Alternate Foraging Strategies and Population Structure of Adult Female
ALTERNATE FORAGING STRATEGIES AND
POPULATION STRUCTURE OF ADULT FEMALE
AUSTRALIAN SEA LIONS
Andrew D. Lowther BSc (Hons)
Submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
University of Adelaide
Faculty of Science
School of Earth and Environmental Sciences
University of Adelaide
February 2012
TABLE OF CONTENTS
LIST OF TABLES...... VI
LIST OF FIGURES...... VIII
DECLARATION OF ORIGINALITY...... X
STATEMENT OF CONTRIBUTION…………………………………………...XI
DEDICATION...... XII
ACKNOWLEDGEMENTS...... XIII
ABSTRACT...... XVI
CHAPTER 1. GENERAL INTRODUCTION...... 1
IMPORTANCE OF ECOLOGY AND GENETICS IN DEFINING POPULATIONS…………………………………………...... 2
INDIVIDUAL SPECIALISATION IN FORAGING BEHAVIOUR AND POPULATION STRUCTURE...... 4
THE OTARIIDAE……………………………...... 6
AUSTRALIAN SEA LIONS……………………...... 7
NEED FOR RESEARCH………………………………………………………….8
THESIS AIMS...... 10
CHAPTER 2. DETECTION OF ALTERNATE FORAGING STRATEGIES IN AUSTRALIAN SEA LION (NEOPHOCA CINEREA) BREEDING COLONIES USING STABLE ISOTOPE ANALYSIS...... 11
ABSTRACT...... 12
INTRODUCTION...... 13
MATERIALS AND METHODS...... 17
I
Study sites...... 17
Sampling – Adult Female Australian Sea Lions...... 17
Sampling – Pups...... 18
Isotope Analysis...... 19
Data Analysis...... 21
RESULTS...... 23
Adult Female Foraging Behaviour...... 23
Fractionation of Isotopes from Mother to Pup...... 23
Pup Whisker Isotope Ratios and Intracolony Foraging Ecotype
Frequencies...... 25
DISCUSSION...... 26
Pups as Surrogate Measures of Maternal Foraging...... 27
Intra-Colony Segregation of Maternal Foraging Behaviour...... 29
Conclusion...... 32
CHAPTER 3. CREATURES OF HABIT: FORAGING SITE FIDELITY OF ADULT FEMALE AUSTRALIAN SEA LIONS...... 43
ABSTRACT...... 44
INTRODUCTION...... 45 MATERIALS AND METHODS...... 49 Study sites and sampling...... 49
Data analysis...... 51
RESULTS...... 56
Inshore and offshore foraging ecotype designation...... 56
Stability of foraging behaviour over time...... 57
II
Movement patterns of individuals...... 58
DISCUSSION...... 59
Individual long-term specialisation in foraging behaviour...... 61
Ecotype-specific differences in movement behaviour and body condition....63
Seasonality of individual foraging behaviour...... 64
Conclusions...... 66
CHAPTER 4. POPULATION STRUCTURE OF ADULT FEMALE AUSTRALIAN SEA LIONS DRIVEN BY FINE-SCALE FORAGING SITE FIDELITY...... 79
ABSTRACT...... 80
INTRODUCTION...... 81
MATERIALS AND METHODS...... 85
DNA Amplification...... 86
Stable Isotope Analysis...... 87
Data Analysis...... 87
RESULTS...... 90
Mitochondrial DNA control region diversity...... 90
Strong genetic partitioning among colonies...... 91
Alternate foraging behaviours...... 91
Environmental determinants of genetic structure and foraging behaviour....92
DISCUSSION...... 93
Matrilineal population structure is unrelated to broad-scale ecological processes...... 93
III
Is individual niche-width specialisation driving matrilineal population structure ?...... 95
Future Directions...... 98
Conclusions...... 100
CHAPTER 5. GENERAL DISCUSSION...... 111
Defining the degree of habitat specialisations………………………………………..112
Stability of individual foraging specialisation over time………………………………113
Individual specialisation as a mechanism to drive population structure……………115
Do adult female Australian sea lions maintain individual foraging specialisation across generations ?...... 116
The evolution of a unique life history………………………………………………..…118
Future research and conclusions………………………………………………………123
APPENDIX. MATERNAL STRATEGIES OF THE AUSTRALIAN SEA LION (NEOPHOCA CINEREA) AT DANGEROUS REEF, SOUTH AUSTRALIA...... 125
ABSTRACT...... 126
INTRODUCTION...... 127
MATERIALS AND METHODS...... 130
Maternal attendance, female body condition and milk collection...... 131
Milk composition...... 132
Pup growth...... 133
Statistical analysis...... 133
RESULTS...... 135
Maternal attendance...... 135
IV
Milk-lipid content...... 136
Pup growth...... 137
Comparison between Seal Bay and Dangerous Reef...... 139
DISCUSSION...... 139
Maternal Strategy...... 139
Conclusions...... 143
LITERATURE CITED...... 152
V
List of Tables
CHAPTER 2. DETECTION OF ALTERNATE FORAGING STRATEGIES IN AUSTRALIAN SEA LION (NEOPHOCA CINEREA) BREEDING COLONIES USING STABLE ISOTOPE ANALYSIS.
Table 1: Foraging behaviour of 15 adult female Australian sea lions instrumented between January 2008 and July 2009...... 34
Table 2: Mean foraging trip and isotope ratio parameter values for alternate foraging ecotypes of adult female Australian sea lions across all colonies...... 35
Table 3: Mean stable isotope ratios from whiskers and blood serum for adult female Australian sea lions and their pups...... 35
Table 4: Mean isotope values and proportional representation of each ecotype at (a) Lilliput I., (b) Blefuscu I. and (c) Olive I. from pup whiskers...... 36
CHAPTER 3. CREATURES OF HABIT: FORAGING SITE FIDELITY OF ADULT FEMALE AUSTRALIAN SEA LIONS.
Table 1: Colony-level mean δ13 C and δ15 N ratios for serially-subsampled vibrissae from 20 adult female Australian sea lions across the South Australian range of the species...... 68
Supplementary Table 1: Descriptive statistics for a) movement and b) dive behaviour calculated from a randomly-selected foraging trip for 20 adult female Australian sea lions tracked at seven colonies in South Australia between October 2006 and June 2009...... 69
Supplementary Table 2: Adult female Australian sea lion deployment and sampling data. Twenty adult females were sampled between October 2006 and June 2009...... 70
Appendix I: Values for (Seasonal) Autoregressive Integrated Moving Average (SARIMA) models fitted to 14 adult female Australian sea lions with >18 isotope values...... 71
VI
CHAPTER 4. POPULATION STRUCTURE OF ADULT FEMALE AUSTRALIAN SEA LIONS DRIVEN BY FINE-SCALE FORAGING SITE FIDELITY
Table 1: Characterisation of fifteen Australian sea lion breeding colonies in South Australia by foraging ecotype using δ13 C and δ15 N present in pup whiskers...... 102
Appendix 1. Median breeding date of colonies sampled…………………………….103
Appendix 2. Haplotype frequency of colonies sampled throughout the South Australian range………………………………………………………………………………………………104
Appendix 3. Pairwise genetic differences between colonies………………………..105
Appendix 4. Pairwise genetic difference P-values……………………………………106
APPENDIX. MATERNAL STRATEGIES OF THE AUSTRALIAN SEA LION (NEOPHOCA CINEREA) AT DANGEROUS REEF, SOUTH AUSTRALIA
Table 1: Summary of foraging trip duration, attendance bout duration and proportion of time spent ashore for individual lactating Australian sea lions in days...... 145
Table 2: Maternal body length, body condition index and milk-lipid levels of adult female Australian sea lions during the first two months of lactation at Dangerous Reef. ‘-‘ indicate milk samples not obtained...... 146
Table 3: Summary of linear regressions of mass on age for 18 individual pups weighed three or more times at Dangerous Reef during the 2006-07 breeding episode...... 147
Table 4. Comparison of maternal strategy data between Seal Bay (Kangaroo Island) and Dangerous Reef colonies...... 148
VII
List of Figures
CHAPTER 2. DETECTION OF ALTERNATE FORAGING STRATEGIES IN AUSTRALIAN SEA LION (NEOPHOCA CINEREA) BREEDING COLONIES USING STABLE ISOTOPE ANALYSIS.
Figure 1: Map of Australian sea lion ( Neophoca cinerea ) breeding colonies sampled in South Australia from January 2008 to July 2009...... 37
Figure 2: Dendrogram based on optimal clusters selected from the maximum-value Bayesian Information Criteria model of hierarchical agglomerative clustering for means of dive depth, distance from shore at the foraging trip inflection point and whisker δ13 C / δ15 N ratios (leave-one-out cross validated classification success = 93.3%)...... 38
Figure 3: Linear regression of (a) pup whisker and (b) capillary blood serum isotope ratios on maternal values...... 39
Figure 4: Isotope biplots for pups from Lilliput Island (n=43), Blefuscu Island (n=52) and Olive Island (n=97)...... 40
CHAPTER 3. CREATURES OF HABIT: FORAGING SITE FIDELITY OF ADULT FEMALE AUSTRALIAN SEA LIONS.
Figure 1: The endemic Australian sea lion is distributed from Kangaroo Island in South Australian through to The Abrolhos Islands in Western Australia...... 72
Figure 2: Dendrogram displaying alternate foraging ecotypes of 20 adult female Australian sea lions sampled between June 2006 and Jun 2009 at seven breeding colonies in South Australia...... 73
Figure 3: Representative tracking data from 20 adult female Australian sea lions tracked at a) The Nuyts Archipelago b) West Waldegrave c) Liguanea Island and d) The Pages Islands and Seal Slide...... 74
VIII
Figure 4: δ13 C and δ15 N isotope plots for serially subsampled adult female Australian sea lion vibrissae (3mm segments) from (a) Lilliput Is., (b) Blefuscu Is., (c) Olive Is., (d) West Waldegrave,(e) Liguanea, (f) Seal Slide and (g) The Pages Is...... 76
CHAPTER 4. POPULATION STRUCTURE OF ADULT FEMALE AUSTRALIAN SEA LIONS DRIVEN BY FINE-SCALE FORAGING SITE FIDELITY.
Figure 1: (a) Locations of sampled ( N=17, pie charts) and unsampled ( N=6, green circles) Australian sea lion breeding colonies along the South Australian coast. (b) Genetic Landscape Surface (GLS) of individual pairwise genetic distances...... 107
Figure 2: Plot of Slatkins Linearised Φst on geographic distance...... 109
Figure 3: Isotope biplots of δ13 C and δ15 N values for 15 colonies sampled highlighting inter-colony variation in the expression of alternate foraging ecotypes...... 110
APPENDIX. MATERNAL STRATEGIES OF THE AUSTRALIAN SEA LION (NEOPHOCA CINEREA) AT DANGEROUS REEF, SOUTH AUSTRALIA
Figure 1: Location of the current study at Dangerous Reef in the Spencer Gulf, and Seal Bay (Kangaroo Island) where a similar study was conducted 16 years earlier. Bathymetry to 100m is shown. Depth increases rapidly towards 100m in the immediate vicinity of Seal Bay whereas potential foraging habitat within the Spencer Gulf rarely exceeds 40m...... 149
Figure 2: Plots of estimated milk-lipid levels among female Australian sea lions against adult female a) length and b) mass. Milk-lipid levels were significantly negatively related to maternal length and mass...... 150
Figure 3: Plots of (a) estimated pup growth rates and (b) mass at 90 days against predicted values generated by GLM modeling of maternal length and proportion of time spent ashore. Maternal length and the proportion of time spent ashore positively influence pup growth rates and masses at 90 days...... 151
IX
Declaration of originality
In presenting this thesis in fulfilment of the requirement for doctoral degree at the
University of Adelaide, I affirm that this work is original and has not been submitted for a higher degree to any other university or institution and consent for this thesis to be made available to the University of Adelaide library, subject to the provisions of the Copyright Act 1968. The following summarises my contribution to the publications involving data analysis included in this thesis.
Signature: Date: 24/1/2012
Mr Andrew Lowther
X
Statement of contribution
Chapter 2: Detecting alternate foraging ecotypes in Australian sea lion (Neophoca cinerea) colonies using stable isotope analysis.
Lowther, A.D. and Goldsworthy, S.D. (2010). Detecting alternate foraging ecotypes in Australian sea lion (Neophoca cinerea) colonies using stable isotope analysis. Marine Mammal Science 27(3): 567-586
Conception: 90%, Data Collection: 100%, Analysis: 100%, Writing: 95%
Chapter 3: Creatures of habit: foraging habitat fidelity of adult female Australian sea lions.
Lowther, A.D., Harcourt, R.G., Hamer, D. and Goldsworthy, S.D. (accepted). Creatures of habit: foraging habitat fidelity of adult female Australian sea lions. Marine Ecology Progress Series.
Conception: 100%, Data Collection: 90%, Analysis: 100%, Writing: 95%
Chapter 4: Population structure of adult female Australian sea lions is driven by fine scale foraging site fidelity.
Lowther, A.D., Harcourt, R.G., Goldsworthy, S.D. and Stow, A. (accepted). Population structure of adult female Australian sea lions is driven by fine scale foraging site fidelity. Animal Behaviour .
Conception: 90%, Data Collection: 90%, Analysis: 100%, Writing: 95%
Appendix I: Maternal strategies of the Australian sea lion Neophoca cinerea at Dangerous Reef, South Australia.
Lowther, A.D. and Goldsworthy, S.D. (2011). Maternal Strategies of the Australian sea lion at Dangerous Reef, South Australia. Australian Journal of Zoology 59: 54-62.
Conception: 70%, Data Collection: 100%, Analysis: 100%, Writing: 80%
Co-authorship was offered to people who assisted me with this project. S. Goldsworthy was involved in the initial conception of this research and provided general supervision. R. Harcourt and A. Stow provided general supervision and commented of manuscript drafts. D. Hamer provided a subset of telemetry data for Chapter 3. By signing this declaration, all co-authors give their consent to allow each published or accepted paper to be included in this thesis.
Signature: Date: 16/11/11 A/Prof Simon Goldsworthy
Signature: Date: 16/11/11 Prof Robert Harcourt
Signature: Date: 16/11/11 Dr Adam Stow
Signature: Date: 16/11/11 Mr Derek Hamer
XI
Dedication
To Brian, Jemma, Charlie, Jessica and Jimmy. My family who silently put up with my
seven year journey of reinvention and gave me their unconditional support
throughout. I am sorry for what this has done.
XII
Acknowledgements
This body of work started out as a neatly conceptualised plan to be completed in 3,5 years. As Sun Tzu states in his book ‘The Art of War’, ‘no plan ever survives first contact’. The unique and enigmatic animal I chose to study ensured not only the initial plan but also plans B, C and D fell by the wayside. As Australian sea lions all along the southern coastline of Australia battered my efforts to understand them, the support and guidance of several people kept me true to the cause. Although I will list a few here, there are doubtless many who I will fail to mention – my omissions are accidental and unintentional.
Firstly to my principal supervisor Associate Professor Simon Goldsworthy; you left me be when I needed to figure things out but you offered direction and ‘out-of-the- box’ ideas when I hit the wall. The support you offered in securing funding, establishing the list of contacts I needed to get through the mammoth amount of fieldwork required to complete this work and your positive and constructive criticism of manuscript drafts will be forever appreciated. To Professor Rob Harcourt; as well as the timely and insightful advice on manuscript drafts, your involvement in various aspects of fieldwork ranging from recapturing GPS transmitters to posing in photos on Western Australian roadsides will always be remembered. To Professor Stephen Donnellan and Dr Adam Stow; molecular ecology is as much an art-of-interpretation as it is a science, and your guidance and input on all things genetics were greatly appreciated.
My sincere and heartfelt gratitude to the staff of the Department of Environment and Natural Resources all along the Spencer Gulf and western Eyre Peninsula: Brett Dalzell, Robbie Sleep and Mark Anderson in particular were fundamental in making this work possible. The Ceduna State Emergency Services Marine Rescue unit headed up by the unflappable and salty Peter Codrington were invaluable particularly when nobody else would come out to remove us from distant islands in questionable seas. Andrew Geering and Airborne Solutions gave me the ability to access islands that would have otherwise remained unsampled.
XIII
Fieldwork is not a one-person job; Kristian Peters, Ian Jupp, Sam Blight, Carly Wishart, Magali Agreil, Andrew Dawson, Kym Collins, Heidi Ahonen, Ben Pitcher and many many others made this body of work possible.
The Australian sea lions made every effort to keep their secrets hidden and without their unwitting cooperation we would still be none-the-wiser. There are several people who without their guidance and training I would have been hopelessly unprepared for the vagaries of Australian sea lion work. Al Baylis, for teaching me how to milk a seal. Derek Hamer for the technicalities of animal capture and restraint, as well as which brandy to take into the field. Kristian Peters, for a wide range of expertise; from how much glue is the right amount to use on a transmitter to how best to cook abalone.
The organisations and funding bodies were key to making this research possible all deserve thanking. SARDI Aquatic Sciences, the University of Adelaide, the Australian Marine Mammal Centre, Holsworth Wildlife Research Endowment, Wildlife Conservation Fund and Sea World Research and Rescue Foundation all contributed to the study of an Australian icon.
Although I was immersed in Australian sea lions continuously for the best part of five years I never lost sight of their unique attributes and enigmatic characters. They are still the most attractive otariid out there, and they still have many secrets left to discover.
XIV
“Caveo !Hic Svnt Leones ”
[“Caveo ! Hic sunt leones” is a Latin expression and literally means ‘Beware ! Here be lions’. It can be found written on uncharted territories of old maps and was used by the early geographers for unknown and unexplored territories they imagined as populated by savages, wild animals and other dangers. It was generally placed at the edges of their known world.]
