Australian sea lion listing assessment · Regression analysis..... 6 2.4. Sensitivity analysis ......

Australian sea lion listing assessment

S.D. Goldsworthy

SARDI Publication No. F2020/000131-1

SARDI Research Report Series No. 1056

SARDI Aquatics Sciences PO Box 120 Henley Beach SA 5022

April 2020

Report to the Department for Environment and Water and Department of Agriculture, Water and the Environment

Goldsworthy, S. D. (2020) Australian sea lion listing assessment

II

Australian sea lion listing assessment

Report to the Department for Environment and Water and Department of Agriculture, Water and the Environment

S.D. Goldsworthy

SARDI Publication No. F2020/000131-1

SARDI Research Report Series No. 1056

April 2020


III

This publication may be cited as: Goldsworthy, S.D. (2020). Australian sea lion listing assessment. Report to the Department for Environment and Water, Department of Agriculture, Water and the Environment. South Australian Research and Development Institute (Aquatic Sciences), Adelaide. SARDI Publication No. F2020/000131-1. SARDI Research Report Series No. 1056. 26pp. South Australian Research and Development Institute SARDI Aquatic Sciences 2 Hamra Avenue West Beach SA 5024 Telephone: (08) 8207 5400 Facsimile: (08) 8207 5415 http://www.pir.sa.gov.au/research DISCLAIMER The authors warrant that they have taken all reasonable care in producing this report. The report has been through the SARDI internal review process, and has been formally approved for release by the Research Director, Aquatic Sciences. Although all reasonable efforts have been made to ensure quality, SARDI does not warrant that the information in this report is free from errors or omissions. SARDI and its employees do not warrant or make any representation regarding the use, or results of the use, of the information contained herein as regards to its correctness, accuracy, reliability and currency or otherwise. SARDI and its employees expressly disclaim all liability or responsibility to any person using the information or advice. Use of the information and data contained in this report is at the user’s sole risk. If users rely on the information they are responsible for ensuring by independent verification its accuracy, currency or completeness. The SARDI Report Series is an Administrative Report Series which has not been reviewed outside the department and is not considered peer-reviewed literature. Material presented in these Administrative Reports may later be published in formal peer-reviewed scientific literature. © 2020 SARDI This work is copyright. Apart from any use as permitted under the Copyright Act 1968 (Cth), no part may be reproduced by any process, electronic or otherwise, without the specific written permission of the copyright owner. Neither may information be stored electronically in any form whatsoever without such permission. SARDI Publication No. F2020/000131-1 SARDI Research Report Series No. 1056 Author(s): Simon D. Goldsworthy Reviewer(s): Dr Jason Tanner and Dr Mark Doubell Approved by: A/Prof Tim Ward Science Leader, Marine Ecosystems

Signed: Date: 21 April 2020 Distribution: DEW, DAWE, SAASC Library, Parliamentary Library, State Library and

National Library Circulation: Public Domain

http://www.pir.sa.gov.au/research


IV

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ........................................................................................................ VII

EXECUTIVE SUMMARY ........................................................................................................... 1

1. INTRODUCTION ................................................................................................................ 2

1.1. Background.................................................................................................................. 2

1.2. Objectives .................................................................................................................... 3

2. METHODS .......................................................................................................................... 4

2.1. Source data ................................................................................................................. 4

2.2. Survey timing evaluation and identification of comparable surveys .............................. 5

2.3. Regression analysis ..................................................................................................... 6

2.4. Sensitivity analysis ....................................................................................................... 7

2.5. Linear Mixed Models .................................................................................................... 8

3. RESULTS ........................................................................................................................... 9

3.1. Population status ......................................................................................................... 9

3.2. Generation length ........................................................................................................ 9

3.3. Time series .................................................................................................................. 9

3.4. Estimating overall reduction ......................................................................................... 9

3.5. Sensitivity analysis ......................................................................................................10

3.6. Linear Mixed Model Analysis ......................................................................................11

4. DISCUSSION ....................................................................................................................12

REFERENCES .........................................................................................................................25


V

LIST OF FIGURES

Figure 1. Location of Australian sea lion breeding colonies within twelve regions (Kangaroo

Island, Spencer Gulf, S-W Eyre, Chain of Bays, Nuyts Archipelago, Nuyts Reef, Bunda Cliffs,

Twilight Cove, Recherche Archipelago, Bremmer Bay, Jurien Bay, Abrolhos Islands). .............13

Figure 2. Plot trends in pup abundance for 30 Australian sea lion subpopulations with three or

more comparable surveys. The plots shows the natural logarithm of pup numbers for each

subpopulation, to which a linear regression model with ±95% confidence limits (shaded) has been

applied. Note that while a log scale is required to compare between sites with large differences in

subpopulation size, it tends to mask the extent of changes over time. ......................................14

Figure 3. Probability distribution of Monte Carlo simulations of the three-generation decline in

Australian sea lion populations. .................................................................................................15


VI

LIST OF TABLES

Table 1. Summary information on the survey history, status and trends in abundance of 80 known

Australian sea lion breeding sites in South (SA) and Western Australia (WA). The location of

breeding sites is indicated (Lat = latitude; Long = longitude), as is the survey method (G = ground

count, CPP = cumulative pup production, MR = mark-recapture, CT = Cliff-top survey, D = drone

survey) used to derive the ‘Pup count’, and ‘Max count, MR/CPP’ (if listed). The number of

comparable breeding season surveys is listed. .........................................................................16

Table 2. Comparable pup abundance time-series data for Australian sea lions with three or more

comparable surveys. .................................................................................................................18

Table 3. Summary results for the 30 listed Australian sea lion subpopulations with three or more

comparable surveys. The year of the earliest and most recent comparable surveys per

subpopulation is provided, along with the time-series duration, the number of surveys, and the

estimated change in past and present pup abundances over a three-generation period (42.3

years) between 1977.2 (1977) and 2019.5 (2019), including the percentage change and intrinsic

rate of increase (r). The mean r, summed pup abundance (1977, 2019) and three-generation

change (%) in pup abundances are also provided at the bottom of the table. ............................23

Table 4. Results of a sensitivity analysis for five subpopulations with large pup abundance

estimates back-projected to 1977 (Rocky (North), West Waldegrave, Rocky (South), Purdie, and

Four Hummocks Islands). Result indicate that the summed pup abundances in 1977 would need

to be reduced by 66% in order for the total listed population assessment (Overall decline) to fall

below a 50% decline over three generations. ............................................................................24


VII

ACKNOWLEDGEMENTS This assessment was funded by the Department for Environment and Water (DEW). I thank Dr

Simon Bryars and Dr Janice Goodwins (DEW) for facilitating this work, and Dr Kelly Waples and

Dr Holly Raudino (Department of Biodiversity, Conservation and Attractions) for providing the

Australian sea lion data for Western Australia. I thank Professor Helene Marsh (Chair of the

Threatened Species Scientific Committee), Professor David Keith (University of New South

Wales) and Dr Ivan Lawler (Department of Agriculture, Water and the Environment) for analytical

advice. I thank Dr Fred Bailleul (SARDI Aquatic Sciences) for statistical support, and Dr Peter

Shaughnessy, Dr Jason Tanner and Dr Mark Doubell for reviewing the draft report.


