were excluded from analyses.

The total number of wetland plant species (≤ 5% and > 5% cover) ranged from 3 to

33 (mean = 14.1, s.d. ± 7.5, mode = 9; Figure 5.2). The number of wetland plant

species in the study ponds, identified to species level and covering > 5% of a pond‟s

area, ranged between 0 and 18 (mean = 5.1, s.d. ± 3.1, mode = 5).

Thirty one point nine percent of the wetland plant species surveyed were seen in only

one pond. However, some wetland plant species were very common and found in

more than 50% of the study ponds (Table 5.1).


53

There was a significant positive correlation between pond size and wetland plant

species richness (r = 0.47, n = 43, p < 0.001;

Figure 5.3Figure 5.1 There was no correlation between the number of amphibian

species detected and the size of the study pond (r = 0.28, n = 34, p > 0.05).

Figure 5.2 Diversity of wetland plant species in study ponds and the frequency of

occurrence (including plants not identified to species and plants at ≤ 5% and > 5%

cover).

Table 5.1 The five most commonly occurring wetland plants across all study ponds

(n = 43).

Species Study ponds occurring in

n %

Soft-rush (Juncus effusus) 27 62.8

Common duckweed (Lemna minor) 26 60.5

Floating sweet grass (Glyceria fluitans) 22 51.2

Creeping buttercup (Ranunculus repens) 22 51.2

Bottle sedge (Carex rostrata) 19 44.2

1.0

10.0

1.0 10.0

Num

ber

of w

etlant

pla

nt

specie

s (

log)

Pond size (log)

0

2

4

6

8

10

12

14

1-5 6-10 11-15 16-20 21-25 26-30 31-35

Num

ber

of ponds

Total number of wetland plant species


54

Figure 5.3 The number of wetland plant species (≤ 5% and > 5% cover) plotted

against pond size (logarithmic scales; y = 0.2675x + 0.8013).

5.3.2 Amphibians as indicators of plant richness and vegetation cover

When controlling for pond size effects, the total number of plant species in ponds

increased significantly with the number of amphibian species present (r = 0.57, n =

34, p < 0.005; Figure 5.4). This relationship held when non-wetland plant species

were excluded (r = 0.56, n = 34, p < 0.005).

Although plant species richness was positively related to the number of amphibian

species present, ponds with a higher coverage of vegetation generally supported

fewer amphibian species, although this trend was not significant (r = -0.32, n = 34, p

> 0.05; Figure 5.5).

Figure 5.4 The mean number (±s.e.) of wetland plant species and all plant species

(wetland and non-wetland) found in ponds according to amphibian species diversity

(plants of ≤ 5% and > 5% cover).

= wetland plant species, = all plant species.

1.0

10.0

1.0 10.0

Num

ber

of w

etlant

pla

nt

specie

s (

log)

Pond size (log)

0

10

20

30

40

50

60

3 (n=6) 4 (n=3) 5 (n=9) 6 (n=12) 7 (n=2) 8 (n=2)

Mean n

um

ber

of pla

nt

specie

s (

±s.e

.)

Number of amphibian species present


55

Figure 5.5 The extent (%) of wetland plant cover (±s.e.) and the number of

amphibian species present.

5.3.3 Wetland plant richness and amphibian occupancy

A priori power analyses run in „G*POWER‟ (Erdfelder et al., 1996) returned > 0.8

for the amphibian species tested, indicating sufficient power to detect an effect if one

existed. Four species were not analysed here due to occupancy at too few (Hyla

arborea, Bufo calamita, Bombina variegata) or too many (Ichthyosaura alpestris)

ponds for comparison. Controlling for pond size effect, on average ponds occupied

by moderately common amphibian species contained more wetland plant species

than unoccupied ponds (Figure 5.6). This trend is statistically significant for T.

cristatus (F1,31 = 4.87, p < 0.05) and Lissotriton vulgaris (F1,31 = 5.65, p < 0.05).

Figure 5.6 Mean number of wetland plant species in ponds occupied / not occupied

by moderately common amphibian species, plants of ≤ 5% and > 5% cover included.

A. obstetricans data include the additional ponds surveyed in 2009.

= occupied, = not occupied; * = significant at p < 0.05; see Appendix 1 for amphibian

species name abbreviations.

0

20

40

60

80

100

3 (n=5) 4 (n=3) 5 (n=7) 6 (n=13) 7 (n=4) 8 (n=2)

Exte

nt of w

etland p

lant

cover (%

)


**

0

5

10

15

20

25

Rt P.spp Bb Ao Tc Lh Lv

Mean n

um

ber

of w

etland

pla

nt

specie

s (

±s.e

.)

Amphibian species


56

Specific relationships between plant species and amphibian species

Nineteen significant relationships were observed between wetland plant species and

amphibian species (Table 5.2), but a sequential Bonferroni correction applied to

adjust the alpha level for simultaneous tests leaves just one significant relationship.

The Bonferroni correction is very conservative, however it is necessary to avoid

incorrect conclusions.

Potamogetan natans was significantly positively associated with A. obstetricans (2

= 13.66, p = 0.0002, significant at the Bonferroni–adjusted alpha level of 0.0004):

both were absent in 60.5% of ponds, they occurred together in 20.9% of ponds and

singly in just 7.0% (P. natans) and 11.6% (A. obstetricans) of ponds (Figure 5.7).

Figure 5.7 Occupancy of Potamogetan natans in ponds by A. obstetricans

occupancy.

= P. natans absent, = P. natans present.

0

20

40

60

80

present absent

All p

onds (

%)

A. obstetricans


57

Table 5.2 Relationships between individual amphibian and plant species, before and

after sequential Bonferroni correction.

Pearson’s 2

Uncorrected p value

(alpha 0.05)

Relationship Direction

Sequential Bonferroni corrected

alpha

A. obstetricans

Potamogetan natans 13.66 < 0.0005 + 0.000446 *

Lysimachia nummularia 7.14 < 0.01 - 0.000455

Salix cinerea 6.29 < 0.05 - 0.000472

Ranunculus repens 6.24 < 0.05 - 0.000476

Iris pseudacorus 6.11 < 0.05 - 0.000481

Dactylis glomerata 6.03 < 0.05 - 0.000485

Bufo bufo

Carex rostrata 7.54 < 0.01 - 0.000450

Lemna minor 5.38 < 0.05 - 0.000495

Glyceria fluitans 5.38 < 0.05 - 0.000500

Mentha aquatica 4.11 < 0.05 + 0.000532

T. cristatus

Juncus inflexus 6.84 < 0.01 + 0.000459

Iris pseudacorus 6.84 < 0.01 + 0.000463

Eleocharis palustris 6.35 < 0.05 + 0.000467

Salix alba 4.44 < 0.05 + 0.000510

Rumex conglomeratus 4.11 < 0.05 + 0.000515

Mentha aquatica 4.11 < 0.05 + 0.000521

Lemna trisulca 4.11 < 0.05 + 0.000526

L. vulgaris

Myostis scorpioides 5.71 < 0.05 + 0.000490

Salix alba 4.89 < 0.05 + 0.000505

* = significant after correction; relationship direction: + positive, - negative.

5.4 Discussion

More than 70% of the plant species recorded during vegetation surveys were wetland

plants (according to Stace, 1997). Pond 34 held the highest number of wetland plants

with 33 species, while ponds 14 and 26 held just three wetland plant species each –

at pond 14 no plant covered 5% of the total area (study ponds listed in Appendix

3). The most speciose pond, 34, was in fact a cluster of small ephemeral pools

around a larger pond, at a site that is managed for its herpetological interest. The

least speciose were both unmanaged woodland ponds with complete canopy cover

and thick leaf litter in the water. The gamma (γ), or regional, diversity of wetland

plants recorded in these 43 Luxembourgish ponds (γ diversity = 113 species) is much


58

greater than was found in the 20 ponds surveyed by Williams et al. (2003) in

southern England (γ diversity = 67).

The number of wetland plant species increased with pond size. This has been

observed previously in small-scale studies of pond plants (e.g. Sewell, 2006) and the

species-area relationship is well documented, originally in Arrhenius‟ species-area

equation (1921) and more recently in the species-area-habitat model by Triantis et al.

(2003). Oertli et al. (2002) found pond size to be unimportant for aquatic plant

diversity, noting that several small ponds could hold more diverse flora than a

solitary big pond, but they conclude that in general the species-area relationship

appears to hold true for aquatic plants in ponds. Gee et al. (1997) suggest that it is in

fact the size of the vegetated area in the pond, rather than the pond size per se, that

determines its biodiversity.

Wetland plant species richness was observed to increase with the number of

amphibian species present in a pond. The additional habitat complexity afforded by a

variety of plants increases the ecological niches available, allowing a greater number

of species to co-exist and a greater diversity at every trophic level (Triantis et al.,

2003; Williams et al., 2008). Van Buskirk (2005) noted that habitat quality was more

important than competition from other amphibians in determining pond occupancy.

The current data set is simply a snapshot of a relationship that varies across seasons

and decades, it cannot demonstrate if, or how closely, plant and amphibian species

richness would co-vary over time. These data support the use of amphibian richness

as an indicator of plant species richness, but they are not sufficient in isolation. The

current study shows that amphibian species richness has potential as a bioindicator

for the richness of plants, but it needs further testing at more sites, across a broader

time scale and with sufficient survey methods and visits for high cumulative

detection rates (Zanini et al., 2009).

Each amphibian species exhibited a tendency to occupy ponds with a greater number

of wetland plant species. However, the difference in plant species richness between

occupied and non-occupied ponds was only significant for T. cristatus and L.

vulgaris. Other researchers have observed similar effects, notably Gustafson et al.


59

(2006) and Sewell (2006) both found that T. cristatus tends to occupy ponds with

higher plant richness, but have more recently concluded from field data and a review

of published papers that T. cristatus alone is not an adequate indicator of biodiversity

(Sewell and Griffiths, 2009). In the present study sample sizes were relatively small

and additional data could have increased the likelihood of detecting significant

differences for other amphibian species in the comparison.

In individual tests of association between plant and amphibian species a number of

relationships were observed, but they did not stand up to the stricter Bonferroni-

corrected alpha levels. Only the positive relationship between A. obstetricans and P.

natans is fully supported. This is perhaps more due to these species having similar

habitat preferences rather than a direct dependence on one another, for example both

thrive in permanent ponds (Bosch et al., 2008; Della Bella et al., 2008).

Plants that can negatively impact a pond‟s suitability for amphibians could have been

expected to show a greater effect on amphibians. For example common duckweed,

Lemna minor, can dominate and create eutrophic conditions (Gee et al., 1997; Janse

and Van Puijenbroek, 1998; Scheffer et al., 2003), but it only interacted with Bufo

bufo presence before the Bonferroni correction was applied. No non-native plants

were observed during the current study, however non-natives (e.g. Crassula helmsii)

can also degrade breeding conditions in amphibian ponds if they crowd out native

plants or choke the water column (Langdon et al., 2004).

Amphibians exhibit many model indicator group qualities, such as wide geographic

ranges, varied population sizes, complex life cycles and sensitivity to modern

environmental threats (Collins and Storfer, 2003). However, in common with similar

studies, here it is observed that single amphibian species do not make good

indicators of plant richness; they show greater indicator potential when used as an

assemblage (Sewell and Griffiths, 2009). There are more amphibian species in

Luxembourg than in the UK where Sewell and Griffiths‟ work was conducted, but

after removing the very common and very rare species, the remaining „suite‟ of four

or seven species is still quite small. A suite of indicators is better able to represent a


60

broad gradient, across both environmental conditions and geographical area (Dale

and Beyeler, 2001).

That there is a relationship between plants and amphibians is intuitive: their ecology

is intricately intertwined (e.g. Braz and Joly, 1994; Miaud, 1994; Marco et al.,

2001). Although few comparative studies of amphibians and plants in Western

Europe have been published and indicator studies have largely focussed on water

quality measures. The results here suggest that amphibian assemblages have

potential to be indicative of other taxa within their breeding ponds. However, the

dataset is relatively small and represents a restricted geographical area, so

generalisations should be made with caution. Further work should include plants and

features surrounding the study ponds, because terrestrial habitat is also important in

dictating amphibian species presence (Cushman, 2006).

6. Macroinvertebrates and amphibians

61

6. Amphibian species richness as an indicator of

macroinvertebrate family richness

Macroinvertebrates and amphibians

Summary: Invertebrates are a key food source for amphibians. In turn,

amphibians – especially eggs and larvae – are often preyed on by

invertebrates. Their interdependence makes them ideal candidates for use

as indicators of each other‟s presence or richness within an amphibian

breeding pond. Invertebrates have often been used as indicators of

general aquatic habitat health, but little has been published on their

relationships with amphibians. Although there was a positive trend in the

relationship between macroinvertebrate richness and amphibian species

richness, this was not supported statistically. One specific relationship

stood out: Lissotriton vulgaris and the Glossiphoniidae leech family

occurred in the same ponds and never separately. In the current study

only a small sample of ponds were surveyed for macroinvertebrates and

the time frame was short. A larger, longer-term dataset may have been

able to substantiate the observed trend.

6.1 Introduction

In the previous chapter amphibians were examined as potential indicators for

wetland plant richness. Correlations between amphibians and wetland plants were

found on several measures and it was concluded that there is potential to use

amphibians as indicators. However, more extensive testing of the relationship is

necessary – for example at a broader geographical scale and across a longer time

period. In this chapter the relationships between macroinvertebrates and amphibians

and plants are tested.

Most adult amphibians feed on live prey comprised principally of invertebrates and

their diets reflect niches within the habitat (Dolmen and Koksvik, 1983). They also

frequently prey on their own larvae and those of sympatric species (Duellman and

Trueb, 1986) and any live animal small enough to be ingested. However, the

relationship is not asymmetric and some invertebrate species also predate on

amphibians. For example, the larvae and adults of the great water beetle (Dytiscus

marginalis) and dragonfly naiads (order Odonata, Anisoptera) are formidable

predators that take amphibians in all life stages and some leeches (family


62

Haemopidae) feed on amphibians without necessarily killing them (Duellman and

Trueb, 1986).

As a group, invertebrates occupy a broader range of environmental niches than

amphibians, and have regularly been used as indicators of habitat health, particularly

to monitor water quality. Many schemes have been developed for use in rivers, all

known by their acronyms, including the BMWP (Biological Monitoring Working

Party, Hawkes, 1998) and RIVPACS (River Invertebrate Prediction and

Classification System, Centre for Ecology & Hydrology, 2009); with variations on

both used extensively in Europe.

Similar schemes have been developed to monitor and classify standing waters, both

in the UK and continental Europe. For example, PSYM (Predictive System for

Multimetrics, Howard, 2002) and NPS (National Pond Survey, UK, Biggs et al.,

1998) in the UK, and IBEM (Indice de Biodiversité des Etangs et Mares,

Indermuehle et al., 2008) and PLOCH (Plans [PL] d‟eau [O] suisses [CH], Oertli et

al., 2005) in Switzerland. These methodologies require a number of standardised

measurements of the biological features of waterbodies, weighting families or

species according to their rarity or sensitivity to environmental conditions, to

produce a single score. The scores can be used to infer water quality or to measure

the „conservation value‟ of a site.

Some studies have found invertebrates to be ideal surrogate measures of plant

richness (e.g. PSYM, BMWP; Hawkes, 1998; Howard, 2002), which is logical

where one taxon is largely dependent on the other, because an increase in the

availability and variety of habitat can influence organisms further up the food chain.

However, in their large scale study Oertli et al. (2005) found very low correlations

between the plant, invertebrate and amphibian taxa tested. Further, they based the

PLOCH scheme on the sum of scores assigned to individual species rather than using

any taxa as an indicator of another.

The current chapter follows on from the previous one on wetland plants and

amphibians. Macroinvertebrates fill a range of ecological niches within ponds, from


63

prey items for amphibians to predators of amphibians. In this chapter the following

questions are addressed:

Is there a correlation in species richness between macroinvertebrates and

wetland plants?

Is amphibian diversity a suitable surrogate measure of macroinvertebrate

diversity?

Do amphibians favour high or low macroinvertebrate diversity ponds?

Are there any strong associations between particular macroinvertebrate

families – especially predatory invertebrates – and amphibian species?

6.2 Methodology

6.2.1 Study area

Table 6.1 The amphibian species present in ponds surveyed for macroinvertebrates.

Pond Amphibian species present

Number of

species Rt P.spp Ha Bb Bc Bv Ao Ia Tc Lh Lv

2 Weiden 3&4 6

5 Laaschtert 2 6

10 Biergwiss 7

15 Werwelslach 1&2 5

17 Giele Botter 6

18 Féitzemuer 5

22 Dréisch 6

25 Neiländigfeld 6

27 Haard 6

31 Weiergewan 7

34 Steinfort 8

See Appendix 1 for species name abbreviations.

Eleven of the ponds surveyed for amphibians and plants in 2007 and 2008 were

chosen for macroinvertebrate surveys (Table 6.1). They were selected to include a

good spread across the study area of south and central Luxembourg, medium to high

amphibian species richness and a variety of amphibian assemblages and pond types.

Ponds 2 and 15 both constituted two pools in very close proximity to each other and

here are considered as a single pond each, as are the big permanent pool and small

ephemeral pools at pond 34.


64

6.2.2 Macroinvertebrate survey method

Two rounds of macroinvertebrate surveys were conducted during late spring and

summer, 6-22 May 2008 and 27 June - 16 July 2008 respectively. Surveys were

usually completed within one morning or afternoon, but on one occasion a survey

started in the late afternoon had to be finished the following morning (pond 34,

survey 1).

Macroinvertebrates were considered to be those which could not easily fit through a

2 mm gauge pond net. Microinvertebrates were often retained in the net within

vegetation and substrate, but they were not included because their populations are

subject to large and rapid fluctuations in number. Common and scientific names

follow those given on the taxonomic nomenclature website

www.zipcodezoo.com/Animals (ZipcodeZoo.com, 2009). Non-aquatic invertebrates

were excluded, assumed to have fallen in from overhanging vegetation.

Macroinvertebrate survey methodology followed the UK‟s National Pond Survey

(NPS) (Biggs et al., 1998):

1. Mesohabitats within the pond were identified (e.g. open water, stands of

emergent vegetation, shallow and deep areas).

2. Three minutes of netting time was divided equally between the mesohabitats.

Netting time includes only the time that the net is in the water: typically this

would constitute multiple sweeps of 10-20 seconds each. In small ponds 100%

coverage was achieved in fewer sweeps and less time. Occasionally in larger

ponds, or where there was thick mud or leaf litter, it was not possible to net for

3 minutes because the sorting and identification time was too great (> 7 hours).

3. A 2 mm gauge net, measuring 26 x 25 cm across the opening and 50 cm deep,

was swept in front of the wading surveyor in a continuous motion through the

water for the required netting time. The net was moved throughout the water

column, including across the surface.

4. Samples were removed from the net to a white sorting tray, from which

invertebrates were identified on site using standard field guides (Unwin, 1984;

Croft, 1986; Chinery, 1993; Tachet et al., 2000; Olsen et al., 2001; Wallace,


65

2006) to family level (genus or species where possible) and counted. Counts for

very numerous specimens present were estimated, based on an accurate count

of a sub-sample. Unidentified invertebrates were compared via photographs

and field notes between ponds to ensure that the total species number is

accurate.

5. All samples were returned to the pond as soon as possible after identification.

6.2.3 Amphibian data

The survey methods for amphibians are described in the General Methodology

(Chapter 2). Here amphibians are simply described as present or absent (1 / 0

respectively). As for the plant analysis (Chapter 5), the cumulative amphibian

detection rates (Pellet and Schmidt, 2005) of multiple methods and survey visits are

high and it is unlikely that there were false negative occupancy errors for amphibian

species in the sub-set of ponds used in the current chapter.

6.2.4 Analysis

A species weighting system for aquatic invertebrates, such as BMWP or PLOCH,

has not been applied as none has been developed for still water in the Luxembourg

area of Europe. Therefore all analyses used basic counts or presence / absence.

Species and family richness data were not adjusted for different sampling times,

counts were extrapolated to give a full three minutes of survey effort in ponds 10

(survey 1) and 27 (survey 2) where the full netting time or 100% of pond could not

be completed. All analyses reported here are of total catch sizes or family richness,

which is generally a good predictor of species richness (Williams and Gaston, 1994).

The same analyses with the less accurate species-level data produced very similar

results and are not reported, except in comparisons of predatory invertebrates and

amphibians.

The Bray-Curtis Dissimilarity Index (BS) (Bloom, 1981) indicated a low level of

dissimilarity in species composition between surveys (all ponds BS ≤ 0.4) and there

was no difference between surveys in terms of numbers caught (t10 = 0.13, p > 0.05).


66

Therefore there was no need to weight the surveys for analysis and data were pooled

by simply adding the surveys together.

The data were described by the percentage identified to family, genus and species

levels and the average and range of catch sizes and family diversity. Outliers (> 2

standard deviations from the mean) were identified with z-scores.

Pearson product-moment correlations were applied to examine the relationships

between all invertebrates – or just predatory macroinvertebrates – and pond size,

amphibian richness, wetland plant diversity and vegetation extent. Only those

invertebrates well known to be predators of amphibians were included here:

dragonfly naiads (Odonata, Anisoptera), great diving beetle (Dytiscidae) larvae and

adults, horse leeches (Haemopidae) and water scorpions (Nepidae). Other aquatic

invertebrates (e.g. other beetles and caddisfly larvae) could predate amphibian eggs

and small larvae, but are not considered here due to limited published information on

exact species interactions.

General macroinvertebrate diversity and predator diversity were compared between

ponds where each moderately common amphibian species was present to those it

was absent from with one-way ANOVA. A priori power analyses in G*POWER

(Erdfelder et al., 1996) indicated sufficient power for statistical analysis.

Chi-square tests were applied to the interactions between presence / absence of pairs

of all moderately common invertebrate (n = 38) and amphibian (n = 4) species.

Sequential Bonferroni corrections (Rice, 1989) were made within amphibian species

to adjust the accepted alpha value for repeated tests.