XV
ABSTRACT
Otariid seals pose several challenges in collecting behavioural data due because of the geographical decoupling of breeding habitat (terrestrial) and foraging grounds
(aquatic). Additionally, life history traits such as breeding chronology and moult state constrain the temporal window over which data can be collected. Expensive telemetry devices coupled with logistical difficulties and the biology of animals often limits the ability to answer questions concerning the role of seasonal or life-history differences in foraging ecology. However, quantifying individual behavioural specialisations such as foraging behaviour that may impede gene flow is an essential prerequisite to understanding population structure.
Several unusual life history traits of the IUCN Red Listed (threatened) Australian sea lion Neophoca cinerea are interesting from an ecological, evolutionary and conservation standpoint. Australian sea lions are endemic to the southern and western coastline of Australia, distributed over approximately 70 breeding colonies with 86% of all breeding occurring in South Australia. Most notably, a unique
17.5month breeding chronology and asynchronous breeding between spatially-close colonies has been proposed to be an adaptive response to a low-quality foraging environment that has little or no seasonality. Contrary to this, recent studies suggest at least the South Australian range of the species is a region replete with mesoscale areas of seasonally rich productivity, further supported by the presence of large numbers of other top marine predators and the largest volume fishery in Australia. In the face of this contradiction, the ecological determinants of population structure clearly have yet to be identified. To address these key knowledge gaps, this study explored the population structure and foraging behaviour of adult female Australian
XVI
sea lions at seventeen of the largest breeding colonies across the South Australian range.
A novel screening technique which employed δ13 C and δ15 N present in the whiskers
of wholly milk-dependent pups was developed and validated as a proxy for maternal
values. Mother-to-pup fractionation was quantified, with a δ15 N enrichment of
+1.92 ‰ (blood) and +1.27 ‰ (vibrissae). Subsequent large-scale sampling of >50%
of all pups born on each colony suggested a degree of ecological partitioning over a
spatial scale never previously described for otariids.
Isotope ratios of serially-subsampled vibrissae from 20 adult female Australian sea
lions across seven breeding colonies revealed individual long-term temporal
consistency in both foraging site (offshore vs inshore) and prey selection.
Furthermore, dive and movement parameters did not differ between alternate
foraging ecotypes, suggesting that the alternate foraging strategies of adult female
Australian sea lions were temporally persistent and unrelated to phenotypic variation.
Finally, individual foraging specialisation and matrilineal pop ulation structure were
integrated by obtaining isotope and mitochondrial DNA (mtDNA) samples from over
50% of all pups born at each of 17 breeding colonies in order to characterise
foraging ecotype composition and matrilineal population structure. Alternate foraging
ecotypes were present at almost every colony with no evidence to support particular
mitochondrial lineages were restricted to specific foraging ecotypes. Spatial analysis
of molecular data identified three putatitive clusters of colonies that share multiple
haplotypes being interspersed with 9 singleton breeding sites, with genetic structure
becoming more apparent at colonies closer to deep water. Colonies with little or no
similarity in mtDNA haplotype composition were more likely to breed asynchronously
XVII
irrespective of the geographic distance between them. Contrary to expectations, the propensity for individual animals to forage offshore was unrelated to the proximity of breeding colonies to deep water.
This study adds considerable knowledge to our current understanding of Australian sea lion foraging behaviour and highlights the inappropriateness of making broad- scale inferences about foraging ecology or population structure in this species.
Individual foraging specialisation may act as an ecological barrier to migration, with individuals only able to disperse to colonies within range of preferred fine-scale foraging habitat where they have appropriate hunting skills.
XVIII Chapter 1: General Introduction
CHAPTER 1
GENERAL INTRODUCTION
______
1
Chapter 1: General Introduction
Importance of ecology and genetics in defining populations
Increasing our ecological knowledge and subsequent conservation management of species relies on the development of robust, effective population management units
(MU) which typically requires the delineation of discrete population groups (Moritz
2002). Historically, the a priori definition of populations required for analysis of spatial molecular data neglected to account for how organisms interact with their environment and often failed to detect cryptic boundaries to gene-flow (Manel et al. 2003).
Subsequently, traditional explanations of population genetic structure that incorporate broad-scale geographical or historical processes may not be sufficient to explain the complex population structures observed in large, mobile apex predators capable of dispersing over large geographic ranges. Thus the importance of ecological and environmental factors in shaping population structure has been reflected in the recent alteration of the MU concept to incorporate non-genetic data (Crandall et al. 2000).
Characterising how patterns of gene flow are influenced by environmental and ecological processes requires detailed knowledge of the interaction of a species with its landscape. The emergence of landscape genetics, a field integrating molecular and landscape ecological data using spatial analytical techniques (Manel et al. 2003) has been used to identify key ecological determinants of population structure. The investigation of non-genetic influences on population structuring typically involves linking geospatial information on the movement and behaviour of animals (from radio telemetry devices or GPS collars) to variables such as (but not limited to) prey abundance, habitat heterogeneity, topography, intersexual differences in reproductive ecology, ontogeny and sociality (Hays et al. 2003, Cronin and McConnell 2008).
2
Chapter 1: General Introduction
Traditionally, this approach was used to characterise the area in which a population can sustain itself by collecting data from a subset of individuals at differing life history stages.
The application of this approach has identified the importance of year-round snow cover to the wolverine Gulo gulo and the effects of different habitat types on the dispersal of the California tiger salamander Ambystoma californiense (Rendell and Whitehead 2001,
Rossiter et al. 2002). Munoz-Fuentes et al. (2009) suggested a break in habitat continuity explained why Grey wolves ( Canis lupus ) in coastal areas of British Columbia remain genetically distinct from wolves in inland areas even though they have the dispersive abilities to interbreed. Ecologically-driven population structuring has been observed in other wolf populations (Musiani et al. 2007) and large cats such as the
Puma Puma concolor and Panther P. concolor coryi (Comiskey et al. 2002, McRae et al. 2005).
Once the habitat range of a species or population had been identified in this manner all other conspecifics are typically treated as ecological equivalents (Bolnick et al. 2003).
Applying such a population-level approach to species may have important ramifications from an evolutionary and conservation standpoint if the utilisation of space by individuals falls outside the broadly-characterised population niche-range. A dramatic increase in computing power has facilitated the integration of analytically-intensive probability-based statistical methods (e.g. Bayesian approaches and coalescent theory) into population studies to identify important processes and localised adaptations at even finer scales. Now it is possible to integrate large ecological, environmental and molecular datasets to identify cryptic barriers to gene flow within and among populations
3
Chapter 1: General Introduction
(Manel et al. 2003) and the relative importance of intrapopulation variation in key behaviours.
Individual specialisation in foraging behaviour and population structure
There is a growing body of literature that suggests how individuals spatially and temporally utilise habitat is of great ecological, environmental and conservation importance (Bolnick et al. 2003). Individual foraging specialisation has been recorded in several species of marine birds and mammals (Bearhop et al. 2006, Cherel et al. 2007,
Bailey et al. 2009a, Cherel et al. 2009, Carroll et al. 2011) with two contrasting approaches emerging to characterise how individual specialisation compares to the population-level niche. Sargeant (2007) terms organisms from these different approaches as either “niche width” or “niche overlap” specialists. The former, such as macaroni penguins (Eudyptes chrysolophus ) and Antarctic fur seals ( Arctocephalus gazella ) occupy niches that are a subset of the population niche (Cherel et al. 2007,
Cherel et al. 2009). In contrast, California sea otters ( Enhyrda lutris nereis ) are niche overlap specialists who exhibit rare or novel foraging behaviours with respect to the population niche (Richardson et al. 1986). The manner in which individual specialisation is manifested can have profound effects on population dynamics. Indeed, although few incidents of sympatric speciation have been documented many proposed cases have been preceded by individual specialisation (Malme et al. 1988).
From an evolutionary perspective, the extent of variation in behaviours under selective pressure may provide a metric of ecological diversity and subsequent evolutionary potential. There are many proximate causes of individual specialisation including (but
4
Chapter 1: General Introduction not limited to) habitat heterogeneity, differences in individual phenotype and social interactions with conspecifics (Richardson and Würsig 1997, Helweg et al. 1998).
Individuals are predicted to increase their niche-width until constrained by some cost
(Sargeant 2007). Accessibility to heterogeneously distributed suitable foraging habitat may force individuals to develop specialist foraging techniques in localised environments that may be suboptimal if employed elsewhere. Alternatively, phenotypic differences between individuals may mean individuals experience better payoffs for exploiting a specific subset of the population habitat (Helweg et al. 1998, Bolnick et al.
2003). Selective pressures can also act strongly on individual specialisation, with selection favouring those individuals who reduce the amount of niche-overlap with conspecifics, provided they remain a minority (frequency-dependent selection).
Additionally, if there is sufficient inter-individual niche-width specialisation, selection may favour specialist over those who are generalist or ‘intermediate’ (disruptive selection).
Selective and competitive pressures operate in markedly different ways on populations depending upon whether individual specialisation is ephemeral, stochastic or temporally-fixed (Bolnick et al. 2003). Individuals who maintain niche-width specialisation over long timescales may be adapted to exploiting their niche more efficiently but are vulnerable to change as they are less able to adapt to new resources when their preferred habitat becomes rare or is no longer able to sustain them.
Conversely, individuals who are generalists or seasonal specialists are used to altering prey capture techniques and are unlikely to be exposed to selection in the same way
(Bolnick et al. 2003, Sargeant 2005).
5
Chapter 1: General Introduction
The otariidae
The key challenge faced by one group of highly-vagile marine mammals, the otariid seals, is the acquisition of food. Constantly addressing the challenges presented by geographic decoupling of breeding (terrestrial) and foraging (aquatic) areas, otariids are constrained by the amount of time they can spend away from dependent offspring and must rely on some form of predictable prey distribution (Gende and Sigler 2006, Willson and Womble 2006, Thayer and Sydeman 2007, Bost et al. 2008). In dynamic, heterogenous environments such as ocean ecosystems investigating the foraging behaviour of otariid seals typically involves the use of expensive geospatial tracking devices and time-depth recorders to describe three-dimensional movements during foraging trips to sea. When trying to characterise individual variation in foraging behaviour, researchers face a further challenge as otariid seals undergo seasonal molts which restricts when tracking devices can be attached and how long they are likely to remain on the animal, thereby defining the onset and duration of the temporal window over which tracking can occur. Thus, prohibitive costs, logistical difficulties and the biology of animals often limits answering questions of seasonal, intersexual and ontogenetic differences in foraging to those that may be answered using data from a small subsample of animals (Hays et al. 2003, Cronin and McConnell 2008).
There is a growing body of literature that suggests individual niche-width specialisation is of great importance in fur seals and sea lions (Otariidae) (Thompson et al. 1998,
Staniland et al. 2004, Chilvers et al. 2005). Therefore to understand how individuals interact with their environment and the subsequent impact on geneflow within and between populations it is crucial to determine what and for how long animals utilise
6
Chapter 1: General Introduction certain habitat types (Ryall and Fahrig 2006). When a large portion of an individuals’ foraging behaviour remains cryptic as is the case with fur seals and sea lions, modelling the impact of individual specialisation on population structure is problematic (Kaschner et al. 2006).
Recently, the use of isotope biochemistry has provided a greater insight into the foraging ecology across a range of mammals. There is strong support for the upward- cascade of stable nitrogen isotopes from primary producers to the top of the food web
(Newsome et al. in press). In marine ecosystems a decreasing stable carbon ratio gradient has been identified with distance from shore (Rounick and Winterbourn 1986,
Cherel and Hobson 2007). These ecological processes have been used in isotopically- derived foraging behaviour models of marine predators. Stable isotopes of carbon
(δ13 C) and nitrogen ( δ15 N) present in metabolically inert tissue (e.g., vibrissae, feathers, hair) have been used in several seal and marine bird species to identify variation in foraging location and trophic level respectively (Forero and Hobson 2003, Aurioles et al.
2006, Bearhop et al. 2006, Awkerman et al. 2007, Cherel et al. 2007, Huckstadt et al.
2007).
The Australian sea lion
The biogeography and unique reproductive ecology of the Australian sea lion
(Neophoca cinerea ) make the study of population structure challenging in this species.
Distributed from The Pages Islands in South Australia to the Houtman Abrohlos Islands in Western Australia, the Australian sea lion is endemic to Australia and listed under
State, Federal and International protective legislature (‘ rare ’ – South Australia National
7
Chapter 1: General Introduction
Parks & Wildlife Act (1972); ‘ threatened (vulnerable) ’-Environmental Protection and
Biodiversity Conservation Act (1999); ‘ endangered ’ – International Union for the
Conservation of Nature Red List). The species is one of the rarest otariid seals in the world with a species census estimate of ~14,000 animals distributed across approximately 80 colonies of which most produce less than 50 pups (Shaughnessy et al. 2011). Australian sea lions have a protracted, aseasonal breeding pattern
(~17.5mo) that is temporally asynchronous across its range which is unique amongst pinnipeds where annual synchronous breeding is the norm (Higgins 1993, Higgins and
Gass 1993, Atkinson 1997, Gales and Costa 1997). The ecological determinants of such a breeding cycle remain unclear however it has been suggested that a proximate cause may be reliance on fine-scale foraging habitat by adult female Australian sea lions (Campbell et al. 2008). The distance over which breeding asynchrony occurs
(<30km) is well within the dispersal abilities of individual adult females (Fowler et al.
2007) and may suggest some form of localised ecological variation sufficiently large enough to generate population subdivision. Indeed, limited genetic data on the species suggests some of the most extreme levels of female natal site philopatry seen in otariids which may be related to fine-scale adaptations in foraging behaviour (Campbell et al.
2008).
Need for research
In conjunction with a post-sealing range expansion, Australian and New Zealand fur seals have increased in population size by an order of magnitude (Kirkwood et al. 2009,
Kirkwood et al. 2010). There appears no evidence of similar recolonisation events by
Australian sea lions and limited demographic data on Australian sea lion colonies
8
Chapter 1: General Introduction suggest at best limited population growth and in some cases population decline
(Goldsworthy et al. 2010).
In their review of Australian sea lion life history, Gales and Costa (1997) proposed a benthic, seasonally influenced foraging strategy adapted to an oligotrophic seascape.
Delayed foetal implantation and gestation, prolonged lactation and one of the lowest milk-fat contents of any otariid were further suggested to reflect an energy-minimizing strategy aimed at buffering against an aseasonal, low production environment.
Disturbingly, prior to this work commencing, there were only four peer-reviewed articles on the foraging movements of Australian sea lions (Costa and Gales 2003, Fowler et al.
2006, Fowler et al. 2007, Baylis et al. 2009). Only one of these (Baylis et al. 2009) described individual variation in foraging behaviour of adult female Australian sea lions at one colony using quantitative fatty-acid signature analysis of milk. However there has been no attempt to characterise the degree, significance or stability over time of adult female Australian sea lion foraging behaviour. Furthermore, given over 80% of the species resides at 39 breeding colonies in South Australian state waters, the single molecular study conducted on Australian sea lions covered only three colonies and lacked any ecological data (Campbell et al. 2008).
Research conducted so far has been insufficient to robustly describe what key ecological factors govern gene flow and the role of individual specialisation, though several testable hypotheses have been generated. The lack of range expansion and recovery combined with the impact of accidental bycatch within somefishing industries operating in South Australian waters has provided the impetus for research into the foraging ecology and population structure of Australian sea lions.
9
Chapter 1: General Introduction
Thesis aims
This thesis focuses on characterising the existence and stability of individual foraging specialisation in Australian sea lion adult females at breeding colonies across their
South Australian range as a precursor to exploring what ecological and environmental factors influence matrilineal population structure. To achieve this, two interdependent assumptions regarding the degree and stability of individual foraging specialisation present within and between colonies needed to be checked. Firstly, I determined the veracity of using stable isotope ratios of carbon and nitrogen present in wholly-milk dependent offspring as accurate and cost-effective proxies for large-scale screening of maternal values. Secondly I utilised isotope ratios in adult female vibrissae to assess the stability of foraging behaviour over time. The ultimate goal of this thesis was to characterise what ecological factors influenced individual adult female foraging specialisation, gene flow and matrilineal population structure within and amongst
Australian sea lion breeding colonies across their South Australian range.
10
Chapter 2: Foraging ecotypes
CHAPTER 2
DETECTING ALTERNATE FORAGING ECOTYPES IN
AUSTRALIAN SEA LION ( NEOPHOCA CINEREA )
COLONIES USING STABLE ISOTOPE ANALYSIS
______
Lowther, A.D. and Goldsworthy, S.D.
University of Adelaide / South Australian Research and Development Institute
Published as: Lowther, A.D. and Goldsworthy, S.D. (2010). Detecting alternate foraging ecotypes in Australian sea lion ( Neophoca cinerea ) colonies using stable isotope analysis. Marine Mammal Science 27 (3): 567-586.
11
Chapter 2: Foraging ecotypes
Abstract
Stable carbon ( δ13 C) and nitrogen ( δ15 N) isotopes are used frequently to describe the trophic ecology of top marine mammal predators. Australian sea lions ( Neophoca cinerea ) are one of the world's rarest otariid seals and exhibit the highest levels of natal site philopatry of any seal. We report the development of a screening technique to identify different foraging ecotypes and assess their relative frequencies in Australian sea lion breeding colonies using stable isotope ratios in pups. Geospatial and dive data from fifteen adult females at three breeding colonies revealed alternate foraging strategies (inshore and offshore foraging) that were reflected in significant changes in
δ13 C and δ15 N. Isotope fractionation from mother to pup was validated using paired whisker and blood serum samples with no significant difference between δ13 C and δ15 N enrichment of +1.27‰ (whiskers) and +1.92‰ (blood serum) from mothers to pups.