1

EXECUTIVE SUMMARY This report provides listing assessment information for the Threatened Species Scientific

Committee, to assist in their evaluation of the Australian sea lion (Neophoca cinerea) against

Criterion 1 A2(b) of the Environmental Protection and Biodiversity Conservation Act (1999)(EPBC

Act).

All available historic Australian sea lion pup abundance time-series data for South and Western

Australia were compiled, and data were cleaned and evaluated to identify comparable breeding

season pup abundance estimates. A total of 30 out of 80 breeding sites for the species had at

least three comparable breeding season surveys. For each subpopulation, the change in pup

numbers over time was estimated using a linear regression of the natural logarithm of pup

numbers against year. Based on these analyses there has been a 64.0% decline in total pup

abundance for the species over a three-generation period (42.3 years).

Given the 30 listed subpopulations used in this assessment account for over 75% of the species-

wide pup abundances, and that the subpopulations span most of the geographic range of the

species, results are considered to be indicative of species-wide trends. Limited trend data

available for some of the remainder subpopulations (with only two comparable breeding season

pup abundance estimates that account for almost half of the remainder pup abundances), suggest

their decline in abundance (67.5% decline) has been similar to that of the listed subpopulations.

Monte Carlo simulations suggest the assessment is relatively insensitive to the error distributions

around past (and present) subpopulation pup abundances. Over 98% of the 1,000 simulations

produced a decline of >50% over a three-generation period.

Keywords: Australian sea lion, Environmental Protection and Biodiversity Conservation Act

(1999)(EPBC Act), listing assessment


2

1. INTRODUCTION

1.1. Background

The extant distribution of the Australian sea lion (ASL, Neophoca cinerea) is restricted to South

Australia (SA) and Western Australia (WA), and with the exception of a number of small mainland

breeding sites (most along the Bunda Cliffs, SA), the majority of breeding sites occur on offshore

islands. The species has a unique life history relative to other pinnipeds, including:

- non-annual and asynchronous breeding: an ~18 month interval between breeding

seasons, breeding is not seasonal and can occur at any time of the year and the timing of

breeding varies markedly across the species range;

- prolonged breeding season: ranging from ~4 to ~12 months in duration, depending on

colony size;

- extreme philopatry and subpopulation structure: most breeding sites are effectively closed

subpopulations;

- the longest gestation period of any pinniped: 13-14m vs ~8m in other species; and

- extended maternal care: 17+ m vs. <12m in most other species (Kirkwood and

Goldsworthy 2013).

The number of pups produced in a breeding season is a commonly used index of abundance for

pinniped populations because pups form the only age-class that is easily identifiable (black/brown

natal coat, lanugo) and most pups are ashore at the end of the breeding season and available for

counting. Although the relationship between pup production and the total population size is

variable, depending on the status of the population (increasing, decreasing, stable), pup counts

can be a reliable indicator of population growth (Berkson and DeMaster 1985).

In 2019, the Threatened Species Scientific Committee (TSSC) of the Australian Government’s

Department of Agriculture, Water and Environment (DAWE) requested assistance with respect to

the listing assessment of the Australian sea lion (ASL, Neophoca cinerea). Following an

Australian sea lion management workshop held in Adelaide (27 November 2019), DAWE staff, in

liaison with the Chair of the TSSC, requested the provision of a short document detailing an

assessment of species-wide trends in pup abundance using regression analysis, including a

description of the survey data and how they were filtered to identify comparable surveys suitable

for analyses.


3

These analyses are required to enable assessment of ASL against Criterion 1 A2(b) of the

Environmental Protection and Biodiversity Conservation Act (1999)(EPBC Act). As the criteria

used by the TSSC to make such assessments are based on those of the International Union for

the Conservation of Nature (IUCN) Red List criteria, this document and its analyses relies on the

IUCN Red List guidelines (IUCN Standards and Petitions Committee 2019).

1.2. Objectives

The objectives of this project were to:

- compile all historic ASL pup abundance time-series data for SA and WA populations;

- summarise survey methodology, data cleaning, filtering and evaluation procedures used

to identify comparable breeding season pup abundance estimates for all ASL populations;

- tabulate comparable pup abundance time-series data for ASL populations;

- undertake regression analyses including assessing the plausibility of back-projected

values;

- evaluate the likelihood that monitored ASL population declines have been >30%

(Vulnerable) or >50% (Endangered) over a 3 –generation period; and

- assess information available on abundance trends for the ‘remainder’ populations to infer

their trends in abundance.


4

2. METHODS

2.1. Source data

A total of 1,776 individual site surveys, for all known ASL breeding sites in SA and WA between

1970 and 2019 were collated (raw data file provided to TSSC: ‘ASL listing advice Tables DAWE-

TSSC.xlsx’). For most breeding sites, all available individual site-survey data were collated. At

three sites in the Kangaroo Island region (Seal Bay, The Pages, and the Seal Slide) where

multiple within season surveys have been undertaken, only the maximum comparable pup counts

per breeding season were collated. For the Bunda Cliffs breeding sites (west of Head of Bight,

SA), site-by-site analysis of trends is challenging due to regular cliff collapses that have resulted

in the loss of some breeding sites over time. It is not clear what happens when a breeding site is

lost or becomes unsuitable, but presumably, some animals move to adjacent suitable habitat or

nearby established breeding sites. For these reasons, within season surveys of pup abundance

for the Bunda Cliffs breeding sites have been pooled for all years prior to the introduction of drone

surveys in 2017.

Pup abundance counts have been obtained using ground (G), aerial (A), boat (B), cliff-top (CT)

and drone (D) surveys. Pup abundances for breeding sites have been estimated using four main

methods:

- standard ground counts of live and dead (or cumulative dead) pups;

- adjusted ground counts where there are a known number of marked (or tagged) pups that

can be added to the maximum count of live and dead (or cumulative dead) unmarked

pups;

- mark-recapture (MR) using the Petersen estimate; and

- cumulative pup production (CPP) based on either twice-weekly surveys of cumulative

births (Seal Bay population), or from estimates of net-pup production between successive

MR surveys (Goldsworthy et al. 2015).

