ORIGINAL PAPER

New methods and technologies for regional-scale abundanceestimation of land-breeding marine animals: applicationto Adelie penguin populations in East Antarctica

Colin Southwell • John McKinlay • Matt Low •

David Wilson • Kym Newbery • Jan L. Lieser •

Louise Emmerson

Received: 26 October 2012 / Revised: 20 February 2013 / Accepted: 22 February 2013 / Published online: 30 March 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract Land-breeding marine animals such as pen-

guins, flying seabirds and pinnipeds are important com-

ponents of marine ecosystems, and their abundance has

been used extensively as an indication of ecosystem status

and change. Until recently, many efforts to measure and

monitor abundance of these species’ groups have focussed

on smaller populations and spatial scales, and efforts to

account for perception bias and availability bias have been

variable and often ad hoc. We describe a suite of new

methods, technologies and estimation procedures for cost-

effective, large-scale abundance estimation within a gen-

eral estimation framework and illustrate their application

on large Adelie penguin populations in two regions of East

Antarctica. The methods include photographic sample

counts, automated cameras for collecting availability data,

and bootstrap estimation to adjust counts for the sampling

fraction, perception bias, and availability bias, and are

applicable for a range of land-breeding marine species. The

methods will improve our ability to obtain population data

over large spatial and population scales within tight

logistic, environmental and time constraints. This first

application of the methods has given new insights into the

biases and uncertainties in abundance estimation for pen-

guins and other land-breeding marine species. We provide

guidelines for applying the methods in future surveys.

Keywords Bootstrap estimation � Detection bias �Distance sampling � Photography � Remotely operating

cameras � Sample designs

Introduction

Animal species that forage in marine environments but

breed on land, such as penguins, flying seabirds and

pinnipeds, are important components of marine ecosystems

and are often used to indicate marine ecosystem status and

change (Montevecchi 1993; Furness and Camphusen 1997;

Piatt et al. 2007). They have several advantages as marine

indicators over other marine species, including their high

visibility, their relative accessibility for study when

breeding on land, and their ability to forage widely in the

marine environment in search of a range of marine prey

(Piatt et al. 2007). These species’ groups can provide

insights into marine ecosystem processes at different spa-

tial and temporal scales through the measurement of a

range of demographic, behavioural and physiological

parameters (Reid et al. 2005). The abundance of breeding

populations is a frequently measured parameter. Abun-

dance reflects the collective outcome of key demographic

processes, is relatively easy to measure compared with

C. Southwell (&) � J. McKinlay � M. Low � D. Wilson �K. Newbery � L. Emmerson

Australian Antarctic Division, Department of Sustainability,

Environment, Water, Population and Communities,

203 Channel Highway, Kingston, TAS 7050, Australia

e-mail: colin.southwell@aad.gov.au

Present Address:M. Low

Department of Ecology, Swedish University of Agricultural

Sciences, Uppsala, Sweden

Present Address:D. Wilson

Ecology and Heritage Partners, 292 Mt Alexander Road,

Ascot Vale, VIC 3032, Australia

J. L. Lieser

Antarctic Climate and Ecosystems Cooperative Research Centre,

Centenary Building, Private Bag 80, Hobart, TAS 7001,

Australia

123

Polar Biol (2013) 36:843–856

DOI 10.1007/s00300-013-1310-z

other demographic parameters (Thompson et al. 1998), is a

primary determinant of biomass which is a key input

parameter for marine ecosystem models (Pauly et al. 2000),

and can reflect ecosystem processes over large spatial and

temporal scales (Reid et al. 2005).

The development of robust methods for estimating

animal abundance is an important and rich area of research

that serves as a foundation for many ecological and man-

agement studies. Over the past three decades, much work

has focussed on developing techniques to estimate the

unknown fraction of animals that are not detected in

abundance surveys because of perception bias and avail-

ability bias (Pollock et al. 2004). A recent emerging area of

methodological research is the development of cost-effec-

tive abundance estimation methods for large-scale infer-

ence, given the scale of human-induced impact on

ecosystems is increasing and agencies need to manage over

large scales but operate within tight logistic and financial

constraints (Pollock et al. 2002; Field et al. 2005; Jones

2011). While research on incomplete detection and cost-

effective methods for large-scale inference has spawned

many abundance estimation techniques for a range of

species, they can all be considered within a general abun-

dance estimation framework (Borchers et al. 2002; Wil-

liams et al. 2002; Pollock et al. 2004). The advantages of a

general framework are that it simplifies apparently com-

plex and unique problems to a small set of common issues,

encourages researchers to consider all components of

estimation, and provides a basis for rigorous statistical

estimation.

These developments in the general field of animal

abundance estimation are particularly relevant to land-

breeding marine animals and their use as marine indicators.

Penguins are a key species group in Southern Ocean eco-

systems and their abundance has been used extensively as

an indication of ecosystem status and change (CCAMLR

2004; Jenouvrier et al. 2006; Trivelpiece et al. 2011; Lynch

et al. 2012). However, until recently, many efforts to

measure and monitor the abundance of penguin popula-

tions have focussed on smaller population and spatial

scales, and efforts to account for perception bias and

availability bias have been variable and often ad hoc. This

situation is now changing as researchers strive to estimate

penguin populations over larger population and spatial

scales in recognition of the increasing scale of human-

induced impacts such as fisheries and climate change

(Lynch et al. 2012; Trathan et al. 2012) and to improve the

comparability and reliability of abundance estimates by

developing rigorous statistical methods for estimating and

accounting for detection bias (Lynch et al. 2009; McKinlay

et al. 2010; Southwell et al. 2010; Trathan et al. 2012).

In this paper, we build on these recent developments by

describing the application of an integrated suite of

methods, technologies and estimation procedures for cost-

effective, large-scale abundance estimation of Adelie

penguin populations (Pygoscelis adeliae) within the Pol-

lock et al. (2004) general framework. Although we illus-

trate the methods through application to Adelie penguin

populations, they have relevance and potential application

for a range of land-breeding marine species in polar

regions. We do this in two regions of East Antarctica with

large Adelie penguin breeding populations: the Rookery

Islands Antarctic Specially Protected Area (hereafter

referred to as the Rookery ASPA region, Fig. 1) off the

Mac.Robertson Land coast, and the Svenner and Steinnes

Islands between the Larsemann Hills and Ranvik Glacier of

the Ingrid Christensen Coast (hereafter referred to as the

Svenner–Steinnes region, Fig. 1). The two regional surveys

illustrate how the general framework and methods can

achieve standardised estimation at large population scales

from counts of different population objects that are subject

to differing degrees of perception and availability bias.