Isotope ratios from whisker samples representing over 50% of pups born at three colonies revealed significant inter- colony differences in maternal foraging ecotype frequencies. These results are unique in that ecological partitioning over such a small spatial scale has not been described in any other otariid species.
12
Chapter 2: Foraging ecotypes
Introduction
The use of isotope biochemistry has provided a greater insight into the foraging ecology of a range of mammals (see Crawford et al. 2008 for review). There is strong support for the upward-cascade of stable nitrogen isotopes from primary producers to the top of marine food webs,(Hobson et al. 1996). Similarly, trophic level enrichment of stable carbon isotopes occurs though in a less predictable manner (Post 2002). In marine ecosystems a decreasing stable carbon ratio gradient has been identified with distance from shore as the influx of terrestrial organic material gives way to recycling of inorganic carbon (Rounick and Winterbourn 1986, Cherel and Hobson 2007). These ecological processes have been used to generate isotopically-derived foraging behavior models in many marine mammals. The type of tissue used for stable isotope analysis can also determine the timescale over which dietary information can be examined. Variation in isotope turnover rates of metabolically active tissues reflect dietary history over relatively short periods of time. Conversely, tissues that are inert after depositon can provide a timeline of feeding history (Crawford et al. 2008). Stable isotopes of carbon
(δ13 C) and nitrogen ( δ15 N) present in metabolically inert tissue ( e.g , vibrissae, feathers, hair) have been used in several seal and marine bird species to identify broad temporal and ontogenetic variation in foraging location and trophic level (Forero and Hobson
2003, Aurioles et al. 2006, Bearhop et al. 2006, Awkerman et al. 2007, Cherel and
Hobson 2007, Huckstadt et al. 2007).
One group of highly-vagile marine mammals, the otariid seals, present challenges to examining non-genetic effects on population structuring. As income breeders (Boyd
2000) otariids are phylogenetically constrained to breeding and nursing on land yet
13
Chapter 2: Foraging ecotypes must forage at sea. Acquiring geospatial data on otariid seals involves the use of expensive satellite transmitters, archival GPS loggers and time-and-depth recorders
(TDR's) often limiting studies to answering narrow questions of seasonal, intersexual and ontogenetic differences in foraging behavior of the individuals tracked (Hays et al.
2003, Cronin and McConnell 2008). A major limitation of such tracking studies is the implicit assumption that the behavior of tracked animals is representative of the broader population (McKelvey and Pearson 2001, Bolnick et al. 2003, Hay 2003).
Stable isotope analysis is becoming increasingly popular as a relatively inexpensive method to extend our understanding of pinniped behavior. Intersexual variation in foraging location has been detected in southern elephant seals ( Mirounga leonina ) using isotope ratios in whiskers (Lewis et al. 2006). Isotope ratios in blood from northern fur seals ( Callorhinus ursinus ) and whiskers from adult female leopard seals
(Hydrurga leptonyx ) have been used to identify seasonal variation in feeding grounds and prey selection (Kurle and Worthy 2001, Hall-Aspland et al. 2005b). Hair from pups has been used in Californian sea lions ( Zalophus californianus ) to detect spatial variability of prey distribution (Porras-Peters et al. 2008) whilst stable isotope variation in blood samples detected intra-specific niche width differences in the southern elephant seal (Ducatez et al. 2008). Adult male Antarctic fur seal ( Arctocephalus gazella ) whiskers have been used to described inter-individual and inter-seasonal variation in prey selection and foraging location (Cherel et al. 2009). Similarly, whiskers of wholly milk-dependent pups have been used as surrogate data to assess inter-colony variation in maternal foraging behavior of Galapagos sea lions (Zalophus wollebaeki ) (Wolf et al.
2008) and northern elephant seals ( Mirounga angustirostris )(Aurioles et al. 2006)With
14
Chapter 2: Foraging ecotypes the exception of Ducatez et al. (2008) the assumption that pup isotope values accurately reflected maternal values was not tested. To our knowledge there has been no validation that different isotope ratios reflect geographic variation in foraging location of otariids by using telemetry data.
The biogeography and unique reproductive ecology of the Australian sea lion
(Neophoca cinerea ) make the study of population structure challenging in this species.
Distributed from The Pages Islands in South Australia to the Houtman Abrohlos Islands in Western Australia (Figure 1), they are one of the rarest otariid species in the world.
The species census estimate is 13,000 animals distributed across approximately 80 colonies of which most produce less than 50 pups (Goldsworthy et al. 2010). Limited molecular data on the species suggests some of the most extreme levels of female natal site philopatry seen in otariids (Campbell et al. 2008). The levels of genetic population structuring seen in Australian sea lions appears driven by a non-annual asynchronous breeding cycle (Gales and Costa 1997) though the ecological determinants of such a breeding cycle remain unclear. Interestingly, the unique breeding cycle of 17.5 months is asynchronous between colonies separated by as little as 30km of water (Higgins 1993, Higgins and Gass 1993, Gales and Costa 1997). This distance is well within the dispersal abilities of individual animals (Fowler et al. 2007) and may suggest some form of localized ecological variation sufficiently large enough to generate population subdivision.
The importance of determining the presence of fine scale population structure in the
Australian sea lion has been emphasised recently by the possible threat that demersal gillnet fisheries pose to the species (Goldsworthy and Page 2007, Goldsworthy et al.
15
Chapter 2: Foraging ecotypes
2010). In order to provide further data we report on the development, validation and use of a novel screening technique using the stable isotope ratios in the whiskers of wholly milk-dependent pups to characterise the degree and significance of fine-scale geographic variation in maternal foraging behavior. Utilising readily available pups allows a more cost-effective and rigorous examination of inter-individual level variation in maternal foraging behavior in comparison with expensive satellite telemetry devices.
Essentially our approach is based on the assumption that pup and maternal isotope levels will show correlation, the nature of which may depend on several factors. As the maternal milk-dependent pups are feeding on their mothers, a trophic level of enrichment is often assumed (Aurioles et al. 2006, Wolf et al. 2008). Recent studies have suggested that offspring may catabolise stable nitrogen in body reserves during periods of physiological stress (such as moulting and nutritional stress) which reduce expected trophic-level enrichment (Jenkins et al. 2001, Sare et al. 2005a). Conversely otariid milk is rich in stable carbon-depleted lipids (Stegall et al. 2008), which may result in offspring δ13 C showing little or no difference from maternal values. After comparing isotopic correlation between mother and offspring in 11 species, Jenkins et al. (2001) recommended species-specific validation of the enrichment process before using offspring samples to infer maternal processes.
To identify differences in inter-individual foraging behavior both within and among breeding colonies, our study had three interdependent aims. Firstly, we tested whether inter-individual variation in adult female Australian sea lion foraging behavior can be characterized from stable isotope ratios of carbon and nitrogen. Secondly we tested the assumption that the isotopic relationship between adult females and their pups is
16
Chapter 2: Foraging ecotypes sufficiently robust to use pup whiskers as surrogate data for maternal foraging behavior.
Thirdly, as the ultimate function of screening colonies is to assess whether inter- individual variation translates to inter-population variation characterised by the presence of groups of individuals that exhibit similar feeding behaviours (henceforth referred to as foraging ecotypes), we report on the screening of three closely-grouped, asynchronously-breeding Australian sea lion colonies using stable isotopes present in pup whiskers.
Materials and Methods
Study sites
Adult females and pups from three breeding colonies in South Australia were sampled
(Figure 1). Australian sea lions breed synchronously on Blefuscu and Lilliput Islands which are separated by ~5km in the Nuyts Archipelago but breed asynchronously with
Olive Island approximately 45km to the south-east. Sampling of pups took place between January 2008 (Lilliput and Blefuscu Islands) and February 2009 (Olive Island) as each colony entered a breeding episode.
Sampling - adult female Australian sea lions
Fifteen adult females from Lilliput, Blefuscu and Olive Islands were GPS tracked and sampled. Females were captured, restrained and anaesthetised using isofluorane (5% induction, 0.5-3% maintenance) (Veterinary Companies of Australia, Artarmon, New
South Wales) delivered through a portable gas anaesthesia machine. Capillary refill, breathing rate and tactile response were used to monitor animals during anaesthesia.
After anaesthetic induction the animal was secured on a stretcher and suspended from
17
Chapter 2: Foraging ecotypes a 100 x0.5kg spring balance (Salter Weigh-Tronix, Blackburn, Victoria, Australia.).
Standard length and axillary girth measurements were recorded and a whisker was sampled by clipping at its base. Blood samples (10 mL) were taken from the caudal gluteal vein of 14 adult females and stored in standard serum tubes. Anticoagulants were not used to avoid altering isotope levels (Drago et al. 2009). A further sample
(~5ml) was taken from a biopsy site on the hind flipper to examine possible variation in isotope ratios between venous and capillary blood (Kraft et al. 2008). Blood serum was removed by allowing whole blood to coagulate and centrifuging within one hour of collection(Drago et al. 2009). Serum was stored in 20ml tubes with 70% ETOH until analysis (Hobson et al. 1997). Archival fastloc GPS logging devices (Sirtrack Ltd,
Havelock North, New Zealand) and time-depth recorders (mk9 TDR) (Wildlife
Computers) sampling at 15 min and 2 m intervals respectively were attached to the pelage distal to the midpoint on the back of each adult female using two-part epoxy glue
(RS Components Pty Ltd). Devices were left attached for at least one foraging trip
(determined either by observed departure and arrival or attendance with and subsequent absence from their pup) whereupon adult females were recaptured and the devices removed.
Sampling - pups
Pups representing 61%, 62% and 56% of estimated pup production were sampled at
Lilliput, Blefuscu and Olive Islands (n=43, 52 and 97 respectively) (Goldsworthy et al.
2010). Sampled pups were < 2months old and were still in their natal pelage. Pups were captured with the aid of a hoop net, manually restrained, sexed and measured
(standard length, straight-line length from tip-of-nose to tip-of-tail, to nearest 0.5 cm). A
18
Chapter 2: Foraging ecotypes whisker from the rear right of the muzzle was sampled by clipping it at the base. When a pup of a sampled adult female was captured (n=14), 2-3 mL of blood was taken from a biopsy site on the hind flipper and stored as for adult females.
Isotope analysis
All whiskers were cleaned in a method similar to that used for human hair (O'Connell and Hedges 1999) and southern elephant seal ( Mirounga leonina ) whiskers. Briefly, whiskers were cleaned individually for 20 min with de-ionised water then with a solution of 2:1 methanol:chloroform for a further 20 min to remove lipids, followed by a final clean with de-ionised water for an additional 20 min. Each whisker was checked under a stereomicroscope for any remaining tissue or dirt; contaminants were removed using a scalpel blade. All samples were then rinsed with distilled water and left to air-dry overnight in a fume cupboard.
In the absence of data on adult female Australian sea lion whisker growth rates we estimated a one month isotopic timeline using growth rates established for Steller sea lions (0.10-0.17 mm/day) (Hirons et al. 2001) by selecting the proximal 5mm of an adult female whisker. Due to the age of pups sampled, one concern was that whisker material towards the distal end would contain material exclusively laid down in utero and not reflect the degree and significance of isotopic enrichment from mother to pup
(Aurioles et al. 2006) . No otariid pup whisker growth rates are available with which to temporally synchronise early ex utero whisker growth and maternal whisker samples.
Consequently a 5mm segment from the proximal end representing early ex utero whisker growth and potentially some in utero deposition was used for stable isotope
19
Chapter 2: Foraging ecotypes analysis. Each whisker portion was clipped into ≈1mm segments and stored in a uniquely numbered glass scintillation vial.
To ensure that isotope levels of mothers and their pups were temporally synchronised, we used isotope levels of blood serum to further assess fractionation. Stable isotope levels present in different components of blood reflect different temporal scales of dietary activity with isotope turnover rates in blood serum having the shortest half-life
(five to seven days) and subsequently representing the most recent record (Kurle and
Worthy 2002, Tierney et al. 2008). Hematocrit has been determined for yearling
Australian sea lions (~52%) (Needham et al. 1980) but not for pups. Assuming a similar hematocrit level in Australian sea lion pups to that determined for Galapagos fur seal pups (~40%) (Horning and Trillmich 2006), we were concerned that lipid extraction and freeze drying may not leave enough blood serum for analysis. Consequently blood serum from both mother and pup was analysed without performing lipid extraction.
Blood serum was freeze-dried, powdered and stored as for whiskers.
Determining the strength and significance of isotope correlation of mother and pup does not require the assignment of maternal foraging behavior to a specific ecotype. As such the power of our analysis was increased by an additional eight mother-pup paired whisker samples collected from The Pages Islands breeding colony as part of other research.
All samples for isotope analysis were sent to the Australian National University
Environmental Biology Stable Isotope Facility. Analysis was performed using a
Microass isoChrom CFIR mass spectrometer coupled to a Carlo Erba EA-1100 CHN-O
20
Chapter 2: Foraging ecotypes analyser. The standard control ratio for 13 C and 15 N were Pee Dee Belemnite and atmospheric nitrogen respectively. Observed error rates were recorded as <±0.1‰
(δ13 C) and <±0.3‰ ( δ15 N).
Data analysis
GPS location and TDR data were downloaded and decoded using the proprietary software and algorithms of the logger used (Sirtrack Ltd Fastloc Admin Tool v1.1.3.34 and Wildlife Computers Instrument Helper v1.0.56). Location positions with <5 satellite records were discarded (Bradshaw et al. 2007). Parameters used to describe adult female Australian sea lion foraging behavior were i) mean distance from the mainland coast at the inflection point of a foraging trip ii) mean dive depth and iii) stable isotope ratios of carbon and nitrogen present in whiskers. Model-based hierarchical agglomerative clustering using Bayesian Information Criteria (BIC) was used to determine the optimal number of clusters present and levels of classification uncertainty in the adult female dataset. Essentially, models varied in one (or more) of the parameters that described the two dimensional spread of the data within each cluster
(volume, shape and size). The optimal model and number of clusters at each colony was determined by selecting the highest-value BIC model of ten possible clustering models (Fraley and Raftery 2002). The a posteriori level of classification success of adult female isotope ratios into significantly discrete groups was conducted by leave- one-out-cross-validated quadratic discriminant analysis (LOOCV-QDA) after the method of Wolf et al. (2008). Clusters were defined as representing inshore or offshore foraging behavioral ecotypes based on mean values of foraging parameters for each adult female.
21
Chapter 2: Foraging ecotypes
The transfer of isotopes from mother to pup was examined by regressing pup values for whiskers and blood serum onto those of the mother. Residuals from regression models were examined for normality of distribution and tested for correlation (ANOVA) with fitted values to determine whether the isotope transfer models were appropriate.
Enrichment factors for δ13 C and δ15 N ratios were defined as the mean difference between maternal and pup values.
Model-based clustering of isotope ratios in pup whiskers was performed for each colony. A posteriori significance and classification success of discrete clusters were calculated as for adult females. As whiskers were used in the screening process, mean isotope values of each cluster were calculated and adjusted to represent adult female values using the generated whisker enrichment discrimination factors described above.
Clusters were then assigned behavioral ecotype designations based on congruence between mean isotope levels with those of adult female ecotype values. Mann-Whitney
U and Welch two-sample t tests described the significance of differences in non- normally distributed parameter values; Students t-tests were used in all other cases. To determine inter-colony differences in proportional representation of each ecotype a likelihood ratio test for goodness-of-fit was performed. R statistical software and
Originlab 8.5 was used for all analyses and unless otherwise stated values are given as mean (±standard error). Results were considered significant at P<0.05.
22
Chapter 2: Foraging ecotypes
Results
Adult female foraging behavior
Diving and geospatial data were collected for five adult females at each of the three colonies. Mean deployment duration was 9.9d (±2.48; range 1.5-33.7d), recording a mean of four foraging trips (±1.2; range 1-15). Four animals performed only one foraging trip during a deployment. Comparison of dive depth profiles to charted water depths confirmed previous findings that adult female Australian sea lions dive continuously to the benthos upon leaving the colony (Costa and Gales 2002, Fowler et al. 2006). While four mk9 TDR’s were lost, sufficiently accurate GPS data were collected to estimate dive depth based on the Australian Hydrographic Service nautical chart of the area (Aus 341). The highest-value BIC clustering model revealed two distinct groups (Figure 2). LOOCV-QDA correctly assigned adult females into the same significantly different behavioral groups with high classification success (93.3%) based on δ13 C and δ15 N ratios, mean dive depth and mean distance from shore at the foraging trip inflection point (Wilkes λ=0.11, F 4,10 =20.7, P<0.001; Figure 2 and Table 1). Based on significant differences in the parameters tested, adult females were assigned to either inshore ( n=9) or offshore ( n=6) foraging ecotypes (Table 2) and could be characterised by significant differences in δ13 C and δ15 N ratios (Students t-test t>2.3,
P<0.05 in both cases).
Fractionation of isotopes from mother to pup
Paired mother and pup whisker samples ( n=14) collected from across the three study sites were supplemented by an additional eight pairs from The Pages Islands. Adult
23
Chapter 2: Foraging ecotypes females expressed a mean whisker δ13 C of -15.35‰(±0.23; range-12.59‰ to -17.98‰) and δ15 N 15.42‰(±0.23; range 13.68‰-18.1‰). As expected there was a very strong linear relationship between mother and pup δ13 C present in whiskers (r 2=0.83,
13 F1,20 =96.1, p<0.001; Figure 3a) but no detectable enrichment in δ C to pups (Students t-test, t=0.63, df=47, P=0.53; Table 3). Linear regression of pup δ15 N on mother isotope ratios from whiskers produced a significantly large regression coefficient for δ15 N
2 (r =0.76, F 1,20 =62.3, p<0.001; Figure 3a) with an enrichment discrimination factor of
+1.27‰ (Students t-test, t=3.86, df=47, P<0.001; Table 3).