67

6.3 Results

6.3.1 Macroinvertebrate descriptives

Overall, 88.4% of invertebrate specimens were positively identified to family level.

A total of 69 macroinvertebrate families were recorded in the 11 ponds surveyed,

with 60.0% of specimens in these families confidently identified to genus level, but

only 25.8% identified to species level. Ponds held a mean of 19.6 macroinvertebrate

species (s.d. ± 6.1, range 8 to 32; not all identified). The number of genera per site

cannot be calculated because it is not known how many genera the unidentified

species comprised.

The total count of macroinvertebrates netted in a single pond (the sum of surveys 1

and 2) ranged from 167 to 3833 individuals (mean 1623.9 ± s.d. 1123.0) and the

number of families ranged from 14 to 34 (mean 24.5 ± s.d. 5.5). Pond 10, which was

heavily shaded and thick with leaf litter, held the fewest macroinvertebrates in both

total catch size and family count, while pond 31 held the highest total catch of

macroinvertebrates and pond 34 had the most families.

In terms of macroinvertebrate counts and richness, pond 17 is an outlier (z = 2.19).

Among the ponds surveyed for macroinvertebrates pond 17 was unusual in that it

was very large, the only pond with a hard rock substrate and little vegetation or leaf

litter in the water. Most of the following analyses are run both with and without pond

17.

Table 6.2 The five most numerous macroinvertebrate families across all 11 ponds

surveyed.

Type Order Family Total number

caught Percentage of total catch (%)

Mayfly Ephemeroptera Baetidae 2880 16.1

Snail Basommatophora Planorbidae 2424 13.6

Snail Basommatophora Lymnaeidae 2330 13.0

Caddisfly Tricoptera Limnephilidae 1872 10.5

True fly Diptera Chaoboridae 1777 10.0


68

Table 6.3 The most commonly occurring macroinvertebrate families across all 11

ponds surveyed.

Type Order Family nponds Percentage of

ponds (%)

Mayfly Ephemeroptera Baetidae 11 100.0

Bug Hemiptera Gerridae 11 100.0

True fly Diptera Chaoboridae 10 90.9

Beetle Coleoptera Dytiscidae 10 90.9

Caddisfly Tricoptera Limnephilidae 10 90.9

Oligochaete worm Haplotaxida Naididae 10 90.9

Bug Hemiptera Notonectidae 10 90.9

Many macroinvertebrate families were found in only one pond (Figure 6.1); 27

families occurred in just one pond and only two occurred in all 11 ponds.

Macroinvertebrate assemblages were generally dominated by the mayfly family

Baetidae (Table 6.2 and Table 6.3), which was present in all 11 ponds surveyed and

very numerous, accounting for 16.1% of the total number of macroinvertebrates

caught during both surveys. The pond skater family Gerridae, comprising only one

species in the current study, Gerris lacustris, was equally widespread (11/11 ponds)

but caught in lower numbers (2.4% of total catch). Snails of the Planorbidae

(ramshorn snail) and Lymnaeidae (freshwater snail) families were also very

numerous, representing 13.6% and 13.0% of the total catch respectively and both at

8/11 ponds. See Appendix 7 for a full list of the invertebrates encountered.

Fish were not found on any survey in any of the ponds surveyed for

macroinvertebrates.

Figure 6.1 The number of ponds that macroinvertebrate families occurred in.

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11

Num

ber

of

macro

invert

ebra

te fam

ilie

s

Number of ponds


69

6.3.2 Pond size and macroinvertebrate numbers and family richness

With all ponds included, a Pearson product-moment correlation indicates that there is

no correlation between pond size and the number of macroinvertebrates counted (r =

0.29, n = 11, p > 0.05) or the number of macroinvertebrate families present (r = 0.01,

n = 11, p > 0.05). However, with the outlying pond 17 removed, pond size does

correlate positively with the number of macroinvertebrates caught (r = 0.86, n = 10,

p < 0.01), but still not with the number of families (r = 0.46, n = 10, p > 0.05)

(Figure 6.2).

Figure 6.2 The relationship between pond size and macroinvertebrate family

richness (y = 0.0033x + 3.1533) and the total count of macroinvertebrates (y =

0.1261x + 6.2569) (logarithmic scales; including pond 17).

= number of families; = total count of macroinvertebrates.

6.3.3 Macroinvertebrates and wetland plant richness and extent

No relationship was observed between the richnesses of wetland vegetation and

aquatic macroinvertebrates (Table 6.4 and Figure 6.3). Excluding the outlying pond

17 did not reveal any significant relationships either.

1

10

1 10

Num

ber

of m

acro

invert

ebra

tefa

mili

es o

r to

tal count (l

ogs)

Pond size (log)


70

Table 6.4 Pearson's product-moment correlation results for wetland vegetation and

macroinvertebrates, including all ponds surveyed for invertebrates (n = 11).

Vegetation Macroinvertebrates r p

Extent Total count 0.28 0.414

Family richness 0.37 0.268

Species richness Total count 0.53 0.096

Family richness 0.24 0.471

Num

ber

of

macro

invert

ebra

te f

am

ilies

Figure 6.3 The number of macroinvertebrate families in surveyed ponds by (a) the

number of wetland plant species present and (b) the extent of wetland plant cover.

6.3.4 Amphibians as indicators of macroinvertebrate diversity

There is no correlation between the number of amphibian species present and the

number of macroinvertebrate families (r = 0.18, n = 11, p > 0.05) or the total count

of macroinvertebrates (r = 0.487, n = 11, p > 0.05), although Figure 6.4 suggests a

trend. A relationship would be difficult to detect statistically with the small sample

size of 11 ponds, especially with the narrow range of amphibian species richnesses

(5 – 8). Removing the outlying pond 17 does not make either of these comparisons

significant (r = 0.171, n = 10, p > 0.05; and r = 0.497, n = 10, p > 0.05 respectively).

pond 17

0

10

20

30

40

0 5 10 15 20

Number of wetland plant species

(a)

pond 17

0

10

20

30

40

0 20 40 60 80 100

Extent of wetland plant cover (%)

(b)


71

Mean c

ount

of

macro

invert

ebra

tes (

±s.e

.)

Mean n

um

ber

of

macro

invert

ebra

te f

am

ilies (

±s.e

.)


Figure 6.4 (a) The mean count of macroinvertebrates and (b) the mean number of

macroinvertebrate families found in ponds according to the number of amphibian

species present.

6.3.5 Macroinvertebrate richness and amphibian species occupancy

The number of macroinvertebrate families found in ponds occupied / not occupied

by moderately common amphibian species was compared using one-way ANOVA:

there were no significant differences between occupancy states (Figure 6.5).

Although sample sizes were low, a priori power analyses (run in G*POWER,

Erdfelder et al., 1996) suggest that there was sufficient power ( > 0.8) to detect an

effect if it had existed. False absences of the amphibian species in these data are

unlikely (as explained above, section 6.2.3), but it is very likely that invertebrate

families were missed, which would have affected the chance of detecting

differences.

The count of predatory invertebrates varied widely between ponds (mean 30, range

6-59), with the greatest number being caught in the pond with the most amphibian

species (Figure 6.6a). There was no relationship between the number of amphibian

species present and the number of predators caught during surveys (r = 0.105, n =

11, p > 0.05), and the removal of outlying pond 17 did not substantially change the

result (r = 0.084, n = 10, p > 0.05).

0

500

1000

1500

2000

2500

3000

3500

4000

5 (n=2) 6 (n=6) 7 (n=2) 8 (n=1)

(a)

0

10

20

30

40

5 (n=2) 6 (n=6) 7 (n=2) 8 (n=1)

(b)


72

Figure 6.5 Mean number of macroinvertebrate families in ponds occupied / not

occupied by moderately common amphibian species.

= occupied, = not occupied; see Appendix 1 for amphibian species name abbreviations.

A priori power analyses (in G*POWER, Erdfelder et al., 1996) indicated sufficient

power ( > 0.8) for ANOVAs comparing amphibian occupancy and the total number

of predatory invertebrates and family and species richness (Figure 6.6b). There were

no differences in the count of predatory invertebrates between ponds occupied or not

occupied by any of the four amphibian species tested. However, there were just

significantly more predatory invertebrate species in ponds occupied by Bufo bufo

than not occupied, and fewer predatory invertebrate families in ponds occupied by A.

obstetricans than not occupied (Table 6.5). The same two relationships are

significant with outlying pond 17 removed.

Table 6.5 One-way ANOVA values comparing the invertebrate predator total count

and the number of families and species in ponds occupied / not occupied by

moderately common amphibian species.

Amphibian species

nponds Number of predatory

invertebrates Direction of relationship Count

F1,9 Families

F3,7 Species

F4,6 Occupied Not

occupied

Bufo bufo 5 6 0.53 0.39 4.64 * +

A. obstetricans 2 9 0.06 5.30 * 0.60 -

T. cristatus 6 5 1.73 0.85 2.01

L. vulgaris 9 2 0.00 0.53 2.18

* = significant at p < 0.05; no comparison was significant after sequential Bonferroni

correction within species; direction of relationship: „+‟ = positive, „-‟ = negative.

n=5 n=2 n=6 n=9n=6 n=9 n=5 n=20

5

10

15

20

25

30

35

Bb Ao Tc Lv

Mean n

um

ber

of m

acro

invert

ebra

te

fam

ilies (

±s.e

.)

Amphibian species


73

Figure 6.6 The mean number of predatory invertebrate families with (a) the number

of amphibian species present and (b) amphibian species‟ occupancy.

= occupied, = not occupied; see Appendix 1 for amphibian species name abbreviations.

6.3.6 Specific relationships between macroinvertebrate and amphibian species

Chi-square analyses revealed eight moderately common macroinvertebrate families

to be associated with one or more moderately common amphibian species: Bufo

bufo, A. obstetricans, T. cristatus and L. vulgaris (Table 6.6). However, at 5% error,

these may mostly have been found significant by chance. Just one relationship stands

up to a sequential Bonferroni correction within amphibian species: the leech family

Glossiphoniidae is positively associated with L. vulgaris, always occurring together,

never singly.

0

1

2

3

4

5

6

5 (n=2) 6 (n=6) 7 (n=2) 8 (n=1)

Mean c

ount of

pre

dato

ry invert

ebra

te

fam

ilies in p

ond (

±s.e

.)


(a)

0

1

2

3

4

5

Bb Ao Tc Lv

Mean n

um

ber

of

pre

dato

ry invert

ebra

te

fam

ilies (

±s.e

.)

Amphibian species

(b)


74

Table 6.6 Moderately common macroinvertebrates exhibiting a significant

relationship with amphibian species‟ occupancy (Nponds = 11) before and after

sequential Bonferroni correction.

Macroinvertebrate

(df = 1) Direction of relationship Order Family Genus / species

Bufo bufo

Coleoptera Dytiscidae Hyphydrus ovatus 4.95 * +

Diptera Stratiomyidae Stratiomys sp. 4.95 * -

Odonata Libellulidae † - 4.41 * -

Hydracarina - - 4.44 * - (excl. pond 17)

Odonata Aeshnidae † - 4.44 * + (excl. pond 17)

A. obstetricans

Coleoptera Dytiscidae Hyphydrus ovatus 6.52 * +

Diptera Stratiomyidae Stratiomys sp. 6.52 * -

T. cristatus

Coleoptera Dytiscidae Hyphydrus ovatus 4.95 * -

Odonata Aeshnidae † - 7.64 ** -

L. vulgaris

Diptera Chironomidae Chironomus sp. 6.52 * +

Odonata Lestidae † - 4.28 * -

Rhynchobdellida Glossiphoniidae - 11.00 **

+

† = predatory invertebrates; 2 significance levels: * p < 0.05, ** p < 0.01; = significant

after sequential Bonferroni correction; pond 17 is included except where noted – excluding

pond 17 made no difference to significance levels in other comparisons; direction of

relationship: „+‟ = positive, „-‟ = negative.

6.4 Discussion

The data collected on invertebrates were limited by the amount of time available and

the time-consuming process of identifying and counting invertebrates. In the present

chapter few relationships that have been observed and tested elsewhere are

substantiated, possibly due to the relatively small dataset.

A weak positive relationship was observed between pond size and the count of

macroinvertebrates, but as with other small scale studies no significant correlation

between macroinvertebrate richness and pond size was found (Sewell, 2006). The

higher count in bigger ponds may have been due to unintentionally covering a

greater area, i.e. netting faster, when space was not limited. Although efforts were

made to standardise the survey technique, the results of a short study cannot be

subjected to an extrapolation to find true richness rather than sample richness (Foggo

et al., 2003).


75

Other studies have found aquatic macroinvertebrates to correlate with measures of

wetland plant richness and abundance (e.g. Gee et al., 1997; Hawkes, 1998; Howard,

2002). In the present study there appears to be a positive trend, although no statistical

support was found. In terms of biodiversity, habitat heterogeneity is more important

than size (Triantis et al., 2003), therefore the greater heterogeneity afforded by

higher plant richness and quantity could be expected to support a higher number of

macroinvertebrates (Williams et al., 2008). A larger sample size may have allowed

detection of such an effect, but even in much bigger samples of 60 or 80 ponds Oertli

et al. (2002) found only very weak correlations between the plant, invertebrate and

amphibian taxa they tested. Alternatively, measures of microinvertebrate diversity

and productivity might more accurately reflect amphibians within a habitat, but they

would need more intensive monitoring to smooth over the population fluctuations.

Amphibians as a taxon and as separate species have not been demonstrated to be

appropriate indicators of invertebrate diversity. There was very little difference

between the number of macroinvertebrate families in ponds where any single

amphibian species was present or absent. Again, a larger sample size would have

increased the likelihood of detecting an effect and given a greater variety of

amphibian assemblages to test – amongst 11 ponds seven of eleven amphibian

species were either too rare or too common for comparison.

Seven of the macroinvertebrate families recorded, comprising 16 species, are capable

of predating adult or larval amphibians. The presence of several predatory

invertebrates may have been negatively related to that of certain amphibian species,

although this could not be confirmed with the present data. These relationships

require further data to confirm, as they may have occurred by chance or be due to

other co-varying variables not considered here. Bufo bufo, T. cristatus and L.

vulgaris were each negatively associated with a single dragonfly (Odonata,

Anisoptera) family, Libellulidae, Aeshnidae and Lestidae respectively, which may

directly influence each other or are perhaps an indirect effect of relationships with

other organisms. Anuran larvae, such as Bufo bufo, which are generally unpalatable

to vertebrate predators, may be readily taken by insects such as Odonata naiads and


76

Dytiscidae larvae (Gunzburger and Travis, 2005) with mouthparts adapted to pierce

the body cavity to avoid skin toxins. The aggregating behaviour of adults and

concentrated masses of eggs and larvae make amphibians an easy prey item for all

classes of predator (Duellman and Trueb, 1986).

No robust relationship between macroinvertebrates and amphibians was observed in

the present study. It is remarkable that little has been published from western /

central Europe looking specifically at amphibians in relation to other pond taxa; the

large scale studies in Switzerland and the UK focussed mainly on measuring water

quality. Further work to build a larger dataset could focus on fewer invertebrates –

perhaps just those identified as key predators or prey of amphibians and would need

a much larger sample of ponds, most usefully over a wider geographic area.

7. Occupancy modelling

77

7. Absent or not detected? Development of an occupancy

modelling approach to amphibian survey design

Occupancy modelling

Summary: When species detection is imperfect, it is difficult to

distinguish between true absences and non-detections. Surveying for

cryptic animals such as amphibians can be time-consuming and

expensive, so it is important to find methods that work, preferably with a

high detection rate. Traditional analyses generally do not account for

imperfect detectability and survey protocols have tended to be designed

by common sense, but without statistical support (e.g. for the number of

repeat surveys necessary). The maximum-likelihood based occupancy

models allow rates of occupancy and detectability to be taken into

account, as well as the site and survey covariates that might influence

them. The detection rates of standard amphibian survey methods alone

were examined: netting returned very poor detection rates for all species,

aural survey was good for anurans with an extended breeding season

(Pelophylax spp. and Alytes obstetricans), visual survey was best for

frogs (Rana temporaria and Pelophylax spp.) and trapping was best for

all newt species. In models including covariates of detection, water

temperature and day of year were most frequently selected by the final

model. Covariates of occupancy were successfully modelled for two

moderately common species, Triturus cristatus and Lissotriton vulgaris:

pond size, length of paths and tracks in the area and elevation appeared

most prominently in the final models, although no clear pattern emerged

for either species. Occupancy modelling is a rapidly developing approach

to analysing survey data of species with low detection. It has great

potential to transform the traditional methodologies in amphibian

surveying and monitoring.

7.1 Introduction

7.1.1 Comparing survey methodologies

In amphibian monitoring it is common to use a range of survey methods to increase

the chances of detecting a species if it is present. Different survey techniques have

varying success rates between habitat types, species and life stages (Ryan et al.,

2002; de Solla et al., 2005), yet inefficient survey protocols may be accepted as

standard practice due to limited money and time, or simply out of habit. The

resources available often dictate the methods used (Kaiser, 2008) and the results

achieved with limited resources can be greatly improved by careful assessment of the


78

value of each method in different habitats and for different species (Gooch et al.,

2006).

In Western Europe a number of survey methods are regularly used to sample pond-

breeding amphibians, which can be tailored to certain species or applied more

broadly to sample all of the potentially occurring species. Survey techniques used in

this region often include dip-netting, head-counts (or torch surveys), funnel / bottle

traps, egg searches, refugia searches and aural surveys (Griffiths and Raper, 1994;

Pellet and Schmidt, 2005). These methods all have their own inherent advantages

and disadvantages (see review in Griffiths and Raper, 1994), but when used in

combination would ideally be able to detect all species, at all life stages, in a variety

of habitat conditions.

Many studies have compared the effectiveness of survey methods between species.

For example, „trappability‟ appears to vary more between newt species of different

body sizes than between more similarly-sized species due to their niche use within

the breeding pond (Griffiths, 1987; Griffiths and Mylotte, 1987; Jehle et al., 2000).

Knowing how efficiently each available method detects the study species is essential

to allocating survey effort appropriately (Pierce and Gutzwiller, 2004).

7.1.2 Detectability

Unless a species is detected with 100% accuracy (a detection probability [p] of 1), its

absence from a site cannot be inferred from non-detection (e.g. MacKenzie, 2005;

MacKenzie et al., 2005; Gooch et al., 2006). Particularly high non-detection errors

are found with rare species, especially those that are cryptic in appearance or

behaviour and that occur in difficult-to-survey habitats (Gu and Swihart, 2004).

Many herpetological studies are based on count data that have not been corrected for

detection probability, thereby violating an assumption of conventional statistical

analyses that the rate of detection is perfect or at least constant (Mazerolle et al.,

2007; Altwegg et al., 2008). The traditional approach to lessen the confounding

effects of imperfect detection is to standardise sampling methods and survey

conditions (Heyer et al., 1994). However, this cannot possibly identify or deal with


79

all of the potential influences on detection – false absences can have a very powerful

effect on model performance (Moilanen, 2002).

It is well established that data obtained by sampling methods such as trapping and

point counts do not correlate with those of exhaustive surveys (e.g. Quinn et al.,

2007), resulting in two types of prediction error: false positives (due to

misidentifications) and false negatives (due to non-detections; Fielding and Bell,

1997). Therefore a more advanced approach than simple presence / absence or

count-based data modelling is required.

There are two ways to ensure that surveys adequately represent the study species: (1)

always apply more survey effort than the minimum necessary to accurately describe

the community, and (2) adjust the results statistically using detection probabilities

(de Solla et al., 2005). New statistical techniques to model survey methods,

incorporating covariates of species occurrence and detectability, are rapidly

improving our ability to assess approaches to monitoring and to employ methods in

the most effective way.

7.1.3 Occupancy modelling

Occupancy modelling is a relatively new and rapidly developing maximum

likelihood-based approach to species distribution modelling introduced by

MacKenzie et al. (2002), with the important advantage over traditional models that it

accounts for imperfect detection. It differentiates between detection probability and

occupancy by modelling both as functions of site or survey covariates, thereby

allowing true and false absences to be distinguished (Altwegg et al., 2008).

Software developed specifically for running occupancy models has been developed

by Hines (2006): Program PRESENCE is freely available and is widely used among

researchers developing this approach to species distribution modelling. A number of

assumptions about the quality of the data are made (see Section 7.2.3), however the

ad hoc methods (i.e. methods that do not account for detectability) make even

stronger assumptions. Moreover, the occupancy modelling method is reasonably

robust to minor violations of its assumptions and also accounts for missing


80

observations by evaluating model likelihood separately for each site (MacKenzie et

al., 2002).

7.1.4 Covariates

Many factors influence the rate of detection – a species‟ detectability will differ

between studies or geographical areas (Mazerolle et al., 2007). Covariates affecting

the detectability of the study species may vary both between sites and over time

(between surveys). For example, anuran breeding behaviour may be triggered by

factors such as rainfall, air and water temperatures, humidity and lunar cycle (e.g.

Banks and Beebee, 1986; Brooke et al., 2000; Marsh, 2000; Friedl and Klump, 2002;

Oseen and Wassersug, 2002; Weir et al., 2005; Kirlin et al., 2006), which, if a

species is present at a breeding pond, will cause its detectability to vary throughout

the season and across sites.

Occupancy is related only to site characteristics which are constant across the season

but vary between sites, for example nearby forest cover, developed land, pond size,

vegetation density, altitude and distance to other ponds (e.g. Halley et al., 1996; Joly

et al., 2001; Knutson et al., 2004; Maletzky et al., 2007; Rinehart et al., 2009).

In the current chapter the detection and occupancy rates of common amphibian

species are examined using presence / absence data in Program PRESENCE

(MacKenzie et al., 2002; Hines, 2006). Initially, basic models were run for each

species and survey method within seasons, with occupancy and detectability as the

only parameters. Finally, full models incorporating covariates detailing the site and

survey conditions are explored.

The objectives of the current chapter are to test the following questions:

Are the commonly used amphibian survey methods appropriate and effective?

How do detection rates vary between survey methods?

How do detection rates vary between species?