As the standard ground count is the default/basic survey method used (Goldsworthy et al. 2020),

these data form the basis to all time series analyses in this assessment.

Where multiple within breeding season surveys have been conducted, individual surveys include

the number of cumulative dead pups. Dead pups are typically marked within surveys to avoid

double counting. Most surveys include details on the number of live pups present in different


5

pelage categories. This information is critical for estimating survey timing relative to the stage of

the breeding season as the pelage categories provide a proxy for pup age: black mate-guarded

(pups whose mother is mate-guarded by an adult male, indicative of a pup aged 0-10 days), black

(post-mate-guard pups, 1-4 weeks), brown (pups approximately 4 - 20 weeks), moulting (pups,

~16+ weeks) and moulted (pups, >20 weeks).

2.2. Survey timing evaluation and identification of comparable surveys

The duration of ASL breeding seasons varies as a function of colony size (Goldsworthy et al.

2015). For most small to medium breeding colonies (<150 pups), the breeding season lasts 4-5

months and the maximum count of pups will occur at the end of the breeding season. A single

survey is adequate, if conducted at the right time. The end of the breeding season will be indicated

by an absence (or low numbers) of mate-guarded or black pups and the presence of some

moulting pups. Fully moulted pups are rarely recorded at the end of breeding season for small to

medium breeding colonies. For large colonies (>150 pups) where the breeding season may

extend between 6 to 9+ months in duration, it is usual for fully moulted pups to be present

alongside newborn (black) pups. For these sites, the peak in pup numbers usually occurs well

before the end of the breeding season, due to the increasing availability biases as moulted pups

become more aquatic (i.e. foraging at sea, dispersing). Multiple within-season surveys are often

undertaken for larger breeding sites to increase the likelihood of obtaining a count at ‘peak’ pup

numbers.

Surveys were evaluated to be ‘Comparable’ for a given site, based on the distribution of pups in

the three main pelage categories (black, brown, moulted), relative to the size of the breeding

population, and on additional observations (notes) made by observers on the timing of the survey

relative to the stage of the breeding season. A challenge with many earlier surveys is that

categories of pups based on pelage were either not recorded (‘Unclassed’), or recorded

inconsistently (e.g. brown pups in early moult were classed as moulted pups), or differently to

recent surveys (e.g. ‘black mate-guard’ and ‘black’ pups have only been regularly recorded in SA

surveys since ~2007, prior to this they were recorded as ‘brown’ pups).

Surveys were excluded from time-series analyses (i.e. not considered comparable) if they were

assessed to be:


6

- ‘Early’ – survey undertaken prior to, or early in the breeding season;

- ‘Late’ - survey undertaken well after the breeding season;

- ‘Unknown’ - where survey timing relative to the breeding season could not be evaluated;

- ‘Incomplete’ - where there was evidence of inconsistent survey effort (i.e. parts of island

not surveyed where breeding may have taken place);

- ‘Incursions’ – where the timing of breeding of neighbouring breeding sites occurs earlier

or is similar to the breeding site being surveyed, pup counts can be confounded (inflated)

if there is significant movement of pups between sites (e.g. tagged Dangerous Reef pups

sighted at Lewis, English and Albatross Islands; Nicolas Baudin Island pups sighted at

Jones Island) (Goldsworthy et al. 2008 , 2009, 2014, Shaughnessy et al. 2005).

If there was any doubt about the timing of past surveys (e.g. early in the breeding season, or well

past the end of the breeding season), or survey quality (due to potential biases), they were omitted

from the time-series analysis.

2.3. Regression analysis The statistical method for calculating population change for taxa with widely distributed or multiple

subpopulations under IUCN Criterion A was assessed to be the most appropriate for the ASL

(IUCN Standards and Petitions Committee 2019).

An exponential decline was assessed to be the most appropriate for ASL, based on the pattern

of decline observed in some subpopulations (Goldsworthy et al. 2020, Goldsworthy et al. 2019).

In addition, observed bycatch mortality rates (in demersal gillnet fisheries) have been shown to

vary as a function of ASL density, hence subpopulations exposed to consistent fishing effort would

be expected to show a constant proportional rate of decline i.e. exponential (Goldsworthy et al.

2010a).

Generation time was estimated by populating the IUCN generation length calculator tool

(https://www.iucnredlist.org/resources/generation-length-calculator), with data on observed ASL

fecundity and survival from the Seal Bay ASL subpopulation (Goldsworthy et al. 2020).

ASL subpopulations with a pup abundance time-series consisting of three or more comparable

breeding season, were used for analyses. The change in pup numbers over time was estimated

https://www.iucnredlist.org/resources/generation-length-calculator


7

using regression analysis, which applied a linear regression of the natural logarithm of pup

numbers against year. All analyses were undertaken in the software R, version 3.5.1 (R Core

Team 2017), using the package lme4 (Bates et al. 2015). The intercept and coefficient terms for

each breeding site regression were used to estimate past and present pup abundances for each

breeding site, and from these the overall change in pup abundance over three generations.

2.4. Sensitivity analysis Two approaches were used to examine sensitivity and uncertainty about back-projected

estimates, and their influence on estimates of population reduction over three generations.

i) Uncertainty in back-projected estimates

The first approach examined the sensitivity of the three-generation change in ASL abundance for

all listed subpopulations, to changes in some of the largest back-projected subpopulation pup

abundance values. This approach selected the largest such estimates of pup abundance, and

incrementally reduced these estimates to determine how much of a percentage reduction was

required in order to change the overall estimate of decline to be less than 50% or 30% over three

generations.

ii) Monte Carlo approach

The second sensitivity analysis followed IUCN Guidelines for dealing with uncertainty (page 41,

IUCN Standards and Petitions Committee 2019). The normal probability distributions for the back-

and forward-projected estimates of pup abundances for each subpopulation with time-series data,

were estimated using the mean value and the 95% CI’s (converted to a standard deviation) for

the earliest and most recent surveys from regression analyses. These probability distributions

were used to provide a measure of the uncertainty around past and present abundance estimates,

from which a pair of past and present breeding site pup abundance estimates were randomly

selected, a three-generation change calculated, and the procedure repeated 1000 times. By

summing all of the past and present breeding site abundances for each Monte Carlo iteration, the

probability distribution for the reduction in ASL population could be estimated, and from this the

percentage of iterations that had declined by more than 30% (Vulnerable) and 50% (Endangered)

determined.