Isotope ratios from blood serum did not differ significantly between capillary or venous blood (Students t-test, t<0.53, df=18, P>0.59 in both cases). Capillary blood serum δ13 C
(n=14 pairs) complemented data from whiskers with no enrichment from mother to pup
(Students t-test, t=0.72, df=27, P=0.48; Table 3) and a significantly strong relationship
2 (r =0.76, F 1,12 =38.1, P<0.001; Figure 3b). Linear regression of pup capillary blood serum δ15 N values onto maternal values produced a very strong, highly significant
2 relationship (r =0.91, F 1,12 =128.8, P<0.001; Figure 3b) and an enrichment discrimination factor of +1.92‰ (Students t-test, t=3.1, P<0.01; Table 3). Model residuals were distributed normally and uncorrelated with fitted values for all enrichment models
-31 (F 1,20 <2.6e , P≈1 in all cases), indicating our isotope enrichment models were appropriate.
24
Chapter 2: Foraging ecotypes
Pup whisker isotope ratios and intra-colony foraging ecotype frequencies
There were no intersexual differences in δ13 C and δ15 N ratios at any colony (Students t- test t<1.9, P>0.05 in all cases) therefore pup data were pooled for each colony. The highest-value BIC model identified three discrete clusters at Lilliput Island and two clusters at Blefuscu Island with very low classification uncertainty (2.2% and 0.5% respectively; Figure 4). LOOCV-QDA also assigned pups to the same clusters at Lilliput
Island (Wilkes λ =0.371, F 2,40 =34, P<0.001) and Blefuscu Island (Wilkes λ =0.15,
F2,49 =137.4, P<0.001) with high classification success (LOOCV = 100% in both cases).
Clustered mean isotope ratios for pups were corrected for enrichment and displayed no significant differences with mean ratios of adult female alternate behavioral ecotypes
(Students t-test, t<1, P>0.2 in all cases). Pup clusters were subsequently characterised as ‘inshore’, ‘intermediate’ and ‘offshore’ ecotypes by significantly different mean carbon isotope ratios (Table 5a and b). The offshore ecotype (71%) was most common at
Blefuscu Island whilst inshore (49%) and intermediate (39%) ecotypes predominated at
Lilliput Island (G=58.9, df=2, P<0.001). Model-based clustering of pup data from Olive
Island resolved three clusters with low classification uncertainty (3.6%), supported by
LOOCV-QDA (Wilks λ=0.89, F 2,91 =5.8, P<0.01) with two significantly different mean
δ13 C values (LOOCV=96.8%). Bayesian Information Criteria values for all cluster models at each colony are provided as supplemental data (Table 1S). Three significantly different δ15 N levels were detected at Olive Island (Figure 4) with two sharing the same δ13 C values (Figure 4 and Table 5c). The offshore ecotype with the highest mean δ15 N value was the most common at Olive Island (71%) whilst the inshore ecotype represented only 9% of the colony (G=16.6, df=2, P<0.001).
25
Chapter 2: Foraging ecotypes
Discussion
Our study represents the first validated technique to detect fine-scale geographic variation in foraging behavior of any otariid through the use of stable carbon and nitrogen isotope ratios in pup whiskers. We have identified significant inter-individual variation in feeding ecology manifested by the presence of alternate foraging ecotypes.
Furthermore, the representation of alternate foraging ecotypes varies significantly within and among geographically-close colonies. These findings are unique in that no other study has described such marked differences in foraging ecotype frequency in an otariid over such small spatial scales. It is possible the temporal incongruity between isotopic sampling (historical feeding record) and telemetry data (current feeding behavior) could lead to incorrect labelling of foraging ecotypes. This would occur if adult female
Australian sea lions alternated between offshore and inshore foraging locations.
Parameter values of adult females tracked for multiple trips in this study suggest short- term temporal stability in ecotype expression, and the congruence between isotope ratios and the foraging locations derived from tracking data follow the trend of latitudinal gradients reported in the literature (Rounick and Winterbourn 1986, Cherel and Hobson
2007, Newsome et al. in press). Should other breeding colonies be studied, then tracking of adult female Australian sea lions would be required to confirm that the isotopic gradient is sufficient to detect alternate foraging ecotypes when using pup whiskers as proxies. However we caution others that these issues should be considered when applying this technique to other species and recommend isotopic sampling after telemetry data has been acquired.
26
Chapter 2: Foraging ecotypes
Pups as surrogate measures of maternal foraging
The development of our technique is dependent on there being a high congruence between the isotope values in Australian sea lion mother-pup pairs. Pinniped milk is rich in lipids, which are 13 C-depleted (Hilderbrand et al. 1996, Polischuk et al. 2001,
Stegall et al. 2008). As such the carbon pool that a wholly milk-dependent pup draws upon should be lower in δ13 C than maternal values. Conversely very young pups may still be catabolising carbon from tissue laid down in-utero, with isotope ratios characterised by values similar to those in the mother (Aurioles et al. 2006). A lack of enrichment in δ13 C from mother to offspring has been documented in many species including coyote ( Canis latrans ), caribou ( Rangifer tarandus ) and cats ( Felis catus )
(Jenkins et al. 2001). Our study follows the same pattern suggesting that δ13 C exhibited by young pups reflects maternal values with no requirement for an enrichment factor.
We caution that whilst isotopic transfer has been validated in this study, pup age should be considered when sampling other Australian sea lion colonies. Sympatrically- breeding New Zealand fur seal pups begin independently supplementing their diets approximately five months prior to independence (Baylis et al. 2005). The weaning process of Australian sea lion pups is poorly understood, however they are physiologically capable of foraging from six months of age (Fowler et al. 2006) though whether foraging occurs is unknown. The onset of independent feeding would almost certainly alter isotope values in pups, and consequently alter the relationship with maternal levels. If older pups are sampled we recommend that a) estimations of whisker growth-rates for adult females and pups should be used to select temporally coupled whisker segments and/or b) further paired blood serum samples should be
27
Chapter 2: Foraging ecotypes taken to determine whether a 13 C enrichment factor is required. Although our study shows no enrichment in 13 C in either whiskers or serum, the use of the (smaller) whisker enrichment factor may have caused the under-estimation of mean carbon and nitrogen isotope values of alternate foraging ecotypes.
Assuming that Australian sea lion pups feed exclusively on maternal milk, our data reflect a trophic level enrichment of 1.92‰ δ15 N derived from blood serum. We were unable to accurately estimate the portion of pup whisker representing exclusively ex utero growth with the potential inclusion of any in utero material resulting in a lower isotopic signal from the pup. The whisker enrichment factor for Australian sea lion pups is considerably lower than diet-tissue enrichment detected in whiskers of captive pinnipeds fed a controlled diet (Hobson et al. 1996), suggesting our inclusion of some in utero material and explaining why our value was lower than that generated using blood serum.
Our findings are similar to studies of isotope fractionation between mothers and offspring in red-backed voles ( Clethrionomys gapperi ) (Sare et al. 2005b) and polar bears ( Ursus maritimus ) (Polischuk et al. 2001), 1.9‰ and 1.0‰ respectively which are smaller than the mean δ15 N enrirchment between diet and maternal values reported in other species(3-5‰) (Gannes et al. 1998, Jenkins et al. 2001). An increase in pup
δ15 N resulting from catabolism of protein by during periods of nutritional stress (i.e. growth and moulting, fasting between feeding bouts etc) (Sare et al. 2005a) may result in an elevated enrichment factor. Adult female Australian sea lion foraging trips seldom lasted longer than two days (Table 1) and the pups sampled in this study were still in their natal pelage suggesting protein catabolism due to nutritional stress was unlikely.
28
Chapter 2: Foraging ecotypes
More likely our data reflect the species-specific isotopic composition of milk and assimilation efficiency of pups (Stegall et al. 2008). We recommend collection of milk samples to characterise milk isotope levels, the degree and significance of inter- individual variation and to quantify the assimilation process of δ15 N by pups. Sampling whiskers and blood from pups suffering malnutrition and comparing with healthy pups would further resolve the effects of nutritional stress on isotope ratios.
Intra-colony segregation of maternal foraging behavior
The difference in δ13 C that characterised variation in foraging behavior at Olive Island (-
2.9‰) is greater than that detected in northern Elephant seals at -1.5‰ (Aurioles et al.
2006), Galapagos sea lions at ~-1.5‰ (Wolf et al. 2008) and northern fur seals at -
1.7‰ (Burton and Koch 1999). Surprisingly the difference in δ13 C between alternate behavior at Blefuscu and Lilliput Islands (-2.7‰ to -5‰ respectively) was over twice the value recorded for studies identifying spatial variation in foraging locations in excess of
600km (-1.5‰) (Burton and Koch 1999, Aurioles et al. 2006) As the spatial scale over which our entire study was conducted was less than 200km, our results were
13 unexpected. Influx of terrestrial organic material (including C -rich C 4 and CAM plants), recycling of organic carbon (Crawford et al. 2008) and the presence of seagrasses (extremely rich in 13 C) (Hemminga and Mateo 1996) influence the δ13 C values of inshore waters. The inshore foraging locations used by adult female
Australian sea lions in the Nuyts Archipelago and around Olive Island are rich in seagrass and protected to some degree from admixture with oceanic-depleted 13 C waters by a series of islands and coastline promontories (Edyvane 1999). These
29
Chapter 2: Foraging ecotypes factors may contribute to a localised elevation of δ13 C values displayed by adult female
Australian sea lions near the colonies screened.
The identification of an intermediate ecotype at Lilliput Island was also unexpected. The detection of intermediate isotope signals similar to that seen at Lilliput Island has been reported in many species of fish and marine mammals and may represent opportunistic and complex feeding behaviors involving prey at both high and low trophic levels
(Davenport and Bax 2002). Australian sea lions commence diving as soon as they depart the colony (Costa and Gales 2002) and it is possible that some adult females opportunistically feed on prey items that occupy relatively high and low trophic positions close to the colony resulting in a mean carbon isotope ratio between inshore and offshore values described here. Intriguingly, the intermediate ecotype displayed δ15 N levels approaching one trophic level higher than those expressed in the offshore ecotype, with a similar degree of enrichment between offshore ecotypes identified at
Olive Island. This may reflect adult female Australian sea lions foraging in similar locations but on sufficiently different prey to confer significantly different δ15 N values, suggesting trophic niche width segregation within the same geographical foraging area which would be undetectable using just geospatial data.
Whilst intra-specific niche segregation has been recorded several pinniped species
(New Zealand fur seals: Page et al. 2006; grey seals Halichoerus grypus : Breed et al.
2006; northern elephant seals: Le Boeuf et al. 2000), intra-sexual segregation is less commonly reported and demonstrates the increased resolving power of stable isotope biochemistry over exclusive reliance on tracking. Commensurate with central place foraging theory (Orians and Pearson 1979), Australian sea lions that expend greater
30
Chapter 2: Foraging ecotypes energy travelling further from the colony should be rewarded by higher quality or more numerous prey items. The significantly different δ15 N between alternate ecotypes we revealed indicates offshore-foraging females are feeding on prey that, on balance, are at a higher trophic level. Contrary to expectations, offshore-foraging animals did not spend longer at sea than those that fed inshore. Our data suggest offshore-foraging animals travel further, possibly spending proportionately less time feeding than inshore conspecifics. This raises interesting questions involving ecotype-specific energetics, particularly if ecotypes exhibit long-term temporal stability. Whether the expression of foraging behavior by individuals extends to inter-seasonal or inter-annual temporal stability is unknown. Examining dietary history by serially subsampling stable isotope ratios of adult female Australian sea lion whiskers would resolve the issue of temporal stability and consequently may provide a useful metric of evolutionary potential for this species (Bolnick et al. 2003).
There is a growing body of literature examining the importance of inter-individual niche width specialisation in population subdivision (Bolnick et al. 2003). When telemetry devices are used to validate the presence of isotopic gradients and set the behavioral ecology of the study organism in context, the use of stable isotope analysis presented in this study represents a powerful, low-impact and cost-effective tool capable of resolving ecological partitioning between individuals and subpopulations. Our technique can be applied to any income-breeding mammal whose offspring are accessible and exhibit a period of nutritional dependency, though accurately identifying the onset of supplementary feeding by offspring determines the timeframe over which adult behavior can be inferred.
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Chapter 2: Foraging ecotypes
Exploiting readily-available and easily sampled offspring as proxies for adults could also offer valuable insights into dietary variation. Isotope mixing models are becoming an increasingly popular method to determine the relative contribution of prey to an individuals' diet (Hall-Aspland et al. 2005a, Phillips et al. 2005). The application of our technique to reconstructing adult diets is underpinned by the development of accurate and appropriate parent-offspring enrichment factors. The collection of blood serum from parent and offspring eliminates the possibility of in utero deposited material distorting true trophic level enrichment values. The presence of lipids in blood serum may artifically reduce δ13 C at both the diet-parent and parent-offspring trophic levels (Kurle
2002). Using isotope mixing models first requires parent-offspring lipid correction values to be calculated using C:N ratios should lipid extraction be unfeasible (Logan et al. 2008). Once accurate enrichment factors have been calculated, a similar technique can be used to correct estimated diet-parent values for lipid depletion.
Conclusions
Our study has described significant inter-individual differences in adult female Australian sea lion foraging behavior that translate to significant inter-colony variation. This represents to our knowledge the most comprehensive assessment of foraging behavior in an otariid breeding colony to date. Ultimately, combining geospatial data with the increased sampling power of stable isotope biogeochemistry to characterise niche width variation represents a powerful tool to increase our understanding of marine predator feeding behavior.
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Chapter 2: Foraging ecotypes
Acknowledgements
All animal handling and experimentation were performed under Department of
Environment and Heritage scientific permit #A24684-7 and University of Adelaide animal ethics permit #S-010-2008. Research was funded through the Department of
Environment, Water, Heritage and the Arts' Australian Marine Mammal Centre,
Australian Antarctic Division and the Commonwealth Environment Research Facilities
(CERF) program, Sea World Research and Rescue Foundation Inc and the Holsworth
Widlife Research Endowment. We acknowledge the logistical assistance of the South
Australian Department of Environment and Heritage rangers and the South Australian
State Emergency Services. We would also like to thank the three anonymous reviewers for feedback which improved the manuscript.
33
Chapter 2: Foraging ecotypes
Table 1. Foraging behavior of 15 adult female Australian sea lions instrumented between January 2008 and July 2009.
Deployment Mean Whisker(‰) Foraging ID Location Date Duration (d) Foraging trips Trip duration (d) Dive Depth (m) Dist from shore (km) δ13 C δ15 N Ecotype 1 Lilliput Jun-09 6.1 1 1.7 52.6 (±0.34) 29.52 -18.22 14.32 Offshore 5 Olive Feb-09 4.5 1 1.6 52.6 (±0.54) 42.42 -18.33 13.87 Offshore 6 Blefuscu Feb-08 3.6 2 0.8 (±0.09) 55 48.93(±2.43) -15.33 17.22 Offshore 7 Blefuscu Feb-08 1.5 2 0.5 (±0.05) 45.7 (±0.25) 31.88(±4.56) -15.94 17.01 Offshore 13 Olive Feb-09 10.2 1 2 65.7 (±0.34) 61.52 -15.49 14.12 Offshore 15 Lilliput Jun-09 33.7 14 1.6 (±0.1) 56.9 (±0.28) 21.96(±1.94) -17.98 15.13 Offshore 2 Lilliput Jun-09 6.1 3 1.2 (±0.19) 15.6 (±0.8) 7.49(±0.97) -14.89 11.12 Inshore 3 Lilliput Jun-09 8.1 6 0.6 (±0.09) 15.4 (±0.12) 9.22(±0.43) -16.42 12.03 Inshore 4 Lilliput Jun-09 4.6 1 0.9 11.3 (±0.19) 5.37 -14.01 10.43 Inshore 8 Blefuscu Feb-08 30.8 15 1.1 (±0.1) 17.1 (±0.22) 3.32(±0.22) -13.26 14.21 Inshore 9 Blefuscu Feb-08 14.8 2 2.1 (±0.4) 10 5.21(±1.8) -12.23 13.52 Inshore 10 Blefuscu Feb-08 6.8 2 1.8 (±0.03) 31 6.98(±1.23) -15.6 17.74 Inshore 11 Olive Feb-09 4.9 2 0.4 (±0.05) 31.2 (±0.28) 8.59(±0.72) -15.2 14.59 Inshore 12 Olive Feb-09 7.3 5 1 (±0.4) 19.3 (±0.16) 1.61(±0.48) -16.02 14.01 Inshore 14 Olive Feb-09 5.4 5 0.4 (±0.13) 14.8 (±0.13) 5.59(±0.51) -16.37 13.9 Inshore
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Chapter 2: Foraging ecotypes
Table 2. Mean foraging trip and isotope ratio parameter values for alternate foraging ecotypes of adult female Australian sea lions across all colonies. Ecotypes can be identified by variation in stable isotope ratios of carbon and nitrogen present in adult female whiskers. Offshore-foraging animals fed at a significantly higher trophic level and exhibited lower δ13 C ratios than inshore foragers.