What site and survey covariates most affect occupancy and detection rates?


81

7.2 Methods

7.2.1 Study area and amphibian data

The current chapter uses data collected specifically for this study at amphibian

breeding ponds in Luxembourg; the study area and survey methods are described in

the General methodology (Chapter 2). All ponds were included in these analyses

(Appendix 3): ponds within 200 m of each other were grouped due to sharing some

of the habitat covariates and in order to maintain independent replicates. This leaves

24 ponds in 2007 and 22 ponds in 2008 for all species, except Alytes obstetricans for

which the first year of surveys („year 1‟) at each pond were used, comprising 40

ponds in total (including the additional ponds surveyed in 2009).

To ensure that a species is available to be detected, only surveys between the first

and last detection dates by any method at any pond are included in the current study

(Appendix 4). The rarest amphibian species (present at only one pond each: Bufo

calamita, Bombina variegata and Hyla arborea) could not be modelled and were

excluded from analysis.

All appropriate survey methods are analysed for each species and life stages are

merged. The life stages recorded by each survey method are detailed in Table 2.2.

Visual encounter surveys (VES), bottle-trapping and dip-netting were modelled for

the four newt species and Rana temporaria. The same methods with the addition of

aural surveys were considered for Pelophylax spp. and Bufo bufo – although most

Bufo bufo calling occurs during a short „explosive‟ breeding period, they were heard

throughout the whole survey period in both 2007 and 2008. Aural survey was the

only methodology considered for A. obstetricans, due to failure of the other methods

to detect them more than once.

7.2.2 Covariate measurements

Thirty-three site-specific covariates (varying between sites, constant within season)

were measured only once per pond per season (Table 7.1 and Table 7.2, detailed in

Appendix 8). Thirteen survey-specific covariates (varying across time and between

sites) were measured during each survey visit to the study ponds (Table 7.3, with

detail in Appendix 8). Measurements of water conductivity and pH varied only


82

slightly over the season, therefore here they are included as site-specific and were

measured once a season in as short a period as possible to minimise any inter-pond

differences due to seasonal changes.

Site-specific covariates describing the landscape within a 100 m buffer around study

ponds (Beebee, 1985) were extracted from the maps described in Chapter 2, section

2.1.1 (with ArcMap v.9.3.1.1850). All covariates were transformed to minimise

skew (Fowler et al., 1998) and to create smaller values which are handled better by

Program PRESENCE.

Table 7.1 Site-specific covariates concerning the occupancy of other species and the

method of normalisation.

Covariate Explanation Norm.

Method Unit

waterfowl Presence / non-detection of any water bird (e.g. duck, moorhen, heron)

- 0/1 (not-detected

/ present)

fish Presence / non-detection of any fish; groups of ponds indicated as fish absent if at least one pond never had fish detected

- 0/1

Rt R. temporaria presence / non-detection - 0/1

P.spp Pelophylax spp. Presence / non-detection - 0/1

Bb Bufo bufo presence / non-detection - 0/1

Ao A. obstetricans presence / non-detection - 0/1

Tc T. cristatus presence / non-detection - 0/1

Lh L. helveticus presence / non-detection - 0/1

Lv L. vulgaris presence / non-detection - 0/1

#_amphib_sps Total number of amphibian species ever detected in pond /mean count

Counts given in Appendix 8.


83

Table 7.2 Site-specific habitat covariates and the method of normalisation.


method Unit

pond_size Surface area of the pond (m2) /mean m

2

pH Yearly mean of three pH measurements taken in each study pond; all ponds tested within 2 days in all years; pH meter: Hanna HI991002

/10 pH

cond Yearly mean of three water conductivity measurements taken in each study pond; all ponds tested within 2 days in all years; Conductivity meter: Orion 125

/mean µs

shade_int Percentage of pond surface area shaded by internal, aquatic vegetation (estimates made during vegetation surveys, for dates see section 5.2.1)

/mean %

shade_ext Percentage of pond surface area shaded by external, non-aquatic vegetation

/mean %

open_water Estimate of the percentage of pond surface area with no vegetation

/mean %

elevation Metres above sea level (m.a.s.l.) /mean m.a.s.l.

#_invert_sps Number of invertebrate species counted during invertebrate surveys (11 ponds only, see Chapter 6)

/mean Count

plant_h' Wetland plant species richness, Shannon-Weiner index (H’), measured during plant surveys (Chapter 5).

/mean H'

paths_tracks Length of paths and tracks within 100 m /1000 m

road_<5.5 Length of small roads (<5.5 m wide) within 100 m /1000 m

road_6.5-7.5 Length of medium-sized roads (6.5 - 7.5 m wide) within 100 m

/1000 m

road_all_widths Length of all roads (any width) within 100 m /1000 m

hedgerow Length of hedgerow within 100 m /1000 m

treerow Length of tree rows within 100 m /1000 m

water_stream_ surface

Length of small surface water courses within 100 m /1000 m

undef_building Area of unclassified buildings (mostly houses) within 100 m

/1000 m2

buildings_undef_ ag_ind

Total area of all buildings (houses, agricultural and industrial) within 100 m

/1000 m2

forest_conif Area of coniferous forest within 100 m /100000 m2

forest_brdlvd Area of broadleaved forest within 100 m /100000 m2

forest_mixed Area of mixed forest within 100 m /100000 m2

orchard Area of orchard forest within 100 m /100000 m2

forest_bwood_ orchard

Total area of all forest, brushwood and orchard within 100 m

/100000 m2

Measurements taken in study pond or within a 100 m radius of pond; summary values given

in Appendix 8.


84

Table 7.3 Survey-specific covariates and the method of normalisation.


method Unit

day_of_yr Day of the survey /100 days (day 1 = 1st Jan.)

full_moon_closest Number of days to/from the closest full moon /10 days

pm_timetaken Total time spent at pond for evening surveys - hours

pm_windspeed Evening wind speed estimate /10 0 - none 1 - breeze 2 - wind 3 - strong

pm_wind_dir Evening wind direction - compass points, clockwise 0.1-0.8

pm_rain Evening rainfall /10 0 - none 1 - light 2 - moderate 3 - heavy

pm_gen Local conditions during evening survey /10 0 - dry 1 - damp 2 - wet 3 - snow

clear_shore Shoreline accessible on foot /100 %

turbidity Water turbidity in torchlight /10 0 - clear 1 - cloudy 2 - very cloudy 3 - no visibility

temp_min Minimum water temperature recorded in pond over night of survey

/10 ˚C

temp_max Maximum water temperature recorded in pond over night of survey

/10 ˚C

#traps Number of traps set /100 count

#nets Number of sweeps made with net /100 count

Summary values given in Appendix 8.

7.2.3 Program PRESENCE analyses

Single season occupancy models make a number of assumptions about the data

quality and independence (MacKenzie et al., 2002): (1) sites are closed to changes in

occupancy for the duration of the survey period (i.e. no colonisation or extinction);

(2) occupancy and detection probabilities are the same for all sites, or are functions

of covariates; (3) sites are independent; and (4) no false detections are made.

Violating these assumptions biases the model estimates or prevents models being

fitted at all. Single season models are used throughout this chapter because multi-

season models require a very large dataset to accommodate the number of

parameters (Donovan and Hines, 2007).

Models were constructed in Program PRESENCE (version 2.3, Hines, 2006) for the

eight amphibian species that occurred at more than one pond. For each species all


85

life stages and both sexes were modelled together; only Lissotriton helveticus and L.

vulgaris larvae were excluded from all surveys and females from VES due to their

similarity preventing certain identification. Survey years 2007 and 2008 were

considered separately, except for A. obstetricans, where „year 1‟ data from each

pond were analysed together and only one year‟s data was considered for each pond,

to uphold the first assumption.

Survey rounds where too few ponds were visited were excluded to minimise missing

data points and achieve well-fitted models. For each model the bootstrap goodness of

fit procedure was applied (500 iterations) to approximate the standard error (s.e.) of

occupancy (ψ) – this non-parametric method produces a good estimate unless

detectability (p) is very low (MacKenzie et al., 2002).

7.2.4 Comparing survey methods

Initially, basic models were run to compare the detection probabilities of survey

methods for each of the eight modelled species. In these preliminary models

maximum overnight temperature was the only covariate added (ψ(.)

p(survey_method, max_temp)), because survey method alone could not account for

sufficient variation to make well-fitting models. Data from all appropriate survey

methods (Table 2.2) were entered onto a single spreadsheet with dummy covariates

to code for the method represented by each column.

Combining the methods this way could be considered a violation of the assumption

of independence – for example, if a newt is trapped and released, its chances of

being netted during the same visit may be affected. However, it overcomes the

alternative problem of repeated analyses on the same data to compare methods.

Years must still be modelled separately, to meet the assumption of continuous

occupancy. Because a continuous survey-specific covariate was included, p and

s.e.(p) had to be averaged to find a single score for each method. The probability of

detection was compared between methods within years using one-way ANOVAs.


86

7.2.5 Predictors of occupancy and detection

Fully parameterised models were run for the common amphibian species: the four

very common species (R. temporaria, Pelophylax spp., Ichthyosaura alpestris and L.

helveticus) were run with detection covariates only and the moderately common

species (Bufo bufo, A. obstetricans, Triturus cristatus and L. vulgaris) were run with

both occupancy and detection covariates. Again, „year 1‟ data from each pond were

used in A. obstetricans models and survey years 2007 and 2008 were modelled

separately for the other species to maintain independence.

Site-specific covariates were tested as predictors of occupancy and survey-specific

covariates as predictors of detection (MacKenzie et al., 2002). Covariates were

chosen a priori, based on published research and assessment of the features and

conditions expected to influence amphibian presence-absence. Covariates (section

7.2.2 and Appendix 8) judged to be appropriate for each species were run

individually, and then covariates in top-ranked models were combined in pairs to

explore whether any better-fitting models could be found. Poorly ranked models (see

next section, 7.2.6) were discarded. The number of covariates was limited to two per

model (in addition to occupancy, ψ, and probability of detection, p) to minimise the

effects of over-parameterisation. A generally accepted rule of thumb is to have no

fewer than ten sites per parameter (Burnham and Anderson, 2004). For example, a

robust model with three parameters should have at least 30 independent samples /

sites.

7.2.6 Model selection

Program PRESENCE ranks models by the Akaike Information Criterion (AIC),

which is a measure of the goodness of fit for estimated models. It is automatically

calculated by Program PRESENCE, but can be expressed as

- Equation 1

where „K‟ is the number of estimated parameters (Mazerolle, 2006). To minimise the

bias caused by small sample sizes the AIC was adjusted to AICc, calculated by


87

Equation 2

where „n‟ is the sample size (Sugiura, 1978, in Mazerolle, 2006). AICc converges

with AIC as the number of samples increases and it gives similar results but is more

conservative for small samples (Burnham and Anderson, 1998).

Over- or under-dispersion of the data is indicated by the ĉ score (variance inflation

factor) given by Program PRESENCE, estimated from the chi-square goodness-of-fit

statistic and the degrees of freedom:

Equation 3

ĉ = 1 is a good fit, > 1 shows over-dispersion and < 1 under-dispersion. Only

models with ĉ close to one were accepted. When the most parsimonious model was

slightly over-dispersed (common in count data, often caused by a lack of true

independence, Anderson and Burnham, 2002) estimates were adjusted by the ĉ score

of the most parsimonious model to give a quasi-AICc (QAICc) score. QAIC is

calculated by Program PRESENCE, but can be represented by the following

equations

-

Equation 4

Equation 5

(Burnham and Anderson, 1998).

Delta AICc or delta QAICc (ΔAICc and ΔQAICc respectively) values are more useful

than simple AICc or QAICc because they describe the difference between candidate

models (Connor et al., 2004). They are calculated as follows:

Equation 6


88

Equation 7

where „i‟ is model number (in rank order by AIC score) and „min QAIC‟ or „min

AIC‟ the QAIC or AIC value of the top-ranked model (Mazerolle, 2006). The most

parsimonious model is always denoted as ΔAICc or ΔQAICc = 0.00. Any ΔQAICc <

2 indicates good support for the model (Zanini et al., 2008).

An additional measure of the strength of support for a model is the AIC (or QAIC)

weights, which indicate the model‟s strength relative to other candidate models. For

example a model with an AIC weight of 0.4 has twice the support of a model with

AIC weight 0.2 (Mazerolle et al., 2007).

Final models were selected if they met the following criteria: ĉ close to 1, AICc or

QAICc < 2, and a test statistic probability > 0.05. If more than one model was

strongly supported the model estimates of occupancy (ψ) and detection probability

(p) were averaged, following Mazerolle et al. (2007) and Sewell et al. (2010).

Detection rates of the most parsimonious models were plotted against their

associated continuous covariates using the following formula:

Equation 8

where „p‟ is the detection rate and β0 and β1 are Beta estimates given in the Program

PRESENCE output, representing the intercept and slope of a regression equation

(Sewell et al., 2010).

7.2.7 Number of survey visits required

Following Pellet and Schmidt (2005) and Sewell et al. (2010) the minimum number

of survey visits necessary (Nmin) to be 95% certain that a non-detection was a true

absence was calculated with Equation 9, where P is the detection rate derived from

Equation 8.


89

Equation 9

7.3 Results

Between five and eleven surveys were conducted at each study pond in 2007, 2008

and „year 1‟; Table 10.2 in Appendix 3 details the ponds included in each year‟s

models. Where more than one life stage was detected all data were modelled

together.

7.3.1 Detectability by different survey methods

ψ(.) p(survey_method, max_temp) models could not be fitted for all species in both

years: R. temporaria from 2008 and Bufo bufo data from both years could not

generate models with a good fit to the data, even when the earliest and latest surveys

were excluded, and including or excluding aural surveys. Unusual detection histories

at some ponds may have prevented models being fitted, for example where there is

perfect detection (p = 1.0) at one pond and low detection rates at other ponds,

particularly with the small sample size in the current study (22 ponds in 2008). AIC

values are not reported individually for these models, because only a single model

was run for each species, therefore ΔAICc was nominally 0.0 and the AIC weight

1.0.

For the two anuran species successfully modelled with more than one survey

method, R. temporaria (2007) and Pelophylax spp. (2007 and 2008), VES had the

highest detection rate, while trapping and netting tended to be less successful (Figure

7.1a-b).

Aural survey was almost as successful as VES for Pelophylax spp., with detection

rates (p) of 0.47 ±0.04 (s.e.) in 2007 and 0.39 ±0.05 in 2008, compared to VES in

2007 0.50 ±0.04 and 2008 0.45 ±0.05. For Pelophylax spp. all survey methods had

slightly higher detection rates in 2007 than 2008. Trapping attained a marginally

higher detection rate for R. temporaria and Pelophylax spp. than netting.


90

Only one survey method was tested for A. obstetricans: aural survey for this species

achieved a detection rate of 0.45 ±0.09 (Figure 7.1c).

D

ete

ction r

ate

(p)

(±s.e

.)

Dete

ction r

ate

(p)

(±s.e

.)

Dete

ction r

ate

(p)

(±s.e

.)

Figure 7.1 „Detectability‟ (±s.e.) of anurans by survey method, ψ(.)

p(survey_method, max_temp).

= 2007, = 2008; VES = visual encounter survey.

For all newt species, trapping returned the highest rate of detection overall, making it

the most successful survey method. Netting was the poorest method, while VES

achieved intermediate detection rates (Figure 7.2a-d).

Contrary to the Pelophylax spp. results, detection rates for newts were generally

lower in 2007 than in 2008. Netting is a relatively poor method for newts, achieving

only very low rates of detection; the highest net detection rate for any newt was for

0.0

0.2

0.4

0.6

0.8

1.0

VES Trap Net

(a) R. temporaria

0.0

0.2

0.4

0.6

0.8

1.0

VES Aural Trap Net

(b) Pelophylax spp.

0.0

0.2

0.4

0.6

0.8

1.0

Year 1 - Aural

(c) A. obstetricans


91

T. cristatus in 2007 (0.24 ±0.06 s.e.). However, in 2008 no T. cristatus were caught

by net.

D

ete

ction r

ate

(p)

(±s.e

.)

Dete

ction r

ate

(p)

(±s.e

.)

Figure 7.2 'Detectability' (±s.e.) of newts by survey method, ψ(.) p(survey_method,

max_temp).

= 2007, = 2008; VES = visual encounter survey.

7.3.2 Predictors of occupancy and detection

The most frequently occurring covariate of detection among the modelled species

was temperature, which occurred in 21/35 of the successfully run year-method

models (7 „temp_min‟ and 14 „temp_max‟). The „day_of_yr‟ was also an important

covariate, in 16/35 accepted models, indicating that detectability does change over

the season. Only covariates of detection were examined for the four most common

species, which were present in too many study ponds for meaningful analysis of

occupancy covariates.

Bufo bufo data could still not be modelled with the addition of site- and survey-

specific covariates. Occupancy modelling may be unsuitable for some explosive

breeders, particularly in this case where surveys were spread across months rather

than days, because these species are highly detectable for a short period only (de

0.0

0.2

0.4

0.6

0.8

1.0

VES Trap Net

(a) I. alpestris

0.0

0.2

0.4

0.6

0.8

1.0

VES Trap Net

(c) L. helveticus

0.0

0.2

0.4

0.6

0.8

1.0

VES Trap Net

(b) T. cristatus

0.0

0.2

0.4

0.6

0.8

1.0

VES Trap Net

(d) L. vulgaris


92

Solla et al., 2005) – larvae tend to be more numerous and more detectable as the

season progresses (Figure 7.3), however they are less conspicuous than breeding

adults. In addition to the skewed detectability over spring and summer, Bufo bufo do

not always use all of the suitable waterbodies available; they congregate to breed and

therefore occupancy is difficult to predict, except perhaps by whether or not a site is

already occupied by that species.

Figure 7.3 The mean number of adult and larval B. bufo seen or captured during

surveys, by day of year.

Based on 2007 and 2008 data from all survey methods; = adults, = larvae.

R. temporaria

Occupancy covariates were not examined for R. temporaria because it was very

common, detected by one or more methods in 87.5% and 90.9% of study ponds in

2007 and 2008 respectively. Although VES and trapping had similar detection rates,

VES found R. temporaria at a greater number of ponds, with naïve and estimated

occupancy rates greater than the other methods (Table 7.4). The final netting models

were under-dispersed and in both years netting detected R. temporaria in fewer

ponds than VES or trapping.

Day of year and temperature emerge as important covariates of detection for R.

temporaria, although across the full range of temperatures encountered there is little

change in detection rate for any method (Figure 7.5a-b), varying by < 0.1. Day of

year had a greater influence on detectability by VES and netting (Figure 7.7), with

the rate of detection by VES decreasing as the year progressed, but increasing for

netting. This may be due to the change in the balance of numbers from largely adults

to largely larvae available to detect (Figure 7.4). Detection rate was positively

0

5

10

15

20

0 50 100 150 200

Mean n

um

ber

seen /

captu

red

Day of year


93

affected by the percentage of clear shore for trapping and netting in 2007 (Figure

7.6) and the amount of time taken during trapping surveys in 2008 (Figure 7.8).

Table 7.4 Most parsimonious models for R. temporaria surveys in 2007 and 2008 by

three survey methods.

Models K -2*LL QAICc QAIC

weight

Est. ψ

Est. p (±s.e.)

Visits (±s.e.)

2007 VES (naïve ψ = 0.83)

ψ(.) p(day_of_yr, temp_max) 4 143.65 0.00 0.64 † 0.85 0.46 (±0.063) 5 (±0.9)

ψ(.) p(day_of_yr) 3 146.78 0.22 0.36 † 0.85 0.46 (±0.052) 5 (±0.8)

ĉ = 0.51 Model averaged: 0.85 0.46 (±0.059) 5 (±0.9)

2008 VES (naïve ψ = 0.86)

ψ(.) p(day_of_yr) 3 95.51 0.00 0.68 0.87 0.45 (±0.053) 5 (±0.8)

ψ(.) p(day_of_yr, temp_max) 4 95.03 2.64 0.32 0.87 0.47 (±0.066) 5 (±0.9)

ĉ = 1.25 Model averaged: 0.87 0.46 (±0.057) 5 (±0.9)

2007 Trap (naïve ψ = 0.58)

ψ(.) p(clear_shore, temp_max) 4 126.93 0.00 1.00 0.63 0.46 (±0.080) 5 (±1.2)

ĉ = 1.73

2008 Trap (naïve ψ = 0.59)

ψ(.) p(pm_timetaken) 3 103.76 0.00 0.73 0.67 0.44 (±0.094) 6 (±1.5)

ψ(.) p(clear_shore) 3 105.76 1.80 0.27 0.62 0.49 (±0.081) 5 (±1.1)

ĉ = 1.11 Model averaged: 0.66 0.45 (±0.090) 5 (±1.4)

2007 Net (naïve ψ = 0.54)

ψ(.) p(clear_shore, temp_max) 4 112.13 0.00 1.00 † 0.74 0.28 (±0.066) 10 (±2.6)

ĉ = 0.87

2008 Net (naïve ψ = 0.50)

ψ(.) p(day_of_yr) 3 93.94 0.00 1.00 † 0.52 0.45 (±0.092) 5 (±1.4)

ĉ = 0.95

Covariates are described in Appendix 8; K = number of model parameters; -2*LL = -2* Log

likelihood (residual deviance); ĉ = TestStat/AvgTestStat of the most parsimonious model; †

= AICc and AIC weight used rather than QAICc and QAIC weight (not adjusted for over-

dispersion); Visits = number of visits required for 95% confidence that non-detection

indicates true absence; all life stages modelled together.

Figure 7.4 The mean number of adult and larval R. temporaria seen or captured

during surveys, by day of year.


0

2

4

6

8

10

12

14

16

0 50 100 150 200

Mean n

um

ber

seen /

captu

red

Day of year


94

Dete

ction r

ate

(p)


models, where water temperature was a covariate of detection.