Methods

General estimation framework

In the general framework proposed by Pollock et al. (2004),

the abundance of population objects in a survey region (N)

can be estimated by obtaining a count (c) of objects and

adjusting this count upwards according to (1) the fraction of

the survey region that is sampled (parea), (2) the fraction of

population objects available to be counted (availability bias,

pa) and (3) the fraction of population objects detected given

availability (perception bias, pda), that is,

N ¼ c

parea:pa:pda

ð1Þ

The abundance of penguins and flying seabirds is

generally estimated from counts of population objects (e.g.

adults, nests or chicks) at their breeding sites, and the

commonly accepted unit for estimating abundance,

including Adelie penguins, is the number of nests or

breeding pairs. For Adelie penguins, an optimal time to

estimate the number of breeding pairs is near the end of egg

laying (CCAMLR 2004) when few pairs have failed their

breeding attempt and the number of occupied nests is at or

close to its maximum. At this stage, most breeding females

and non-breeding penguins have left the breeding site and the

population remaining at the site is comprised almost entirely

of single adult males on their nests incubating eggs.

However, one of the practical difficulties in estimating the

abundance of Adelie penguin breeding pairs is that it is often

impossible to visit breeding sites at an optimal time due to

unpredictable weather and ice conditions and limited logistic

844 Polar Biol (2013) 36:843–856

123

support. If, as logistic or environmental conditions may

necessitate, one-off counts of occupied nests are made before

or after the peak in nest construction and attendance,

abundance estimates will be negatively biased unless

adjusted for nest construction or attendance. Another

practical issue is that many remote Antarctic sites can only

be visited and observed from an aircraft. In these

circumstances, it may not be possible to see nest structures

or distinguish breeding adults from non-breeding adults with

sufficient accuracy, and consequently, only counts of all

adults may be reliably made. As the proportion of breeding

and non-breeding adults changes across the breeding season,

counts of adults may lead to biased abundance estimates

unless adjusted for adult attendance. These issues were

considered when adapting the general framework for

estimating Adelie penguin breeding abundance. We

describe below how each of the components of Eq. 1 were

addressed and estimated in these case studies, and then how

these components were merged to estimate abundance.

Population sample counts (c) and the sampling

fraction (parea)

Given that the unit for abundance estimation is the number of

nests or breeding pairs, nests are the preferred population

object for counting. However, as indicated above, in some

circumstances, it may only be possible to count other pop-

ulation objects, such as adults, from which an estimate of nest

abundance must be derived. The two surveys in this study

illustrate abundance estimation from counts of different

population objects: a ground survey in the Rookery ASPA

region where a count of occupied nests was possible, and an

aerial survey in the Svenner–Steinnes region where only a

count of adults could be reliably made.

Given the expected large size of the population in each

region and time constraints on survey work, we used a

simple random sample design for cost-effective counting.

The general sampling methods involved (1) establishing a

boundary or set of boundaries that encompassed all

breeding nests in a region (hereafter referred to as

‘breeding areas’, where a breeding area was a geographic

area that included all breeding penguins as well as some

open space between sub-colonies (see Fig. 1 in Southwell

et al. 2012), (2) counting all visible occupied nests or adults

within randomly located sample plots within the breeding

areas and (3) adjusting the sample counts upwards by the

sampling fraction (parea) to estimate the observed abun-

dance of occupied nests or adult penguins (C) in the survey

region at the time of the survey (hereafter termed observed

abundance). The third step was achieved with plot count,

plot area and breeding area data using the weighted

estimator:

C ¼Xn

i¼1

ci

ai� ai

Ap

� �A ¼ A

Ap

Xn

i¼1

ci ð2Þ

where ci is the count of occupied nests or adult penguins in

plot i, n is the number of plots, ai is the area of plot i, Ap is the

total area of all plots within breeding area boundaries, and A

is the total area contained within breeding area boundaries.

The standard error of observed abundance was estimated as:

SE C� �¼ 1� Ap

A

� �0:5A

Ap=n

1

n n� 1ð ÞXn

i¼1

ci � �cð Þ2" #0:5

ð3Þ

We aimed to estimate C from a sample size (number of

plots) sufficient to return a precision (CV = SE as a

Fig. 1 Map of a the Rookery Islands ASPA region and b the Svenner–Steinnes Islands region. The dashed lines encompass the two survey

regions. Mawson and Davis are the closest research stations to the survey regions

Polar Biol (2013) 36:843–856 845

123

percent of the mean) of 10 % for observed abundance in

each region.

The detailed survey-specific methods for the ground-

based sample survey in the Rookery ASPA region were as

follows. We mapped the boundaries of all breeding areas

by walking around groups of sub-colonies with a hand-held

Garmin Etrex GPS set to log locations at 5-s intervals.

While walking these boundaries, we remained within

5–20 m from sub-colony perimeters, and within these

distance constraints, attempted to minimise the boundary

length encompassing groups of sub-colonies. Simulation

studies (Southwell et al. 2012) showed that this strategy of

‘coarse’ boundary mapping was more efficient than ‘exact’

mapping of sub-colony boundaries. We then converted the

GPS-defined boundaries into a set of GIS polygons, used

the GIS survey tool developed by Southwell et al. (2012)

for planning and evaluating sample survey designs to

generate locations and boundaries of sample plots across

the entire survey region and selected 320 plots (the sample

size predicted from a pilot study to return a 10 % CV) that

completely or partially overlapped with breeding areas for

sample counts. Each sample plot consisted of two 4 9 5 m

sub-plots facing in opposite directions and offset by 2 m

from a central location. To locate plots in the field, we

imported their central locations, obtained as output from

the GIS survey planning tool, into a GPS and used the GPS

to navigate to each location. At each sample plot, we used a

camera mounted on top of a 3-m pole to take oblique

photographs of the area covered by the two sub-plots sur-

rounding the central point (Fig. 1 in Low et al. 2008). This

was done by placing the camera-pole at the central point,

holding it vertically using a spirit level, and taking a

photograph in each of the two directions at 180� to each

other. To avoid bias in the areas photographed, we used

predetermined rules for the direction of the camera: (1) for

flat ground, photographs were taken in magnetic north and

south directions, and (2) for sloping ground, photographs

were taken directly up-slope and down-slope. We used

upslope–downslope photographs because this allowed us to

correct for distortions in perspective with greater accuracy

than would have been possible for cross-slope photographs.

To make these corrections, we also recorded the slope of

the ground in each sub-plot in a 5-point category scale [flat

(0�–3�), moderate up (4�–10�), moderate down (4�–10�),

steep up ([10�) and steep down ([10�)]. To minimise time

in the field and disturbance to penguins, sub-plot counts

were made in the laboratory from the photographs after

digitally overlaying slope-specific templates to delineate

‘virtual’ sub-plot boundaries. Low et al. (2008) showed that

delineating plots in this way resulted in only minor errors

(*2–3 %) in classifying penguins as inside or outside the

plots, provided slopes were not too steep (\17�). We

counted the number of occupied nests in each sub-plot and

summed sub-plot counts to obtain a total count of occupied

nests for each plot (ci). Values for ai, Ap and A in Eqs. 2

and 3 were obtained from shapefiles produced by the sur-

vey planning tool, which along with values for ci and n

(320) were used to estimate the observed abundance of

occupied nests and its standard error.

