Components of plant species diversity in the New Zealand forest

Jake Overton

Landcare Research

Hamilton

Acknowledgements

NVS data contributors and curators

Simon Ferrier and Glenn Manion for development of GDM and collaboration on modelling

General Question

Investigate components of richness

• Alpha diversity

• Beta diversity

• Gamma diversity

How do these compare between groups?

Approach: Use a new modelling technique, Generalised Dissimilarity Modelling (GDM) to estimate components of diversity

Components of diversity (sensu Cody 1986)

Alpha diversity = local richness

Beta diversity = turnover in species due to habitat or environment

Gamma diversity = turnover in species due to geographic distance or barriers

All three components contribute to regional richness

Presence-absence of all vascular plant species in each plot

Plots approx 20x20 m (sometimes unbounded)

Almost 20000 plots

1220 species

NVS recce (= recon) plots

Biotic Data

Variable abbrev. Description

Geographic position Geographic position of plot

MAT Mean Annual Temperature

Tseas A measure of cold stress, relative to mean annual temperature

MAS Mean Annual Solar Radiation

Deficit Vapor Pressure deficit

VPD Vapor Pressure deficit

Calcium Soil Calcium

Age Soil age

N Total Soil Nitrogen

AcidP Available P

Drain Soil Drainage

Psize Soil Particle Size

Indur Soil Induration

Slope Topographic slope

Discoast Distance to coastline

Notho Nothofagus abundance from Leathwick

En

viro

nm

enta

l va

riab

les

(sp

atia

l)

alpha diversity (local richness)

beta diversity

gamma diversity

‘dissimilarity’‘turnover’‘complementarity’

richness = f (rainfall, temperature, veg type …)

dissimilarity = f ( (rainfall, temperature, veg type …), geographical separation)

Modelling of richness:

can be supplemented by modelling of compositional dissimilarity between locations:

What is Generalised Dissimilarity Modelling?

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Compositional dissimilarity between pairs of survey sites

Environmental& geographicalseparation

Generalised dissimilarity modelling (GDM)

Biotic Information

Environmental and Geog Space

Ecological Space

Same units scaled by importance

Diff

erin

g un

its a

ndim

port

ance

All

sp

ecie

s m

od

el

Results 1

All

sp

ecie

s va

lid

atio

n

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All species

Total species pool 1020 species

Alpha diversity component = Proportion accounted for by local richness = Mean plot richness/ Total species pool

Unexplained component = 1 – proportion deviance explained

Gamma Diversity component = Proportion deviance explained by geography

Beta Diversity component = Deviance explained by environment

All plant speciesSnails

Group # spp local rich

Ferns 147 6.8

Lianes-epiphytes-parasites

49 1.9

Trees 107 8.9

Shrubs 255 4.3

Dicot herbs 360 2.4

Monocot herbs 282 1.3

All Species 1020 25.9

Results 1

All

Ferns

Shrubs

Trees

Monocot herbs

Dicot Herbs

All species

Dicot HerbsFerns

Monocot herbs

Shrubs

Trees

Biological survey data

Environmentalpredictors

Visualisation of spatial pattern in community composition

Predicted distributions of species

Constrained environmental classification

Survey gap analysis

Conservation assessment

Climate-change impact assessment

Generalised dissimilarity modelling

Ferrier, S. et al (in press) Using generalised dissimilarity modelling to analyse and predict patterns of beta-diversity in regional biodiversity assessment. Diversity & Distributions

Ferrier, S. et al (2004) Mapping more of terrestrial biodiversity for global conservation assessment. BioScience 54: 1101-1109

Conclusions

GDM is an exciting new tool for biodiversity analyses

Its main application is for biodiversity modelling and planning, but it has promise for untangling components of diversity

Plant species show relatively strong environmental influence and some geographic influence on turnover

Groups differ in the explained turnover, and in relative importance of different variables.

test

Dense sampling relative to grain of compositional turnover - relatively few species, each with many records

Sparse sampling relative to grain of compositional turnover - huge number of species, each with very few (or no) records

Environmental space (beta diversity)

Geogra

phic

al sp

ace

(gam

ma d

ivers

ity)

Geogra

phic

al sp

ace

(gam

ma d

ivers

ity)

Environmental space (beta diversity)

An example from the arid rangelands of central Australia

All species

All species

Environmental predictors•Radiometrics – Total Count•Landsat TM – Band 2•Radiation of Warmest Quarter•Topographic Wetness Index •Precipitation of Driest Period•Isothermality•Minimum Temperature of Coldest Period•Elevation Diversity for 300m radius•Landsat TM – PD54 vegetation index•Mean Temperature of Wettest Quarter•Radiometrics – Uranium

f (T

c10

d)

f (W

etn

ess

)

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1

1ln

Biological responseBray-Curtis compositional dissimilarity between all pairs of 248 field survey sites (based on perennial woody plant species)

Models species turnover (dissimilarity) between locations as a function of geography and environment

Uses matrix regression, using GLMs.

Developed by Simon Ferrier, (Department of Environment and Conservation, Armidale New South Wales, Australia)

Programmed by Glenn Manion, DEC, Armidale.

What is Generalised Dissimilarity Modelling?

All species

Dicot HerbsFerns

Monocot herbs

Shrubs

Trees

FernsFerns

test

L-E-P

Monocot herbs

Monocot herbs

Shrubs

Shrubs

Trees

test

Dicot Herbs

Ferns

test

L-E-P

Monocot herbs

Shrubs

Trees

Trees