Based on (a) 2007 VES data: ψ(.) p(day_of_yr, temp_max), β0 = 5.13, β1 = -0.81; (b) 2007 trap data: ψ(.) p(clearshore, temp_max), β0 = -6.03, β1 = 0.85; (c) 2007 net data: ψ(.)

p(clearshore, temp_max), β0 = -8.91, β1 = 1.47; where β0 and β1 are equivalent, respectively,

to the intercept and slope of a regression equation.

Dete

ction r

ate

(p)


models, where „clear shore‟ was a predictor of detection rate.

Based on (a) 2007 trap data: ψ(.) p(clearshore, temp_max), β0 = -6.03, β1 = 5.77; (b) 2007

net data: ψ(.) p(clearshore, temp_max), β0 = -8.91, β1 = 6.81; where β0 and β1 are equivalent,

respectively, to the intercept and slope of a regression equation.

0.94

0.96

0.98

1.00

(a) Rt VES 2007

-0.02

0.00

0.02

0.04

0.06(b) Rt Trap 2007

-0.02

0.00

0.02

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Maximum overnight water temperature (C)

(c) Rt Net 2007

-0.25

0.00

0.25

0.50

0.75(a) Rt 2007 Trap

-0.25

0.00

0.25

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Clearshore (%)

(b) Rt 2007 Net


95

Dete

ction r

ate

(p)

Figure 7.7 Detection rates (±s.e.) of R. temporaria by top models, where „day of

year‟ was a covariate of detection.

Based on (a) 2007 VES data: ψ(.) p(day_of_yr, temp_max), β0 = 5.13, β1 = -3.48; (b) 2008 VES data: ψ(.) p(day_of_yr), β0 = 7.32, β1 = -7.04; (c) 2008 net data: ψ(.) p(day_of_yr), β0 =

-3.52, β1 = 3.06; where β0 and β1 are equivalent, respectively, to the intercept and slope of a

regression equation.

Figure 7.8 Detection rates (±s.e.) of R. temporaria by trapping in 2008, where time

taken was a covariate of detection.

Based on 2008 trap data: ψ(.) p(pm_timetaken), β0 = -0.96, β1 = 1.33; where β0 and β1 are

equivalent, respectively, to the intercept and slope of a regression equation.

-0.2

0.0

0.2

0.4

0.6

0.8

1.0(a) Rt 2007 VES

-0.2

0.0

0.2

0.4

0.6

0.8

1.0(b) Rt 2008 VES

0.0

0.2

0.4

0.6

0.8

1.0

50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

Day of year

(c) Rt 2008 Net

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 100

Dete

ction r

ate

(p)

Time taken in evening(minutes)

Rt 2008 Trap


96

Pelophylax spp.

Table 7.5 Most parsimonious models for Pelophylax spp. surveys in 2007 and 2008

by four survey methods.


weight

Est. ψ

Est. p (±s.e.)

Visits (±s.e.)

2007 VES (naïve ψ = 0.79)

ψ(.) p(temp_max) 3 153.59 0.00 0.72 † 0.82 0.52 (±0.055) 4 (±0.6)


ĉ = 0.92 Model averaged: 0.82 0.53 (±0.058) 4 (±0.7)

2008 VES (naïve ψ = 0.73)

ψ(.) p(temp_max) 3 90.67 0.00 0.71 † 0.79 0.39 (±0.061) 6 (±1.2)


ĉ = 0.65 Model averaged: 0.79 0.40 (±0.066) 6 (±1.3)

2007 Trap (naïve ψ = 0.58)

ψ(.) p(temp_max) 3 125.16 0.00 1.00 0.73 0.25 (±0.062) 11 (±3.1)

ĉ = 1.16

2007 Net (naïve ψ = 0.50)

ψ(.) p(day_of_yr) 3 82.68 0.00 0.59 0.54 0.32 (±0.054) 8 (±1.6)

ψ(.) p(day_of_yr, temp_min) 4 81.39 1.96 0.41 0.54 0.31 (±0.063) 9 (±2.1)

ĉ = 1.37 Model averaged: 0.54 0.31 (±0.058) 8 (±1.8)

2007 Aural (naïve ψ = 0.67)

ψ(.) p(day_of_yr, temp_min) 4 109.78 0.00 0.72 † 0.69 0.51 (±0.061) 5 (±0.7)

ψ(.) p(temp_min) 3 113.63 0.94 0.28 † 0.69 0.51 (±0.050) 5 (±0.6)

ĉ = 0.87 Model averaged: 0.69 0.51 (±0.057) 5 (±0.7)

2008 Aural (naïve ψ = 0.59)

ψ(.) p(temp_max) 3 76.28 0.00 0.39 0.65 0.39 (±0.066) 6 (±1.3)

ψ(.) p(day_of_yr, temp_max) 4 74.53 1.95 0.34 0.62 0.42 (±0.082) 6 (±1.4)

ψ(.) p(day_of_yr) 3 77.06 0.48 0.26 0.60 0.46 (±0.060) 5 (±0.9)

ĉ = 1.64 Model averaged: 0.63 0.42 (±0.070) 6 (±1.2)



= AICc and AIC weight used rather than QAICc and QAIC weight (not adjusted for over-

dispersion); Visits = number of visits required for 95% confidence that non-detection


Figure 7.9 The mean number of adult and larval Pelophylax spp. seen or captured

during surveys by day of year.


0

20

40

60

80

100

120

0 20 40 60 80 100 120 140 160 180 200

Mean n

um

ber

seen

/ captu

red

Day of year


97

Like R. temporaria, Pelophylax spp. were too common to assess their covariates of

occupancy. No model could be fitted to the 2008 trap data for Pelophylax spp. and

few detections were made by netting in 2008, resulting in a poorly fitted model that

is not reported here (Table 7.5). All of the successful models run for Pelophylax spp.

contained temperature and / or the day of year covariate(s), no other covariates

featured in the most parsimonious models. Detectability increased with temperature

for all survey methods (Figure 7.11). By net and aural survey in 2007 detectability

also increased as the season progressed (with „day of year‟; Figure 7.10) and the

mean number of individuals counted increased throughout the season (Figure 7.9).

Dete

ction r

ate

(p)


models, where „day of year‟ was a covariate of detection rate.

Based on (a) 2007 net data: ψ(.) p(day_of_yr), β0 = -10.97, β1 = 7.67; (b) 2007 aural data: ψ(.) p(day_of_yr, temp_min), β0 = -6.78, β1 = 3.68, under-dispersed; β0 and β1 are equivalent,


-0.2

0.0

0.2

0.4

0.6

0.8

1.0

(a) P.spp 2007 Net

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

Day of year

(b) P.spp 2007 Aural


98

Dete

ction r

ate

(p)


models, where water temperature was a covariate of detection.

= minimum temperature, = maximum temperature; based on (a) 2007 VES data: ψ(.) p(temp_max), β0 = -3.27, β1 = 2.45, under-dispersed; (b) 2008 VES data: ψ(.)

p(temp_max), β0 = -5.24, β1 = 3.99, under-dispersed; (c) 2007 trap data: ψ(.) p(temp_max), β0 = -3.51, β1 = 1.59; (d) 2007 aural data: ψ(.) p(temp_min, day_of_yr), β0 = -6.78, β1 = 2.19,

under-dispersed; (e) 2008 aural data: ψ(.) p(temp_max), β0 = -5.83, β1 = 4.46; β0 and β1 are


A. obstetricans

The most parsimonious occupancy model for A. obstetricans was „ψ(.) p(temp_max)‟

(Table 7.6) – none of the additional covariates strengthened the model reported in

section 7.3.1 and models with more covariates could not be fitted. However the

0.0

0.2

0.4

0.6

0.8

1.0

(a) P.spp 2007 VES

0.0

0.2

0.4

0.6

0.8

1.0

(b) P.spp 2008 VES

0.0

0.2

0.4

0.6

0.8

(c) P.spp 2007 Trap

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0 (d) P.spp 2007 Aural

0.0

0.2

0.4

0.6

0.8

1.0

0 5 10 15 20 25

Overnight water temperature (c)

(e) P.spp 2008 Aural


99

single final model is under-dispersed (ĉ = 0.08) and therefore is not reliable. The

naïve A. obstetricans occupancy rate (ψ) of „year 1‟ study ponds was 0.33, but the

model estimates occupancy at just 0.16 (±0.074 s.e.), indicating either that false

detections were made or again that the model is unreliable. Further interpretation is

not made due to the poor fit of the model.

Table 7.6 Most parsimonious models for A. obstetricans 'year 1' data using aural

surveys.

Models K -2*LL AICc AIC

weight Est. ψ

Est. p (±s.e.)

Visits (±s.e.)

‘Year 1’ Aural (naïve ψ = 0.33)

ψ(.) p(temp_max) 3 38.33 0.00 1.00 0.16 0.45 (±0.093) 5 (±1.4)

ĉ = 0.08

Covariates are described in Appendix 8; K = number of model parameters; -2*LL = -2* Log likelihood (residual deviance); ĉ = TestStat/AvgTestStat of the most parsimonious model;

Visits = number of visits required for 95% confidence that non-detection indicates true

absence.

I. alpestris

I. alpestris was too common in the study ponds to examine its covariates of

occupancy, and no model could be fitted for the 2008 trap data. The most frequently

occurring detection rate covariates were day of year, time taken and temperature

(Table 7.7). Although „temp_min‟ and „temp_max‟ occurred in three of the top

models, they had only a very slight effect on detection rate: a positive relationship

existed between VES detection rate in 2007 and minimum temperature (β0 = 2.34, β1

= 0.29) and between net detection rate in 2008 and maximum temperature (β0 = -

0.48, β1 = 2.14), while minimum temperature negatively affected detection by net in

2007 (β0 = -5.97, β1 = -1.03).

Detection rate of I. alpestris by VES and trap decreased during the season, while

detection rate by netting increased (Figure 7.14), due to more larvae being available

to net (Figure 7.12). Overall netting was a poor method, requiring many additional

visits to achieve confidence in non-detections (Table 7.7). The general evening

conditions („pm_gen‟: dry / damp / wet / snow) exhibited a negative relationship

with I. alpestris detection by trapping in 2007, however only as the less important of

two covariates in both models. There was a positive relationship between detection


100

rate by VES and the time taken to complete evening surveys for I. alpestris in 2008

(Figure 7.13).

Figure 7.12 The mean number of adult and larval I. alpestris seen or captured during

surveys by day of year.


Table 7.7 Most parsimonious models for I. alpestris surveys in 2007 and 2008 by



weight

Est. ψ

Est. p (±s.e.)

Visits (±s.e.)

2007 VES (naïve ψ = 0.96)

ψ(.) p(day_of_yr, temp_min) 4 187.27 0.00 0.60 0.99 0.46 (±0.068) 5 (±1.0)

ψ(.) p(day_of_yr) 3 190.04 -0.33 0.40 0.99 0.45 (±0.057) 5 (±0.9)

ĉ = 1.08 Model averaged: 0.99 0.46 (±0.064) 5 (±0.9)

2008 VES (naïve ψ = 0.91)

ψ(.) p(pm_timetaken) 3 116.66 0.00 0.40 † 0.91 0.71 (±0.063) 3 (±0.4)

ψ(.) p(turbidity, pm_timetaken) 4 114.67 1.03 0.39 † 0.91 0.70 (±0.078) 3 (±0.5)

ψ(.) p(pm_rain, pm_timetaken) 4 115.92 2.28 0.21 † 0.91 0.70 (±0.078) 3 (±0.5)

ĉ = 0.82 Model averaged: 0.91 0.70 (±0.072) 3 (±0.5)

2007 Trap (naïve ψ = 0.92)

ψ(.) p(day_of_yr, pm_gen) 4 167.93 0.00 0.59 0.92 0.69 (±0.062) 3 (±0.4)

ψ(.) p(pm_gen, temp_max) 4 168.66 0.49 0.41 0.92 0.69 (±0.063) 3 (±0.5)

ĉ = 1.48 Model averaged: 0.92 0.69 (±0.063) 3 (±0.4)

2007 Net (naïve ψ = 0.75)

ψ(.) p(day_of_yr, temp_min) 4 125.81 0.00 1.00 † 1.00 0.20 (±0.051) 14 (±3.8)

ĉ = 0.79

2008 Net (naïve ψ = 0.36)

ψ(.) p(temp_max) 3 62.1 0.00 1.00 † 0.65 0.16 (±0.079) 18 (±9.8)

ĉ = 0.71



= AICc and AIC weight used rather than QAICc and QAIC weight (not adjusted for over-dispersion); Visits = number of visits required for 95% confidence that non-detection


0

10

20

30

40

50

60

70

0 50 100 150 200

Mean n

um

ber

seen /

captu

red

Day of year


101

Dete

ction r

ate

(p)

Figure 7.13 The detection rate (±s.e.) of VES for I. alpestris in 2008, where

„pm_timetaken‟ was a covariate of detection.

Based on 2008 I. alpestris VES data, most parsimonious model: ψ(.) p(pm_timetaken); β0 = -0.14, β1 = 2.13, under-dispersed; β0 and β1 are equivalent, respectively, to the intercept and

slope of a regression equation.

Dete

ction r

ate

(p)

Figure 7.14 Detection rates (±s.e.) of I. alpestris by top models where the day of

year was a covariate of the detection rate.

Based on (a) 2007 VES data: ψ(.) p(day_of_yr, temp_min), β0 = 2.34, β1 = -2.36; (b) 2007

trap data: ψ(.) p(day_of_yr, pm_gen), β0 = 3.14, β1 = -1.54; (c) 2007 netting data: ψ(.) p(day_of_yr, temp_min), β0 = -5.97, β1 = 4.52, under-dispersed; β0 and β1 are equivalent,


T. cristatus

A variety of covariates occurred in the top models for T. cristatus (Table 7.8). Pond

size was a predictor of occupancy in the most parsimonious models of VES data in

both years: on average, ponds occupied by T. cristatus were larger (2187.27 m2

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 100

Time taken in evening (minutes)

Ia 2008 VES

-0.2

0.0

0.2

0.4

0.6

0.8

1.0(a) Ia 2007 VES

0.0

0.2

0.4

0.6

0.8

1.0(b) Ia 2007 Trap

0.0

0.2

0.4

0.6

0.8

1.0

50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200Day of year

(c) Ia 2007 Net


102

±299.50 s.e.) than non-occupied ponds (1029.60 m2 ±790.61), but the difference is

not significant (t24 = 1.53, p > 0.05). The amount of clear shore (i.e. percentage of

shoreline accessible for survey) was a predictor of T. cristatus detection rate by VES

in three of four well-fitted models in 2007. However, contrary to expectation,

according to Equation 8 it is a negative relationship: detection rate decreases with

increasing shoreline accessibility (Figure 7.15). This was not anticipated because it is

more logical that a species is more likely to be detected where a habitat can be more

completely searched.

Table 7.8 Most parsimonious models for T. cristatus surveys in 2007 and 2008 by



weight

Est. ψ

Est. p (±s.e.)

Visits (±s.e.)

2007 VES (naïve ψ = 0.29)

ψ(pond_size) p(clear_shore) 4 79.55 0.00 0.36 0.29 0.64 (±0.091) 3 (±0.7)

ψ(tracks) p(clear_shore) 4 80.06 0.41 0.30 0.30 0.63 (±0.092) 3 (±0.8)

ψ(pond_size) p(turbidity) 4 81.22 1.36 0.18 0.30 0.54 (±0.105) 4 (±1.1)

ψ(pH) p(clear_shore) 4 81.65 1.71 0.16 0.30 0.63 (±0.092) 3 (±0.8)

ĉ = 1.23 Model averaged: 0.30 0.62 (±0.094) 4 (±0.8)

2008 VES (naïve ψ = 0.27)

ψ(pond_size) p(pm_timetaken) 4 43.92 0.00 1.00 † 0.28 0.80 (±0.102) 2 (±0.6)

ĉ = -0.70

2007 Trap (naïve ψ = 0.29)

ψ(tracks) p(temp_max) 4 59.95 0.00 0.58 0.29 0.75 (±0.081) 3 (±0.5)

ψ(Bb) p(temp_max) 4 61.48 0.88 0.27 0.29 0.75 (±0.082) 3 (±0.5)

ψ(tracks) p(day_of_yr) 4 62.65 1.55 0.15 0.29 0.77 (±0.080) 2 (±0.5)

ĉ = 1.74 Model averaged: 0.25 0.59 (±0.063) 3 (±0.5)

2008 Trap (naïve ψ = 0.41)

ψ(.) p(turbidity, pm_timetaken) 4 67.68 0.00 0.69 0.41 0.62 (±0.150) 4 (±1.3)

ψ(.) p(turbidity) 3 74.44 0.60 0.31 0.42 0.63 (±0.081) 3 (±0.7)

ĉ = 1.87 Model averaged: 0.41 0.62 (±0.129) 4 (±1.1)

2007 Net (naïve ψ = 0.25)

ψ(#invert_sps) p(temp_min) 4 28.54 0.00 1.00 † 0.64 0.34 (±0.079) 8 (±2.1)

ĉ = 0.08






103

Figure 7.15 The detection rate (±s.e.) of VES for T. cristatus in 2007, where the

percentage of clear shore was a covariate of detection.

Based on 2007 T. cristatus VES data, most parsimonious model: ψ(pond_size) p(clear_shore); β0 = 3.56, β1 = -3.83; β0 and β1 are equivalent, respectively, to the intercept

and slope of a regression equation.

Figure 7.16 The detection rate (±s.e.) of T. cristatus in the top 2007 trapping model,

where „max_temp‟ was a covariate of detection.

Based on 2007 T. cristatus trap data, most parsimonious model: ψ(tracks) p(temp_max); β0 = -2.29, β1 = 2.70; β0 and β1 are equivalent, respectively, to the intercept and slope of a


Turbidity occurs in one of the top T. cristatus models for VES in 2007 and in the top

2008 trapping models (Table 7.8) – it is logical that turbidity affects detectability by

VES, but less so for trapping. In 2007 detectability by trapping for T. cristatus was

predicted by maximum overnight water temperature, there is a positive relationship

between them (Figure 7.16).

The length of tracks and paths in the 100 m buffer surrounding study ponds is a

predictor of T. cristatus occupancy for both VES in 2007 and trapping in 2008.

There is a significant difference between the ponds where T. cristatus were and were

not recorded (t22 = 2.55, p < 0.05), with more tracks near occupied ponds (not

recorded mean = 96.82 m ±29.73 s.e.; occupied mean = 494.14 m ±239.43).

0.0

0.2

0.4

0.6

0.8

1.0

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Dete

ction r

ate

(p)

Clear shore (%)

Tc VES 2007

0.0

0.2

0.4

0.6

0.8

1.0

0 5 10 15 20 25

Dete

ction r

ate

(p)

Maximum overnight water temperature (C)

Tc Trap 2007


104

However, this effect is probably an artefact of the data collection or analysis

methods.

No T. cristatus were netted in 2008, giving a detection rate of 0.0, and the best 2007

model is under-dispersed. As shown in the previous section (7.3.1), netting was not a

good method compared to VES and trapping. Achieving 95% confidence that non-

detections of T. cristatus were genuine absences would take 2-4 repeat surveys with

VES or trapping, whereas approximately 8 surveys would be required to gain

confidence in the 2007 netting model with a much poorer detectability rate (Table

7.8).

Figure 7.17 shows VES and trapping to estimate occupancy at similar levels in 2007,

but the under-dispersed model-estimated value for netting in 2007 demonstrates the

potential of a small dataset to distort results. During 2007 seven study ponds were

found to be occupied by T. cristatus by ≥ 1 survey method; in two ponds T. cristatus

were only detected by a single method (one each by trapping and VES). VES and

trapping detected T. cristatus on 53.8% and 67.3% of occupied pond surveys

respectively in 2007, while netting made positive detections on only 26.9% of

surveys in ponds found to be occupied by other methods. In 2008 T. cristatus

occupancy was determined at three ponds by trapping where VES failed, but overall

the rate of positive detections by VES and trapping in 2008 at T. cristatus-occupied

ponds were similar to 2007: 50.0% of VESs and 68.9% of trap surveys were

successful in 2008. Except for the under-dispersed netting model in 2007, there is

not much difference between the naïve and model-estimated occupancy rates (Figure

7.17).


105

Occupancy r

ate

(ψ

)

Figure 7.17 Naïve and model-estimated occupancy (ψ) rates (±s.e.) of T. cristatus

by survey method and year.

= naïve ψ, = estimated ψ (uses averaged values from accepted models, Table 7.8);

dotted bars = under-dispersed model; VES = visual encounter survey.

L. helveticus

Very few detections of L. helveticus were made by netting, giving low naïve rates of

occupancy, estimated occupancy and estimated detection rates compared to VES and

trapping (excluding the under-dispersed 2007 VES model; Table 7.9). L. helveticus

was detected in > 90% of study ponds by one or more methods in both survey years

and therefore potential covariates of occupancy were not included in models.

Temperature covariates occurred as predictors of detection rate in 3/6 of the final,

most parsimonious, models for L. helveticus (Table 7.9 and Figure 7.18a-c), both

minimum and maximum overnight temperatures were tested separately for all

models, although they are clearly strongly correlated with each other (2007: r146 =

0.95, p < 0.001; 2008: r107 = 0.96, p < 0.001).

0.0

0.5

1.0

VES Trap Net

(a) 2007

0.0

0.5

1.0

VES Trap Net

(b) 2008


106

Table 7.9 Most parsimonious models for L. helveticus surveys in 2007 and 2008 by



weight

Est. ψ

Est. p (±s.e.)

Visits (±s.e.)