The detailed survey-specific methods for the aerial-

based sample survey in the Svenner–Steinnes region were

as follows. We mapped the boundaries of all breeding

areas in the survey region from aerial photographs of the

breeding sites. Photographs were taken from an AS350BA

Squirrel helicopter flying at 80 knots speed and 750 m

altitude. The helicopter carried a downward-looking Has-

selblad H3DII-50 camera fitted with a 150 mm lens. This

set-up resulted in ground coverage of 245 m by 184 m for

each photograph. A 3-s interval between photographs

ensured that consecutive photographs overlapped along a

flight line, and a flight line spacing of 200 m ensured that

photographs overlapped between flight lines. We aimed to

achieve complete photographic coverage of each breeding

site. The camera auto-focussed effectively at infinity using

the software Phocus and used a shutter speed of 1/800th s.

Following the survey flight, groups of 3–4 photographs

taken along a flight line within a breeding site were stit-

ched together using the software Microsoft Image Com-

posite Editor. These mosaics were then geo-referenced

against shapefiles of the coastline in a GIS so that a

complete photo-mosaic of each breeding site could be

viewed. We then imported the geo-referenced photo-

mosaics into a GIS and drew polygon boundaries around

sub-colonies or groups of sub-colonies to delineate all

breeding areas in the region. The boundaries had a buffer

of 1–5 m from the outermost penguins. The GIS survey

planning tool was then used to generate a shapefile with a

grid of 10 9 10 m plots across the entire sub-region and

to then generate a second shapefile that contained only the

parts of those plots that overlapped with the breeding

areas. We then randomly selected 90 plots (the sample

size predicted from a pilot study to return a 10 % CV) and

counted the number of adult penguins (both lying and

standing) within each selected plot. We chose to count all

penguins rather than just penguins on nests because we

were not confident in distinguishing penguins on nests

from penguins off nests. The plot counts were then scaled

up by the sampling fraction in the same way as for the

ground survey to estimate the observed number of adult

penguins present in all the breeding areas at the time of

the survey.

Estimating the availability fraction (pa)

The term availability refers to counting bias resulting from

population objects that are present but completely obscured

846 Polar Biol (2013) 36:843–856

123

from view and hence not available for counting (Pollock

et al. 2004). In this classic sense, the availability fraction is

always B1. This concept can be extended to surveys of

land-breeding marine species, such as Adelie penguins,

where some breeding adults may forage away from the

breeding site during the breeding season to provision their

chicks, or some nests that were occupied early in the

breeding season may be abandoned at a later time if the

eggs fail to hatch or the chicks die, and hence, population

objects such as breeding adults or occupied nests may not

always be available for counting at the breeding site. There

are also times during the breeding season when the pres-

ence of non-breeders at the breeding site results in a pop-

ulation of adult penguins that is greater than the number of

breeding adults or breeding pairs, and hence, the avail-

ability fraction can be[1 for counts of adults. In this study,

we have interpreted the concept of availability in its

broadest sense when applying it to Adelie penguins by

having no upper bound to pa.

We applied new remote camera technology to collect

availability data and new analysis methods to estimate

availability fractions. Remotely operating cameras (New-

bery and Southwell 2009) were established at several

Adelie penguin breeding sites close to the two survey

regions prior to the ground and aerial surveys (nine

cameras on four islands near Mawson station 15 km east

of the Rookery ASPA region; five cameras on two islands

near Davis station 60 km east of the Svenner–Steinnes

region). The cameras were established adjacent to breed-

ing sub-colonies and overlooked areas covering 30–50

nests. Once established, each camera took a single pho-

tograph at solar midday each day. We counted the number

of adults and occupied nests in a fixed region of each daily

photograph and then standardised the time series counts of

adults and occupied nests against a count of occupied

nests at the stage in the breeding phenology recommended

in the Standard Methods of the CCAMLR Ecosystem

Monitoring Program for penguin population counts

(1 week after peak egg laying, Method A3 in CCAMLR

2004). At this stage, the population at the breeding site

comprises mostly adult males incubating eggs on nests,

and the number of occupied nests is at or close to its

maximum. The peak in egg laying can be difficult to

determine through observation of eggs in the photographs.

However, since females leave the breeding site shortly

after laying their second egg, we used the maximum rate

of decline in adult numbers after the initial peak in arrival

as a proxy for the peak in egg laying (Southwell et al.

2010). The standardised time series counts of adults and

occupied nests were then modelled using a generalised

additive model (GAM), and the modelled time series were

used to generate object- and stage-specific factors to adjust

sample counts (see below).

Estimating the detection fraction given availability (pda)

Counts of animals are often negatively biased because

some are not detected even though they are present and

potentially visible in the area searched (perception bias,

Pollock et al. 2004). Consistent with this generalisation,

experimental work using model penguins (Low et al. 2008)

showed that sample plot counts using the pole-mounted

camera may be slightly negatively biased as a result of the

camera’s oblique view and fixed position. In the case of

counting penguins from aerial photographs, counts could

also be positively biased if objects such as rocks or shad-

ows are mistaken for penguins. Thus, estimates of observed

abundance from both the ground and aerial sample surveys

may have been biased in relation to true abundance as a

result of perception bias. We used different methods to

assess perception bias for the two surveys.

For the ground survey, we used distance sampling to

estimate the probability of detecting occupied nests that

were present in the sample plots. Each of the sub-plot

boundary templates was divided in 5 equal-width 1-m bins

and counts of occupied nests were made within each bin.

We then used DISTANCE 5.0 software (Thomas et al.

2006) to estimate detection probability from the bin count

data. Four candidate detection functions were considered

(uniform ? cosine; uniform ? simple-polynomial; half-

normal ? hermite-polynomial; and half-normal ? cosine),

and the best model was selected on the basis of AIC values.

For the aerial survey, we assessed how similar counts

from aerial photographs were to counts made directly by

observers on the ground at 14 small- to medium-sized sub-

colonies (B300 penguins) situated on four islands off the

Vestfold Hills close to where the remotely operating

cameras were located. The work was undertaken on the

same day as the regional aerial survey. Ground counts of

all penguins in each of the 14 sub-colonies were carried out

by two observers counting independently, and the average

of their counts was taken as the ‘best’ count. Aerial pho-

tographs of the same sub-colonies were taken within 3 h of

the ground counts using the same survey and photographic

settings as for the regional survey, and counts were made

from the photographs by the same observer who counted

the survey photographs. Ground and aerial counts of the

sub-colonies were then compared using a paired t test.

Combining the estimation components

This was achieved using a parametric bootstrap imple-

mentation using the software ICESCAPE (McKinlay et al.