8

2.5. Linear Mixed Models A linear mixed model (LMM) approach was used to examine if changes in pup abundance over

time were affected by any unrelated variation attributed to different subpopulations, survey timing

and effort. Analysis was restricted to SA data where details were generally available on survey

timing relative to the timing of the particular breeding season. A LMM was developed using survey

effort and timing as co-variables, site (subpopulation) as a random variable, and year as a fixed

effect. Survey effort was the number of surveys within a breeding season attributed to any

comparable site-breeding season survey (range 1-80). Survey timing was a largely subjective

assessment of whether the survey timing was optimal (1 = optimal, 2 = uncertain). LMM

statistical analyses were performed in software R, version 3.5.1 (R Core Team 2017), using the

package lme4 (Bates et al. 2015). Data were standardised prior to analysis using the standardise

function from the standardise package in R, p values were obtained with the ANOVA function in

the lme4 package.


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3. RESULTS

3.1. Population status There are a total of 80 known ASL breeding sites (subpopulations), 48 (60%) in SA, 32 (40%) in

WA (Table 1). The location of extant sites is presented in Figure 1. Total species-wide pup

abundance based on pup counts is 2,551 (2,058, 81% SA; 493, 19% WA), or 2,686 (2,193, 82%

SA; 493, 18% WA) including cumulative pup production estimates from Seal Bay and drone

surveys counts from the Bunda Cliffs colonies (Table 1). The species-wide median pup

abundance per subpopulation is 14.5 (24.0 SA, 8.5 WA) (Table 1).

3.2. Generation length Generation length in ASL was estimated to be 14.1 years. Therefore, a three-generation period

was 42.3 years.

3.3. Time series A total of 228 individual site-surveys across 60 subpopulations were judged to be comparable,

189 of these from 30 subpopulations, had three or more breeding season surveys (Table 2). The

duration between the earliest and most recent survey ranged between 1.5 and 44.5 years (mean

= 16.4 years) for all subpopulations, and between 2.9 and 44.5 years (mean = 18.5 years) for

subpopulations with three or more comparable surveys (Table 2).

3.4. Estimating overall reduction i) Listed subpopulations

Listed subpopulations include those with three or more comparable surveys from which a change

in abundance over three generations can be estimated (p 37, IUCN Standards and Petitions

Committee 2019). Regression analyses were undertaken on the 30 listed subpopulations (Figure

2), to estimate the reduction between past and present pup abundances over a three-generation

period (42.3 years) between 1977.2 (1977) and 2019.5 (2019) (Table 3).

Pup abundance estimates were 5,477 in 1977 and 1,973 in 2019, a three-generation decline of

64.0% (Table 3).


10

ii) Remainder subpopulations

The 30 listed subpopulations account for 75.6% of the species-wide pup abundance (based on

the most recent pup count surveys, 1,929 pups), and the 50 remainder subpopulations account

for 24.4% of total pup abundance (622 pups) (Table 1).

If there had been no change in pup abundance over three generations for the 50 remainder

subpopulations, the total population pup abundance estimate would have been 6,099 (5,477 +

622) in 1977, and 2,595 (1,973 + 622) in 2019, a three-generation decline of 57.5%.

However, eight of the remainder subpopulations have two comparable surveys of pup abundance.

Acknowledging the statistical limitations of regression analysis on two data points, applying it to

these data provides pup abundance estimate of 849 in 1977 and 276 in 2019, a three-generation

decline of 67.5%. The pup abundances for these eight remainder subpopulations (294) makes

up nearly half (47%) of the total remainder population pup abundances (Table 3).

Assuming that the decline in the eight remainder subpopulations with two comparable surveys is

indicative of trends for those sites, and conservatively assuming no change in pup abundances

(328 pups) over three generations for the remainder subpopulations with no time-series, the best

estimates of pup abundances for the listed and remainder subpopulations in 1977 and 2019 are

6,654 (5,477 + 849 + 328), and 2,577 (1,973 + 276 + 328), respectively. This provides an overall

three-generation decline of 61.3%.

3.5. Sensitivity analysis i) Uncertainty in back-projected estimates

There are 13 subpopulations with pup abundance estimates back-projected to 1977 that

exceeded 100 pups (Table 3). For three of these (Dangerous Reef, The Pages, Seal Bay), the

back-projected estimates fall well within observed values, and for another (Olive Island), the

maximum observed value is within 23% of the back-projected estimate and the estimate is

considered plausible (Table 3, Goldsworthy et al. 2007, Goldsworthy et al. 2010b, McIntosh et al.

2012, Shaughnessy 2005, Shaughnessy et al. 2013).

A sensitivity analysis on the next five largest back-projected estimates greater than 100 pups

(Rocky (North), West Waldegrave, Rocky (South), Purdie, and Four Hummocks Islands) indicated


11

that their summed back-projected pup abundances would need to be reduced by 66% in order for

the total listed population assessment to fall below a 50% decline over three generations (Table

4).

Three subpopulations (Rocky (North), West Waldegrave, Rocky (South) Islands) have back-

projected estimates that ranged between 23 and 65 times current estimates. Even with the

removal of these subpopulations, the remaining listed subpopulations would still have a three-

generation decline >50% (namely 52.9%).

ii) Monte Carlo approach

Monte Carlo simulations of past and present breeding site abundances were undertaken to

estimate the probability distribution of the reduction in ASL population over three generations

(Figure 3).

Based on 1000 simulations:

- the probability that the listed subpopulation pup abundances have declined by at least

30% over a three-generation period was 100% (1000 of 1000 simulations);

- the probability that the listed subpopulation pup abundances have declined by at least

50% over a three-generation period was >98% (>980 of 1000 simulations); and

- the median decline in pup abundance for the listed subpopulations over three generations

was 61%.

3.6. Linear Mixed Model Analysis Neither of the co-variables ‘survey effort’ and ‘timing’ were significant. The variance explained by

the random effect (site), was significantly different to zero, indicating that there were site

(subpopulation) differences in the relationship between year and pup abundance. The final linear

model indicated there was significant effect of year (negative, Wald Test, P<0.001) on pup

abundance.


12

4. DISCUSSION Regression analyses undertaken on 30 Australian sea lion subpopulations with three or more

comparable pup abundance surveys estimated there has been a 64.0% decline in total pup

abundances over a three-generation period.

Given the 30 listed subpopulations used in this assessment account for over 75% of the species-

wide pup abundances, and that the subpopulations span most of the geographic range of the

species, results should be indicative of species-wide trends.

The limited trend data available for some of the remainder subpopulations (that account for almost

half of the remainder pup abundances) suggest their decline in abundance (67.5%) is similar to

that of the listed subpopulations.