2007 VES (naïve ψ = 0.71)

ψ(.) p(pm_gen, temp_max) 4 134.26 0.00 1.00 † 0.75 0.39 (±0.078) 7 (±1.6)

ĉ = 0.73

2008 VES (naïve ψ = 0.73)

ψ(.) p(pm_ timetaken) 3 119.04 0.00 0.59 0.76 0.59 (±0.077) 4 (±0.7)

ψ(.) p(clear_shore) 3 120.47 0.73 0.41 0.75 0.57 (±0.074) 4 (±0.7)

ĉ = 1.96 Model averaged: 0.76 0.58 (±0.076) 4 (±0.7)

2007 Trap (naïve ψ = 0.83)

ψ(.) p(temp_min, pm_rain) 4 147.89 0.00 1.00 0.84 0.58 (±0.053) 4 (±0.5)

ĉ = 1.20

2008 Trap (naïve ψ = 0.86)

ψ(.) p(pm_timetaken) 3 96.68 0.00 0.54 0.85 0.75 (±0.061) 3 (±0.4)

ψ(.) p(pm_timetaken, #traps) 4 95.03 1.84 0.46 0.85 0.76 (±0.073) 3 (±0.5)

ĉ = 1.40 Model averaged: 0.85 0.75 (±0.066) 3 (±0.4)

2007 Net (naïve ψ = 0.42)

ψ(.) p(clearshore) 3 103.59 0.00 1.00 0.43 0.41 (±0.085) 6 (±1.5)

ĉ = 1.10

2008 Net (naïve ψ = 0.36)

ψ(.) p(temp_min) 3 74.29 0.00 0.63 0.38 0.49 (±0.110) 5 (±1.4)

ψ(.) p(temp_max) 3 75.36 0.86 0.37 0.38 0.47 (±0.112) 5 (±1.6)

ĉ = 1.24 Model averaged: 0.38 0.48 (±0.110) 5 (±1.5)





In contrast to T. cristatus, L. helveticus showed a negative relationship between

detection rate and water temperature for VES and trapping in 2007: at the lowest

temperatures encountered detectability by VES and trap for L. helveticus was very

high (Figure 7.18a-b). However, the two accepted models for netting in 2008 also

included water temperature covariates as predictors of detectability, but here there

was a positive relationship (Figure 7.18c). There is no clear reason why temperature

would have an opposite effect on netting to VES and trapping, although again, it

may be due to the small sample size.


107

Dete

ction r

ate

(p)

Figure 7.18 Detection rates (±s.e.) of L. helveticus by top models where water

temperature was a covariate of detection rate.

= minimum temperature, = maximum temperature; based on (a) 2007 VES data: ψ(.) p(pm_gen, temp_max), β0 = 2.08, β1 = -1.52, under-dispersed; (b) 2007 trap data: ψ(.) p(temp_min, pm_rain), β0 = 2.54, β1 = -1.62; (c) 2008 netting data: ψ(.) p(temp_min), β0 = -

1.97, β1 = 2.15; β0 and β1 are equivalent, respectively, to the intercept and slope of a


In 2008 the amount of time spent at study ponds was a predictor of L. helveticus

detectability. Detection rate increased slightly with time taken for VES and it

decreased for trap surveys (Figure 7.19). Ponds that took longer to survey generally

had more complex structure (e.g. scrubby or steep banks), which is perhaps reflected

in the diminishing returns from trapping, whereby newts could be less likely to

encounter and enter traps in complex ponds.

Three or four trapping surveys would be needed to be 95% certain that the L.

helveticus-unoccupied ponds were truly unoccupied (Table 7.9). The well-fit 2008

VES model also suggests a minimum of four visits, although the under-dispersed

2007 VES model indicates seven. To achieve the lower occupancy estimate using

netting only, five to six repeat surveys would be required.

0.0

0.2

0.4

0.6

0.8

1.0 (a) Lh VES 2007

0.0

0.2

0.4

0.6

0.8

1.0 (b) Lh Trap 2007

0.0

0.2

0.4

0.6

0.8

1.0

0 5 10 15 20 25

Overnight water temperature (C)

(c) Lh Net 2008


108

L. helveticus were present in nearly all of the ponds surveyed in 2007 and 2008:

91.7% and 95.5% respectively with all the survey methods taken together, although

none of the individual methods quite achieved naïve occupancy rates this high

(Figure 7.20). The naïve and estimated occupancy rates match each other quite

closely within methods, even for the under-dispersed VES model. As with T.

cristatus, trapping revealed L. helveticus occupancy at more ponds than the other

methods: trap surveys found L. helveticus in four 2007 and five 2008 ponds where

they were never detected by VES or netting. Netting performed very poorly, failing

to detect L. helveticus on any survey in 12 and 13 ponds (2007 and 2008

respectively) that were proven to be occupied by VES and/or traps. A contributory

factor to the weakness of netting as a survey method is that it tends to catch larvae

more easily than adults and the larvae of L. helveticus and L. vulgaris cannot be

distinguished in the field, although this limitation also applies to the other methods.

Dete

ction r

ate

(p)

Figure 7.19 Detection rates (±s.e.) of L. helveticus by top models, where „time

taken‟ was a covariate of detection.

Based on (a) 2008 VES data: ψ(.) p(pm_timetaken), β0 = 0.20, β1 = 0.35; (b) 2008 trap data:

ψ(.) p(pm_timetaken), β0 = 2.51, β1 = -2.47; β0 and β1 are equivalent, respectively, to the

intercept and slope of a regression equation.

0.0

0.2

0.4

0.6

0.8

1.0 (a) Lh VES 2008

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 100

Time taken in evening (mins)

(b) Lh Trap 2008


109

Occupancy r

ate

(ψ

)

Figure 7.20 Naïve and model-estimated occupancy (ψ) rates (±s.e.) of L. helveticus

by survey method and year.


dotted bars = under-dispersed model; VES = visual encounter survey; where β0 and β1 are


L. vulgaris

Well-fitted models were found for L. vulgaris with all three survey methods in both

years and only the 2008 netting model being under-dispersed. The detection rate of

L. vulgaris was strongly associated with the „day of year‟ covariate – for VES and

trap surveys in 2007 the detection rate decreased as the year progressed (Table 7.10

and Figure 7.22a-b), whereas for netting in 2008 detectability was minimal until

approximately day 120 (end of April, Figure 7.22c). However, this model was under-

dispersed and therefore should be interpreted with caution.

L. vulgaris 2008 trap data show the opposite relationship with „time taken‟ to L.

helveticus. Detectability of L. vulgaris by trap in 2008 increased with time spent

(Figure 7.21), which is more intuitive than the decrease shown by L. helveticus

(Figure 7.19b).

0.0

0.2

0.4

0.6

0.8

1.0

VES Trap Net

(a) 2007

0.0

0.2

0.4

0.6

0.8

1.0

VES Trap Net

(b) 2008


110

Table 7.10 Most parsimonious models for L. vulgaris surveys in 2007 and 2008 by



weight

Est. ψ

Est. p (±s.e.)

Visits (±s.e.)

2007 VES (naïve ψ = 0.38)

ψ(Tc) p(day_of_yr) 4 75.55 0.00 1.00 0.55 0.17 (±0.068) 17 (±7.1)

ĉ = 1.19

2008 VES (naïve ψ = 0.41)

ψ(.) p(turbidity) 3 73.72 0.00 1.00 0.61 0.21 (±0.080) 13 (±5.5)

ĉ = 1.12

2007 Trap (naïve ψ = 0.71)

ψ(hedgerow) p(day_of_yr) 4 146.48 0.00 1.00 0.71 0.49 (±0.062) 5 (±0.8)

ĉ = 1.42

2008 Trap (naïve ψ = 0.68)

ψ(elevation) p(pm_timetaken) 4 94.24 0.00 1.00 0.77 0.58 (±0.074) 4 (±0.7)

ĉ = 1.77

2007 Net (naïve ψ = 0.13)

ψ(forest_brdlvd) p(.) 3 30.59 0.00 0.42 0.27 0.09 (±0.084) 32 (±31.1)

ψ(roads_all) p(.) 3 30.83 0.22 0.37 0.19 0.13 (±0.083) 22 (±14.7)

ψ(roads_<5.5m) p(.) 3 31.93 1.24 0.21 0.22 0.11 (±0.083) 26 (±20.6)

ĉ = 1.08 Model averaged: 0.23 0.11 (±0.083) 26 (±21.0)

2008 Net (naïve ψ = 0.09)

ψ(elevation) p(day_of_yr) 3 11.05 0.00 0.68 † 0.27 0.09 (±0.073) 32 (±27.0)

ψ(elevation) p(.) 3 14.56 1.51 0.32 † 0.27 0.07 (±0.047) 42 (±28.8)

ĉ = 0.52 Model averaged: 0.27 0.08 (±0.065) 35 (±27.7)





Dete

ction r

ate

(p)

Figure 7.21 Detection rate (±s.e.) of L. vulgaris by the top trapping model in 2008,

where „pm_timetaken‟ was a covariate of detectability.

Based on 2008 Trap data: ψ(elevation) p(pm_timetaken), β0 = -0.84, β1 = 2.34; β0 and β1 are


0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 100

Time taken in evening(minutes)

Lv Trap 2008


111

Dete

ction r

ate

(p)

Figure 7.22 Detection rates (±s.e.) of L. vulgaris by top models, where „day of year‟

was a covariate of detection.

Based on (a) 2007 VES data: ψ(.) p(day_of_yr), β0 = 1.73, β1 = -2.96; (b) 2007 trap data: ψ(hedgerow) p(day_of_yr), β0 = 3.24, β1 = -2.70; (c) 2008 netting data ψ(elevation)

p(day_of_yr), β0 = -12.63, β1 = 8.11, under-dispersed; β0 and β1 are equivalent, respectively,

to the intercept and slope of a regression equation.

Covariates predicting L. vulgaris occupancy of study ponds varied between models

and survey methods, because the ponds found to be occupied varied between

methods. In the most parsimonious 2007 trap model, hedgerow length within 100 m

of the pond was selected – the unoccupied ponds all had no hedgerow nearby, while

the occupied ponds averaged 163.9 m (±49.89 s.e.; t22 = 2.08, p = 0.049). Both the

net and trap models of the 2008 season selected elevation as predictive of

occupancy: on average occupied ponds (detected by any method) were at lower

elevations than unoccupied ponds (mean = 284.0 m.a.s.l. ±6.51 and 350.5 m.a.s.l.

±19.96 respectively; t20 = 4.06, p < 0.005). The 2007 netting model has broadleaved

forest as a predictor of occupancy, but this is strongly biased by the method‟s failure

to detect occupancy at most sites – when occupied sites detected by any method are

compared to unoccupied sites there is no difference in broadleaved forest coverage

(t22 = 0.34, p > 0.05). The presence of T. cristatus in ponds was retained as a

predictor of L. vulgaris occupancy in the most parsimonious VES model in 2007;

they occurred together more frequently than individually (Figure 7.23).

-0.2

0.0

0.2

0.4

0.6

0.8(a) Lv VES 2007

0.0

0.2

0.4

0.6

0.8

1.0(b) Lv Trap 2007

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

Day of year

(c) Lv Net 2008


112

Again, netting achieved very low detection rates and consequently low occupancy

rates for L. vulgaris. VES also performed relatively badly with naïve and estimated

occupancy rates far lower than those for trapping and many surveys required for

95% confidence in non-detections (Table 7.10).

L. vulgaris were found in 70.8% and 68.2% (2007 and 2008) of study ponds by ≥ 1

survey method. Traps detected L. vulgaris at least once per season in each of the

occupied ponds, while VES failed in 47.1% and 40.0% and netting failed in 82.4%

and 86.3%. Neither VES nor netting detected L. vulgaris in ponds where they were

not also detected by trapping (Figure 7.24).

Figure 7.23 Occupancy of 2007 study ponds by T. cristatus and L. vulgaris.

Occupancy determined by ≥ 1 detection by any method within th 2007 season.

Occup

an

cy r

ate

(ψ

)

Figure 7.24 Naïve and model-estimated occupancy (ψ) rates (±s.e.) of L. vulgaris by

survey method and year.


dotted bars = under-dispersed model; VES = visual encounter survey.

0

2

4

6

8

10

12

Tc & Lv Tc only Lv only No Tc or Lv

Nu

mb

er

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on

ds

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0.0

0.2

0.4

0.6

0.8

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VES Trap Net

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0.0

0.2

0.4

0.6

0.8

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(b) 2008


113

7.4 Discussion

The probability of detection of any of the methods and species studied here is far

from perfect, ranging from p = 0.00 for T. cristatus by net in 2008 to 0.75 for L.

helveticus by trap in 2008, often with variation between years. Comparison of the

survey methods appropriate for each amphibian species highlighted that some

methods are far more effective than others in terms of achieving higher detection

rates and requiring fewer survey visits for an acceptable level of confidence that non-

detections represent true absences.

For the frogs, R. temporaria and Pelophylax spp., VES had the highest detection

rate: larvae of these species are easily identifiable and adults are quite conspicuous in

their behaviour, especially the Pelophylax spp.. Among the newts, trapping was

clearly the best method and netting was of very limited use, while VES had mostly

intermediate detection rates but was poor for L. vulgaris. Netting is routinely used in

surveys for European newt species (Griffiths and Raper, 1994; Arntzen, 2002),

therefore its inefficiency is surprising. In the current study netting was restricted to

avoid creating too much disturbance or damage to vegetation. It is a relatively quick,

simple method, which is perhaps explains its popularity, but here it has not proven

worthwhile for any species. Netting was never the sole method of detection of a

species on any survey visit and made far fewer detections than VES or trapping, or

aural for anurans.

Aural survey was only modelled for Pelophylax spp. and A. obstetricans.

Unfortunately the A. obstetricans model is unreliable, although it indicates a low-

medium detection rate of 0.45. The population size of A. obstetricans at some study

sites may have been so low that survey conditions could not predict when they

would be detected (Mazerolle et al., 2007), for example at ponds 9-10 just two

detections were made in ten surveys in year 1 and the maximum number of

individuals heard calling was estimated to be just four. For Pelophylax spp. aural

survey had medium success; it achieved higher detection rates than netting, but was

not as effective as VES or trapping. Both Pelophylax spp. and A. obstetricans exhibit

an extended breeding season, so are more likely to be repeatedly detected by aural


114

survey, although A. obstetricans mating activity is mostly concentrated into several

peaks across a long season (Marquez, 1992).

7.4.1 Covariates of detection

The survey-specific covariates expected to influence detection, such as turbidity for

VES and dense vegetation for netting, did not often make well-fitted models and

therefore are not reported in the model tables. The most frequently occurring

covariates of detection for the fully modelled species were temperature (maximum or

minimum) and day of year. „Time taken‟ and „clear shore‟ were also in a number of

top models and overnight water temperature generally had a positive effect in the

well-fitted models. Lower oxygen content in warmer water (Steen, 1958)

necessitates more frequent surface movements to breathe and previous studies have

found greater amphibian activity at higher temperatures (e.g. Oseen and Wassersug,

2002; Blair, 1960 in Kirlin et al., 2006). Therefore there is a greater chance of

entering a trap or being seen moving in the torch beam. Maximum temperature was

the only detection covariate fitted to A. obstetricans data, supporting the idea that

anuran call intensity increases with temperature (Marquez, 1992; Navas, 1996;

Navas and Bevier, 2001). For VES and trapping detection rates of all species

decreased as the season progressed, while for netting it increased. Although netting

was generally a poor method, this suggests that it was more effective later in the

season, when VES and traps were less useful. This is probably due to the numbers of

adults available to detect being lower as they leave the water post-breeding and the

number of larvae being higher (Figures 7.3, 7.5, 7.9 and 7.12).

In reality the effects of survey-specific covariates are likely to vary across the

season. For example, early in the year there is a minimum threshold temperature

above which Bufo bufo begin migration to breeding ponds (Reading, 1998), but after

the threshold has been reached and the temperature continues to rise, other covariates

may become more important (Kirlin et al., 2006).

7.4.2 Covariates of occupancy

Only two species, T. cristatus and L. vulgaris, were modelled with covariates of

occupancy. The other moderately common species either did not fit any site-specific


115

covariates (A. obstetricans) or could not form acceptable models (Bufo bufo). In

some of the most parsimonious models for T. cristatus, pond size and the length of

tracks in the vicinity were both positively related to the likelihood of occupancy, and

elevation occurred in three of the top models for L. vulgaris. However, overall no

strong patterns emerged and no covariates stood out for either species. With a small

sample size the models can easily be disproportionately influenced by a covariate,

particularly here where each survey method produced slightly different sets of

„occupied‟ and „unoccupied‟ ponds.

7.4.3 Occupancy modelling

Occupancy modelling had variable success with the amphibian species surveyed in

Luxembourg. The rarest species were excluded because they occurred at too few

ponds and the most common species could only be modelled for detection rate (not

for occupancy). Moreover, the sample size in both study years (2007 and 2008) was

only just sufficient to run models with additional parameters – using AICc addresses

the problem of small sample sizes, although a larger dataset would allow

simultaneous modelling of more covariates and produce better-fitting models.

Occupancy modelling is a very „data-hungry‟ analysis technique, it cannot be done

with the low resolution, presence-only data typically collected by record centres. To

collect a more adequately sized dataset within a season the current study would have

required more than one person, and an even greater survey effort would have been

needed for sufficient data for multi-season models.

The present set of models is a preliminary exploration of the application of

occupancy modelling and the types of covariates to be used for species‟ occupancy

and detectability. Occupancy modelling represents a considerable advance from the

traditional logistic regression and discriminant analysis approaches, which tend to

ignore detection rates (Mazerolle et al., 2007) and lend false confidence to

management practices (MacKenzie, 2005). Data collection needs to be undertaken

with the demands of occupancy modelling in mind to ensure that the appropriate

type and standard of data are collected. Typical record centre data are currently not

suitable, but as current revisions of the National Amphibian and Reptile Recording


116

Scheme (NARRS) in the UK demonstrate, volunteer schemes can be designed to

collect the required data (Sewell et al., 2010).

The cryptic behaviour of many amphibian species means that a single survey method

is unlikely to detect all species in all sites (Ryan et al., 2002). Variation in the cost

and time required to implement a method add to the considerations necessary when

designing a survey protocol (O‟Donnell et al., 2007). It is important to know the

efficiency of each method and the sampling effort required (de Solla et al., 2005).

Further work should include pairs of survey methods modelled together to find the

most efficient method combinations, however this would also benefit from a larger

dataset.

8. General discussion

117


General discussion

The analyses in Chapters 3 – 7 employed two different types of data, firstly broad

scale, presence-only data, collected by a large number of people using mixed survey

methods, which provided an overview of the status of amphibian species in

Luxembourg. Secondly a detailed dataset on a subset of sites used to examine

relationships between amphibians and biotic and abiotic variables in and around

breeding ponds. In view of the population declines and extinctions reported among

amphibians globally (e.g. Barinaga, 1990; Alford et al., 2001; Houlahan et al., 2001;

Collins and Storfer, 2003; Beebee and Griffiths, 2005), the record centre data

showed quite a positive outlook for most amphibian species in Luxembourg.

Common species‟ ranges have generally expanded over recent decades and, despite

the identification of L. helveticus as endangered in the Benelux countries by the

European herpetological atlas (Gasc et al., 1997), it was the most widespread newt

species according to the MNHN data. Even the locally threatened A. obstetricans

appears from these data to have increased its range. Range sizes of H. arborea, T.

cristatus and L. vulgaris have shrunk, but while the latter two remain moderately

common, H. arborea has become very rare and more recently is thought to have

gone extinct at all but one pond. National species action plans for H. arborea, T.

cristatus and Bufo calamita are already in place to address specific threats and

ensure their long-term survival in Luxembourg (MNHNL - Groupe herpétologique,

2009a, b; Proess, 2009). These conservation measures will also benefit the non-

targeted species such as L. vulgaris, which was found in association with T.

cristatus.

In Chapters 3 and 4 the problems associated with low resolution data were discussed,

however Chapter 7 introduced an additional consideration: detection rates. The

highest detection rate for any species by any method was 0.75, although most were

much lower. Imperfect detection raises a key issue with record centre data, which is

that some or all species may have been routinely under-detected, and therefore

under-represented, throughout the historical records. As argued by Telfer (2002),

although detection rates are not perfect, survey methods have not changed greatly

therefore detection should have remained fairly constant over time, so the broad


118

trends demonstrated are probably true. However, for the rare species the margin of

error could have a greater effect, especially when coupled with the unknown bias in

recording (Dennis et al., 1999) – for example there may be many populations of

common species such as L. helveticus that are known by individual surveyors but not

reported to the MNHN, and there are probably no known unrecorded populations of

Bufo calamita. For the latter species one additional population would represent a

50% increase, but it would make little difference to the common species.

Accounting for a species‟ rate of detection could increase estimates of its range size.

However, in the present study the combination of imprecise historical data and weak

occupancy models make for dubious extrapolations. For example, R. temporaria was

found in 54.89% of the grid squares surveyed (Figure 3.3) and its rate of detection

(p) was approximately 0.47 by all survey methods: ideally it would be possible to

deduce that it was likely to occupy 53% more squares than it had been detected in

(83.98%), but the type of surveys and the survey effort were not reliably recorded in

the MNHN data. Further development of the models and a higher quality of record

centre data are needed before such inferences can be made.

Findings from Chapters 5 and 6 suggested that there is potential to use amphibians as

a surrogate measure of general biodiversity in their breeding ponds, but no strong

relationships were established here and surrogates ideally have high detection rates.

The detection rates for macrophytes and macroinvertebrates will also have been

below 100% – potentially very low for invertebrates, but also for small,

inconspicuous or submerged plants. Where conservation or management planning is

required for a range of taxa, there may be no substitute for conducting thorough

surveys appropriate to each group. Although the conservation measures intended for

a specific species, for example the removal of invasives and limiting local farming

activity prescribed for H. arborea (MNHNL - Groupe herpétologique, 2009a),

probably benefit other native species too. Occupancy modelling could be applied to

the species of interest to help design more efficient surveys, but it would be too time-

consuming to gather the initial data to apply it to every species within a large

taxonomic group.


119

On the whole PAs appear to perform well for amphibians in Luxembourg, with the

common species found both inside and outside. The three very rare species, H.

arborea, Bufo calamita and Bombina variegata, occupied very few sites, all of

which were within PAs. These species are afforded a high level of protection, but

currently have little chance of colonising new areas because the PAs they inhabit are

surrounded by roads, towns and intensive agriculture (Bennett, 2003; Hannah et al.,

2007). The modern protected areas approach promotes connectivity between sites

(Haslett et al., 2010) and new action plans for these species address their isolation,

with new ponds planned for the area around the sole remaining H. arborea site and

new scrapes as potential breeding ponds already made in the large park where

Bombina variegata occupied the only waterbody. According to the MNHN data the

rarest amphibian species in Luxembourg have always been rare, so their general

status has not changed, but the threats have perhaps intensified and at least H.

arborea is now much rarer than a decade ago. Unfortunately the rarest species were

unsuitable for statistical analysis by any of the methods used here, but it is clear that

they are dependent on conservation action to avoid becoming nationally extinct.