2010). The procedure uses the estimates of observed

abundance and detectability, and availability time series

data, to generate a distribution of estimates of the number

of occupied nests at the standardised stage of the breeding

Polar Biol (2013) 36:843–856 847

123

phenology, and in these applications operated as follows:

(1) a distribution of observed abundance estimates for

occupied nests or adults was generated by drawing 1,000

replicates from a normal distribution defined by the

observed abundance estimate and its standard error; (2) a

distribution of availability adjustment fractions was

obtained by (i) drawing 1,000 dates from a uniform dis-

tribution defined by the date-range over which the survey

took place, (ii) randomly selecting a date and an avail-

ability curve from the pool of curves (nine curves for the

Rookery ASPA region and five for the Svenner–Steinnes

region), (iii) drawing an availability fraction replicate from

a normal distribution with mean equal to the predicted

value for that date and standard deviation equal to the

standard error of the fitted GAM function at that point, and

(iv) repeating this process to generate 1,000 availability

fraction replicates; (3) a distribution of detection fractions

was generated by drawing 1,000 replicates from a normal

distribution defined by the detection probability estimate

and its standard error (obtained from DISTANCE for the

Rookery ASPA region and from the air-ground comparison

counts for the Svenner–Steinnes region); (4) having gen-

erated distributions for observed abundance, availability

and detectability, a distribution of 1,000 final adjusted

occupied nest estimates for the standardised stage of the

breeding phenology was obtained by iteratively calculating

the product of an observed abundance replicate, the inverse

of an availability fraction replicate and the inverse of a

detection fraction replicate; and (5) the distribution of final

adjusted estimates was summarised by the median and

95 % confidence interval, where a 100.(1 - a) % confi-

dence interval is taken as the a/2 and 1 - a/2 percentile

points.

Uncertainty

We explored options for reducing or minimising uncer-

tainty in future surveys that use the new methods by sim-

ulating abundance estimates obtained in different years,

over different date-ranges and with different sample sizes

(number of sample plots) with data obtained from the

Rookery ASPA region. First, we assumed that the same

estimates of observed abundance and detectability were

obtained over the same date-range as the Rookery ASPA

survey, but used availability data from the region in the

four subsequent breeding seasons (i.e. from 2008–2009 to

2011–2012) to obtain a distribution of 1,000 simulated final

adjusted abundance estimates for these seasons. Second,

we assumed that the same estimates of observed abundance

and detectability were obtained within a shorter, earlier

date-range (23–25 November) and used availability data

for this date-range from the five breeding seasons

2007–2008 to 2011–2012 to obtain distributions of simu-

lated final adjusted abundance estimates for these seasons.

Finally, we simulated final adjusted abundance estimates

for the five seasons and two date-ranges assuming that the

sampling error in estimating observed abundance was

successively reduced from a CV of 10 % through to 0 %

(the latter inferring a census or complete count) by

increasing the number of sample plots counted. We then

used the distributions of final abundance estimates to

examine uncertainty for the various survey design options.

Results

Rookery ASPA region

We mapped breeding areas in the Rookery ASPA region

over a three-day period at the end of November 2007.

Although sample plot photographs could have also been

completed in a few days, the work extended over a two-

week period from 3–15 December 2007 (Table 1; Fig. 2)

because of unsuitable weather and the need for the survey

team to work on other projects. A total of 2,684 occupied

nests were counted in the 320 sample plots which covered

3.6 % of the mapped breeding areas. Adjusting the sample

count upwards by the sampling fraction gave an observed

abundance estimate (C) of 73,630 (SE = 6,268) occupied

nests at the time of the survey.

Table 1 Survey and estimation details for the Rookery ASPA and Svenner–Steinnes regional abundance surveys

Survey details Rookery ASPA region Svenner–Steinnes

region

Dates when sample plot photographs were taken 3–15 December 2007 20 November 2010

Population object counted Occupied nests Adults

Observed abundance of population objects at the time of the survey (C, SE) 73,630 (6,268) 44,022 (4,583)

Availability fraction (pa: median, 95 % CI) 0.872 (0.411–0.984) 1.713 (1.574–1.923)

Detection fraction given availability (pda: mean, 95 % CI) 0.943 (0.916–0.969) 1.000 (1.000–1.000)

Estimated number of occupied nests 1 week after the peak in egg laying

(N: median, 95 % CI)

90,627 (70,685–197,832) 25,658 (20,092–31,654)

848 Polar Biol (2013) 36:843–856

123

The dates selected for the standardised stage of the

breeding phenology at the nine camera sites ranged from

27–30 November and included the period when the number

of occupied nests was at its maximum (Fig. 2b). There was

a strong decline in the number of occupied nests from

early- to late-December 2007 at three of the nine camera

sites and a small-moderate decline at the other six sites

(Fig. 2b). The strong decline at the three sites commenced

during a 2-day blizzard across the region and occurred at

sites where the microhabitat appeared to offer little shelter.

As a consequence, the standardised counts of occupied

nests from the nine camera sites became increasingly dis-

parate over the 2-week period that sample plot photographs

were taken (Fig. 2b). This disparity resulted in a strongly

skewed distribution of availability fractions for the date-

range of the survey period, with a median value of 0.872

and a long tail of low fractions (95 percentile range

0.411–0.984, Table 1; Fig. 4b).

The number of occupied nests counted within 1-m bins

declined slightly as distance from the camera point

increased (Fig. 3), indicating reduced detection at greater

distances. A uniform ? cosine detection function gave the

best fit to bin count data, and detectability of occupied

nests present in the sub-plots (pda) was estimated to be

0.943 (Table 1; Fig. 4c).

Combining these estimation components gave a skewed

distribution of occupied nest abundance estimates at the

standardised stage of the breeding phenology, with a

median of 90,627 and a long tail of high estimates

(95 percentile range 70,685–197,832; Table 1; Fig. 4d).

Svenner–Steinnes region

In contrast to the ground survey photography in the

Rookery ASPA region which extended over nearly

2 weeks, aerial photography of the Svenner–Steinnes

region was completed in a single day. However, a heli-

copter was only available for survey work on 20 November

2010 when the number of adults present at the camera sites

was considerably higher than the number of occupied nests

(Fig. 2c, d). After stitching and geo-referencing the aerial

photographs, we counted 4,142 adult penguins in the 90

sample plots covering 9.4 % of the mapped breeding areas.

Adjusting the sample count upwards by the sampling

fraction gave an observed abundance estimate of 44,022

(SE = 4,583) adult penguins at the time of the survey

(Table 1).

The dates selected for the standardised stage of the

breeding phenology at the five camera sites ranged from

7–8 December and again included the period when the

number of occupied nests was at its maximum (Fig. 2d).