The assessment is not overly sensitive to marked reductions (e.g. 50%) in the largest back-

projected estimates of pup abundance. Indeed many of the back-projected pup abundance

estimates for the largest subpopulations fall well within their observed historic ranges.

Results from Monte Carlo simulations suggest the assessment is relatively insensitive to the error

distributions around past (and present) subpopulation pup abundances, with over 98% of

simulations producing a decline of >50% over a three-generation period.

The LMM analysis did not add much to the assessment, principally because the number of site-

surveys per breeding season has no bearing on survey quality or pup abundances for the majority

of sites where a single, appropriately timed survey at the end of the breeding season is sufficient.


13

Figure 1. Location of Australian sea lion breeding colonies within twelve regions (Kangaroo Island, Spencer Gulf, S-W Eyre, Chain of Bays, Nuyts Archipelago, Nuyts Reef, Bunda Cliffs, Twilight Cove, Recherche Archipelago, Bremmer Bay, Jurien Bay, Abrolhos Islands).


14

Figure 2. Plot trends in pup abundance for 30 Australian sea lion subpopulations with three or more comparable surveys. The plots shows the natural logarithm of pup numbers for each subpopulation, to which a linear regression model with ±95% confidence limits (shaded) has been applied. Note that while a log scale is required to compare between sites with large differences in subpopulation size, it tends to mask the extent of changes over time.


15

Figure 3. Probability distribution of Monte Carlo simulations of the three-generation decline in Australian sea lion populations.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0% -5%

-10%

-15%

-20%

-25%

-30%

-35%

-40%

-45%

-50%

-55%

-60%

-65%

-70%

-75%

-80%

-85%

-90%

-95%

-100

%

Prob

abili

ty

3-Generation decline

>50% decline


16

Table 1. Summary information on the survey history, status and trends in abundance of 80 known Australian sea lion breeding sites in South (SA) and Western Australia (WA). The location of breeding sites is indicated (Lat = latitude; Long = longitude), as is the survey method (G = ground count, CPP = cumulative pup production, MR = mark-recapture, CT = Cliff-top survey, D = drone survey) used to derive the ‘Pup count’, and ‘Max count, MR/CPP’ (if listed). The number of comparable breeding season surveys is listed.

No. State Region Breeding site Lat Long Survey yearSurvey

methodPup

countMax count,

MR/CPPSources

No. comparable breeding season

surveys

1 SA Kangaroo Island The Pages Islands -35.756 138.300 2014 G 313 313 Goldsworthy et al. (2015) 152 SA Kangaroo Island Seal Slide (Kangaroo Is.) -36.028 137.539 2018 G 14 14 Goldsworthy et al. (2019) 113 SA Kangaroo Island Seal Bay (Kangaroo Is.) -35.995 137.317 2019, 2019 G, CPP 178 222 Goldsworthy unpublished data 124 SA Kangaroo Island Cape Bouguer (Kangaroo Is.) -36.042 136.909 2014 G 9 9 Goldsworthy et al. (2015) 15 SA Kangaroo Island North Casuarina Is. -36.068 136.703 2014 G 11 11 Goldsworthy et al. (2015) 16 SA Spencer Gulf Peaked Rocks -35.183 136.483 2010 G 18 18 Goldsworthy et al. (2012) 17 SA Spencer Gulf North Islet -35.121 136.476 2011 G 21 21 Goldsworthy et al. (2012) 18 SA Spencer Gulf Dangerous Reef -34.817 136.217 2019 G 359 359 DEW unpublished data 119 SA Spencer Gulf English Is. -34.637 136.197 2011 G 34 34 Goldsworthy et al. (2012) 010 SA Spencer Gulf Albatross Is. -35.069 136.181 2011 G 69 69 Goldsworthy et al. (2012) 211 SA Spencer Gulf South Neptune Islands -35.333 136.117 2014 G 4 4 Goldsworthy et al. (2015) 312 SA Spencer Gulf North Neptune Islands -35.226 136.077 2014 G 9 9 Goldsworthy et al. (2015) 113 SA Spencer Gulf Lewis Is. -34.957 136.032 2014 G 83 83 Goldsworthy et al. (2015) 314 SA Spencer Gulf Williams Is. -35.035 135.974 2019 G 2 2 Goldsworthy et al. (2020) 215 SA Spencer Gulf Curta Rocks -34.934 135.874 2019 G 5 5 Goldsworthy et al. (2020) 216 SA Spencer Gulf Liguanea Is. -34.998 135.619 2019 G 27 27 Goldsworthy et al. (2020) 317 SA SW-Eyre Price Is. -34.717 135.283 2014 G 32 32 Goldsworthy et al. (2015) 118 SA SW-Eyre Four Hummocks Is. -34.769 135.031 2019 G 10 10 Goldsworthy et al. (2020) 319 SA SW-Eyre Rocky (South) Is. -34.810 134.718 2019 G 6 6 Goldsworthy et al. (2020) 320 SA SW-Eyre Rocky (North) Is. -34.267 135.267 2014 G 36 36 Goldsworthy et al. (2015) 321 SA SW-Eyre Cap Island -33.947 135.113 2014 G 31 31 Goldsworthy et al. (2015) 222 SA Chain of Bays West Waldegrave Is. -33.600 134.783 2015 G 89 89 Goldsworthy et al. (2015) 323 SA Chain of Bays Jones Is. -33.183 134.367 2014 G 19 19 Goldsworthy et al. (2015) 624 SA Chain of Bays Point Labatt -33.152 134.261 2013 G 2 2 Goldsworthy et al. (2014) 025 SA Chain of Bays Pearson Is. -33.950 134.267 2019 G 32 32 Goldsworthy et al. (2015) 526 SA Chain of Bays Ward Is. -33.750 134.300 2019 G 42 42 Goldsworthy et al. (2020) 427 SA Chain of Bays Nicolas Baudin Is. -33.010 134.126 2019 G 70 70 Goldsworthy et al. (2020) 428 SA Chain of Bays Olive Is. -32.717 133.983 2019 G 86 86 Goldsworthy et al. (2020) 1129 SA Nuyts Archipelago Lilliput -32.449 133.669 2019 G 63 63 Goldsworthy et al. (2020) 630 SA Nuyts Archipelago Blefuscu -32.462 133.639 2019 G 50 50 Goldsworthy et al. (2020) 731 SA Nuyts Archipelago Breakwater/Gliddon Is. -32.322 133.561 2015 G 27 27 Goldsworthy et al. (2015) 432 SA Nuyts Archipelago Lounds Is. -32.273 133.366 2019 G 30 30 Goldsworthy et al. (2020) 433 SA Nuyts Archipelago Fenelon Is. -32.583 133.283 2019 G 31 31 Goldsworthy et al. (2020) 234 SA Nuyts Archipelago West Is. -32.517 133.250 2019 G 36 36 Goldsworthy et al. (2020) 435 SA Nuyts Archipelago Purdie Is. -32.283 133.233 2019 G 74 74 Goldsworthy et al. (2020) 436 SA Nuyts Reef Nuyts Reef -32.117 132.133 2019 G 122 122 Goldsworthy et al. (2020) 237 SA Bunda Cliffs Bunda 01 -30.481 131.061 2016, 2018 CT, D - 2 DEW38 SA Bunda Cliffs Bunda 06 -30.414 130.562 2016, 2019 CT, D 1 8 DEW39 SA Bunda Cliffs Bunda 07 -30.413 130.069 2016, 2019 CT, D - 4 DEW40 SA Bunda Cliffs Bunda 09 -30.412 130.046 2016, 2019 CT, D 6 15 DEW41 SA Bunda Cliffs Bunda 11 -30.392 129.784 2016, 2017 CT, D - 1 DEW42 SA Bunda Cliffs Bunda 12 -30.390 129.763 2016, 2017 CT, D - 7 DEW 1043 SA Bunda Cliffs Bunda 18 -30.363 129.429 2016, 2019 CT, D - 1 DEW44 SA Bunda Cliffs Bunda 19 -30.359 129.377 2016, 2017 CT, D 5 14 DEW45 SA Bunda Cliffs Bunda 20 -30.357 129.357 2016, 2017 CT, D 2 1 DEW46 SA Bunda Cliffs Bunda 22 -30.351 129.308 2016, 2017 CT, D - 13 DEW47 SA Bunda Cliffs 152 -30.364 129.447 2016, 2017 CT, D - 11 DEW48 SA Bunda Cliffs 155 -30.369 129.479 2016, 2017 CT, D - 28 DEW