Ecotype Test p Inshore Offshore statistic value Distance (km) 5.9(±0.81) 39.4(±5.91) 54* <0.001 Depth (m) 18.4(±2.57) 54.8(±2.68) 54* <0.001 δ13 C (‰) -14.9(±0.54) -16.9(±0.59) 2.34** <0.05 δ15 N (‰) 13.3(±0.64) 15.3(±0.59) 2.34** <0.05 *=Mann-Whitney U, **=Welch 2-sample t
Table 3. Mean stable isotope ratios from whiskers and blood serum for adult female
Australian sea lions and their pups. Enrichment of δ15 N from mother to pup was significant in whiskers (+1.27‰) and capillary blood serum (+1.92‰).
Mean isotope ratios (‰) Whiskers Capillary blood serum δ13 C (n=22) δ15 N (n=22) δ13 C (n=14) δ15 N (n=14)
Maternal - 15.35(±0.23) 15.42(±0.23) - 17.72(±0.45) 13.79(±0.45) Pup - 15.56(±0.23) 16.69(±0.23) - 17.95(±0.45) 15.71(±0.45) Enrichment -0.21‰ns +1.27‰* -0.23‰ns +1.92‰* ns =non significant, * = Students t-test, t>3.1, p<0.001
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Chapter 2: Foraging ecotypes
Table 4. Mean isotope values and proportional representation of each ecotype at (a) Lilliput I., (b) Blefuscu I. and (c) Olive I. from pup whiskers. Mean values for both δ13 C and δ15 N differed significantly between alternate ecotypes at each island. Adjusted δ15 N represents values incorporating the whisker-derived enrichment discriminating factor. No enrichment in δ13 C was detected from mothers to pups therefore no adjustment was required for δ13 C. Adult female Australian sea lions that foraged offshore fed at a higher trophic level and with lower carbon ratios than inshore-foragers.
a)
Lilliput Island Kruskal - Ecotype Inshore Intermediate Offshore Wallis χ2 p n 21 17 5 - - Proportion of colony (%) 49 39 12 - - mean δ13 C (‰) -12.5 (±0.17) -15.3 (±0.21) -17.5 (±0.46) 33.1 <0.001 mean δ15 N (‰) 14.5 (±0.17) 17.2 (±0.22) 15.6 (±0.32) 31.8 <0.001 Adjusted δ15 N (‰) 13.2 15.9 14.3 33.1 <0.001 b)
Blefuscu Island Kruskal - Ecotype Inshore Offshore Wallis χ2 p n 15 37 - - Proportion of colony (%) 29 71 - - mean δ13 C (‰) -13.3(±0.38) -16(±0.04) 23.8 <0.001 mean δ15 N (‰) 14.8(±0.29) 18(±0.12) 27.9 <0.001 Adjusted δ15 N (‰) 13.5 16.7 - - c)
Olive Island Offshore (low Offshore Kruskal - Ecotype Inshore δ15 N) (high δ15 N) Wallis χ2 p n 9 19 69 - - Proportion of colony (%) 9 20 71 - - mean δ13 C (‰) -12.6 (±0.27) -15.7 (±0.15) -15.5(±0.05) 25.5 <0.001 mean δ15 N (‰) 12.7(±0.17) 13.7(±0.12) 15.5(±0.05) 58.9 <0.001 Adjusted δ15 N (‰) 11.4 12.4 14.2 - -
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Chapter 2: Foraging ecotypes
Figure 1. Map of Australian sea lion ( Neophoca cinerea ) breeding colonies sampled in
South Australia from January 2008 to July 2009. Distribution of the species is highlighted in blue. Adult female Australian sea lions were GPS tracked and whisker- sampled from each colony (n=15 total). Pup sampling to detect intra-population structuring was conducted at Lilliput, Blefuscu and Olive Islands as colonies entered a breeding episode.
37
Chapter 2: Foraging ecotypes
Figure 2. Dendrogram based on optimal clusters selected from the maximum-value
Bayesian Information Criteria model of hierarchical agglomerative clustering for means of dive depth, distance from shore at the foraging trip inflection point and whisker δ13 C /
δ15 N ratios (leave-one-out cross validated classification success = 93.3%). Numbers represent individual adult female ID number (see Table 1).
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Chapter 2: Foraging ecotypes
Figure 3. Linear regression of (a) pup whisker and (b) capillary blood serum isotope ratios on maternal values. Transfer of isotopes from mother to pup was supported using both isotope sources, though no significant difference between maternal δ13 C and their pup could be detected.
39
Chapter 2: Foraging ecotypes
40
Chapter 2: Foraging ecotypes
41
Chapter 2: Foraging ecotypes
O v
Nitrogen 12 14 16 18 20
-18 -17 -16 -15 -14 -13 -12 -11
Carbon
Figure 4. Isotope biplots for pups from Lilliput Island (n=43), Blefuscu Island (n=52) and
Olive Island (n=97). Clusters were determined by selecting the maximum-value
Bayesian Information Criteria (BIC) model of ten hierarchical agglomerative clustering algorithms. Symbols represent discrete clusters. Ellipses highlight co-variance between
δ13 C and δ15 N for each cluster.
42
Chapter 3: Foraging site fidelity
CHAPTER 3
CREATURES OF HABIT: FORAGING HABITAT
FIDELITY OF ADULT FEMALE AUSTRALIAN SEA
LIONS
______
Lowther, A.D., Harcourt, R.G., Hamer, D. and Goldsworthy, S.D.
University of Adelaide / South Australian Research and Development Insitute
Marine Ecology Progress Series; 2011; accepted paper
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Chapter 3: Foraging site fidelity
Abstract
We examined the movement characteristics and seasonality of feeding behaviour for an endemic Australian otariid, the Australian sea lion ( Neophoca cinerea ). By combining tracking data and stable isotope analysis of serially subsampled vibrissae from twenty adult females at seven colonies, we were able to characterise individual foraging specialisation across 80% of the species range. Adult females expressed long-term temporal consistency in both foraging site (offshore vs inshore) and prey selection.
When seasonality in foraging behaviour was detected (n=7), there was no consistency in variation of isotope ratios between individuals or colonies. Offshore-foraging animals fed at higher trophic levels than inshore foragers. Potentially, inshore foragers could be subdivided into those who targeted heterogeneously-distributed seagrass meadows or calcanerite reef systems for different payoffs. This data highlights the importance of understanding individual specialisation and the dangers of generalising behaviour at the colony-level. Individual specialisation in foraging behaviour may be a mechanism that reduces intra-specific competition, but its effectiveness will be a function of the temporal stability of individual differences. Our study is the first to identify multi-season consistency of individual foraging behaviour for any otariid. Given the long-term stability of adult female foraging behaviour, categorising individuals using a proxy measure such as whisker isotopic signature appears robust, economical and appropriate. Such data is critical to modelling population response to anthropogenically-driven fine-scale habitat modification.
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Chapter 3: Foraging site fidelity
Introduction
A major problem faced by marine mammal apex predators such as seals, who must constantly address the challenges presented by geographic decoupling of breeding
(terrestrial) and foraging (aquatic) areas, is the acquisition of food. In dynamic, heterogenous environments such as ocean ecosystems predators that are constrained by the amount of time they can spend away from dependent offspring must rely on some form of predictable prey distribution (Gende and Sigler 2006, Willson and Womble
2006, Thayer and Sydeman 2007, Bost et al. 2008). In response to the challenges of locating sufficient food to survive and reproduce, three broad foraging strategies are utilised seals; epipelagic foragers such as California sea lions Zalophus californianus
(Feldkamp et al. 1989) and sub-Antarctic fur seals Arctocephalus tropicalis (Georges et al. 2000) focus their foraging effort in the upper water column to depths of 200m, mesopelagic depths (200- 2000m) are the realm of predators such as the northern and southern elephant seals Miroungia angustirostris and M. leonina (LeBoeuf 1994,
Campagna et al. 1995) while benthic foragers such as Weddell seals (Hindell et al.
2002), southern sea lions Otaria flavescens (Werner and Campagna 1995, Thompson et al. 1998, Campagna et al. 2001) and New Zealand sea lions Phocarctos hookeri feed on the sea floor (Crocker et al. 2001a).
Typically, epipelagic and mesopelagic foragers rely on predictable dynamic oceanographic features such as frontal zones and upwellings where enhanced productivity supports higher densities of prey species (Olson and Backus 1985,
Schneider 1990). This imparts a degree of seasonality to foraging behaviour and this
45
Chapter 3: Foraging site fidelity has been observed in a wide variety of predators including king penguins Aptenodytes patagonicus (Guinet et al. 1997), southern elephant seals (Field et al. 2002), New
Zealand fur seals (Harcourt et al. 2002) and sub-Antarctic fur seals (De Bruyn et al.
2009). As postulated by the ‘meeting point’ hypothesis, benthic or epibenthic prey tend to congregate around permanent topographical features such as shelf-breaks and reef systems (Langton et al. 1995, Klimley and Holloway 1999, Fréon and Dagorn 2000), making such features important to benthic predators such as yellow-eyed penguins
Megadyptes antipodes (Mattern et al. 2007) and the Australian fur seal A. pusillus doriferus (Arnould and Kirkwood 2007).
Investigating the foraging behaviour of air-breathing marine predators typically involves the use of expensive geospatial tracking devices and time-depth recorders to describe three-dimensional movements of these large, wild, elusive predators during foraging trips to sea. Researchers face a further challenge when working with otariid seals that undergo seasonal molts as this restricts when tracking devices can be attached thereby defining the onset and duration of the temporal window over which tracking can occur.
Thus, prohibitive costs, logistical difficulties and the biology of animals often limits answering questions of seasonal, intersexual and ontogenetic differences in foraging to those that may be answered using data from a small subsample of animals (Hays et al.
2003, Cronin and McConnell 2008).
Once a population or species niche range has been defined, conspecifics tend to be treated as equals (Bolnick et al. 2003) which from both evolutionary and conservation perspectives has important ramifications. Yet there is a growing body of literature that
46
Chapter 3: Foraging site fidelity suggests individual niche-width specialisation is of great importance in fur seals and sea lions (Otariidae) (Thompson et al. 1998, Staniland et al. 2004, Chilvers et al. 2005).
However, the temporal window afforded by tracking is frequently relatively narrow and so long term temporal consistence of individual specialisation remains poorly documented. Variation in behaviours under selective pressure may provide a metric of ecological diversity and subsequent evolutionary potential. However, selective and competitive pressures will operate in markedly different ways on populations depending upon whether individual specialisation is ephemeral, stochastic or temporally-fixed
(Bolnick et al. 2003).
Individuals who are long-term foraging specialists may be able to exploit their niche more efficiently but are vulnerable to change as they are less able to adapt to new resources when their preferred habitat becomes rare or is no longer able to sustain them. Conversely, individuals who are generalists or seasonal specialists are used to altering search images and prey capture techniques and are unlikely to be exposed to selection in the same way. Therefore to effectively model population responses to changes in habitat it is crucial to determine what habitat types animals use and for how long they are used (Ryall and Fahrig 2006). When a large portion of an individuals’ foraging behaviour remains cryptic, as is the case with fur seals and sea lions, modelling potential changes in species distribution under climate change-driven habitat alteration becomes problematic at best (Kaschner et al. 2006).
Attempts to characterise the individual specialisation in foraging behaviour must overcome temporal constraints yet remain financially and logistically plausible.
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Chapter 3: Foraging site fidelity
Recently, the use of cost-effective stable isotope biogeochemistry has provided great insights into foraging ecology across a range of marine mammals (see Crawford et al.
2008 for review). There is strong support for the upward-cascade of stable nitrogen isotopes ( δ15 N) from primary producers to the top of the food web. In marine ecosystems a decreasing stable carbon ratio ( δ13 C) gradient has been identified with distance from shore (Hobson et al. 1996, Post 2002). Isotope ratios provide ecological geo-trophic data that has been used to describe temporal and individual variation in foraging location and diet outside the scope of traditional tracking methods using δ13 C and δ15 N from metabolically active (blood) and inert (vibrissae, feathers etc) tissues for several seal and marine bird species (Forero and Hobson 2003, Aurioles et al. 2006,
Bearhop et al. 2006, Awkerman et al. 2007, Lowther and Goldsworthy 2010). Recently multi-year, individual variation in foraging behaviour has been described using isotope values from serially-subsampled adult male Antarctic fur seal vibrissae (Cherel et al.
2009).
The biogeography and unique reproductive ecology of the Australian sea lion
(Neophoca cinerea ) make characterising its foraging ecology challenging. Distributed from the Pages Islands in South Australia to the Houtman Abrolhos Islands in Western
Australia, a species census estimate of ~13,000 animals makes the species one of the rarest otariids in the world and listed as “Endangered” on the IUCN Red List. Adult female Australian sea lion natal site philopatry is amongst the strongest observed in mammals, with temporally asynchronous breeding episodes occurring between colonies
48
Chapter 3: Foraging site fidelity separated by as little as 30km of water (Campbell et al. 2008) which may suggest that adult female Australian sea lions are not dependent on seasonality of prey resource.
Characterised as a benthic predator (Costa and Gales 2003) two recent studies have identified inter-individual variation in foraging behaviours of adult female Australian sea lions; individuals who forage in shallow, coastal waters (<10m) and those who forage offshore in waters up to 120m (Baylis et al. 2009, Lowther and Goldsworthy 2010).
However, neither study was able to characterise the temporal stability of these two behaviour types, though previous short-term deployments (>30d) of telemetry devices on adult females suggested foraging site fidelity over the duration of tracking events
(unpublished data). With this in mind, our study had two interdependent aims 1) establish the temporal fidelity to previously-detected alternate foraging behaviours of adult female Australian sea lions across a large portion of their range using a synergistic combination of tracking and stable isotope analysis of serially-subsampled vibrissae and 2) determine whether individual foraging specialisation resulted in significantly different phenotypes using proximal metrics of movement, diving and morphology.
Materials and methods
Study sites and sampling
Whisker samples, depth and location data were collected from 20 adult female
Australian sea lions at seven breeding colonies spanning the South Australian range of the species between October 2006 and June 2009 (Figure 1). Females were captured, restrained and anaesthetised using isofluorane delivered through a portable gas
49
Chapter 3: Foraging site fidelity anaesthesia machine (5% induction, 0.5-3% maintenance, Veterinary Companies of
Australia, Artarmon, New South Wales). After anaesthetic induction the animal was secured on a stretcher and suspended from a 100 x 0.5 kg spring balance (Salter
Weigh-Tronix, Blackburn, Victoria, Australia.). Standard length and axillary girth measurements were recorded and a whisker was sampled by clipping at its base.
Kiwisat ARGOS satellite-linked transmitters (pre-2008) or archival fastloc GPS logging devices (Sirtrack Ltd, Havelock North, New Zealand) sampling at 15 minute intervals in conjunction with time-depth recorders (mk9 TDR) (Wildlife Computers) sampling at depth increments of 2m and 1s intervals were attached to the pelage one hand span distal to the midpoint on the back of each adult female using two-part epoxy glue
(Araldite 2017, Vantico, Basel, Switzerland or RS Components Pty Ltd). Devices were left attached for at least one foraging trip (determined either by observed departure and arrival or attendance with and subsequent absence from their pup) whereupon adult females were recaptured and the devices removed.
All vibrissae were cleaned in a method similar to that used for human hair (O'Connell and Hedges 1999) and southern elephant seal ( Mirounga leonina ) vibrissae(Lewis et al.
2006). Briefly, vibrissae were cleaned individually for 20min with de-ionised water then with a solution of 2:1 methanol:chloroform for a further 20 min to remove lipids, followed by a final clean with de-ionised water for an additional 20min. Each whisker was checked under a stereomicroscope for any remaining tissue or dirt; contaminants were removed using a scalpel blade. All samples were then rinsed with distilled water and left to air-dry overnight in a fume cupboard.
50
Chapter 3: Foraging site fidelity
Available data on otariid whisker growth rates suggest linear growth at a rate of approximately 3mm / month (Hirons et al. 2001, Cherel et al. 2009). This may apply to
Australian sea lions, but in the absence of species specific data, we refer to each 3mm segment as a time-step. Vibrissae were cut into 3mm segments starting at the base and placed in labelled 7ml glass scintillation vials. All samples for isotope analysis were analysed at the Australian National University Environmental Biology Stable Isotope
Facility. Analysis was performed using a Microass isoChrom CFIR mass spectrometer coupled to a Carlo Erba EA-1100 CHN-O analyser. The standard control ratio for 13 C and 15 N were Pee Dee Belemnite and atmospheric nitrogen respectively. Observed error rates were recorded as <±0.1‰ ( δ13 C) and <±0.3‰ ( δ15 N).
Data analysis
Geospatial data processing
Raw GPS, PTT and TDR data were downloaded and processed using proprietary software (Sirtrack Fastloc Admin Tool v1.1.5.8 and Wildlife Computers Mk9 Host v1.09.1028). We divided geospatial data into two groups based on spatial resolution of the telemetry data 1) high-quality data comprising both GPS and TDR data (n=12) or just GPS data (n=4) were filtered by excluding locations fixed by 4 or less satellites
(Bradshaw et al. 2007), and dive data extracted at one second intervals; 2) lower-quality
PTT datasets (n=4) were filtered using an iterative backwards-forwards filter that excludes locations associated with implausible swim speeds (McConnell et al. 1992).
For the 16 GPS datasets, foraging trip start and end times were identified by the first and last in-water GPS locations. The mean swim-speed between the first and last two
51
Chapter 3: Foraging site fidelity filtered in-water PTT positions of a trip were used to estimate departure and arrival times of the remaining four animals . For each trip of each animal, new positions were interpolated at 10 minute intervals assuming straight-line travel and constant speed between GPS or Argos fixes using the 'trip' package in R (R Development Core Team
2010).