2.0

1.0

0.0

A

2.0

1.0

0.0S

tand

ardi

sed

coun

ts

B

2.0

1.0

0.0

C

2.0

1.0

0.0Oct Nov JanDec

D

Fig. 2 Standardised time series counts from nine cameras near the

Rookery Islands ASPA region in the 2007–2008 breeding season

(a adults; b occupied nests) and five cameras near the Svenner–

Steinnes region in 2010–2011 breeding season (c adults; d occupied

nests). Adult and occupied nest counts have been standardised to the

count of occupied nests 1 week after peak egg laying for each camera

site (see text). The dark grey shaded area shows the range of

standardisation dates across cameras. The light grey shading shows

the range of dates of the ground (a, b) and aerial (c, d) surveys

Polar Biol (2013) 36:843–856 849

123

The number of adults present at the camera sites on the

date of the aerial survey was 60–90 % higher than the

maximum number of occupied nests (Fig. 2c). Although

the photographic survey was conducted in a single day, the

availability fraction distributions for the date-range of the

survey show considerable spread or variation (Figs. 2c, 5),

reflecting spatial variation in adult attendance from one

camera site to the next.

When the air-ground comparative counts were pooled

over sub-colonies, counts from aerial photographs were

4 % lower than counts made from the ground, but a paired

t test using sub-colonies as the replicate found no signifi-

cant difference between aerial and ground counts

(t = 0.896, df = 13, p = 0.387). Consequently, counts

from aerial photographs were taken to be unbiased relative

to a ground count, and detectability of penguins present in

sample plots was assumed to be perfect (i.e. pda = 1,

SE = 0) when estimating the number of occupied nests in

the region.

After adjusting the estimated observed abundance of

adults at the time of the aerial survey for availability and

detectability, our estimate of the current Adelie penguin

breeding population at the standardised stage of the

breeding phenology was 25,658 (20,092–31,654)

(Table 1).

Uncertainty

There was substantially less between-site variation in nest

attendance in the Rookery ASPA for the four breeding

seasons 2008–2009 to 2011–2012 than in 2007–2008 when

the survey was conducted (Fig. 6). Consequently, if the

Rookery ASPA survey had been conducted over the date-

range 3–15 December in any of these four breeding

seasons, uncertainty around the final abundance estimates

would have been considerably lower and less skewed than

in 2007–2008 (Fig. 7, upper panel). Uncertainty would

have been further reduced, particularly in 2007–2008, if the

0 1 2 3 4 50.0

0.2

0.4

0.6

0.8

1.0D

etec

tion

prob

abili

ty

Distance (m)

Fig. 3 Detection histogram and modelled detection function for

sample plot counts of occupied nests in the Rookery Islands Antarctic

Specially Protected Area

100000 200000 300000 4000000

30

60

90

120

Observed abundance of occupied nests

0.4 0.6 0.8 1.0 1.20

40

80

120

160

Availability fraction

Num

ber

of b

oots

trap

est

imat

es

0

40

80

120

160

Detection fraction0.90 0.95 1.00 1.05

100000 200000 300000 4000000

50

100

150

200

Adjusted abundance of occupied nests

A

B

C

D

Fig. 4 Distribution of 1,000 bootstrap estimates for each stage of the

estimation process (a estimated observed abundance of population

object counted; b estimated availability fraction; c estimated detec-

tion fraction; d final estimated number of occupied nests) for the

Rookery ASPA region ground survey

850 Polar Biol (2013) 36:843–856

123

sample counts had been made over the shorter, earlier date-

range of 23–25 November instead of 3–15 December

(Fig. 7, lower panel). Further reductions in uncertainty

could be achieved by counting a larger number of sample

plots to reduce uncertainty in the estimate of observed

abundance, but some residual uncertainty would remain

even if a census count were made (and hence there was no

sampling error). This residual uncertainty is related to

between-site variation in nest attendance and detectability.

Discussion

Our adaptation of the general estimation framework

involved the development and integration of a suite of

methods, technologies and software for data collection and

estimation. Several of these are new developments,

including a GIS tool for sample survey planning (Southwell

et al. 2012), an elevated camera for sample counts within

‘virtual’ plot boundaries (Low et al. 2008), remotely

operating cameras for collecting availability data (Newbery

and Southwell 2009; Southwell et al. 2010), and software

for new estimation procedures (McKinlay et al. 2010).

Other methods, including sample survey designs for cost-

effective inference over large areas or large populations

and distance sampling for estimating detectability (Thomas

et al. 2006), are routinely used for many species but to our

knowledge have rarely or never been applied to breeding

penguin populations. Although we developed the methods

specifically for use on Adelie penguin populations, the

methods have potential application for a range of land-

breeding marine species.

Our need to make inferences about abundance of large

populations while working within tight logistic, environ-

mental and time constraints was a driving force in devel-

oping cost-effective and efficient methods. The

development of optimal sample survey designs using vir-

tual simulation (Southwell et al. 2012) was critical to the

application of the ground survey in the Rookery ASPA

region, given the time-window for survey work was limited

to the time when sea-ice allowed access to islands. Our

development of the elevated camera and use of ‘virtual’

sub-plot boundaries for making sample plot counts in the

ground survey was also driven by the desire to minimise

the time spent at each sample plot by allowing counts to be

made post-survey. Sample survey designs have only

occasionally been used in previous surveys of penguin

abundance but are now receiving increased attention as

researchers strive to estimate abundance over larger

regions and populations (e.g. Lynch et al. 2012).

Researchers working in polar environments often face

difficulties in planning and timing their work around

unpredictable logistics and environmental conditions. This

is a particular problem when undertaking abundance sur-

veys of land-breeding marine species, such as the Adelie

penguin, because surveys ideally need to be timed around

the species’ breeding phenology. Our surveys clearly

20000 40000 600000

20

40

60

80

Observed abundance of adults

1.0 1.2 1.4 1.6 1.8 2.0 2.20

25

50

75

100

Availability fraction

Num

ber

of b

oots

trap

est

imat

es

0.95 1.00 1.05 1.100

250

500

750

1000

Detection fraction

20000 40000 600000

20

40

60

80

Adjusted abundance of occupied nests

A

B

C

D

Fig. 5 Distribution of 1,000 bootstrap estimates for each stage of the

estimation process (a estimated observed abundance of population

object counted; b estimated availability fraction; c estimated detec-

tion fraction; d final estimated number of occupied nests) for the

Svenner–Steinnes region aerial survey

Polar Biol (2013) 36:843–856 851

123

demonstrate how the idiosyncracies of location and logistic

support can affect the timing of population counts in

relation to breeding phenology, and the importance of

adjusting population counts to account for availability bias.

While this importance has long been recognised (e.g.

Croxall and Kirkwood 1979), the enormous amount of

work by the scientific community in collecting one-off

penguin population count data has not been matched by

simultaneous collection of availability data. This is

understandable if availability data are collected manually

because it requires time-consuming, repeated visits or long

stays by observers at remote breeding sites. The use of

remotely operating cameras is a significant development in

opening up new possibilities for widespread, cost-effective

collection of availability data for land-breeding marine

species and will allow improved interpretation of one-off

population count data as the pool of availability data

grows.