Identification of low levels of the fungal disease Batrachochytrium dendrobatidis in

amphibian populations in Luxembourg (see Appendix 9; Wood et al., 2009) adds

further threat to the amphibian population, and is worthy of close monitoring over

future years.

The data collected by the MNHN are a very useful resource both for identifying

populations for further study and to monitor species‟ distributions; similar databases

are kept for all other plant and animal taxa found in Luxembourg. However, for the

purposes of the current study finer details would have increased the strength of

analysis and inference greatly. For example, full grid references would have allowed

every record to be pin-pointed and the surrounding landscape variables to be

measured precisely (rather than simply sharing a grid square) and records of non-

detections (or „null‟ records) would allow exploration of local extinctions more

accurately than the abrupt ending of records. Such details could easily be supplied by

database contributors if the data submission process was simpler, which could now

be done via a website rather than the current unwieldy spreadsheet.


120

There was some conflicting information between the various datasets employed in

the chapters – for example the MNHN data showed an increase in A. obstetricans

range, but at several sites selected for intense study where it had previously been

recorded it was not found, suggesting local extinctions (and / or low detection rates)

had occurred. Also the map data could not be ground-truthed, and may have

contained inaccuracies, or have had low resolution. For example some of the smaller,

non-permanent study ponds were not included in the ACT maps, and therefore there

may be other small waterbodies, such as ditches, that are suitable amphibian

breeding habitat, but do not show up on the maps.

8.1.1 Future data collection

Natural habitats are rapidly changing under increasingly intense anthropogenic

pressures. The effect on amphibians and other taxa has been largely negative and is

mostly attributable to loss of habitat (Cushman, 2006). Climate change projections

present an uncertain picture for the future of amphibian populations (Araújo et al.,

2006), but heightened monitoring activity – both of amphibians and the factors

threatening their survival – is fast improving knowledge of the ecosystems and

effective conservation measures. Imperfect detection has typically been inadequately

addressed in previous studies by use of multiple methods and ample surveys in the

field, but occupancy modelling represents a big step forward in survey design by

properly accounting for detectability.

The main barrier to the adoption of occupancy modelling for designing all fauna and

flora survey protocols is that it needs high quality data (e.g. repeated visits,

independent / discrete sites) and it requires records of non-detections as well as

presences, and therefore cannot be used on the historical data typically held by

record centres. In countries where data collection capacity is limited occupancy

modelling would be difficult to implement. However, as Luxembourg and the

neighbouring countries have many amateur and professional contributors to their

natural history records, it would be possible to collect a sufficiently large database to

build much stronger models than are presented in the current thesis, from which

standard survey protocols could be developed. Ongoing monitoring is very important

to inform conservation management decisions: Luxembourg has a high level of


121

monitoring and good information on the highly threatened amphibians. Building on

the existing database and recording in more detail could greatly strengthen the

information that can be drawn from it.

9. References

122

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10. Appendices

10. Appendices

Appendix 1 – Amphibian nomenclature ........................................................... 148

Appendix 2 – Contributors to the MNHN database .......................................... 150

Appendix 3 – Study ponds ............................................................................... 151

Appendix 4 – Species dates .............................................................................. 152

Appendix 5 – Species distribution maps ........................................................... 153

Appendix 6 – Plants species surveyed .............................................................. 155

Appendix 7 – Macroinvertebrate families surveyed .......................................... 159

Appendix 8 – Occupancy modelling covariates ................................................ 161

Appendix 9 – Publications ............................................................................... 163

Appendix 1 – Amphibian nomenclature

148


Twelve amphibian species in Luxembourg were studied here, species names

throughout follow Speybroeck et al. (2010), who are the most recent authority on

European herpetological nomenclature. In figures and tables species are often

referred to by their initials, which are also given below.

Speybroeck et al. (2010) recommend retaining the natterjack toad (Bufo calamita) in

the Bufo genus, concluding that there is not enough evidence yet to re-group it into

Epidalea as suggested by others.

Two green frog species are found in Luxembourg: Pelophylax lessonae and P. kl.

esculentus. P. kl. esculentus originally arose as a hybrid of P. lessonae and P.

ridibundus, but persists in the absence of one parent species by only producing

gametes with the genome of the absent parent and breeding with the available parent

species and (Holenweg Peter, 2001). P. kl. esculentus x P. kl. esculentus offspring

are not viable, necessitating the persistence of sexual parasitism on a parent species.

P. lessonae potentially occupies some sites without P. kl. esculentus, although their

individual distributions have not been studied in Luxembourg. Green frogs can be

distinguished in the field (Mutz, 2009), but this requires morphometric measures of

multiple individuals at every site – a time consuming task outside the scope of the

current study; they were grouped for all analyses and are collectively referred to as

Pelophylax spp. (or abbreviated to P.spp) throughout.


149

Table 10.1 Nomenclature of study species (following Speybroeck et al., 2010).

Initials Order Family Genus Species

Anura (frogs and toads) Ranidae Rafinesque-Schmaltz, 1814 (true frogs) Rt

Rana Linnaeus, 1758 temporaria Linnaeus, 1758 – common/grass frog

Pelophylax Fitzinger, 1843

P.spp kl. esculentus (Linnaeus, 1758) – edible (green) frog lessonae (Camerano, 1882) – pool (green) frog

Ha

Hylidae Rafinesque, 1815 (tree frogs) Hyla Laurenti, 1768 arborea (Linnaeus, 1758) – common tree frog

Bb

Bufonidae Gray, 1825 (true toads) Bufo Laurenti, 1768 bufo (Linnaeus, 1758) – common toad

Bc calamita (Laurenti, 1768) – natterjack Ao

Alytidae Fitzinger, 1843 (painted frogs and midwife toads) Alytes Wagler, 1829 obstetricans (Laurenti, 1768) – common midwife toad

Bv

Bombinatoridae Gray, 1825 (fire-bellied toads) Bombina Oken, 1816 variegata (Linnaeus, 1758) – yellow-bellied toad

Caudata or Urodela (salamanders and newts) Salamandridae Goldfuss, 1820 (true salamanders and newts)

Ia

Ichthyosaura Sonnini and Latreille, 1801 alpestris (Laurenti, 1768) – Alpine newt

Tc

Triturus Rafinesque, 1815 cristatus (Laurenti, 1768) – great crested newt

Lv Lh

Lissotriton Bell, 1839 vulgaris (Linnaeus, 1758) – smooth newt helveticus (Razoumowsky, 1789) – palmate newt

Ss

Salamandra Garsault, 1764 salamandra (Linnaeus, 1758) – fire salamander

Appendix 1 References

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Appendix 2 – Contributors to the MNHN database

150

Appendix 2 – Contributors to the MNHN database

1955-2005 - Musée national d‟histoire naturelle, 25 rue Münster, L-2160

Luxembourg.

Arendt, Alexandra

Backes, S.

Baghli, A.

Baum

Bechet, Georges

Braunert, C.

Carrières, Evelyne

Casagranda, Béatrice

Conzemius, T.

Cungs, Josy

Diederich, Jules

Dion, M.

Dolisy, D.

Dupont, R.

Engel, Edmée

Erpelding, Andre

Ewert, M.P.

Felten, C.

Felten, P.

Gerend, J.

Gerend, R.

Grégoire, Christian

Grof, Marc

Groh, K.

Haag, Guillaume

Heidt, C.

Hengemühle, M.

Hentgen

Hermes, Jean

Hoffelt, J.

Hoffmann, Jos

Isekim

Junck, Claudine

Jungers, P.

Kalmes, Pierre

Klopp, F.

Kraus, C.

Kraus, M.

Krippel, Yves

LANIUS

Lauff, Max

Manderscheid

Mangen, Jean-Marie

Meyer, Marc (MNHNL)

Origer, G.

Owaller, Marc

Pelles, A.

Pfeiffenschneider, Manou

Philippo, S.

Pir, Jacques

Poncelet, M.

Proess, Roland (Ecotop)

Pütz, F.

Reichling, Léopold

Reuter, C.

Risch, J.-P.

Risch, Stéphane

Roesgen

Scheer, Anne

Schley, Laurent

Schmidt, Gérard

Schmit, J.

Schmit, R.

Schmitz, C.

Schmitz, R.

Schneider, Nico

Schoentgen, F.

Schoos, Fernand

Schoos, R.

Schotel, Jan

Schrankel, Isabelle

Seiler, R.

Sowa, F.

Steichen, Claude

Stomp, N.

Strozyk, Ulrich

Thilmany, B.

Thonon, Philippe

Thorn, Robert

Weimerkirch, S.

Weis, A.

Weiss, J.

Willems, G

Appendix 3 – Study ponds

151

Appendix 3 – Study ponds

Table 10.2 Study ponds in 25 m and 200 m groupings.

Years

Ponds Grid Reference

Pond size (at start)

(m2) 07 08 ‘1’ 25 m 200 m

1 Weiden 1&2

Weiden 1-5

LC 648 926 486

2 Weiden 3&4 LC 648 924 1134

3 Weiden 5 LC 646 923 780

4 Laaschtert 1 Laaschtert 1&2

LC 722 934 128

5 Laaschtert 2 LC 722 932 150

6 Laaschtert 3 Laaschtert 3-5

LC 725 933 323

7 Laaschtert 4&5 LC 726 934 409

8 Pesch 1&2 Pesch 1&2 LC 744 801 3600

9 Flakewiss Flakewiss & Biergwiss

LC 800 870 270

10 Biergwiss LC 798 869 270

11 Jakobsbierg Jakobsbierg LC 955 913 2750

12 Scheedchen Scheedchen LC 964 900 225

13 Kléiwiss Kléiwiss & Briderfeld

LC 681 791 900

14 Briderfeld LC 681 793 200

15 Werwelslach 1&2 Werwelslach 1&2 LC 683 790 234

16 Loorlach Loorlach LC 693 796 221

17 Giele Botter Giele Botter LC 592 674 8000

18 Féitzemuer Féitzemuer LC 684 663 840

19 Laangwiss Laangwiss LC 713 640 1250

20 Lamidden Lamidden LC 715 630 270

21 Looreck Looreck LC 712 632 700

22 Dréisch Dréisch LC 708 635 700

23 Hongerbruch Hongerbruch LC 753 682 250

24 Véierhärebesch Véierhärebesch LC 749 683 108

25 Neiländigfeld Neiländigfeld LC 752 669 80

26 Leiwesdallchen Leiwesdallchen LC 745 668 225

27 Haard Haard LC 725 602 137

28 Bloklapp 1&2

Bloklapp 1-3

LC 754 600 693

29 Bloklapp 3 LC 754 600 1000

30 Stueden Stueden LC 763 650 1232

31 Weiergewan Weiergewan LC 900 670 6000

32 Stuppicht 1

Stuppicht 1&2

LC 800 890 1250

33 Stuppicht 2 LC 799 890 700

34 Steinfort Steinfort LC 610 813 3989

35 Renkert Merscheid Renkert Merscheid LA 754 1140 912

36 Alersang Putscheid Alersang Putscheid LA 781 1141 899

37 Biwelser Béral Biwelser Béral LA 800 1135 160

38 Vianden Teggelbaach Vianden Teggelbaach LA 833 1124 704

39 Hosingen Niklosboesch 1&2

Hosingen Niklosboesch 1-6

LA 764 1178 1496

40 Hosingen Niklosboesch 4-6 LA 750 1180 2029

41 Follebur 1

Follebur 1&2

LA 682 1244 680

42 Follebur 2 LA 680 1243 680

43 Lellingen Ortschaft Lellingen Ortschaft LA 689 1163 834

Plant and invertebrate chapters (5 and 6) group ponds less than 25 m apart; occupancy models (Chapter 7) group ponds within 200 m of each other; Gauss-Luxembourg grid

reference system.

Appendix 4 – Species dates

152

Appendix 4 – Species dates

Table 10.3 Survey dates in 2007.

Species First

detection in pond(s)

Last detection

in pond(s)

R. temporaria 25.02.07 3 13.06.07 11

Pelophylax spp. 07.03.07 1 07.08.07 11

H. arborea 19.04.07 37 25.06.07 37

Bufo bufo 06.03.07 24-27 11.06.07 24-27

Bufo calamita - - - -

A. obstetricans 16.04.07 11 07.08.07 11

Bombina variegata 26.04.07 32 16.07.08 32

I. alpestris 06.03.07 24-27 25.06.07 37

T. cristatus 07.03.07 1-5 25.06.07 37

L. helveticus 13.03.07 28-31 21.06.07 16

L. vulgaris 06.03.07 24-27 11.06.07 25

Table 10.4 Survey dates in 2008.

Species First


Last detection

in pond(s)

R. temporaria 27.02.08 6-9 30.05.08 24-27

Pelophylax spp. 01.04.08 38-39 15.07.08 40

H. arborea 23.04.08 37 02.06.08 37

Bufo bufo 03.03.08 36 30.05.08 24-27

Bufo calamita 07.04.08 40 27.05.08 40

A. obstetricans 24.04.08 40 15.07.08 40

Bombina variegata 05.05.08 32 17.06.08 32

I. alpestris 27.02.08 6-9 16.06.08 37

T. cristatus 29.02.08 1-5 16.06.08 37

L. helveticus 27.02.08 6-9 29.05.08 13-14

L. vulgaris 28.02.08 17-21 30.05.08 24-27

Table 10.5 Survey dates in 2009 – additional ponds used in „year 1‟ data for A.

obstetricans.

Species First


Last detection

in pond(s)

A. obstetricans 12.05.09 10, 32-34 02.06.09 34, 39-40

nb: surveying in 2009 was all within the A. obstetricans breeding season, therefore the first

and last dates reflect survey dates more than in years 2007 and 2008.

Appendix 5 – Species distribution maps

153


(a) Rana temporaria

(b) Pelophylax spp.

(c) Hyla arborea

(d) Bufo bufo

(e) Alytes obstetricans

(f) Bufo calamita

Figure 10.1 Distributions of amphibian species in Luxembourg in 1 km squares,

1996-2005, data from the MNHN database.

Continued…

20 km

N


154

…continued from previous page.

(g) Bombina variegata

(h) Ichthyosaura alpestris

(i) Triturus cristatus

(j) Lissotriton helveticus

(k) Lissotriton vulgaris

(l) Salamandra salamandra

20 km

N

Appendix 6 – Plants species surveyed

155


Table 10.6 All wetland plants surveyed and the percentage of study ponds in which

each plant occurred (names following Stace, 1997).

Plants marked „*‟ could not be identified to species level. „~‟ indicates plants never

occurring at ≥ 5% cover.

Common name Latin name Ponds

occurring in (%)

* ~ Algal pond weed / Blanket weed - 9.3

Creeping bent Agrostis stolonifera L. 7.0

~ Narrow-leaved water plantain Alisma lanceolatum With. 9.3

Water plantain Alisma plantago-aquatica L. 39.5

Alder Alnus glutinosa (L.) Gaertn. 9.3

~ Marsh foxtail Alopecurus geniculatus L. 2.3

* ~ unID'd Alopecurus sps Alopecurus L. sp. 7.0

Meadow foxtail Alopecurus pratensis L. 11.6

~ Wild angelica Angelica sylvestris L. 16.3

~ Fool's-water-cress Apium nodiflorum (L.) Lag. 2.3

~ Lesser water-parsnip Berula erecta (Huds.) Coville 2.3

~ Autumnal water-starwort Callitriche hermaphroditica L. 4.7

~ Various-leaved water-starwort Callitriche platycarpa Kütz. 4.7

~ Common water-starwort Callitriche stagnalis Scop. 4.7

Marsh-marigold Caltha palustris L. 18.6

~ Hedge bindweed Calystegia sepium (L.) 2.3

Slender tufted-sedge Carex acuta L. 2.3

~ Water sedge Carex aquatilis Wahlenb. 2.3

~ Brown sedge Carex disticha Huds. 2.3

Common sedge Carex nigra (L.) 14.0

Cyperus sedge Carex pseudocyperus L. 9.3

Bottle sedge Carex rostrata Stokes 44.2

* unID'd Carex sps Carex sp. 16.3

~ Bladder-sedge Carex vesicaria L. 7.0

True fox-sedge Carex vulpina L. 4.7

Rigid hornwort Ceratophyllum demersum L. 4.7

Soft hornwort Ceratophyllum submersum L. 2.3

~ Opposite leaved golden saxifrage Chrysoplenium oppositifolium L. 2.3

~ Marsh thistle Cirsium palustre (L.) Scop. 16.3

* ~ unID'd Marsh-orchid sp. Dactylorhiza sp. 2.3

* ~ unID'd hair-grass sp Deschampsia sp. 2.3

Common Spike-rush Eleocharis palustris (L.) Roem. & Schult. 32.6

* unID'd Spike-rush Eleocharis sp. 4.7

~ Canadian Waterweed Elodea canadensis Michx. 2.3

~ Nuttall's pondweed** Elodea nuttallii (Planch.) H. St. John 2.3

~ Great willowherb Epilobium hirsutum L. 11.6

Water horsetail Equisetum fluviatile L. 2.3

Marsh Horsetail Equisetum palustre L. 18.6

~ Hemp-agrimony Eupatorium cannabinum L. 2.3

Alder buckthorn Frangula alnus Mill. 2.3

Common Marsh-bedstraw Galium palustre L. 27.9

Floating sweet-grass Glyceria fluitans (L.) B. Br. 51.2

~ Reed sweet-grass Glyceria maxima (Hartm.) Holmb. 11.6

* ~ unID'd sweetgrass sp Glyceria sp. 2.3

Mare's tail Hippuris vulgaris L. 9.3

~ Square-stalked St John's-wort Hypericum tetrapterum Fr. 2.3

Yellow iris Iris pseudacorus L. 32.6

Continued…


156

…continued from previous page


occurring in (%)

Jointed rush Juncus articulatus L. 4.7

~ Round-fruited rush Juncus compressus Jacq. 2.3

~ Compact rush Juncus conglomeratus L. 4.7

Soft-rush Juncus effusus L. 62.8

Hard rush Juncus inflexus L. 34.9

* ~ unID'd Juncus sp Juncus sp. 2.3

Common duckweed Lemna minor L. 60.5

* ~ unID'd Duckweed sp Lemna sp. 2.3

Ivy-leaved duckweed Lemna trisulca L. 16.3

~ Water lobelia Lobelia dortmanna L. 4.7

~ Greater bird's-foot trefoil Lotus pedunculatus Cav. 7.0

~ Ragged robin Lychnis flos-cuculi L. 7.0

Gypsywort Lycopus europaeus L. 39.5

Creeping-jenny Lysimachia nummularia L. 25.6

~ Tufted loosestrife Lysimachia thyrsiflora L. 2.3

Purple-loosestrife Lythrum salicaria L. 41.9

Water mint Mentha aquatica L. 16.3

~ Tufted forget-me-not Myostis laxa Lehm. 2.3

Water forget-me-not Myostis scorpioides L. 25.6

~ Alternate water-milfoil Myriophyllum alternifolium DC. 2.3

Whorled water-milfoil Myriophyllum verticillatum L. 2.3

~ Least water-lily Nuphar pumila (Timm) DC. 2.3

White water-lily Nymphaea alba L. 7.0

~ Fine-leaved water-dropwort Oenanthe aquatica (L.) Poir. 2.3

Amphibious bistort Persicaria amphibia (L.) Gray 16.3

Reed canary-grass Phalaris arundinacea L. 2.3

Common reed Phragmites australis (Cav.) Trin. ex Steud. 9.3

Swamp meadow-grass Poa palustris L. 11.6

Curled pondweed Potamogetan crispus L. 9.3

Broad-leaved pondweed Potamogetan natans L. 27.9

Fennel pondweed Potamogetan pectinatus L. 2.3

* unID'd Potamogetan sps Potamogetan sp. 2.3

~ Common fleabane Pulicaria dysenterica (L.) Bernh. 2.3

Common water-crowfoot Ranunculus aquatilis L. 2.3

~ Lesser spearwort Ranunculus flammula L. 4.7

Greater spearwort Ranunculus lingua L. 11.6

Round-leaved crowfoot Ranunculus omiophyllus Ten. 4.7

Creeping buttercup Ranunculus repens L. 51.2

Aquatic liverwort (a bryophyte) Riccia fluitans 2.3

Narrow-fruited water-cress Rorippa microphylla (Boenn.) Hyl. ex Á. & D. Löve 4.7

~ Water-cress Rorippa nasturtium-aquaticum (L.) Hayek. 11.6

~ Clustered dock Rumex conglomeratus Murray 16.3

~ Curled dock Rumex crispus L. 11.6

~ Marsh Dock Rumex palustris Sm. 9.3

~ Wood dock Rumex sanguineous L. 7.0

White willow Salix alba L. 27.9

Goat willow Salix caprea L. 11.6

Grey willow Salix cinerea L. 23.3

Crack-willow Salix fragilis L. 25.6

Purple willow Salix purpurea L. 2.3

Osier Salix viminalis L. 9.3

Common club-rush Schoenoplectus lacustris (L.) Palla 9.3

Wood club-rush Scirpus sylvaticus L. 9.3

~ Skullcap Scutellaria galericulata L. 16.3

Continued…


157



occurring in (%)

~ Woody nightshade Solanum dulcamara L. 16.3

Unbranched bur-reed Sparganium emersum Rehmann 4.7

Branched bur-reed Sparganium erectum L. 25.6

* Aquatic moss Sphagnum sp. 25.6

Greater duckweed Spirodela polyrhiza (L.) Schleid. 9.3

* Algal pond weed Charophyta sp. 2.3

Lesser bulrush Typha angustifolia L. 18.6

Bulrush Typha latifolia L. 41.9

Greater bladderwort Utricularia vulgaris L. 7.0

~ Common valerian Valeriana officinalis L. 2.3

Brooklime Veronica beccabunga L. 11.6

Marsh speedwell Veronica scutellata L. 14.0

Table 10.7 Non-wetland plants surveyed and the percentage of study ponds in which

each plant occurred (names following Stace, 1997).