Segregation of adult female Australian sea lions into alternate foraging ecotypes
We employed the methods in Lowther and Goldsworthy (2010) to classify individual animals into significantly different foraging behaviours (ecotypes). To determine the broad-scale distribution of foraging effort, a fixed-kernel core range at the 50% utilisation distribution (UD-50) using least squares cross validation (LSCV) (Worton
1989, Borger et al. 2008) was calculated for each animal over all trips using the R package 'adehabitat'.
A foraging trip representative of the calculated 50% UD was selected at random for each animal and used for all subsequent analyses. Zero offset correction and behavioural analysis of dive data was conducted using the R package diveMove. Data exploration suggested that the definition of diving (submergence event >6m) used in other studies of Australian sea lions (Costa and Gales 2003) may exclude large portions of dive data for some animals foraging close to shore. We therefore define a dive as submergence below 4m depth as a compromise between capturing the foraging behaviour of all animals and avoiding misidentification of dives (Waluda et al. 2010) .
Distance from mainland coast at the foraging-trip inflection point and mean dive depth
52
Chapter 3: Foraging site fidelity were used to group individuals using a model-based agglomerative hierarchical clustering approach (Lowther and Goldsworthy 2010).
Bayesian Information Criteria (BIC) was used to select the optimal number of clusters, and a posteriori classification success was assessed using leave-one-out cross- validated quadratic discriminant analysis (LOOCV-QDA). Clusters were defined as representing inshore and offshore foraging ecotypes based on significant differences in mean behavioural parameters of individuals.
Stability of foraging behaviour over time
As vibrissae were clipped and not plucked, we assumed the first time-step was contained in the folicle and the first segment analysed represented feeding from two time-steps prior to sampling. Mean values for δ13 C and δ15 N (±SE) (across multiple time-steps, range 1-24), were calculated for each animal and for each ecotype. To quantify temporal variation in foraging behaviour we fitted univariate ARIMA ( p,d,q )
(AutoRegressive Integrated Moving Average) models to stable carbon ( δ13 C) and nitrogen ( δ15 N) isotope values using the three-step Box Jenkins method (Box et al.
1994). As SARIMA modelling requires sample sizes greater than the number of parameters to be estimated (Makridakis et al. 2008), only individuals with >18 isotopic measurements/time steps were considered (n=14).
Visual inspection of isotope plots and autocorrelograms were used to determine whether data required differencing to achieve stationarity and to identify the number of parameters to be estimated. Conditional sum of squares (to assess initial parameter
53
Chapter 3: Foraging site fidelity values) followed by exact maximum likelihood methods were employed to estimate each parameter (Gardner et al. 1980). Finally, models were validated by checking for autocorrelation of residuals using autocorrelograms and portmanteau tests (Ljung-Box
Q statistic) (Ljung and Box 1978). Optimal SARIMA models were then selected using
Bayesian Information Criteria (BIC) as this method penalises overfitting and is widely used in time series analyses (Hurvich and Tsai 1989).
Identification of individual foraging areas
First Passage Time (FPT) was used to identify the scale at which Area Restricted
Search (ARS) was conducted (Pinaud 2008, Bailey et al. 2009b). To perform FPT, each track was redescretized into 50m step lengths and a new time index interpolated for each new position. The FPT of circle radii ranging from 0.05km to twice the maximum daily step length (Bradshaw et al. 2007) were calculated and the radii responsible for varlog max FPT identified for each animal (herein referred to as ARS patch size). FPT values for this radii were then plotted against time for each trajectory, with
ARS behaviour being inferred at segments of tracks that took the longest time to traverse (i.e expressed maximum FPT values) (Hamer et al. 2009, Bailleul et al. 2010,
Votier et al. 2011).
Mean parameters of time spent, distance covered and speed(kmh -1), depth (m), bottom time (time spent at >80% of maximum depth, min), dive duration (min), dive rate
(number of dives / time, min), dive efficiency (bottom time / [dive duration+post-dive interval]) and proportion of dives conducted were calculated for all locations that occurred within ARS patches and during transit. To characterise whether individuals
54
Chapter 3: Foraging site fidelity preferred foraging in areas of variable bottom topography, we used the standard error of mean dive depths during transit and within ARS patches as a metric for bathymetric gradient (Burns et al. 2008) making the assumption that Australian sea lions dive benthically throughout a foraging trip (Costa and Gales 2003, Fowler et al. 2006).
Nautical twilight times for each foraging trip were collated from Geoscience Australia and used to assess the propensity of adult female Australian sea lions to forage nocturnally by calculating the proportion of ARS dives that occurred at night.
Morphometric and ecotype-specific differences in movement and diving behaviour
As model fitting has the potential to provide a close fit to data without being biologically informative, we refrained from including every parameter into a single model and opted to separate behavioural parameters into 'movement' (speed, distance, ARS patch size and time spent transiting and foraging) and 'diving' (rate, efficiency, depth, SE of mean depth, duration, bottom time and number of dives) (Kimura and Weiss 1964, Villegas-
Amtmann et al. 2008). We modelled the relative importance of mean behavioural parameters in predicting ecotype membership using a series of generalised additive models (GAM) using the 'gam' package in R. Parameters that were normally distributed were modelled against foraging ecotype using generalised linear models (GLM).
Residuals were checked for normality of distribution (Shapiro Wilks test) and lack of correlation with fitted values (ANOVA). For all GAM we assumed a binomial distribution for the response variable (ecotype) and a logit function.
We were also interested to examine the interplay between foraging behaviour, body condition and age. For the 12 adult females that were measured we used body length
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Chapter 3: Foraging site fidelity
(as a proxy for maternal age) (McKenzie et al. 2007) and the residuals from regressing mass on length (as a Body Condition Index - BCI) (Trites 1991, McDonald et al. 2009) as predictors in two Generalised Additive Mixed Models (GAMM) using a Poisson distribution with an identity link. We included ecotype as a fixed effect and behavioural parameters as random effects. A stepwise forwards and backwards model selection process based on Akaike Information Criteria (AIC) was used to select the parsimonious model (Hastie and Tibshirani 1990).
Statistical analyses
All statistical analyses were conducted within the R framework and OriginPro Lab v8.
Values are reported as mean (±SE) and significant at P<0.05 unless otherwise stated.
Results
Inshore and offshore foraging ecotype designation
Between October 2006 and June 2009 GPS (n=16) and PTT (n=4) datasets were collected from 20 adult female Australian sea lions along the South Australian range of the species (Figure 1) recording an average of 616 (±139) GPS and 802 (±259) Argos locations per animal. Average tracking duration was 30d (±8.5) (range 2.7-113.1d) capturing approximately 10 (±2.4) foraging trips per animal (range 1-45)
(Supplementary Table 1). Filtering and removal of hauled-out locations left approximately 67 (±10) GPS and 28 (±9) Argos locations per foraging trip at an average of 1.3 (±0.19) and 0.25 (±0.05) GPS and Argos positions respectively per at- sea hour. Archival dive recorders were recovered from 14 animals and dive data
56
Chapter 3: Foraging site fidelity collected at 1s resolution. Throughout the tracking period an average 1534 (±491) dives were recorded for each animal (range 344-6121) with individual mean depths ranging from 5.3m (±0.41) to 86.4m (±11.4) (Supplementary Table 1). The highest-value
BIC model incorporating mean parameters of distance from shore and dive depth at the foraging trip inflection point calculated for all trips combined for each individual described two distinct clusters (Figure 2). These clusters were strongly supported by
LOOCV-QDA (99% classification success) and henceforth animals are referred to as expressing either 'inshore' or 'offshore' foraging ecotypes. Kernel density estimates of
UD-50 for individuals were invariant between ecotypes and scale-independent of the number of foraging trips recorded (Wilcoxon W = 32, P=0.19 and Kruskal-Wallis χ2 =
13.7, P=0.24 respectively), ranging from 5.2 to 1582.7km 2 (mean 227.7±90.2 km 2)
(Figure 3).
Stability of foraging behaviour over time
Adult female Australian sea lion whisker lengths were normally distributed (Shapiro-
Wliks W=0.95, p=0.36), ranging from 56-241mm (mean 125.2mm ±9.35) with between
12-24 whisker segments being analysed for δ13 C and δ15 N (Supplementary Table 1).
Individual animal isotope signatures varied between -11.9 to -18.4 ‰ ( δ13 C) and 11.6 to
16.8 ‰ ( δ15 N) (Figure 4). Generally, significant within-colony differences in mean δ13 C values followed expectations for characterising inshore and offshore foraging behaviour with the exception of Liguanea and The Pages Islands (Students t-test T>13.5, p<0.001 in all significant cases). Within-colony differences between alternate foraging ecotype
δ15 N ratios were all significantly different (Students t-test T>4.9, P<0.05 in all cases)
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Chapter 3: Foraging site fidelity however a priori expectations of offshore foragers expressing higher δ15 N ratios were reversed at Olive Island, West Waldegrave and Seal Slide (Table 1).
Autoregressive modelling detected significant seasonal components to temporal variation in δ15 N and / or δ13 C ratios of seven animals, with no significant autocorrelation of residual values indicating our models were appropriate (Ljung-Box Q < 2.8, P<0.1 in all cases). Seasonality of δ13 C and δ15 N SARIMA models were mainly characterised in six or 12 unit time-steps with three individuals expressing identical time-step seasonality in both δ13 C and δ15 N (Supplementary Table 2).Stable isotope data from an additional three animals required differencing in order to achieve stationarity (LIG2, OL138B and
BL94). Only three animals (L1, L6 and 55938) displayed AR(1) structured isotope measurements whilst one (L3) was characterised by an AR(2) structure.
Movement patterns of individuals
Using data from randomly selected foraging trips representative of UD-50, instrumented animals undertook foraging trips between 0.4 and 3.5d duration (mean 1.5d ±0.17) with corresponding variation in total distance travelled (range 15.5-216.3km, mean 87.8
2 ±13.23km; ANOVA F 1,18 = 36.1, P<0.001, R =67%). Total time spent and distance travelled whilst at sea did not differ significantly between adult females from alternate foraging ecotypes (Students T-test T <0.6, P>0.58 in both cases). Optimal FPT radius identified by varlog max FPT ranged from 0.05 to 8.9km (mean 2.3±0.56km) and was unrelated to foraging ecotype (ANOVA F 1,18 = 2.6, P=0.13). Locations identified within
ARS patches along each track are highlighted at Figure 3. In all cases where both behavioural modes were identified, individuals swam significantly faster and travelled
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Chapter 3: Foraging site fidelity further during transit than when inside an ARS patch (Mann Whitney U=154, P<0.05 in both cases; Supplementary Table 1). Individual animals displayed no significant differences in time spent, distance covered, diving rate and diving efficiency in each behaviour (Mann Whitney U=83, P=0.53 in all cases). Transit distance was the only movement parameter to successfully predict ecotype membership, with offshore- foraging animals travelling further than inshore-foragers (bGAM Χ2 = 6.4, P<0.05) although ARS patch size differences did approach significance (bGAM Χ2 = 7.28,
P=0.06). Offshore-foraging adult females also appeared to prefer foraging in regions of variable benthic topography (bGLMz (mean foraging depth SE) = 1.9, P=0.05) (Shapiro Wilk
W(model residuals) =0.98, P ≈1, ANOVA (fitted vs residuals) F1,12 = 0, P ≈1). Adult female body length and mass were unrelated to foraging ecotype, however body condition was significantly poorer in offshore foragers and animals with slow transit swimming speeds (GAMM
T>6.7, df=7, P<0.01 in both cases) irrespective of the trophic level of foraging (ANOVA
F1,10 = 0.18, P=0.68).
Discussion
Our study supports the partitioning of adult female Australian sea lion foraging behaviour into significantly different ecotypes (Lowther and Goldsworthy 2010) and extends these findings over a large part of the species range. To our knowledge this is the first study identifying multi-season consistency in individual foraging behaviour of a temperate otariid species.
Top predators characterised as generalists tend to have diets dominated by relatively few main prey species but include a large number of less numerous secondary prey
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Chapter 3: Foraging site fidelity species, necessitating a diverse prey community structure. As such they tend to be spatially or temporally associated with prey biodiversity and are commonly used to identify areas of ecological significance (Sergio et al. 2006, Baylis et al. 2008).
However, whether a predator population consists of true generalists or a collection of long-term individual specialists is likely to have profound effects on how conservation management and policy is formulated. The incorrect labelling of a predator population as generalists may result in the protection of habitat-types that are inappropriate for individual specialists (Bolnick et al. 2003). Furthermore, the successful implementation of policy designed to protect species against threatening processes such as fisheries bycatch is contingent on understanding interaction rates (Goldsworthy and Page 2007).
These rates are likely dependent upon the spatial and temporal usage of habitat which may be very different for generalist and specialist foragers. Examples of individual variation in foraging behaviour that is independent of environmental or life history effects are seldom reported though are more prevalent in systems with high levels of intraspecific competition (Hoffman et al. 2006). As such, several authors suggest a model of frequency-dependent prey-choice, where density-dependent depletion of intrinsically valuable prey may make it more profitable for some individuals to specialise on less valuable but more available prey types (Beauchamp et al. 1997, Bolnick et al.
2003)
The presence of alternate, stable foraging ecotypes within Australian sea lion breeding colonies suggests that adult female foraging behaviour is not influenced by broad-scale environmental variability as theoretically any adult female from a colony can reach the
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Chapter 3: Foraging site fidelity foraging habitat of any ecotype present at that colony. We identify long-term temporal stability of previously-identified alternate foraging ecotypes of adult female Australian sea lions (Lowther and Goldsworthy 2010) and extend these findings over a substantial part of the species range. Thus our data fit the criteria outlined by Estes et al. (2003) that true differences in foraging behaviour must be temporally persistent and unrelated to environmental or phenotypic variation.
Individual long-term specialisation in foraging behaviour
This study demonstrates clear and unequivocal long-term individual specialisation in female Australian sea lions, previously characterised as generalist predators. The accessibility of multiple suitable habitats may require an animal to develop specialist foraging techniques to successfully exploit one, which may preclude exploitation of others (McLaughlin et al. 1999, Bolnick et al. 2003, Knudsen et al. 2011). Whilst no ecotype-specific bias to either detected seasonality existed, tracking and isotopic data from this study suggest individual adult female Australian sea lions are repeatedly targeting the same foraging locations and similar trophic levels of prey.
13 Influx of terrestrial organic material (including C -rich C 4 and CAM plants), recycling of organic carbon (Crawford et al. 2008) and the presence of seagrasses (extremely rich in
13 C) (Hemminga and Mateo 1996) influence the δ13 C values of inshore waters.
Seagrass meadows in South Australia represent one of the largest temperate seagrass ecosystems in the world (Shepherd and Robertson 1989) and are heterogeneously distributed in sheltered inshore waters throughout the range of this study. Stable carbon ratios were congruent with tracking data and followed a priori expectations of a
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Chapter 3: Foraging site fidelity latitudinal cline with the two notable exceptions; Liguanea and The Pages Islands lie in a high-energy marine environment that promote admixture with oceanic-depleted 13 C waters possibly reducing any latitudinal cline to undetectable levels over the spatial scale of our study.
Limited dietary analysis of Australian sea lions suggests a broad range of prey from crustacean, teleost fish, elasmobranchs and Little penguins ( Eudyptula minor ) spanning several trophic levels (Richardson and Gales 1987, Gales and Cheal 1992, McIntosh et al. 2006). This is consistent with our findings of δ15 N variation spanning almost three trophic levels. δ15 N values for piscivorous fishes that typically inhabit seagrass beds such as flathead ( Platycephalus speculator: 11.5‰) and Australian salmon ( Arripis truttacea: 12‰) (Jenkins and Wheatley 1998, Hindell et al. 2000, Davenport and Bax
2002, Hindell 2006) are approximately one trophic level (1-2‰) lower than adult female
Australian sea lions at the inshore Lilliput, Blefuscu and Pages Islands foraging locations where seagrass beds are most dense (Edyvane 1999).
Inshore foraging locations of animals from Olive Island, West Waldegrave, Liguanea and the Seal Slide where seagrass was absent (Edyvane 1999) were characterised by
δ15 N ratios approximately one trophic level higher than other inshore foragers (Table 2).
The inshore habitat occupied by these adult females is dominated by heavy limestone or calcarenite reefs in high-energy marine environments (Edyvane 1999). Inshore- foraging adult female Australian sea lions from these colonies appear to prefer foraging around areas of variable bottom topography, supporting our assumption that they are targeting such habitat. All offshore-foraging animals (with the exception of #WW5)
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Chapter 3: Foraging site fidelity expressed δ15 N values that were indistinguishable from inshore-reef foraging animals and shared a similar propensity for diving on high-relief bottom topography. Nitrogen isotope values from both these groups suggest feeding on a mixture of benthopelagic piscivores and secondary-piscivores such as large fish, small sharks and Little penguins. Conversely, it is possible both inshore groups forage on the same prey species, but the increased complexity of inshore reef systems may result in the same species being at a higher trophic level than in seagrass beds (Nagelkerken et al. 2002).
We must caution that the absence of isotope data on local species necessitated basing our interpretation on isotope values of prey sampled in Bass Strait (Davenport and Bax
2002). Australian sea lions dive benthically throughout a foraging trip so it appears that offshore-foraging animals are targeting reefs and steep bathymetric gradients, capturing prey items that on balance are one trophic level higher than prey in seagrass.