The camera data presented here provide new insights

into spatial and temporal variation in nest attendance and

nest attrition and the implications of this variation for

population estimation. We found that stochastic, regional-

scale events, such as blizzards, can have spatially and

temporally heterogeneous impacts on Adelie penguin nest

attendance, which increases uncertainty in making infer-

ences about abundance from one-off, large-scale counts.

These kinds of uncertainties have long been qualitatively

recognised (Croxall and Kirkwood 1979; Woehler 1993)

and were identified as one of the primary unaccounted

sources of uncertainty in a recent global synoptic assess-

ment of emperor penguin abundance from satellite imagery

(Fretwell et al. 2012). The methods presented here provide

a means of characterising and quantifying this spatial and

temporal variation and propagating the resulting uncer-

tainty through to the final abundance estimate.

This first application of the new methods in the Rookery

ASPA region appears by chance to have occurred in a year

when spatial variation in nest attendance and nest attrition

was unusually large. This variation was the dominant

contributor to the large uncertainty around the abundance

estimate for this region. The simulated survey results show

2007-08

2010-11

2011-12

Sta

ndar

dise

d co

unts

of o

ccup

ied

nest

s

2008-09

Oct Nov Dec Jan

1.0

1.0

1.0

1.0

1.0

0.5

0.5

0.5

0.5

0.5

0.0

0.0

0.0

0.0

0.0

2009-10

Fig. 6 Standardised time series counts of occupied nests from nine

cameras near the Rookery ASPA region for five breeding seasons

from 2007–2008 to 2011–2012. Counts have been standardised to the

number of occupied nests 1 week after peak egg laying for each

camera site (see text). The dark grey shaded area in each panel shows

the date-range of survey work in the Rookery ASPA in 2007–2008

(3–15 December). The light grey shading in each panel shows an

alternate date-range (23–25 November) for which simulated abun-

dance estimates were derived

-20

0

20

40

60

80

100

120

140

-20

0

20

40

Unc

erta

inty

of f

inal

abu

ndan

ce e

stim

ate

(% o

f med

ian)

23-25 Nov

3-15 Dec

0 2.5 5 7.5 10

Precision of observed abundance estimate (SE as % of mean)

Fig. 7 Uncertainties for simulated final abundance estimates under

scenarios of surveys in 5 years (2007–2008 to 2011–12), over two

date-ranges (3–15 December and 23–25 November) and for five

precisions for observed abundance estimates (CVs of 10, 7.5, 5, 2.5

and 0 %). Uncertainties for final abundance estimates are shown as

the percent difference of upper and lower 95 percentile values from

the median value. Results for 2007–2008 are in bold, and results for

all other years are not in bold

852 Polar Biol (2013) 36:843–856

123

that uncertainty would have been substantially smaller had

the same survey design been applied in the same region in

any of the four subsequent seasons when spatial variation

in nest attendance and attrition was smaller. The large

uncertainty that resulted from the events in 2007–2008

should not be interpreted as a weakness of the methods;

indeed, we argue that rigorous quantification of all the

major sources of uncertainty is a strength of these new

methods that has not been fully addressed in most previous

studies of penguin abundance. Indeed, heterogeneous

availability may be a general and largely unrecognised

source of uncertainty in abundance surveys for a wide

range of species because the difficulty of collecting avail-

ability data has hindered researchers’ ability to quantify or

assess heterogeneity (Thomson et al. 2011). Failure to

quantify all the major sources of uncertainty in abundance

estimates can lead to overconfidence in the estimates and

potentially to spurious conclusions about variation in

abundance over time or space by mistaking apparent

change in abundance resulting from estimation error for

real change due to ecological processes (Link and Nichols

1994).

Future application of these methods can benefit from the

new insights into the sources and magnitudes of uncer-

tainty provided by this first application of the methods. In

particular, the power to detect change in abundance over

time or space will be improved by reducing or minimising

overall uncertainty in future survey results. One way of

reducing uncertainty would be through the sample size

(number of sample plots counted) and its effect on uncer-

tainty in the observed abundance estimate. We recommend

that future sample count surveys were possible aim to

estimate observed abundance with greater precision than

attempted here (e.g. a target CV of 5 % instead of 10 %)

given that spatial variation in nest attendance can some-

times substantially inflate overall uncertainty. A second

possible means of minimising uncertainty given a fixed

sample size is through the date of surveys and its effect on

uncertainty in the availability estimate. The availability

data in Figs. 2 and 6 highlight optimal times for Adelie

penguin surveys in this regard, but as previously discussed,

the date of surveys may not be under the control of

investigators. However, other factors must also be taken

into account when deciding on the timing of surveys (e.g.

disturbance for ground surveys, see below), and these

competing requirements may not be complementary. If

both of these options had been applied in our surveys,

confidence intervals for the Rookery ASPA region would

have been around 10–12 % of the median value in every

year from 2007–2008 to 2011–2012 (Fig. 7).

As the remotely operating cameras were located at

breeding sites close to but outside the two survey regions,

we caution that the adjustments for availability bias assume

nest attendance in the survey regions was similar to the

sites where the cameras were located. More cameras have

since been deployed in these and other regions of East

Antarctica, so adjustment data will be more comprehensive

for future surveys. As the body of availability data grows

into the future, better insights should be possible on the

influence of environmental conditions, such as sea-ice

extent and weather on nest attendance and attrition. It may

then be possible to develop predictive models relating

variation in adjustment data to environmental covariates

(e.g. Lynch et al. 2009) and use these models to minimise

the resulting uncertainty in abundance estimates.

Incomplete detection is a common source of negative

bias in wildlife abundance estimation if not incorporated

into the overall estimation procedure (Borchers et al.

2002). The large size of Adelie and other penguins, their

conspicuous plumage, and the open features used as a

breeding substrate all serve to enhance detectability, and

accordingly, most surveys of penguin abundance have

assumed perfect detection. However, uneven terrain and

high density of nesting penguins may reduce detectability

when counts are made from the ground (Trathan 2004), and

shadow and a complex background substrate may affect

detectability when counts are made from the air. Our use of

distance sampling demonstrated that detectability was high

but not perfect in the ground survey of the Rookery ASPA

region. We attribute the level of detectability in this survey

to the combined effect of the small plot size, the use of an

elevated vantage point for photography, and the uneven

and boulder terrain of the region. Our air-ground compar-

ison counts supported the conclusions of other studies (e.g.

Taylor et al. 1990; Barbraud et al. 1999) that detection of

breeding penguins is high in aerial surveys. However,

detectability is likely to be strongly related to lighting

conditions at the time that aerial photographs are taken and

we recommend that wherever possible aerial photography

surveys are carried out when lighting conditions are uni-

form and there are no or few shadows.