Plants marked „*‟ could not be identified to species level. „~‟ indicates plants never

occurring at ≥ 5% cover.


occurring in (%)

* ~ unID'd Grasses - 2.3

~ Field maple Acer campestre L. 2.3

~ Garlic mustard Alliaria petiolata (M. Bieb.) Cavara & Grande 2.3

~ Silver birch Betula pendula Roth. 4.7

~ Hornbeam Carpinus betulus L. 2.3

~ Common knapweed Centaurea nigra L. 2.3

~ Rosebay willowherb Chamerion angustifolium (L.) Holub 18.6

~ Enchanter's-nightshade Circaea lutetiana L. 2.3

~ Dogwood Cornus sanguinea L. 2.3

~ Hazel Corylus avellana L. 4.7

~ Hawthorn Crataegus monogyna Jacq. 16.3

* ~ Hawthorn hybrid Crataegus sp. 2.3

~ Cock's-foot Dactylis glomerata L. 20.9

~ Broad-leaved willowherb Epilobium montanum L. 4.7

* ~ unID'd Horsetail sp Equisetum sp. 4.7

Beech Fagus sylvatica L. 7.0

~ Ash Fraxinus excelsior L. 7.0

~ Cleavers Galium aparine L. 9.3

~ Hedge bedstraw Galium mollugo L. 2.3

~ Wood avens Geum urbanum L. 4.7

~ Ground-ivy Glechoma hederacea L. 2.3

* ~ unID'd hogweed Heracleum sp. 2.3

~ White dead nettle Lamium album L. 2.3

~ Lesser hawkbit Leontodon saxatilis Lam. 2.3

* ~ unID'd pine tree Pinus sp. 2.3

~ Annual meadow-grass Poa annua L. 7.0

~ Wood meadow-grass Poa nemoralis L. 2.3

Continued…


158



occurring in (%)

~ Smooth meadow-grass Poa pratensis L. 2.3

* ~ unID'd meadow-grass sp Poa sp. 2.3

* ~ Poplar sp Populus sp. 14.0

~ Aspen Populus tremula L. 4.7

~ Creeping cinquefoil Potentilla reptans L. 2.3

~ Blackthorn Prunus spinosa L. 7.0

~ Dog-rose Rosa canina L. 2.3

~ Bramble Rubus fruticosus L. agg. 18.6

~ Broad-leaved dock Rumex obtusifolius L. 7.0

~ Eared willow Salix aurita L. 7.0

* unID'd Willow sp Salix sp. 7.0

~ White campion Silene latifolia 2.3

~ Prickly sow-thistle Sonchus asper L. Hill 2.3

~ Dandelion Taraxacum officinale Wigg. group 2.3

~ Red clover Trifolium pratense L. 2.3

~ White clover Trifolium repens L. 2.3

* unID'd Elm tree sp Ulmus sp. 2.3

~ Common nettle Urtica dioica L. 14.0

Appendix 6 References

Stace, C. (1997) New flora of the British Isles. 2nd edition. Cambridge University

Press, Cambridge.

Appendix 7 – Macroinvertebrate families surveyed

159


Table 10.8 The macroinvertebrates found and their frequency of occurrence (names

following ZipcodeZoo.com, 2009).

Eleven ponds surveyed; ? = unknown / unidentified.

Phylum Class Type Order Family

Total number of individuals

counted

Number of ponds

Annelida Clitellata Worm Haplotaxida Naididae 339 10

Leech Arhynchobdellida Erpobdellidae 9 2

Haemopidae 23 3

Rhynchobdellida Glossiphoniidae 66 9

Arthropoda Arachnida Watermite Hydracarina ? 102 5

Trombidiformes Hydrodromidae 1 1

Hydryphantidea 14 4

Limnocharidae 4 1

Branchiopoda Waterflea Cladocera ? 140 2

Daphniidae 20 1

Insecta Beetle Coleoptera ? 9 3

Chrysomelidae 8 1

Dytiscidae 230 10

Elmidae 10 1

Gyrinidae 5 3

Haliplidae 37 6

Helophoridae 18 3

Hydrochidae 5 1

Hydrophilidae 71 7

Noteridae 88 8

Scirtidae 5 2

True fly Diptera ? 4 1

Chaoboridae 1777 10

Chironomidae 328 8

Culicidae 9 3

Heleinae 16 1

Psychodidae 2 1

Ptychopteridae 10 4

Sciomyzidae 1 1

Stratiomyidae 83 9

Tabanidae 4 1

Tipulidae 3 2

Mayfly Ephemeroptera Baetidae 2880 11

Caenidae 1 1

Leptophlebiidae 13 1

Potamanthidae 3 1

Siphlonuridae 210 2

Bug Hemiptera Corixidae 113 5

Gerridae 364 11

Hydrometridae 3 1

Naucoridae 105 4

Nepidae 16 4

Notonectidae 862 10

Pleidae 344 6

Continued…


160


Phylum Class Type Order Family

Total number of individuals

counted

Number of ponds

Arthropoda Insecta Dragonfly Odonata ? 3 2

Aeshnidae 100 7

Corduliidae 120 9

Gomphidae 1 1

Libellulidae 28 6

Damselfly Odonata Coenagrionidae 391 8

Lestidae 372 6

Platycnemididae 17 1

Stonefly Plecoptera Capniidae 2 1

Caddisfly Tricoptera Glossosomatidae 3 1

Hydropsychidae 1 1

Lepidostomatidae 1 1

Limnephilidae 1872 10

Phryganeidae 15 1

Polycentropodidae 18 1

Sericostomatidae 10 1

Malacostraca Crustacean Amphipoda Gammaridae 8 1

Isopoda Asellidae 10 2

Ostracoda ? ? 1000 1

Mollusca Bivalvia Bivalve Veneroida Corbiculidae 20 2

Pisidiidae 747 7

Gastropoda Snail Basommatophora Lymnaeidae 2330 8

Planorbidae 2424 8

Platyhelminthes Turbellaria Flatworm Tricladida Planariidae 1 1

Number of families identified: 62

Number of families not identified: 6

Appendix 8 – Occupancy modelling covariates

161


Table 10.9 Summary of survey-specific covariate values for each study year.

Covariate 2007 ponds 2008 ponds Year 1 ponds

min max mean mode min max mean mode min max mean mode

day_of_yr 56 219 - - 58 197 - - 56 219 123.58 72

full_moon_ closest

0.07 14.72 7.19 - 0.21 14.67 8.38 - 0.07 14.74 7.58 -

pm_timetaken 0.08 2.25 0.57 - 0.08 1.50 0.40 - 0.08 2.25 0.53 -

pm_windspeed 0 2 - 0 0 3 - 0 0 2 - 0

pm_wind_dir - - - 0.3 (E)

- - - 0.7 (NW)

- - - 0.3 (E)

pm_rain 0 3 - 0 0 3 - 0 0 3 - 0

pm_gen 0 2 - 0 0 2 - 0 0 2 - 0

clear_shore 10 100 76.21 - 5 100 74.66 - 10 100 75.55 -

turbidity 0 3 - 1 0 3 - 2 0 3 - 1

temp_min 1 22 10.95 - 1 20 8.76 - 1 22 11.28 -

temp_max 3 25 14.09 - 4 23 11.47 - 3 25 14.03 -

#traps 5 30 19.57 - 0 30 18.87 - 0 30 19.45 -

#nets 5 30 19.57 - 0 30 18.92 - 0 30 19.44 -

See explanations in Chapter 7, Table 7.3.


162

Table 10.10 Summary of site-specific covariates for each study year.

Covariate 2007 ponds 2008 ponds 'year 1' ponds

mean s.e._ mean s.e._ mean s.e._

pond_size 1247.33 387.38 1242.64 409.04 1159.34 274.37

pH 7.36

(5.97-8.20)

- 7.32

(6.27-8.20)

- 7.68

(5.91-8.99)

-

cond 423.75 52.82 386.54 53.37 352.26 42.42

shade_int 63.20 5.99 63.72 6.23 59.20 5.36

shade_ext 21.58 4.96 22.41 5.30 21.23 4.11

open_water 30.94 5.32 30.57 5.47 35.29 5.25

elevation 299.64 7.80 305.17 10.06 332.48 11.73

#_invert_sps 29.70 2.20 31.09 2.43 31.09 2.43

plant_h' 4.07 0.60 4.31 0.64 3.71 0.44

paths_tracks 212.71 78.84 268.68 86.39 282.31 69.21

road_<5.5 70.54 30.17 76.95 32.62 90.83 27.67

road_6.5-7.5 16.75 11.63 18.27 12.66 11.49 8.03

road_all_widths 87.29 33.03 95.23 35.60 102.31 28.94

hedgerow 116.08 38.31 120.64 41.51 97.26 27.20

treerow 46.71 27.37 50.95 29.74 37.17 19.10

water_stream_surface 40.71 17.53 44.82 19.11 80.14 21.93

undef_building 5.63 5.58 6.14 6.09 36.49 32.74

buildings_undef_ag_ind 5.63 5.58 6.14 6.09 53.71 49.89

forest_conif 254.54 145.74 335.55 176.96 2442.71 1184.18

forest_brdlvd 8295.67 3818.29 11770.27 4788.81 10076.86 3172.21

forest_mixed 2338.88 1528.61 2398.23 1687.31 3073.83 1442.03

orchard 651.83 524.52 711.09 571.58 446.97 360.98

forest_bwood_orchard 11650.63 4170.52 15334.82 5239.51 16115.60 3741.00

See explanations in Chapter 7, Table 7.2.

Table 10.11 Site-specific variables concerning the presence / non-detection of other

species.

Covariate 2007 2008 ‘year 1’ ponds

1 0 1 0 1 0

waterfowl 12 12 11 11 21 14

fish 4 20 3 19 4 31

Rt 22 2 21 1 24 2

P.spp 22 2 22 0 26 2

Bb 12 12 11 11 18 13

Ao 2 22 3 19 13 22

Tc 11 13 10 12 11 15

Lh 23 1 22 0 31 1

Lv 17 7 17 5 18 8

#_amphib_sps 5.58 0.27 5.91 0.20 5.65 0.27

1 = present, 0 = not detected; see explanations in Chapter 7, Table 7.1 and species name

abbreviations in Appendix 1.

Appendix 9 – Publications

163

Appendix 9 – Publications

Wood, L. R., Griffiths, R. A. Groh, K., Engel, E. & Schley, L. (2008)

Interactions between freshwater mussels and newts: a novel form of

parasitism? Amphibia-Reptilia, 29, 457-462.

Amphibia-Reptilia 29 (2008): 457-462

Interactions between freshwater mussels and newts:a novel form of parasitism?

Laura R. Wood1,*, Richard A. Griffiths1, Klaus Groh2, Edmée Engel3, Laurent Schley4

Abstract. Small freshwater mussels are sometimes found attached to the toes of aquatic phase amphibians, but the ecologicalimplications of this interspecific relationship are unknown. Toe condition and mussel presence were recorded for newts caughtin 37 ponds in Luxembourg between March and June 2007. All four local newt species were affected (Lissotriton helveticus,L. vulgaris, Mesotriton alpestris and Triturus cristatus), primarily by the mussel Sphaerium nucleus but also by Pisidiumsubtruncatum. Mussel attachment was observed in three ponds, with a particularly high occurrence in one pond, where 23%of newts were affected and significantly more toes were damaged than in other ponds. Mussels caused local tissue and bonedamage to their host and may interfere with egg-laying. Twenty-two newts with attached mussels were observed in aquariafor up to 3 days: 13 mussels detached when the newt’s toe fell off and nine remained attached. If the mussels benefit fromthe interaction through, for example, enhanced dispersal then the relationship between the two taxa represents a novel formof parasitism.

Keywords: dispersal, Lissotriton, Mesotriton, Sphaerium nucleus, symbiosis, Triturus.

Introduction

Interactions between organisms of differentspecies may have a mutually positive or nega-tive outcome for both parties, or an overall pos-itive result for one species at the expense of theother. The term ‘symbiosis’ describes a rangeof interspecific associations, the exact type de-pending on the advantage or disadvantage eachparticipant derives from the relationship. Hostorganisms may serve a range of purposes for thepartner, from providing temporary food, shelteror transport, to hosting its entire life (Ricklefsand Miller, 1999).

In a parasitic relationship the host is neg-atively affected while the parasite benefits. Incontrast a commensal relationship is where oneparticipant benefits whilst the other incurs no

1 - The Durrell Institute of Conservation and Ecology, Uni-versity of Kent, Marlowe Building, Canterbury, CT27NR, UK

2 - VHÖ/BBN, Mainzer Str. 25, D-55546 Hackenheim,Germany

3 - Musée national d’histoire naturelle, 25 rue Münster,L-2160 Luxembourg

4 - Service de la Conservation de la Nature, Direction desEaux et Forêts, 16 rue Eugène Ruppert, L-2453 Luxem-bourg∗Corresponding author; e-mail: [email protected]

notable benefit or harm. Phoresy is a specificform of commensalism in which a mobile or-ganism affords provisional transport to a ses-sile one (Kwet, 1995). A phoretic relationshipbetween invertebrates living in bromeliad wa-ter reservoirs and visiting vertebrates has beenwell studied (Lopez et al., 2002; Lopez et al.,2005). Bromeliad annelids and ostracods ori-ent towards and attach to frog or lizard skin,thereby gaining transport to another reservoirand succeeding in dispersal (Lopez et al., 2005).Furthermore, the ostracods can survive pass-ing through the gut of amphibians and mam-mals, potentially achieving dispersal by beingingested while the animal drinks (Lopez et al.,2002).

Seidel (1990) recorded high rates of ostracod‘attacks’ on yellow-bellied toads (Bombina var-iegata) at a pond complex, and instances of indi-vidual toads carrying ostracods between ponds.Furthermore, the rate of association increased asponds dried out, indicating that attachment wasnot accidental.

Some freshwater molluscs need another ani-mal as a host during parts of their life cycle. Forexample freshwater unionid or margaritiferidmussel larvae that parasitise fishes’ gills or fins,

© Koninklijke Brill NV, Leiden, 2008. Also available online - www.brill.nl/amre

458 L.R. Wood et al.

gaining shelter, sustenance and ultimately dis-persal (Howerth and Keller, 2006). A heavy par-asite load is costly to the host, and potentiallyfatal, however this is not always a one-sided re-lationship, some fish also use the mussels to in-cubate their embryos (Reichard et al., 2006).

The present study examines the occurrence offreshwater bivalve mussels found clamped ontonewts’ toes, which might be a form of phoresyor dispersal parasitism. This phenomenon islittle-reported in the literature, although infor-mal communication with professional herpetol-ogists indicates that it has been sporadically ob-served in several European countries. There area few published accounts from the late 19th andearly 20th centuries including two short notes byDarwin (1878, 1882) detailing a mussel foundclipped onto a duck’s foot and, later, on a greatdiving beetle (Dytiscus marginalis) and a frog(species not given). The only recent account, toour knowledge, is that of Kwet (1995) detail-ing mussels found on common toads’ toes (Bufobufo) in Germany, but with only two individualtoads observed with mussels and no analysis.

Freshwater mussels were observed on newts’toes at three ponds in Luxembourg, but in par-ticularly high numbers at one pond, affectingall four local newt species (Lissotriton helveti-cus, L. vulgaris, Mesotriton alpestris and Tritu-rus cristatus). Mussels were firmly shut aroundthe tip of the newt’s digit (see fig. 1), often withapparent tissue and bone damage to the newt.The data collected so far allow simple compar-isons between the newt sexes and species, anddescribe the local incidence of the phenomenon.

Materials and methods

Ponds in central and southern Luxembourg were surveyedby bottle trapping up to six times between March and June2007. In total 37 ponds at 17 sites were surveyed. A pondwas defined as “a body of water, of man-made or naturalorigin, between 1 m2 and 2 ha, which usually holds waterfor at least four months of the year” (National Pond Survey,Biggs et al., 1998). Sites were considered distinct if studyponds were separated by >1 km. Sites were selected fromhistorical records of amphibian presence in Luxembourgand from topographical maps showing waterbodies.

Bottle traps, constructed from 2 litre drinks bottles withthe top section cut off and inverted (see Griffiths, 1985),were set underwater resting on the substrate of the pond,held in place with a cane and with an air bubble retainedinside. Up to 25 traps were set per pond, or a maximum of 30between immediately adjacent ponds, at 2 m intervals alongthe margin in multiples of 5 or in a continuous line. Trapswere removed and emptied the following morning, within12 hours of setting. All captured newts were identified tospecies, sexed and counted, and their feet and toes wereexamined for damage or attached mussels. The conditionof each toe was recorded as intact, damaged or with musselattached.

In pond ‘Dréisch’, where the incidence of mussel at-tachment to newts was highest, six visits were made dur-ing the fieldwork period. From five of these visits 22 newts(14 Mesotriton alpestris, 4 Triturus cristatus, and 4 Lissotri-ton vulgaris) caught with mussels clamped to their toes wereobserved for 24 or 36 hours. They were kept in a 30 litre co-vered plastic box, with between 1 and 10 other newts, innatural shade on site or indoors in a cool day-lit room with20 cm depth of pond water and some vegetation. The timeelapsed between capturing the newt and the mussel becom-ing detached was measured to the following 12 hour intervaland the affected toe was checked for damage.

Mussel sampling

On 16th March 2007 mussels in Dréisch were sampled insix 10 × 10 cm quadrats. Quadrats were spaced along theaccessible shallow pond margin, random placing was notpossible due to dense vegetation and deep water. Substrateand plant matter from the pond bottom were lifted by handinto a bucket and then sorted and sieved carefully to collectall the freshwater mussels.

Mussels in each sample were counted and preserved inethanol for later identification. They were identified accord-ing to Glöer and Meier-Brook (2000) and Killeen, Aldridgeand Oliver (2004).

Analysis

Pearson’s chi-square test was used to compare the incidenceof mussel attachment between sexes and species of newtand fore/hind legs at Dréisch, the rate of damaged toes be-tween Dréisch and all other ponds, and the difference be-tween middle toes and outer toes. Pseudoreplication mighthave occurred as newts were not individually recognisable;however recapture rates would be expected to be relativelylow and not to adversely affect analyses.

Results

Mussels were found attached to newts in threeponds at two sites: Dréisch (Gauss-Luxembourggrid reference: LC 708 636) and Laaschtert3 & 4 (LC 725 934 & LC 727 935). Two

Interactions between freshwater mussels and newts 459

Figure 1. Male M. alpestris with a mussel attached to the fourth toe of the hind left foot.

species of freshwater mussel were identified atDréisch: Sphaerium nucleus and Pisidium ob-tusale, although only S. nucleus was seen at-tached to newts. Only one mussel species, P.subtruncatum, was found at Laaschtert, how-ever mussels at this site were not surveyed or

collected with quadrats. The density of mus-sels at Dréisch was very high – S. nucleuswas recorded at a mean density of 2216.7/m2

(standard error: ±371.0/m2, range: 1100-3300,n = 6) and P. obtusale at 716.7/m2 (standard er-ror: ±293.7/m2, range: 0-1900, n = 6) with


Figure 2. The condition of the toes of newts caught in pond ‘Dréisch’ compared to those caught in 34 other ponds. Newtsrecorded as ‘Intact’ had all their toes present uninjured, ‘Damaged’ newts had some shortened or missing toes, and newts‘With mussels’ had one or more mussels attached and possibly some additional damage; n indicates the total number of newtsin each category.

an overall mean mussel density of 2933.3/m2

(standard error: ±315.9/m2, range: 1700-4000,n = 6).

Individuals of all four newt species presentin Luxembourg were observed with musselsattached to their toes: Mesotriton alpestris (n =23), Triturus cristatus (n = 10), Lissotritonhelveticus (n = 2) and L. vulgaris (n = 7).Of all newts captured in Dréisch 23% (totaln = 161) were carrying mussels (see fig. 2).The occurrence was less frequent at Laaschtert3 and 4 where mussel attachment was recordedon 3.6% (n = 1, total n = 28) and 7.0%(n = 4, total n = 57) of newts respectively.Laaschtert 3 and 4 data were excluded fromstatistical analysis of the frequency of musselattachment due to low overall capture rates andthe corresponding lack of data.

Individuals of all three newt species presentin Dréisch were found with one or more mus-sels attached (fig. 2); there was no significantdifference in frequency of mussel attachmentbetween the species (χ2 = 3.21, df = 2, P =0.20). T. cristatus males were significantly more

likely to have a mussel attached than females(χ2 = 5.98, df = 1, P < 0.05), but there wasno significant difference between sexes of theother species (M. alpestris: χ2 = 1.75, df = 1,P = 0.18; L. vulgaris: χ2 = 1.18, df = 1,P = 0.28). One female M. alpestris was foundwith a mussel on each hind foot and her owneggs adhered to the mussels, showing that mus-sels can interfere with oviposition.

Newts in Dréisch (n = 161) were nearlyfive times more likely to have damaged, miss-ing or split toes than newts from other ponds(excluding Laaschtert, n = 1982, see fig. 2;χ2 = 155.73, df = 1, p < 0.001). Middle toeswere significantly more likely to have musselsattached than the shorter outer toes on each foot(χ2 = 7.27, df = 1, P < 0.001). There was nodifference in rate of attachment between frontand hind legs (χ2 = 3.04, df = 1, P = 0.08).In captivity newts only lost mussels when the af-fected toe fell off with the mussel still clampedonto it (n = 13). Nine out of 22 mussels re-mained attached for longer than the captivity pe-

Interactions between freshwater mussels and newts 461

riod of 24 or 36 hours (24 hrs: n = 3; 36 hrs:n = 6).