17

Table 1. cont.

No. State Region Breeding site Lat Long Survey yearSurvey

methodPup

countMax count,

MR/CPPSources

No. comparable breeding season

surveys

49 WA Twilight Cove Twilight Cove -32.277 126.006 1996 G 4 4 Dennis & Shaughnessy 1999 050 WA Recherche Archipelago Spindle Is. -33.763 124.161 1990 G 53 53 Gales (1990); Gales et al. (1994) 051 WA Recherche Archipelago Ford (Halfway) Is. -33.766 124.041 1990 G 17 17 Gales (1990); Gales et al. (1994) 152 WA Recherche Archipelago Six Mile Is. -33.640 123.968 2017 G 45 45 DPAW 353 WA Recherche Archipelago Round Is. -34.105 123.888 2013 G 13 13 DPAW, Goldsworthy unpub. data 154 WA Recherche Archipelago Salsibury Is. -34.360 123.552 2014 G 10 10 DPAW 255 WA Recherche Archipelago Wickham (Stanley ) Is. -34.020 123.291 2014 G 5 5 DPAW 256 WA Recherche Archipelago George Is. -34.050 123.259 2011 G 13 13 DPAW 157 WA Recherche Archipelago Glennie Is. -34.096 123.105 1999 G 21 21 DPAW 258 WA Recherche Archipelago Taylor Is. -33.920 122.873 2013 G 4 4 DPAW, Goldsworthy unpub. data 059 WA Recherche Archipelago Kimberley Is. -33.949 122.469 2014 G 32 32 DPAW 360 WA Recherche Archipelago Cooper Is. -34.231 123.607 2014 G 8 8 DPAW 161 WA Bremer Bay Investigator (Rocky Is. -34.083 120.867 1989 G 17 17 Gales et al. (1994) 162 WA Bremer Bay West Is. -34.082 120.485 1992 G 20 20 Gales et al. (1994) 063 WA Bremer Bay Red Islet -34.040 119.780 2017 G 25 25 DPAW 364 WA Bremer Bay Middle Doubtful Island -34.376 119.609 2012.5 G 1 1 DPAW 165 WA Bremer Bay Haul Off Rock -34.702 118.661 2016 G 24 24 DPAW 466 WA Jurien Bay Buller Is. -30.657 115.115 2019 G 44 44 DPAW 1267 WA Jurien Bay Beagle Is. -29.808 114.877 2019 G 57 57 DPAW 1268 WA Jurien Bay North Fisherman Is. -30.130 114.944 2019 G 40 40 DPAW 1369 WA Abrolhos Islands (Easter) Morley Is -28.746 113.813 2006 G 1 1 DPAW 070 WA Abrolhos Islands (Easter) Soumi Is. -28.719 113.837 2006 G 4 4 DPAW 171 WA Abrolhos Islands (Easter) Rat Is. -28.715 113.784 2014 G 1 1 DPAW 072 WA Abrolhos Islands (Easter) Campbell Is -28.694 113.836 2004 G 1 1 DPAW 173 WA Abrolhos Islands (Easter) Leo Is. -28.688 113.860 2006 G 2 2 DPAW 074 WA Abrolhos Islands (Easter) Gibson Is -28.687 113.829 2006 G 6 6 DPAW 175 WA Abrolhos Islands (Easter) Serventy Is. -28.680 113.832 2006 G 3 3 DPAW 176 WA Abrolhos Islands (Easter) Stokes Is -28.673 113.852 2013 G 2 2 DPAW 177 WA Abrolhos Islands (Easter) Alexander Is. -28.673 113.830 2006 G 3 3 DPAW 178 WA Abrolhos Islands (Easter) Gilbert Is. -28.667 113.827 2006 G 9 9 DPAW 179 WA Abrolhos Islands (Wallabi) Long Is. -28.471 113.774 2006 G 2 2 DPAW 080 WA Abrolhos Islands (Wallabi) Eastern Is. -28.468 113.814 2006 G 6 6 DPAW 1

Total SA 2058 2193Total WA 493 493

Overall total 2551 2686Pup abundance - time-series (listed) sites (3+ surveys) 1929 76%

Pup abundance - remainder sites (total) 622 24%Remainder sites with 2 surveys 294


18

Table 2. Comparable pup abundance time-series data for Australian sea lions with three or more comparable surveys.