Our ground survey methods required surveyors to enter

sub-colonies to access some plot locations. As this is

potentially disturbing, it needs to be done with care and

attention to penguin response behaviour. The level of

potential disturbance associated with sample survey

designs was investigated in simulation studies (Southwell

et al. 2012), and the results were taken into consideration

when designing the ground survey. In particular, surveyors

used a buffer distance around sub-colony perimeters when

mapping breeding area boundaries and used virtual sub-

plot boundaries for plot counts to reduce the time spent at

sample plots. We also restricted plot counts to the incu-

bation period when there is only a single penguin at each

nest and few non-breeding penguins are present. Behav-

ioural observations made by surveyors showed that at this

Polar Biol (2013) 36:843–856 853

123

time of the breeding season, penguins remained at their

nests even during close approaches and in no cases were

penguins observed leaving their nests. However, we would

not recommend applying the ground survey methods ear-

lier in the breeding season when penguins are forming pair

bonds and are present in greater numbers.

Our use of a general estimation framework showed that,

for the two applications described here, detection bias was

mainly related to the availability of penguins for counting

due to varying attendance of adults at nests rather than

difficulty in counting penguins that were present in

breeding areas. This may not be the case, however, for

other survey circumstances. For example, detection of

Adelie penguins in sample plots may have been much

lower if we had used larger sample plots, and detection of

cryptic species such as the cavity-nesting Wilson’s storm

petrel (Oceanites oceanicus) can be low even with inten-

sive search effort (Southwell et al. 2011). Because the

nature and extent of bias may not always be evident or

predictable, explicitly considering all components of the

general estimation framework during the design stage of an

abundance survey is the best way of ensuring that abun-

dance estimates are accurate and comparable over space

and time. General guidelines for the application of the

methods within the general framework are given in

Table 2.

The methods described here were designed to be con-

sistent with the long-established Standard Methods of the

CCAMLR Ecosystem Monitoring Program for penguin

population size (Method A3, CCAMLR 2004) in regard to

the stage of the breeding season (1 week after the peak in

egg laying) and the population metric (occupied nests) for

estimation, but also to extend the potential for application

to larger populations and variable dates of data collection.

The CCAMLR Standard Method, which involves a census

count, is recommended for colonies of up to 1,000–2,000

nests and requires either a detailed study of breeding phe-

nology (Method A9, CCAMLR 2004) or counts as close as

possible to the same date each year. In these new methods,

sample counts allow extension of abundance estimation and

monitoring to much larger populations than intended by the

CCAMLR method, remote cameras allow cost-effective

collection of breeding phenology data, and the availability

adjustment methods allow counts made on variable dates to

be standardised to the CCAMLR-recommended stage of the

breeding season. However, while the new methods retain the

intent of the CCAMLR method with regard to standardising,

there is a trade-off in their broader application in the form of

increased uncertainty which needs to be propagated through

to the final abundance estimate.

There is growing interest in the use of satellite tech-

nology for regional-scale estimation of wildlife abundance

as an alternate or complementary approach to the more

direct methods described here. Satellite technology is

particularly relevant in remote areas that cannot be visited

on the ground or in aircraft, but may also be a cost-effec-

tive alternate to established methods in accessible areas.

While high-resolution satellite imagery can be used to

detect and count individuals of large species such as seals

(LaRue et al. 2011), use of this technology for smaller

species, such as penguins, currently relies on detection of

guano from satellite imagery to estimate colony area and

estimation of population density within colonies using

direct counts (e.g. Fretwell et al. 2012; Lynch et al. 2012).

In estimating the circumpolar abundance of ice-breeding

emperor penguins using this approach, Fretwell et al.

Table 2 General guidelines for implementing the methods for estimating abundance

Identify the spatial extent of the target population

Establish a number of remotely operating cameras adjacent to breeding areas within or close to the target population

Map boundaries around breeding areas

Overlay a grid of sample units across the breeding areas

Randomly select a number of sample units and count the number of population objects within them. In deciding on the number of sample

units to count, consider the desired precision for the final abundance estimate and the contribution of uncertainty in observed abundance to

overall uncertainty

Collect auxiliary count data to estimate detectability. For ground surveys, this can involve counting in distance bins. For aerial surveys, this

can involve ground counts at a sample of breeding areas for comparison with aerial counts

Count population objects within photographs taken by the remotely operating cameras

Develop availability or attendance curves by standardising camera counts to the population unit and stage of the breeding season for

abundance estimation

Estimate observed abundance from sample unit counts

Estimate detectability from auxiliary count data

Use a bootstrap procedure such as in program ICESCAPE to merge estimates of observed abundance, detectability and availability and derive

a distribution of final abundance estimates

Summarise the final abundance distribution using statistics such as the median and upper/lower 95 percentile limits

854 Polar Biol (2013) 36:843–856

123

(2012) listed a number of potential biases and uncertainties

that required further investigation to improve their abun-

dance estimate. One of the most important issues they

identified was the difficulty of obtaining satellite imagery

and counts at the optimal time in mid-winter when only one

adult per breeding pair is at the colony (the earliest possible

time for collecting data is in September or October, when

an unknown and variable number of adults may have left

the colony following breeding failure). Lynch et al. (2012)

also point out the difficulty of obtaining cloud-free satellite

images at predictable times for land-breeding penguins in

the Antarctic Peninsula. The methods described in this

paper provide a means of addressing the issue of sub-

optimal or unpredictable timing of surveys and so can

contribute to improved abundance estimation for both

direct- and satellite-based applications.

Acknowledgments We thank the pilots and engineers from Heli-

copter Resources for aerial support, Luke Einoder, Susan Doust and

Shavawn Donoghue for stitching and geo-referencing aerial photo-

graphs, Barbara Wienecke for assistance in air-ground counts, Steve

Candy for advice on abundance estimators and David Smith for

producing the map. This work was conducted as part of AAD ASAC

project 2722 in accordance with permits issued under the Antarctic

Treaty (Environmental Protection) Act 1980 and was approved by the

Australian Antarctic Animal Ethics Committee. The paper was

improved by constructive comments from three anonymous

reviewers.

References

Barbraud C, Delord KC, Micol T, Jouventin P (1999) First census of

breeding seabirds between Cap Bievenue (Terre Adelie) and

Moyes Islands (King George V Land), Antarctica: new records

for Antarctic seabird populations. Polar Biol 21:146–150

Borchers DL, Buckland ST, Zucchini W (2002) Estimating animal

abundance. Closed populations. Springer, London

CCAMLR (2004) CCAMLR Ecosystem Monitoring Program: stan-

dard methods for monitoring studies. CCAMLR, Hobart

Croxall JP, Kirkwood ED (1979) The distribution of penguins on the

Antarctic Peninsula and islands of the Scotia Sea. British Antarctic

Survey, Natural Environment Research Council, Cambridge

Field SA, Tyre AJ, Possingham H (2005) Optimizing allocation of

monitoring effort under economic and observational constraints.