Discussion

During amphibian surveys in Luxembourg,newts were found with small freshwater musselsattached to their toes – in one pond, ‘Dréisch’,23% of the newts captured were carrying one ormore mussels. The current data set is limited toa single season and three ponds at two sites; toofew newts were captured at one site, so analyseswere restricted to pond Dréisch. In addition, theoccurrence of damaged digits was significantlyhigher in newts from Dréisch, possibly as a re-sult of previous mussel attachment. Of the 22newts observed in captivity for up to three days,the 13 mussels that became detached only did sobecause the toe fell off. Therefore attachment ofmussels does have a negative impact on newts.The ability to regenerate digits ensures that theimpact is only temporary, although full regener-ation takes at least 1.5 years (Arntzen, Smithsonand Oldham, 1999). A different kind of impactwas observed on a single occasion: the femaleMesotriton alpestris with a mussel attached toeach hind foot and eggs adhered to the mussels.Clearly her ability to wrap eggs was reduced, atleast in the short-term, indicating that perhapsmussels regularly prevent or hinder reproduc-tive behaviours in which the feet play a part,such as egg-laying and courtship.

Although a greater percentage of the largerbodied newts carried mussels, this trend wasnot significant. If the inter-species differencesare real, they may be due to differences in thespecies’ behaviour and habitat use, or simplydue to toe size and structure. For example, Tritu-rus cristatus spends more time on the pond bot-tom than Lissotriton vulgaris (Dolmen, 1988)and feeds predominantly on benthic prey (Jolyand Giacoma, 1992), so would be more likelyto come into contact with mussels.

Why mussels attach to newts, and why theydo not release, is open to speculation. Newtsresting and travelling on the pond bottom will

regularly contact freshwater mussels, especiallywhen the mussels are at a very high density asin Dréisch. An open, filter feeding mussel willclamp shut defensively when prodded, whichwould probably occur if a newt steps on it. Fol-lowing such accidental attachment, the musselmay be unable to release due to constant stimu-lation from the trapped toe. Alternatively, Kwet(1995) hypothesised that attachment to amphib-ians may be a dispersal mechanism for musselsfrom overcrowded ponds, although presumablystill with an element of accidental attachment inthe first place.

Dréisch is a closed pond (no water flowing inor out) and is relatively isolated. Isolation andideal conditions for Sphaerium nucleus preventeasy dispersion and create the extraordinarilyhigh mussel density. Although most Sphaeriumand Pisidium species are able to climb plantstems underwater, they cannot traverse dry landwithout the assistance of some kind of vector(Boycott, 1936). There are only two waterbod-ies within 500 m: the nearest pond (400 m) isan inaccessible motorway run-off pool and thesecond (480 m) is separated from Dréisch by arailway track and embankment. In this case, am-phibians may provide transport within Dréisch,but are very unlikely to be useful vectors fordispersal to other ponds. However if there wereother ponds in close proximity, movement ofnewts between ponds during the breeding sea-son (e.g. Hagström, 1979; Dolmen, 1981; Grif-fiths, 1984) could facilitate very local musseldispersal. Over greater distances passive mus-sel dispersal could occur through attachmentto larger animals: mussels might become stuckin the mud on other animals’ feet (Pip, 1986;Bilton, Freeland and Okamura, 2001), or even“nip” onto ducks’ toes (Darwin, 1882; Boycott,1936).

Based on the present observations it is notpossible to determine whether freshwater mus-sel attachment to newts represents an interactionthat has evolved by natural selection or is simplyaccidental. If dispersal is facilitated in this way,the relationship appears to be parasitic, because


the mussel benefits while the newt is harmed.On land, mussels seem to impede a newt’s lo-comotion, but an experimental approach wouldbe necessary to determine whether newts are ca-pable of transporting mussels a sufficient dis-tance on land for such ‘dispersal parasitism’ tosucceed. Subject to environmental conditions,Sphaerid mussels can survive extended periodsin anoxic conditions: approximately 20 days at16◦C, increasing to more than 100 days below7◦C (Holopainen, 1987), and avoid desiccationby closing tightly. Therefore if the host newtmoves to another pond during the same season,and before its toe falls off, a mussel may sur-vive the transfer. Further work should build onthe present study, allowing greater descriptionof the phenomenon and developing an under-standing of where and under what conditionsfreshwater mussels attach to amphibians.

Acknowledgements. This project was funded by a re-search scholarship (‘bourse de formation-recherche’) fromthe Luxembourg Ministère de la Culture, de l’EnseignementSupérieur et de la Recherche to LW (BRF06/056), with ad-ditional funding for fieldwork equipment from The Am-phibian Conservation and Research Trust, UK. A licence totrap amphibians was granted by the Luxembourg Ministèrede l’Environnement (ref. 64512). We thank L. Glesener andM. Sylla for fieldwork assistance and two referees for usefulcomments on the manuscript.

References

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Biggs, J., Fox, G., Nicolet, P., Walker, D., Whitfield, M.,Williams, P. (1998): A guide to the methods of theNational Pond Survey. Oxford, Pond Action.

Bilton, D.T., Freeland, J.R., Okamura, B. (2001): Dispersalin freshwater invertebrates. Ann. Rev. Ecol. Syst. 32:159-181.

Boycott, A.E. (1936): The habitats of fresh-water molluscain Britain. J. Anim. Ecol. 5: 116-186.

Darwin, C. (1878): Transplantation of shells. Nature 18:120-121.

Darwin, C. (1882): On the dispersal of freshwater bivalves.Nature 25: 529-530.

Dolmen, D. (1981): Local migration, rheotaxis, andphilopatry by Triturus vulgaris within a locality in cen-tral Norway. Br. J. Herpetol. 6: 151-158.

Dolmen, D. (1988): Coexistence and niche segregation inthe newts Triturus vulgaris (L.) and T. cristatus (Lau-renti). Amphibia-Reptilia 9: 365-374.

Glöer, P., Meier-Brook, C. (2000): Süsswassermollusken.Ein Bestimmungsschlüssel für die BundesrepublikDeutschland, 13th Edition. Hamburg, Deutscher Jugend-bund für Naturbeobachtung.

Griffiths, R.A. (1984): Seasonal behaviour and intrahabitatmovements in an urban population of Smooth newts,Triturus vulgaris (Amphibia: Salamandridae). J. Zool.Lond. 203: 241-251.

Griffiths, R.A. (1985): A simple funnel trap for studyingnewt populations and an evaluation of trap behaviourin smooth and palmate newts, Triturus vulgaris andTriturus helveticus. Herpetol. J. 1: 5-10.

Hagström, T. (1979): Population ecology of Triturus crista-tus and T. vulgaris (Urodela) in SW Sweden. HolarcticEcol. 2: 108-114.

Holopainen, I.J. (1987): Seasonal variation of survival timein anoxic water and the glycogen content of Sphaeriumcorneum and Pisidium amnicum (Bivalvia, Pisidiidae).Amer. Malac. Bull. 5: 41-48.

Howerth, E.W., Keller, A.E. (2006): Experimentally in-duced glochidiosis in smallmouth bass (Micropterusdolomieu). Vet. Pathol. 43: 1004-1008.

Joly, P., Giacoma, C. (1992): Limitation of similarity andfeeding habits in three syntopic species of newts (Tritu-rus, Amphibia). Ecography 15: 401-411.

Killeen, I., Aldridge, D., Oliver, G. (2004): Freshwaterbivalves of Britain and Ireland. Cardiff and Cambridge,Field Studies Council.

Kwet, A. (1995): Erdkröten (Bufo bufo) als Transportwirtevon Kugelmuscheln (Sphaerium corneum). Salamandra31: 61-64.

Lopez, L.C.S., Filizola, B., Deiss, I., Rios, R.I. (2005):Phoretic behaviour of bromeliad annelids (Dero) andostracods (Elpidium) using frogs and lizards as dispersalvectors. Hydrobiologia 549: 15-22.

Lopez, L.C.S., Gonçalves, D.A., Mantovani, A., Rios, R.I.(2002): Bromeliad ostracods pass through amphibian(Scinaxax perpusillus) and mammalian guts alive. Hy-drobiologia 485: 209-211.

Pip, E. (1986): A study of pond colonization by freshwatermolluscs. J. Mollus. Stud. 52: 214-224.

Reichard, M., Ondrackova, M., Przybylski, M., Liu, H.,Smith, C. (2006): The costs and benefits in an un-usual symbiosis: experimental evidence that bitterlingfish (Rhodeus sericeus) are parasites of unionid musselsin Europe. J. Evol. Biol. 19: 788-796.

Rickleffs, R.E., Miller, G.L. (1999): Ecology. Basingstoke,W.H. Freeman.

Seidel, B. (1990): Amphibien als Transporteure limnis-cher Muschelkrebse: Ein Parameter zur Analyse derVerteilung von Bombina variegata. Amphibia-Reptilia11: 253-261.

Received: January 25, 2008. Accepted: April 25, 2008.

Appendix 9 - Publications

170

Wood, L. R., Griffiths, R. A. & Schley, L. (2009) Amphibian

chytridiomycosis in Luxembourg. Bulletin de la Société des

Naturalistes Luxembourgois, 110, 109-114.

Bull. Soc. Nat. luxemb. 110 (2009) 109

Amphibian chytridiomycosis in Luxembourg

Laura R. Wood1, Richard A. Griffiths1 & Laurent Schley2

1 Durrell Institute of Conservation & Ecology, University of Kent, Marlowe Building, Canterbury, CT2 7NR, UK ([email protected]; [email protected])2 Administration de la nature et des forêts, 16 rue Eugène Ruppert, L-2453 Luxembourg ([email protected])

Wood, L.R., R.A. Griffiths & L. Schley, 2009. Amphibian chytridiomycosis in Luxembourg. Bulletin de la Société des naturalistes luxembourgeois 110: 109-114.

Abstract. Aquatic-phase amphibians at eight sites in Luxembourg were tested for the fungus Batrachochytrium dendrobatidis, which causes a disease responsible for population declines among many amphibian species worldwide. Infected amphibians were found at two of the sites tested; two further sites also showed marginally positive results. The ecology of B. den-drobatidis and the necessity for biosecurity protocol implementation by fieldworkers are discussed.

Key words. Batrachochytrium dendrobatidis, biosecurity, conservation, fieldwork, mass mortality, population decline.

1. IntroductionIn recent years much attention has been focused on the apparent global decline of amphibians. According to the IUCN Red List many more amphibian species are threatened than birds or mammals, with 32.5% amphibians, 23% mammals and 12% birds assessed as ‘globally threatened’ (Stuart et al. 2004). The threats facing amphibians are complex and mostly caused by humans, including non-native species introductions, exploitation, habitat loss, climate change, pollution and disease (Collins & Storfer 2003).Following its relatively recent discovery almost simultaneously in wild and captive populations (Berger et al. 1998, Pessier et al. 1999), the chytridiomycete fungus Bat-rachochytrium dendrobatidis (hereafter Bd) has been implicated in amphibian declines and extinction events worldwide. The reason for the rapid global emergence of Bd is the subject of much debate, but likely due to a suite of factors including introduction by human agency into wild populations, for example release of non-native species, espe-cially the North American bullfrog (Rana catesbeiana) (Garner et al. 2006) and the cumulative effect of environmental changes

modifying an already present host-parasite relationship (Pounds et al. 1999).Prior to the identification of Bd, chytridi-omycete fungi had not been described in parasitic relationships with vertebrates, they were thought to be either free-living or parasitic on other fungi, algae, plants or invertebrates (Pessier et al. 1999). There-fore Bd is the first member of the phylum known to parasitise vertebrates and in a very short space of time has been associated with amphibian declines on every continent they inhabit (see www.spatialepidemiology.net/bd/). Bd infection has been observed to travel very rapidly, described by Lips et al. (2006) as an ‘epidemic wave’ and its interac-tions with amphibians and the environment appear to vary geographically (Morehouse et al. 2003).Infection with Bd causes a disease called chytridiomycosis. The fungus only occurs in keratinised tissue and may cause death by interfering with the respiration and osmoregulation properties of amphibian skin (Daszak et al. 1999). However not all amphibian species are susceptible. Out of 28 European species tested by Garner et al. (2005) 20 were found to be infected in some area or at some stage of their life history. According to the latest data available on the

110 Bull. Soc. Nat. luxemb. 110 (2009)

website dedicated to sharing information about Bd, 29 species have tested positive in nine European countries (http://www.spa-tialepidemiology.net/bd/).Bd has been detected in both France and Germany and at very high prevalence in Switzerland. However this does not mean that it is not present in other nearby coun-tries as sampling effort has been patchily distributed and not all data are in the online database. All but two of the amphibian species present in Luxembourg have been found to be susceptible to the disease else-where in Europe: only Triturus cristatus and Bombina variegata have not tested positive so far, but the extent of survey effort has not been quantified.Increasing knowledge of the global distribu-tion of Bd infections is vital. Accordingly, we tested amphibians in Luxembourg for the disease in a simple presence/absence study. The resulting data are useful from both local and global perspectives, informing manage-ment decisions on site and contributing to wider knowledge of the disease.

2. Materials and methodsAmphibians from 14 ponds in eight differ-ent locations in Luxembourg were tested for Bd infection (see Table 1). Test sites were selected mainly (but not exclusively) for having an historical record of the Common

midwife toad, Alytes obstetricans, a threat-ened species known to be particularly sus-ceptible to the disease. Up to 34 individuals were tested at each site between May and July 2008.During all fieldwork, appropriate biosecu-rity measures were applied. Footwear and survey equipment were disinfected between sites and clean disposable vinyl gloves were worn for handling amphibians.Amphibians were caught in bottle traps, by hand or by net. Only species known to be susceptible to Bd were tested; see Table 1. Keratinised skin parts were targeted with a cotton swab that was swept three times across the drink patch, feet and thighs of adult animals or rotated on the mouthparts of anuran larvae.Swabs were stored in individual airtight tubes in a cool, dry environment. The real-time polymerase chain reaction (PCR) Taqman procedure of Boyle et al. (2004) was used to extract and amplify Bd DNA from the swabs. Samples were compared to ampli-fication standards of 100, 10, 1 and 0.1 Bd zoospore equivalents. This method is widely used and is specific to Bd.Each sample was replicated twice. Samples that amplified ≥1.0 Bd zoospore equiva-lent in both replications were taken to be true positives, single amplification samples were retested, and samples that did not

Nearest town or village Pond name Grid reference

Number of swabs by species Total swabsA.o B.c R.kl.e M.a L.h L.v

Putscheid Alersang LA 78152 14160 17 6 2 25Bissen Laaschtert 2 LC 72266 93271 1 1 2 4

Laaschtert 3 LC 72520 93396 1 1 2Laaschtert 4&5 LC 72679 93499 1 1 2 4

Useldange Weiden 3&4 LC 64855 92458 30 30Schoos Stuppicht 1&2 LC 80024 89074 3 27 30Hunnebour Mersch LC 74102 88067 25 25Blaschette Flakewiss2 LC 80068 87043 1 1 2

Biergwiss LC 79823 86966 1 4 14 19Steinfort Steinfort Carrières LC 61072 81361 4 6 5 6 13 34Niedercorn Giele Botter LC 59207 67483 19 19

Table 1. Sites of Bd testing and the species tested at each site. A.o = Alytes obstetricans; B.c = Bufo calamita; R.kl.e = Rana klepton. esculenta (green frog complex); M.a = Mesotriton alpestris; L.h = Lissotriton helveticus; L.v = L. vulgaris.


amplify in either replicate were taken to be negative. Due to the very small quanti-ties of DNA involved, and the potential for contamination between samples, those that amplified only once and at very low levels were ignored. The quantity of Bd zoospores detected on positive animals is taken from the mean value of Bd zoospore equivalents detected in the replicates of each sample.This study aimed only to establish whether Bd is present in Luxembourg, therefore inferences about the prevalence of disease can not be made.

3. ResultsAmphibians tested positive for Bd zoospores at two of the eight sites surveyed. Two fur-ther sites may also be positive, however the number of zoospores detected was very low – if greater confidence in results was required these samples would need further testing. The sites tested and the results are shown in Fig. 1.No infection was found at Alersang, Laas-chtert 2 or 3, Weiden 3&4, Mersch or Biergwiss and Flakewiss2.A single green frog (R. kl. esculenta) tested positive at Laaschtert 4&5; this is a clear positive because Bd DNA amplified in both sample replicates on the first PCR run (mean 8.3 zoospores). A clear positive result was also found in one Alpine newt (Mesotriton alpestris) at the forest ponds Stuppicht 1&2 (mean 2.5 zoospores).At Giele Botter pond a palmate newt (Lissot-riton helveticus) returned a marginal positive result on both replicates, only one of which was ≥0.5 zoospore equivalents (0.07 and 0.6). Screening was unable to give conclu-sive results for two newts swabbed at Stein-fort Carrières: a smooth newt (L. vulgaris) sample that was tested twice (4 replicates) amplified only a single replicate each time (0.4 and 2.2 zoospores); one palmate newt (L. helveticus) gave a marginally positive test on both replicates (mean 0.7 zoospores).The amplification standards, with a known quantity of Bd zoospores, all amplified as expected, indicating that the tests had run successfully.

4. Discussion4.1. Bd in LuxembourgBd was found at half of the sites tested, although two of the positive results are not clear. The highest infection rate among the animals randomly sampled was 10% at the Laaschtert ponds near Bissen (n = 10): how-ever finding true prevalence rates would require more extensive testing.The sites tested were selected for their amphibian fauna, with at least four of the eight sites presently hosting the Common midwife toad (A. obstetricans) and two other sites from where it has recently become extinct. According to the Luxem-bourg Musée national d’histoire naturelle (MNHN) records A. obstetricans was present at the Laaschtert pond complex until at least 1997 (F. Schoos, ‘Amphibia Data’ record 466, MNHN, 2007) but it was not detected

Fig. 1. The locations of ponds tested for Bd infection and test results (1 = Alersang; 2 = Laaschtert 2; 3 = Laaschtert 3; 4 = Laaschtert 4+5; 5 = Weiden 3+4; 6 = Stuppicht 1+2; 7 = Mersch; 8 = Flakewiss; 9 = Biergwiss; 10 = Steinfort Carrières; 11 = Giele Botter).


during two seasons of intensive survey effort by LRW in 2007 and 2008.

4.2. Biology of BdBd was only discovered recently (Berger et al. 1998, Pessier et al. 1999), so relatively little is known about its ecology, and its modes of transmission are still not fully understood. Long-distance transmission seems likely to have occurred principally via infected ani-mals in the global amphibian trade (Garner et al. 2006) for the pet, science and food industries (Kriger & Hero 2007), and then subsequent releases or escapes into the wild. Besides its own motility in water, on a local level there are many possible vectors of Bd; for example animals travelling between sites (e.g. on bird feathers) and human vectors such as vehicles and hiking boots (Daszak et al. 2003, Johnson & Speare 2005). It can survive, and remain viable, away from a host amphibian for up to 7 weeks in lake water (Johnson & Speare 2003) and can survive up to three hours out of the water (Johnson & Speare 2005). Therefore it could easily be transported locally.The effects of Bd infection vary widely. In some places mass-mortality events have been observed, for example the large num-bers of dead and dying A. obstetricans found around ponds in Peñalara National Park in central Spain (Bosch et al. 2001). Yet some-times there may be a very high prevalence of Bd, but no pathological symptoms or any apparent population decline (Kriger & Hero 2007). Furthermore, many of the so-called ‘enigmatic declines’ (Stuart et al. 2004) could have been caused by undetected disease events. To the authors’ knowledge no mass-mortality events of amphibians have been reported in Luxembourg. However, because of the patchy nature of amphibian recording, it is possible that they may have occurred without being witnessed. Alternatively Bd may simply be present but not causing ani-mals to die.Although several treatments or cures for Bd infection have been found (Woodhams et al. 2003, Bishop et al. 2008) these are imprac-ticable in the field, therefore Bd has not yet been mitigated for in the wild (Garner 2008).

Ascertaining the presence of Bd in Luxem-bourg, and neighbouring countries (Garner et al. 2005), is a key step to understanding the pressures faced by declining amphib-ian species in the region. The actual rate of infection, whilst regulating the impact of the disease, does not alter the biosecurity pro-tocol that should be implemented by field-workers. The disease is present, and care should be taken to prevent further spread by human agency.

4.3. Biosecurity issuesSimple disinfection procedures, such as washing boots in commercially available disinfectants, can be used by fieldworkers to lessen the risk of carrying Bd between study sites. At first this is time-consuming, how-ever it rapidly becomes part of the survey routine. It can be argued that such biose-curity measures are ‘too little, too late’ and even futile because wild animals move freely and frequently between ponds. Despite the counter-opinions, it is generally consid-ered that precautionary principles should be applied because of the potentially severe consequences of spreading Bd.A guideline leaflet produced by UK herpeto-logical conservation organisations provides up-to-date, simple advice on how to mini-mise the risk of transferring Bd between sites and the recommended disinfectants. The leaflet ‘Amphibian disease precautions: a guide for UK fieldworkers’ (Version 1, Feb-ruary 2008) is free to download from www.arg-uk.org.uk/Publications.htm. Anybody working in or near to amphibian aquatic habitat is strongly recommended to follow the guidelines, to report unusual die-off events and to be aware of the symptoms of disease.A great deal of research is being conducted on Bd around the world. The data from Lux-embourg contribute to knowledge of the extent of the disease. Further work could include retesting the samples that gave bor-derline positive results and making detailed local investigations to determine more pre-cisely where Bd infection occurs and its prevalence.


AcknowledgementsThe disease testing was funded by the Administration de la nature et des forêts, during research work under LRW’s student bursary from the Ministère de la culture, de l´enseignement supérieur et de la recherche (Luxembourg, BFR 06/056). Many thanks to fieldwork assistants Liza Glesener and Véronique Ludwig who sped up the swabbing process greatly, and to Edmée Engel (Musée national d’histoire naturelle) for her collaboration and support throughout the project. Trent Garner and Frankie Clare from the Zoological Society of London (ZSL) kindly instructed LRW in sample collection and processing and provided laboratory facilities. We also thank Jennifer Sears, of ZSL and the Durrell Institute of Conservation and Ecology, whose knowledge of Bd was very helpful in writing this paper.

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