Ecotype-specific differences in movement behaviour and body condition
A tenet of central place foraging theory is that animals must be suitably rewarded for undertaking longer foraging trips (Orians 1979). The foraging behaviour of several marine apex predators such as Cooks petrel Pterodroma cookie (Rayner et al. 2010), common guillemot Uria aalge (Burke and Montevecchi 2009), Antarctic fur seal
(Staniland et al. 2007) and chinstrap penguin Pygoscellis antarctica (Ichii et al. 2007) follow this prediction, targeting larger presumably more energy-dense prey items when foraging at greater distances from breeding locations. Our data appear in-line with this expectation; offshore-foraging adult female Australian sea lions that travelled further to foraging grounds fed at the highest trophic levels. More interestingly, adult females who
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Chapter 3: Foraging site fidelity foraged inshore were in better condition than offshore foragers. Those animals that we assume fed on nearby inshore reef systems may have encountered the same quality prey items as offshore foragers, yet would have expended less resource accessing them. If no significant differences between ecotypes in foraging time equate to similar levels of prey-acquisition then an assumed increase in body condition is reasonable.
The question of whether fine-scale environmental heterogeneity significantly influences body condition is intriguing. Further intensive sampling of animals foraging in both inshore habitat types would prove informative.
Seasonality of individual foraging behaviour
Seasonal and geographical variation in foraging behaviour and diet has been inferred in several otariids by changes in dive and movement metrics (Boyd et al. 2002), hard part analysis of scats and regurgitates (Casaux et al. 2003), combinations of the above
(Harcourt et al. 2002) and more recently using stable isotope analysis (Cherel et al.
2007, Cherel et al. 2009). Two other Australian otariids notable for either breeding in geographical sympatry (New Zealand fur seals) or sharing a similar benthic foraging strategy (Australian fur seals) to Australian sea lions also appear to display seasonality in foraging behaviour. New Zealand fur seals, which are epipelagic predators, move from continental shelf foraging areas into deeper oceanic waters near the subtropical front during the winter (Page 2001, Baylis et al. 2008). Australian fur seals also appear to work harder during winter months, undergoing significantly longer foraging trips with a recent study identifying significant individual variation in preferred prey types (Arnould
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Chapter 3: Foraging site fidelity and Hindell 2001a, Arnould et al. 2011) though no data exists as to the temporal stability of these preferences.
To detect temporal or spatial variation in foraging behaviour and diet of apex predators using stable isotopes, a relatively large change in the trophic makeup of ingested prey is required (Bolnick et al. 2003, McCutchan et al. 2003). Adult female Australian sea lions do not appear to display uniform seasonality in foraging behaviour or diet. Our findings show limited congruence with the only other study examining Australian sea lion temporal variation in foraging behaviour which reported seasonal variability in foraging energetics and dive behaviour (Costa and Gales 2003). Unlike New Zealand and
Australian fur seals, Australian sea lions breed in a mosaic-like fashion; a species-level
17-18 month breeding cycle and asynchronously-breeding colonies means colonies are temporally decoupled with respect to the stage of lactation. If there were a lactation influence on foraging strategy then we would expect within-colony variation of foraging behaviour to be uniform between individuals but significantly different between colonies at different stages of breeding. With no such pattern observed, it appears that the foraging behaviour of adult female Australian sea lions is unaffected by lactation stage, and the presumed increase in energetic demands of nursing and gestation do not necessitate a change in foraging strategy. Weak, spatially and temporally stochastic seasonality observed in the current study may reflect opportunistic feeding on seasonal influx of prey items into a preferred foraging area and not a change in the location of foraging per se .
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Chapter 3: Foraging site fidelity
Conclusions
This study clearly illustrates that long-term individual specialisation may confound population level generalisations. We demonstrate that the synergistic use of biogeochemical markers and tracking data is a powerful, cost-effective tool that can be used to determine the spatial and temporal utilisation of foraging habitat. In the case of central-place foragers, this technique could prove informative across a wide range of marine and terrestrial predators. Characterisation of Australian sea lions as generalist predators appears an over-simplification and may be misleading. Adult female
Australian sea lions express foraging behaviour that appears to be geographically fixed, temporally stable, invariant to lactation stage and probably influenced by the presence of fine-scale heterogeneity in habitat structure. The large degree of inter-individual variation in movement and dive parameters we observed suggest that extrapolating colony or population-level processes from a small subset of animals is inappropriate in this species. The specialisation-disturbance hypothesis predicts populations made up of individual specialists are more sensitive to habitat loss than generalists (Vázquez and
Simberloff 2002). If, as our data suggests, adult female Australian sea lions are long- term individual specialists then the characteristic low-density of breeding colonies and the vulnerability of seagrass meadows to the potential effects of climate change means the loss of even a small proportion of adult females may result in localised extirpation.
Given that adult female foraging behaviour is stable the inferences made by employing proxies of maternal foraging behaviour can now be extended to characterise the degree and significance of individual specialisation within and between colonies across
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Chapter 3: Foraging site fidelity seasons and years (Lowther and Goldsworthy 2010). Collection of fine-scale habitat data using techniques such as benthic swath mapping and baited underwater remote video will enable us to test the relationship between individual foraging behaviour and habitat type. Such data can then be used to underpin modelling colony-level reactions to alteration of critical habitats such as seagrass meadows and reef-fish assemblages.
Acknowledgements
All animal handling and experimentation were performed under Department of
Environment and Heritage scientific permit #A24684-7 and University of Adelaide animal ethics permit #S-010-2008. Research was funded through the Department of
Environment, Water, Heritage and the Arts' Australian Marine Mammal Centre,
Australian Antarctic Division and the Commonwealth Environment Research Facilities
(CERF) program, Sea World Research and Rescue Foundation Inc and the Holsworth
Widlife Research Endowment. We acknowledge the logistical assistance of the South
Australian Department of Environment and Heritage rangers and the South Australian
State Emergency Services. We would also like to thank the three anonymous reviewers for feedback which improved the manuscript.
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Chapter 3: Foraging site fidelity
Table 1. Colony-level mean δ13 C and δ15 N ratios for serially-subsampled vibrissae from 20 adult female Australian sea
lions across the South Australian range of the species. Numbers in parentheses represent sample size. Mean inshore
δ13 C were significantly lower than offshore values with the exception of Liguanea and The Pages Islands(Students t-test
T>13.5, p<0.001 in all significant cases). Offshore foragers also fed at a significantly higher trophic level with the
exception of individuals at Olive Island, West Waldegrave and Seal Slide.
δ13 C δ15 N Colony Inshore ±SE Offshore ±SE Inshore ±SE Offshore ±SE
Lilliput Is. -14.7 (3) 0.21 -15.6 (2) 0.05 12.9 0.12 15.9 0.04 Blefuscu Is. -11.9 (1) 0.12 -15.3 (1) 0.03 11.6 0.1 15.6 0.04 Olive Is. -15.4 (1) 0.08 -16.4 (1) 0.03 15.8 0.04 14.9 0.09 West Waldegrave -14.9 (1) 0.04 -18.4 (1) 0.13 16.5 0.04 16 0.12 Liguanea -16.3 (1) 0.09 -15.4 (2) 0.06 15.4 0.11 16.2 0.08 Seal Slide -15.4 (1) 0.03 -16.4 (1) 0.06 15.9 0.06 13.9 0.07 Pages Is. -14.9 (2) 0.04 -14.9 (2) 0.03 14.3 0.04 15.5 0.27
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Chapter 3: Foraging site fidelity
Supplementary Table 1. Descriptive statistics for a) movement and b) dive behaviour calculated from a randomly-selected foraging trip for 20 adult female Australian sea lions tracked at seven colonies in South Australia between October 2006 and June 2009. Abbreviation of Animal #ID in parentheses. Ecotypes designated as 'Inshore' (I) or 'Offshore' (O). '-' denotes data missing due to a) no detectable transit phase or b) no available dive data. UD- 50 represents the fixed-kernel core range area used by animals at the 50% volume contour . For those adult females where transit and foraging behaviour was detected, all except three inshore-foraging animals travelled faster and further in transit than whilst foraging and none significantly altered their diving behaviour throughout a foraging trip.
Overall deployment data Foraging Trip Representative trip data Distance (km) Time (h) Mean speed (km/h) Animal #ID Deployment date Duration (d) No. Trips UD-50 (km 2) Ecotype Duration (d) Distance (km) ARS scale (km) Foraging Transit Foraging Transit Foraging ± SE Transit ± SE 2 Lilliput #5 (L5) 13/06/2009 3.74 2 12.7 I 1.05 36.34 0.53 13 23.3 11.3 14.12 1.15 0.08 1.65 0.11 3 Lilliput #3 (L3) 12/06/2009 7.45 6 20.6 I 0.69 34.2 2.37 16.52 17.68 10.07 30.49 1.59 0.09 1.84 0.09 4 Lilliput #2 (L2) 10/06/2009 6.1 3 22.9 I 1.25 66.98 0.1 36.53 30.45 25.65 4.08 1.34 0.07 4.1 0.11 6 Olive #138B (OL138B) 27/05/2009 32.83 17 12.4 I 1.38 50.2 0.1 5.9 44.4 10.68 21.33 0.51 0.04 1.99 0.06 8 Liguanea #4 (LIG4) 27/04/2009 4.89 2 4.7 I 0.93 37.1 1.85 14.6 22.5 15.05 7.84 0.97 0.09 2.87 0.11 12 Blefuscu #93 (BL93) 27/02/2008 30.81 18 15.5 I 0.73 77.56 2.65 54.76 22.8 7.9 9.62 1.84 0.07 3.85 0.18 14 West Waldegrave AF3 (WW3) 30/01/2008 4.33 2 121.1 I 1.1 66.5 0.25 14.2 52.3 6.45 20.12 2.2 0.06 2.6 0.09 15 Seal Slide AF1 (SS1) 22/10/2007 34.13 12 46.9 I 2.58 141.5 1.21 108.6 32.9 49.14 13.82 2.21 0.07 2.38 0.09 20 South Pages #982 (55938) 31/10/2006 112.58 45 161.2 I 1.87 91.1 1.1 22.9 68.1 28.99 17.46 0.79 0.05 3.9 0.26 19 South Pages #958 (55974) 30/10/2006 110.42 25 449.7 I 2.68 213.16 4.13 81.3 137.93 31.3 33.02 2.4 0.04 4.17 0.13 1 Lilliput #6 (L6) 14/06/2009 1.72 1 171.4 O 1.64 120.9 8.49 24.4 96.5 13.71 27.65 1.78 0.05 3.49 0.13 5 Lilliput #1 (L1) 10/06/2009 6.12 3 71 O 1.7 109.2 4.29 35.6 73.6 25.8 26.67 1.38 0.06 2.76 0.12 7 Olive #135B (OL135B) 26/05/2009 33.49 12 120 O 1.43 93.5 3.21 19.4 74.2 10.6 22.83 1.83 0.05 3.25 0.11 9 Liguanea #3 (LIG3) 27/04/2009 4.35 3 100.3 O 1.82 74.87 0.93 74.87 - 45.65 - 1.64 0.03 - 10 Liguanea #2 (LIG2) 25/04/2009 7.92 3 5.2 O 0.43 15.5 3.77 4.1 11.4 3.1 7.12 0.37 0.04 1.32 0.08 11 Blefuscu #94 (BL94) 28/02/2008 2.68 2 39.8 O 0.5 34.8 0.05 9.45 25.4 5.28 8.25 1.79 0.15 3.08 0.37 13 West Waldegrave AF5 (WW5) 30/01/2008 7.84 3 5.9 O 1.32 16.3 1.65 16.3 - 27.79 - 0.33 0.04 0.51 0.05 16 Seal Slide AF2 (SS2) 16/09/2007 34.89 11 633.9 O 1.61 90.2 0.05 39.7 50.5 21.69 17.06 1.83 0.06 2.96 0.13 17 South Pages #959 (55940) 3/11/2006 113.16 19 956.1 O 2.27 201.5 0.85 64 137.4 20.06 36.54 3.19 0.19 3.76 0.14 18 South Pages #954 (55976) 1/11/2006 41.26 9 1582.7 O 2.18 216.3 8.13 84.5 131.1 29.34 25.71 2.88 0.08 5.1 0.63
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Chapter 3: Foraging site fidelity
Supplementary Table 2. Adult female Australian sea lion deployment and sampling data. Twenty adult females were
sampled between October 2006 and June 2009. '-' denotes no data collected.
Adult female Whisker Animal Mass Animal #ID Deployment Date Length (mm) Segments Length (cm) (kg) Body Condition Index (BCI)
Lilliput #6 14/06/2009 122 24 173 104 -4.1 Lilliput #5 13/06/2009 139 24 153.5 96 -10.5 Lilliput #3 12/06/2009 123 18 165.5 102 -2.2 Lilliput #2 10/06/2009 169 24 162 91 1 Lilliput #1 10/06/2009 82 18 154 81 -0.8 Olive #138B 27/05/2009 85 18 160 80 5.7 Olive #135B 26/05/2009 110 24 169 100 2.5 Liguanea #4 27/04/2009 114 24 165 96 0.9 Liguanea #3 27/04/2009 56 12 151 79 -2.7 Liguanea #2 25/04/2009 99 24 163 92 1.4 Blefuscu #94 28/02/2008 153 24 146 71 -2.8 Blefuscu #93 27/02/2008 101 18 157 79 3.3 West Waldegrave AF3 30/01/2008 138 24 - - - West Waldegrave AF5 30/01/2008 121 12 - - - Seal Slide AF1 22/10/2007 241 24 - - - Seal Slide AF2 16/09/2007 179 12 - - - South Pages #959 3/11/2006 120 12 - - - South Pages #954 1/11/2006 149 12 - - - South Pages #982 31/10/2006 68 12 - - - South Pages #958 30/10/2006 134 24 - - -
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Appendix I. Values for (Seasonal) Autoregressive Integrated Moving Average (SARIMA) models fitted to 14 adult female Australian sea lions with >18 isotope values. Abbreviation of Animal #ID in parentheses. Numbers in parentheses indicated fitted autoregressive ( p), differencing order ( d) and variance of the error term ('moving average') parameters ( q) with additional parameters for seasonal components. Subscripted numbers next to SARIMA models indicate the time step duration of detected seasonal cycles. No significant residual autocorrelation was detected in any model (Ljung-Box Q <2.8, P>0.1 in all cases). Seasonality in one or both isotopes was detected in seven animals (bold) with only three being characterised by random temporal correlation (italicized).
δ13 C δ15 N dif BIC BIC Ljung -Box dif BIC BIC Ljung -Box Animal #ID AR(1) (AR(1)) ARIMA SARIMA (AR1) (selected) Q p AR(1) (AR(1)) ARIMA SARIMA (AR1) (selected) Q p
Lilliput #6 (L6) (1,0,0) - - - -29.6 - 1.37 0.24 (1,0,0) - - - -31.67 - 0.27 0.61
Lilliput #5 (L5) - (1,1,0) - - 7.61 - 0.18 0.89 - - - (1,0,0)(1,0,0) 9 -22.11 -25.43 0.96 0.33 Lilliput #3 (L3) (1 ,0,0) - - - 3.07 - 0.43 0.51 (1,0,0) - - - -19.83 - 0.009 0.92
Lilliput #2 (L2) - 1,1,0 - - -1.65 - 0 0.99 - - - (1,1,0)(1,1,0) 12 9.95 8.33 0.01 0.92 Lilliput #1 (L1) (1,0,0) - - - 19.47 - 0.73 0.39 (1,0,0) - - - -1.77 - 2.8 0.1 Olive #138B (OL138B) (1,0,0) - - - 3.65 - 0.84 0.36 - (1,1,0) - - 9.6 - 0.04 0.85
Olive #135B (OL135B) (1,0,0) - - - -32 - 2.29 0.13 - - - (1,1,0)(1,0,0) 12 -37.16 -37.72 0.41 0.52
Liguanea #4 (LIG4) - - - (1,1,0)(1,1,0) 12 31.42 24.05 0.02 0.89 - - - (1,1,0)(1,1,0) 12 47.82 28.82 0.61 0.42 Liguanea #2 (LIG2) - 1,1,0 - - -6.8 -18.49 1.96 0.16 - 1,1,0 - - 3.76 0.23 0.89 0.34
Blefuscu #93 (BL93) - - - (1,1,0)(1,0,0) 6 - - 0.05 0.81 - (1,1,0) - - -42.16 - 0.002 0.96 Blefuscu #94 (BL94) (1,0,0) - 18.15 0.89 0.22 0.64 - - (1,1,0) - 4.6 -5.1 0.02 0.89
West Waldegrave AF3 (WW3) - - - (0,0,2)(0,0,1) 12 -6.46 -27.5 0.04 0.83 - - - (0,0,2)(0,0,1) 12 -19.71 -20.87 0.26 0.61
Seal Slide AF1 (SS1) - - - (1,1,0)(1,0,0) 6 -29.17 -29.55 - - - - - (0,1,1)(0,0,1) 6 -10.1 -16.01 0.04 0.85 South Pages #958 (55974) (1,0,0) - - - -15.65 - 0.09 0.92 - - (0,1,0) - -28.48 -30.63 1.16 0.28
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Chapter 3: Foraging site fidelity
Western Australia Abrolhos Is. South Australia
Great Australian Bight Kangaroo Island
Blefuscu (n=84) Lilliput (n=67) S ° 2 3 S ° 3 3 Olive Island (n=206) Eyre Peninsula S ° 4 West Waldegrave (n=157) 3 Spencer Gulf S ° 5 3 Liguanea (n=43) The Pages islands (n=589)
Kangaroo Island S ° 6
3 200km
133 °E 134 °E 135 °E 136 °E 137 °E 138 °E Seal Slide(n=16) A
Figure 1. The endemic Australian sea lion is distributed from Kangaroo Island in South
Australian through to The Abrolhos Islands in Western Australia (light relief). Inset - study sites along the South Australian range of Australian sea lions, encompassing 80%
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Chapter 3: Foraging site fidelity of the entire species. Numbers in parentheses represent approximate pup production
(Goldsworthy et al. 2010). Bathymetry lines are at 50m increments to 200m.