Site StateNo.

comparable surveys

Year Pup abundance

The Pages SA 15 1990.1 522The Pages SA 15 1991.4 431The Pages SA 15 1993.0 448The Pages SA 15 1996.0 439The Pages SA 15 1997.4 381The Pages SA 15 1998.6 445The Pages SA 15 2000.0 491The Pages SA 15 2001.5 461The Pages SA 15 2002.8 607The Pages SA 15 2004.3 485The Pages SA 15 2005.8 543The Pages SA 15 2007.1 403The Pages SA 15 2008.5 478The Pages SA 15 2010.0 478The Pages SA 15 2014.5 313Seal Bay SA 12 2003.4 185Seal Bay SA 12 2004.8 208Seal Bay SA 12 2006.2 197Seal Bay SA 12 2007.8 198Seal Bay SA 12 2009.4 197Seal Bay SA 12 2010.9 189Seal Bay SA 12 2012.3 166Seal Bay SA 12 2013.7 126Seal Bay SA 12 2015.6 170Seal Bay SA 12 2016.9 192Seal Bay SA 12 2018.4 178Seal Bay SA 12 2019.6 178Dangerous Reef SA 11 1999.5 383Dangerous Reef SA 11 2001.2 393Dangerous Reef SA 11 2002.7 426Dangerous Reef SA 11 2004.1 499Dangerous Reef SA 11 2005.5 585Dangerous Reef SA 11 2007.0 575Dangerous Reef SA 11 2008.7 441Dangerous Reef SA 11 2009.9 435Dangerous Reef SA 11 2011.5 329Dangerous Reef SA 11 2014.4 294Dangerous Reef SA 11 2019.0 382


19

Table 2. cont.

Site StateNo.

comparable surveys

Year Pup abundance

Olive Island SA 11 2003.5 121Olive Island SA 11 2005.0 131Olive Island SA 11 2006.3 150Olive Island SA 11 2007.7 124Olive Island SA 11 2009.2 134Olive Island SA 11 2010.6 116Olive Island SA 11 2012.1 109Olive Island SA 11 2013.6 98Olive Island SA 11 2014.9 103Olive Island SA 11 2016.5 104Olive Island SA 11 2019.3 86Seal Slide SA 11 2003.4 9Seal Slide SA 11 2004.7 11Seal Slide SA 11 2006.4 10Seal Slide SA 11 2007.8 14Seal Slide SA 11 2009.6 10Seal Slide SA 11 2010.9 9Seal Slide SA 11 2012.1 9Seal Slide SA 11 2014.0 9Seal Slide SA 11 2015.1 8Seal Slide SA 11 2016.7 15Seal Slide SA 11 2018.4 14Bunda Cliffs SA 10 1991.6 62Bunda Cliffs SA 10 1995.7 75Bunda Cliffs SA 10 1997.5 39Bunda Cliffs SA 10 2001.6 27Bunda Cliffs SA 10 2007.3 24Bunda Cliffs SA 10 2008.7 25Bunda Cliffs SA 10 2010.2 16Bunda Cliffs SA 10 2013.2 18Bunda Cliffs SA 10 2014.5 23Bunda Cliffs SA 10 2016.1 14Blefuscu Island SA 7 2005.2 84Blefuscu Island SA 7 2008.1 68Blefuscu Island SA 7 2010.9 85Blefuscu Island SA 7 2012.5 52Blefuscu Island SA 7 2013.8 81Blefuscu Island SA 7 2015.3 51Blefuscu Island SA 7 2019.6 50


20

Table 2. cont.

Site StateNo.

comparable surveys

Year Pup abundance

Jones Island SA 6 2005.0 15Jones Island SA 6 2007.8 15Jones Island SA 6 2009.2 11Jones Island SA 6 2012.2 12Jones Island SA 6 2013.5 16Jones Island SA 6 2014.8 19Lilliput Island SA 6 2005.2 67Lilliput Island SA 6 2008.2 55Lilliput Island SA 6 2010.8 48Lilliput Island SA 6 2013.8 73Lilliput Island SA 6 2015.3 61Lilliput Island SA 6 2019.6 63Pearson Island SA 5 2003.7 29Pearson Island SA 5 2005.6 35Pearson Island SA 5 2013.6 28Pearson Island SA 5 2015.3 30Pearson Island SA 5 2019.7 32Breakwater/Gliddon SA 4 2005.4 24Breakwater/Gliddon SA 4 2008.2 22Breakwater/Gliddon SA 4 2015.1 27Breakwater/Gliddon SA 4 2018.2 27Lounds Island SA 4 2008.3 34Lounds Island SA 4 2015.3 20Lounds Island SA 4 2018.2 27Lounds Island SA 4 2019.6 30Nicolas Baudin Island SA 4 2006.4 98Nicolas Baudin Island SA 4 2007.9 92Nicolas Baudin Island SA 4 2014.9 63Nicolas Baudin Island SA 4 2019.2 70Purdie Island SA 4 2005.4 132Purdie Island SA 4 2008.3 95Purdie Island SA 4 2015.3 67Purdie Island SA 4 2019.6 74West Island SA 4 2005.4 56West Island SA 4 2008.3 39West Island SA 4 2015.4 20West Island SA 4 2019.6 36


21

Table 2. cont.

Site StateNo.

comparable surveys

Year Pup abundance

Ward Island SA 4 2019.7 42Ward Island SA 4 2006.4 45Ward Island SA 4 2013.6 46Ward Island SA 4 2015.3 44Four Hummocks Islands SA 3 2011.9 20Four Hummocks Islands SA 3 2014.9 10Four Hummocks Islands SA 3 2019.2 10Lewis Island SA 3 2005.9 78Lewis Island SA 3 2013.2 79Lewis Island SA 3 2014.7 83Liguanea Island SA 3 2004.9 41Liguanea Island SA 3 2015.4 25Liguanea Island SA 3 2019.8 27Rocky (North) Island SA 3 2011.9 44Rocky (North) Island SA 3 2013.2 47Rocky (North) Island SA 3 2014.7 36Rocky (South) Island SA 3 2011.9 12Rocky (South) Island SA 3 2014.7 11Rocky (South) Island SA 3 2019.2 6South Neptune Islands SA 3 1970.0 10South Neptune Islands SA 3 2008.1 6South Neptune Islands SA 3 2014.5 4West Waldegrave Island SA 3 2003.5 157West Waldegrave Island SA 3 2013.6 91West Waldegrave Island SA 3 2015.3 89Fisherman Island WA 13 1988.4 43Fisherman Island WA 13 1991.2 42Fisherman Island WA 13 1998.6 66Fisherman Island WA 13 2000.1 43Fisherman Island WA 13 2001.7 44Fisherman Island WA 13 2004.5 47Fisherman Island WA 13 2005.9 39Fisherman Island WA 13 2007.6 66Fisherman Island WA 13 2008.9 50Fisherman Island WA 13 2011.9 36Fisherman Island WA 13 2014.9 48Fisherman Island WA 13 2016.3 37Fisherman Island WA 13 2019.4 40


22

Table 2. cont.