J Wildl Manag 69:473–482

Fretwell PT, LaRue MA, Morin P, Kooyman GL, Wienecke B,

Ratcliffe N, Fox AJ, Fleming AH, Porter C, Trathan PN (2012)

An emperor penguin population estimate: the first global,

synoptic survey of a species from space. PLoS One 7:1–11

Furness RW, Camphusen CJ (1997) Seabirds as monitors of the

marine environment. ICES J Mar Sci 54:726–737

Jenouvrier S, Barbraud C, Weimerskirch H (2006) Sea ice affects the

population dynamics of Adelie penguins in Terre Adelie. Polar

Biol 29:413–423

Jones JPG (2011) Monitoring species abundance and distribution at

the landscape scale. J Appl Ecol 48:9–13

LaRue MA, Rotella JJ, Garrott RA, Siniff DB, Ainley DG, Stauffer

GE, Porter CC, Morin PJ (2011) Satellite imagery can be used to

detect variation in abundance of Weddell seals (Leptonychotesweddellii) in Erebus Bay, Antarctica. Polar Biol 34:1727–1737

Link WA, Nichols JD (1994) On the importance of sampling variation

to investigations of temporal variation in animal population size.

Oikos 69:539–544

Low M, Meyer L, Southwell C (2008) Experimental evaluation of a

new ground-based survey method for estimating the density and

abundance of nesting Adelie penguins Pygoscelis adeliae. Polar

Biol 31:309–315

Lynch HJ, Fagan WF, Naveen R, Trivelpiece SG, Trivelpiece WZ

(2009) Timing of clutch initiation in Pygoscelis penguins on the

Antarctic Peninsula: towards an improved understanding of off-

peak census correction factors. CCAMLR Sci 16:149–165

Lynch HJ, Naveen R, Trathan PN, Fagan WF (2012) Spatially

integrated assessment reveals widespread changes in penguin

populations on the Antarctic Peninsula. Ecology 93:1367–1377

McKinlay J, Southwell C, Trebilco R (2010) Integrating count

effort by seasonally correcting animal population estimates

(ICESCAPE): a method for estimating abundance and its

uncertainty from count data using Adelie penguins as a case

study. CCAMLR Sci 17:213–227

Montevecchi WA (1993) Birds as indicators of change in marine prey

stocks. In: Furness RW, Greenwood DJ (eds) Birds as monitors

of environmental change. Chapman and Hall, London,

pp 217–266

Newbery K, Southwell C (2009) An automated camera system for

remote monitoring in polar environments. Cold Reg Sci Technol

55:47–51

Pauly D, Christensen V, Walters CJ (2000) Ecopath, Ecosim, and

Ecospace as tools for evaluating ecosystem impact of fisheries.

ICES J Mar Sci 57:697–706

Piatt JF, Sydemann WJ, Wiese F (2007) Introduction: a modern role

for seabirds as indicators. Mar Ecol Prog Ser 352:199–204

Pollock KH, Nichols JD, Simons TR, Farnsworth GL, Bailey LL,

Sauer JR (2002) Large scale wildlife monitoring studies:

statistical methods for design and analysis. Environmetrics

13:105–119

Pollock KH, Marsh H, Bailey LL, Farnsworth GL, Simons TL,

Alldredge MW (2004) Separating components of detection

probability in abundance estimation: an overview with diverse

examples. In: Thompson WL (ed) Sampling rare and elusive

species: concepts, designs and techniques for estimating popu-

lation parameters. Island Press, Washington, pp 43–58

Reid K, Croxall JP, Briggs DR, Murphy EJ (2005) Antarctic

ecosystem monitoring: quantifying the response of ecosystem

indicators to variability in Antarctic krill. ICES J Mar Sci

62:366–373

Southwell C, McKinlay J, Emmerson L, Trebilco R, Newbery K

(2010) Improving estimates of Adelie penguin breeding popu-

lation size: developing factors to adjust one-off population

counts for availability bias. CCAMLR Sci 17:229–241

Southwell DM, Einoder LD, Emmerson LM, Southwell CJ (2011)

Using the double-observer method to estimate detection prob-

ability of two cavity-nesting seabirds in Antarctica: the snow

petrel (Pagadroma nivea) and the Wilson’s storm petrel

(Oceanites oceanicus). Polar Biol 34:1467–1474

Southwell CJ, Driessen R, Candy SG (2012) Using virtual simulation

in a geographic information system to optimize abundance

survey designs when logistic and biological conditions are

constrained. Wildl Soc B 36:784–795

Taylor RH, Wilson PR, Thomas BW (1990) Status and trends of

Adelie penguin populations in the Ross Sea region. Polar Rec

26:293–304

Thomas L, Laake JL, Strindberg S, Marques FFC, Buckland ST,

Borchers DL, Anderson DR, Burnham KP, Hedley SL, Pollard

JH, Bishop JRB, Marques TA (2006) Distance. Research Unit

for Wildlife Population Assessment, University of St. Andrews,

St. Andrews

Polar Biol (2013) 36:843–856 855

123

Thompson WL, White GC, Gowan C (1998) Monitoring vertebrate

populations. Academic Press, London

Thomson JA, Cooper AB, Burkholder DA, Heithaus MR, Dill LM

(2011) Heterogeneous patterns of availability for detection

during visual surveys: spatio-temporal variation in sea turtle

dive-surfacing behaviour on a feeding ground. Methods Ecol

Evol 3:378–387

Trathan PN (2004) Image analysis of color aerial photography to

estimate penguin population size. Wildl Soc B 32:332–343

Trathan PN, Ratcliffe N, Masden EA (2012) Ecological drivers of

changes at South Georgia: the krill surplus, or climate variabil-

ity. Ecography 35:983–993

Trivelpiece WZ, Hinke JT, Miller AK, Reiss CS, Trivelpiece SG,

Watters GM (2011) Variability in krill biomass links harvesting

and climate warming to penguin population changes in Antarc-

tica. Proc Natl Acad Sci USA 108:7625–7628

Whitehead MD, Johnstone GW (1990) The distribution and estimated

abundance of Adelie penguins breeding in Prydz Bay, Antarc-

tica. Polar Biol 3:91–98

Williams BK, Nichols JD, Conroy MJ (2002) Analysis and manage-

ment of animal populations. Modeling, estimation, and decision

making. Academic press, San Diego

Woehler EJ (1993) The distribution and abundance of Antarctic and

Subantarctic penguins. SCAR, Scott Polar Research Institute,

Cambridge

Woehler EJ, Johnstone GW, Burton HR (1989) The distribution and

abundance of Adelie penguins, Pygoscelis adeliae, in the

Mawson area and at the Rookery Islands (Specially Protected

Area 2), 1981 and 1988. Report No. 71, Australian Antarctic

Division, Hobart

856 Polar Biol (2013) 36:843–856

123