Site StateNo.

comparable surveys

Year Pup abundance

Beagle Island WA 12 1988.4 63Beagle Island WA 12 1991.2 69Beagle Island WA 12 1998.6 73Beagle Island WA 12 2000.1 70Beagle Island WA 12 2003.0 46Beagle Island WA 12 2004.5 57Beagle Island WA 12 2007.4 54Beagle Island WA 12 2008.9 62Beagle Island WA 12 2011.8 83Beagle Island WA 12 2014.9 100Beagle Island WA 12 2016.5 71Beagle Island WA 12 2019.4 57Buller Island WA 12 1989.8 39Buller Island WA 12 1998.6 49Buller Island WA 12 2001.7 46Buller Island WA 12 2004.6 49Buller Island WA 12 2007.6 43Buller Island WA 12 2009.0 39Buller Island WA 12 2010.7 61Buller Island WA 12 2012.0 39Buller Island WA 12 2012.1 39Buller Island WA 12 2015.2 41Buller Island WA 12 2016.6 51Buller Island WA 12 2019.5 44Haul Off Rock WA 4 1989.7 29Haul Off Rock WA 4 2012.9 22Haul Off Rock WA 4 2014.0 22Haul Off Rock WA 4 2016.9 24Kimberley Island WA 3 1992.1 42Kimberley Island WA 3 2008.4 33Kimberley Island WA 3 2014.2 32Red Islet WA 3 1989.7 27Red Islet WA 3 2008.4 27Red Islet WA 3 2017.0 25Six Mile Island WA 3 1991.3 43Six Mile Island WA 3 2000.2 40Six Mile Island WA 3 2017.9 45


23

Table 3. Summary results for the 30 listed Australian sea lion subpopulations with three or more comparable surveys. The year of the earliest and most recent comparable surveys per subpopulation is provided, along with the time-series duration, the number of surveys, and the estimated change in past and present pup abundances over a three-generation period (42.3 years) between 1977.2 (1977) and 2019.5 (2019), including the percentage change and intrinsic rate of increase (r). The mean r, summed pup abundance (1977, 2019) and three-generation change (%) in pup abundances are also provided at the bottom of the table.

Listed Subpopulation StateEarliest survey

Most recent survey

Time series duration (years)

Number of surveys

r Pups 1977 Pups 2019 3 gen change

Blefuscu Island SA 2005.2 2019.6 14.4 7 -0.035 225 51 -77.5%Breakwater/Gliddon SA 2005.4 2018.2 12.7 4 0.014 15 28 79.8%Bunda Cliffs SA 1991.6 2016.1 24.6 10 -0.059 150 12 -91.9%Dangerous Reef SA 1999.5 2019.0 19.4 11 -0.014 639 359 -43.8%Four Hummocks Islands SA 2011.9 2019.2 7.3 3 -0.088 366 9 -97.6%Jones Island SA 2005.0 2014.8 9.8 6 0.017 8 17 109.3%Lewis Island SA 2005.9 2014.7 8.8 3 0.005 67 84 25.5%Liguanea Island SA 2004.9 2019.8 14.9 3 -0.032 95 25 -73.6%Lilliput Island SA 2005.2 2019.6 14.4 6 0.005 52 63 21.5%Lounds Island SA 2008.3 2019.6 11.3 4 -0.015 49 26 -47.7%Nicolas Baudin Island SA 2006.4 2019.2 12.8 4 -0.031 234 63 -73.1%Olive Island SA 2003.5 2019.3 15.8 11 -0.027 286 90 -68.4%Pearson Island SA 2003.7 2019.7 16.0 5 -0.001 32 30 -6.1%Purdie Island SA 2005.4 2019.6 14.2 4 -0.041 375 66 -82.5%Rocky (North) Island SA 2011.9 2014.7 2.9 3 -0.074 615 26 -95.7%Rocky (South) Island SA 2011.9 2019.2 7.3 3 -0.099 396 6 -98.5%Seal Bay SA 2003.4 2019.6 16.2 12 -0.010 253 167 -33.9%Seal Slide SA 2003.4 2018.4 15.0 11 0.011 7 12 58.9%South Neptune Islands SA 1970.0 2014.5 44.5 3 -0.018 9 4 -53.5%The Pages SA 1990.1 2014.5 24.4 15 -0.005 514 418 -18.6%Ward Island SA 2006.4 2019.7 13.3 4 -0.005 53 43 -18.7%West Island SA 1992.0 2019.6 27.6 4 -0.041 149 26 -82.4%West Waldegrave Island SA 2003.5 2015.3 11.8 3 -0.050 584 70 -88.0%Beagle Island WA 1988.4 2019.4 31.0 12 0.004 59 69 16.3%Buller Island WA 1989.8 2019.5 29.7 12 0.001 43 45 6.1%Fisherman Island WA 1988.4 2019.4 31.0 13 -0.004 51 43 -15.1%Haul Off Rock WA 1989.7 2016.9 27.3 4 -0.009 32 22 -32.6%Kimberley Island WA 1992.1 2014.2 22.1 3 -0.013 51 29 -41.9%Red Islet WA 1989.7 2017.0 27.4 3 -0.002 28 25 -9.5%Six Mile Island WA 1991.3 2017.9 26.5 3 0.002 40 44 10.7%South Australia -0.025 5174 1694 -67.2%Western Australia -0.003 304 278 -8.4%Overall -0.020 5477 1973 -64.0%


24

Table 4. Results of a sensitivity analysis for five subpopulations with large pup abundance estimates back-projected to 1977 (Rocky (North), West Waldegrave, Rocky (South), Purdie, and Four Hummocks Islands). Result indicate that the summed pup abundances in 1977 would need to be reduced by 66% in order for the total listed population assessment (Overall decline) to fall below a 50% decline over three generations.

% reduction in 1977

estimateOverall decline

0% -64.0%10% -62.4%20% -60.6%30% -58.7%40% -56.6%50% -54.2%60% -51.6%66% -49.9%


25

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Goldsworthy, S. D., Page, B. and Shaughnessy, P. D. (2010a). Maintaining the monitoring of pup production at key Australian sea lion colonies in South Australia (2009/10). Final report to the Australian Marine Mammal Centre. South Australian Research and Development Institute (Aquatic Sciences), Adelaide. SARDI Publication No. F2010/000665-1. SARDI Research Report Series No. 491.

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