Geospatial and isotopic data were collected from twenty adult females across seven
breeding colonies between 2006-2009.
y
t
i
r
a
l
i
m
i
s
s
i D
Offshore Inshore
Figure 2.Dendrogram displaying alternate foraging ecotypes of 20 adult female
Australian sea lions sampled between June 2006 and Jun 2009 at seven breeding colonies in South Australia. Optimal clusters were determined using maximum-value
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BIC modelling of hierarchical agglomerative clustering of mean dive depth and mean distance from the mainland coast at foraging trip inflection point for all trips of each animal (leave-one-out cross-validated classification success = 99%). Numbers represent individual adult female ID number (see Supplementary Table 1.).
Figure 3. Representative tracking data from 20 adult female Australian sea lions tracked at a) The Nuyts Archipelago b) West Waldegrave c) Liguanea Island and d) The
Pages Islands and Seal Slide. Red points are locations identified by FPT as representing Area Restricted Searching. Areas of seagrass habitat are shown in blue
(Edyvane 1999). Individuals travelled similar distances during foraging trips irrespective
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Chapter 3: Foraging site fidelity of foraging ecotype. Elipses represent the 50% home range Utilisation Distribution of space of all trips for each individual animal calculated by fixed kernels and are colour- coordinated with individual tracks. Kernel home range size estimates were not affected by deployment duration and did not differ significantly between alternate foraging ecotypes. Grey lines represent changes in bathymetric slope derived from a 9 arc- second bathymetry Digital Elevation Model. Animals who foraged offshore displayed a preference for more variable bottom topography characterised by greater mean foraging dive depth SE.
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Chapter 3: Foraging site fidelity
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Chapter 3: Foraging site fidelity
[Figure 4 continued]
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Chapter 3: Foraging site fidelity
Figure 4. δ13 C and δ15 N isotope plots for serially subsampled adult female Australian sea lion vibrissae (3mm segments) from (a) Lilliput Is., (b) Blefuscu Is., (c) Olive Is., (d) West
Waldegrave,(e) Liguanea, (f) Seal Slide and (g) The Pages Is. Each subsample approximates feeding behaviour incorporated into one time step assumed to represent one month of growth. Most recent sample is at time step #1. * denotes adult females with 18 or more measurements (n=14) who expressed seasonal structure (either six or
12 month) in one or both stable isotopes (n=7). Stable isotope data from an additional four animals required differencing in order to achieve stationarity. The remaining individuals were parsimoniously characterised as correlated random walkers with respect to stable isotope ratios
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CHAPTER 4
POPULATION STRUCTURE OF ADULT FEMALE
AUSTRALIAN SE A LIONS IS DRIVEN BY FINE SCALE
FORAGING SITE FIDELITY
______
Lowther, A.D., Harcourt, R.G., Goldsworthy, S.D. and Stow. A.
University of Adelaide / South Australian Research and Development Insitute
Animal Behaviour; 2011; accepted paper
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Abstract
The Australian sea lion ( Neophoca cinerea ) is one of the rarest otariids in the world and is notable for an asynchronous, aseasonal breeding chronology. Determining the ecological features that shape the genetic structure of marine predators such as
Australian sea lions is challenging because their demersal foraging habitat is difficult to observe and quantify. Recent development in stable isotope screening techniques using milk-dependent pups as proxies for maternal isotope signatures identified temporally stable, alternate (inshore and offshore) foraging ecotypes in adult female
Australian sea lions. We combine this technique with mitochondrial DNA (mtDNA) analysis of samples of 40-60% of all pups produced at seventeen of the largest South
Australian colonies to determine if ecological specialisation in foraging ecotype within and between colonies has shaped maternal population structure within the species.
Genetic isolation by distance was apparent at very fine geographic scales (>40km) with three distinct clusters of colonies that share multiple haplotypes being interspersed with isolated breeding sites. There was no congruence between mtDNA haplotype distribution and foraging ecotypes suggesting that observed behavioural specialisation was not maintained along matrilines. We propose that foraging specialisation within discrete fine-scale foraging areas and habitats at the individual level limits the dispersive capacity of individual adult female Australian sea lions which in turn drives population structure. Given the vulnerability of this species to extant anthropogenic impacts and given the high degree of female population structure, determining the extent of male-mediated gene-flow in this species is critical. Only then can breeding
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Chapter 4: Population structure colony connectivity be established and appropriate management units identified for the species.
Introduction
Conservation management relies on the development of robust, effective population management units (MU) which typically requires the delineation of discrete genetic groups (Moritz 1994). Traditional explanations for population genetic structure that incorporate broad-scale geographical or historical processes may not be sufficient to explain the complex population structures observed in large, mobile organisms capable of dispersing over large geographic ranges. The importance of ecological and environmental factors in shaping genetic structure has been reflected in re-emphasis on the MU concept, incorporating information about distinctiveness in ecological functions and the timescales over which they arise (Crandall et al. 2000). The investigation of how habitat structure and resource heterogeneity influences population structuring in terrestrial predators typically involves linking geospatial information on the movement and behaviour of animals (from radio telemetry devices or GPS devices) to variables such as prey abundance, habitat heterogeneity, topography, intersexual differences in reproductive ecology, and sociality (Pilot et al. 2006, Crompton et al. 2008, Haag et al.
2010, de Silva et al. 2011).
As tracking studies are expensive, generally few animals are tracked and the data are used to characterise population range for management purposes. Once a population or species niche range has been defined, conspecifics have traditionally been treated as
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Chapter 4: Population structure ecologically equivalent (Bolnick et al. 2003). However there is a growing body of literature that suggests individual niche-width specialisation is common, making such generalisations in the absence of data questionable. In otarid seals (fur seals and sea lions), individual niche specialisation appears common (Thompson et al. 1998,
Staniland et al. 2004, Chilvers et al. 2005) and its extent within populations may provide a metric of ecological diversity and subsequent evolutionary potential (Bolnick et al.
2003). Capturing true levels of individual specialisation in a population is often hampered by limited sampling due to logistical difficulties and life history constraints
(Hays et al. 2003, Cronin and McConnell 2008), which may have important implications for conservation.
Quantifying ecological parameters important in shaping the population structure of air- breathing marine predators such as the otariid seals (fur seals and sea lions) present a series of complex challenges. Researchers must address the geographic decoupling of observable (terrestrial) breeding habitat and cryptic (aquatic) foraging areas. This typically involves the use of expensive geospatial tracking devices and time-depth recorders to describe the three-dimensional movements of large, wild, elusive predators during foraging trips to sea. Although this can now be achieved with some level of precision, information on the habitat, prey diversity and abundance in the places where animals forage is still a major challenge.
Recently, cost-effective stable isotope biogeochemistry has provided great insights into foraging ecology across a range of marine mammals (see Crawford et al. 2008 for
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Chapter 4: Population structure review). There is strong support for the upward-cascade of stable nitrogen isotopes
(δ15 N) from primary producers to the top of the food web. In marine ecosystems a decreasing stable carbon ratio ( δ13 C) gradient has been identified with distance from shore(Hobson et al. 1996, Post 2002). Isotope ratios provide ecological geo-trophic data that has been used to describe temporal and individual variation in foraging location and diet outside the scope of traditional tracking methods. Using δ13 C and δ15 N from metabolically active (blood) and inert tissues (whiskers, feathers etc.) individual variation in foraging location and diet has been detected in several seal and seabird species(Forero and Hobson 2003, Aurioles et al. 2006, Bearhop et al. 2006, Awkerman et al. 2007, Lowther and Goldsworthy 2010).
Several life history traits of the endangered Australian sea lion Neophoca cinerea that are unusual from an ecological and evolutionary perspective are also relevant from a conservation and management standpoint. This IUCN Redlisted species is distributed from The Pages islands in South Australia to the Abrolhos Islands in Western Australia
(Figure 1) with 85% of pup production occurring across 49 breeding sites within South
Australian waters (Shaughnessy et al. 2011). A protracted, aseasonal breeding pattern
(~17.5mo) that is temporally asynchronous across its range is unique amongst pinnipeds where annual synchronous breeding is the norm (Atkinson 1997).
Furthermore, Australian sea lions breed sympatrically with two other synchronous breeding species, the Australian and New Zealand fur seals ( Arctocephalus pusillus doriferus and A. australis forsteri ). These species display limited genetic population structure within their Australasian ranges (Lento et al. 1997, Lancaster et al. 2010)
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Chapter 4: Population structure suggesting frequent migration of at least adult females between breeding colonies and recolonisation events post-sealing (Lancaster et al. 2010).
By contrast the single molecular study conducted on Australian sea lions reported strong population subdivision and extremely high adult female natal site philopatry
(Campbell et al. 2008). These data suggested almost no effective migration of adult females between colonies within known travelling distances (Lowther and Goldsworthy
2010) however their study covered only a small part of the Australian sea lion range in
South Australia and lacked any ecological data. Negligible gene-flow between breeding colonies in close proximity has important implications for conservation management because the potential for recolonisation events appear minimal (Campbell et al. 2008).
Australian sea lions in South Australia are vulnerable to significant bycatch in demersal gillnet fisheries therefore if this high degree of fidelity occurs across the entire range, local extinctions are inevitable (Goldsworthy and Page 2007). The mechanisms underpinning such unusual life history traits are poorly understood however Campbell et al. (2008) suggested foraging site fidelity and breeding asynchrony between colonies may drive selection for adult female natal site philopatry.
A recently developed technique screens milk-dependent pups as proxies for maternal values using stable isotope ratios (Lowther and Goldsworthy 2010). This technique has identified significant individual variation in adult female Australian sea lion foraging behaviour that is manifested in alternate foraging ecotypes (inshore or offshore) which appear to remain stable over years (Lowther et al. 2011). This large-scale screening
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Chapter 4: Population structure technique when used in conjunction with matrilineally inherited mitochondrial DNA
(mtDNA) provides a means to simultaneously compare both the matrilineal relatedness and foraging ecotype of large numbers of individuals. Potentially, if offspring remain faithful to maternally-introduced regions of fine-scale foraging habitat this might reinforce selection for natal site philopatry by adult females. This may be observable as congruence between mitochondrial DNA haplotypes and foraging ecotypes similar to that seen in southern right whales (Valenzuela et al. 2009) and killer whales (Hoelzel et al. 2007).
As such, we test whether maternally-directed foraging site fidelity occurs in Australian sea lions. Ultimately, our main objective was to determine whether breeding asynchrony, geographic distance between colonies and bathymetry significantly influence fine-scale matrilineal population substructure within and amongst Australian sea lion breeding colonies.
Materials and methods
Mitochondrial DNA (hair) and stable isotope (whisker) samples were collected from newborn (<3mths) Australian sea lion pups at seventeen colonies along the southern coast of South Australia (Figure 1a) between January 2008 and October 2009 (although whisker samples were not available for Seal Bay and English Island). Approximately
60% of all pups produced in each colony were captured with the aid of a hoop net, manually restrained, sexed and measured for standard length (straight-line length from tip-of-nose to tip-of-tail, to nearest 0.5cm) and mass (25x0.1kg spring balance, Salter
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Weigh-Tronix, Blackburn, Victoria, Australia.). Individual unique identifying patterns were clipped into the hair across the back of each pup with scissors and a whisker was clipped (right hand side, proximal to the face) at the base of the muzzle. Sampling instruments were cleaned after each use. Hair and whiskers were stored inside individually-labelled glycine bags until analysis.
All whiskers were cleaned in a method similar to that used for human hair (O'Connell and Hedges 1999) and southern elephant seal ( Mirounga leonina ) whiskers (Lewis et al.
2006). Briefly, whiskers were cleaned individually for 20mins with de-ionised water then with a solution of 2:1 methanol:chloroform for a further 20 mins to remove lipids, followed by a final clean with de-ionised water for an additional 20mins. Each whisker was checked under a stereo microscope for any remaining tissue or dirt; contaminants were removed using a scalpel blade. All samples were then rinsed with distilled water and left to air dry overnight in a fume cupboard. A 1mm segment was cut from the proximal end of each whisker and stored in a uniquely labelled 7mm glass scintillation vial. DNA was extracted from five to 50 hair samples (Table 1) using the Gentra
Puregene © DNA purification kit (Qiagen Pty Ltd, Victoria Australia).
DNA Amplification
All laboratory work was conducted at the University of Adelaide Evolutionary Biology
Unit (EBU). We used a 1:150 dilution of extracted DNA in TLE as the PCR template.
Amplification of a 650bp fragment of the mtDNA control region was performed using the following specifically designed primer pair: forward 5’ ACACCCAAAGCTGACGTTCTC
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3’ (EBU nomenclature: M1214) and reverse 5’ TGAAATGCACCTCATGGTTGTA 3’
(EBU nomenclature: M1216). The PCR reactions were performed in 12.5microL volumes. PCR conditions were: 15 minutes denaturing at 95 oC followed by 35 cycles of
95 oC for 30s, 45 oC for 30s and 68 oC for one minute. Cleaned PCR product was sent to the Australian Genome Research Facility (AGRF) for sequencing on an AB 3730 DNA
Sequencer (Applied Biosystems Inc).
Stable Isotope Analysis
All samples for isotope analysis were sent to the Australian National University
Environmental Biology Stable Isotope Facility. Analysis was performed using a
Microass isoChrom CFIR mass spectrometer coupled to a Carlo Erba EA-1100 CHN-O analyser. The standard control ratio for 13 C and 15 N were Pee Dee Belemnite and atmospheric nitrogen respectively. Observed error rates were recorded as <±0.1‰
(δ13 C) and <±0.3‰ ( δ15 N).
Data Analysis – mtDNA
Summary Statistics
Sequences were aligned manually using BioEdit 7.0.5.3 (Hall 1999). Kimura's two- parameter model of nucleotide substitution (Kimura 1980) was selected as the most appropriate model of nucleotide substitution using Akaike Information Criterion (AIC) in jModelTest v0.1.1 (Posada 2008) . Haplotype composition, haplotype diversity (h) and nucleotide diversity ( π) were estimated, with Tajima's D (Tajima 1989) and Fu's Fs
(Yun-xin 1997) tests being employed to detect deviations from neutrality and recent
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Chapter 4: Population structure population expansion events in DNAsp v5.10.01 (Librado and Rozas 2009).
Geographical distance was calculated as the shortest swimming distance in kilometres between colonies (Gonzalez-Suarez et al. 2009).
Characterising Genetic Structure
The presence of isolation by distance (IBD) was assessed using a plot of Slatkins linearised Φst (calculated in Arlequin v3.5.1.2) (Excoffier et al. 2005) with geographic distance, and a Mantel Test (Mantel 1967) was conducted in Arlequin (significance being determined after 10,000 permutations).
The use of spatial AMOVA (SAMOVA) to detect clusters of colonies that are maximally differentiated can be confounded in the presence of IBD (Dupanloup et al. 2002).
Therefore we employed SAMOVA 1.0 to develop hypotheses of clustered colonies from
K=2 to K=16, and tested each hypothesis using AMOVA in Arlequin (Manier and Arnold
2006). Optimal cluster selection was based on maximised between-cluster significant differences ( Φct ) as recommended by Dupanloup et al (2002). We also employed
Monmoniers Maximum Distance algorithm and genetic landscape surfaces (GLS) implemented in AIS (Miller 2005) to visually check that geographic regions where genetic differentiation was maximised were congruent with the optimal clustering identified by SAMOVA. GLS interpolation was parameterised at multiple scales with surfaces based on the midpoints of edges derived from Delauny triangulation and residual genetic distances. Surface topology did not vary with differing scales and we
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Chapter 4: Population structure display the topology based on X and Y coordinates in bins of 50 with a residual genetic distance weighting of one.
Determinants of Genetic Structure and Foraging Ecotype
Isolation by distance makes evaluating the relative contribution of ecological and environmental factors to genetic partitioning difficult. Partial Mantel tests (with the significance of results based on 999 permutations) were conducted to investigate whether geographic distance, breeding asynchrony and distance to shelf waters were significantly related to observed genetic structure and alternate foraging ecotypes.
Given the considerable literature supporting linear pup growth during the early stages of lactation (Guinet et al. 1999, Guinet and Georges 2000, Arnould and Hindell 2002,
Goldsworthy 2006) we used mass to estimate age-at-capture of sampled pups.
Regression equations were derived using data from 18 serially-resampled pups monitored from birth to c.125 days at Dangerous Reef during the 2006-07 breeding episode (Lowther and Goldsworthy 2011). Each colony was then assigned a median birthing date (Appendix 1). A 9 arc-second (250m) resolution bathymetric digital elevation model of the study area (Whiteway 2009) was used to determine the shortest swimming distance to the 100m bathymetry line from each colony.
Isotope datasets for Olive, Lilliput and Blefuscu Islands were used from an earlier study
(Lowther and Goldsworthy 2010). δ13 C and δ15 N isotope data available for genetically- sampled individuals ( N=413) were explored using isotope biplots for each colony.
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Alternate foraging ecotypes were identified using agglomerative hierarchical clustering and leave-one-out-cross-validated quadratic discriminant analysis (LOOCV-QDA).
Individuals within clusters were defined as representing inshore or offshore foragers based on congruence with existing mean values (Lowther and Goldsworthy 2010). The
Bray-Curtis method was selected to construct an individual pairwise dissimilarity matrix of δ13 C and δ15 N values as it appears well suited to ecological data (Clarke 1993).
Unless otherwise stated, mean values (±SE) are reported and significance is assumed at P<0.05. All animal handling and experimentation were performed under Department of Environment and Heritage scientific permit #A24684-7 and University of Adelaide animal ethics permit #S-010-2008.
Results
Mitochondrial DNA Control Region Diversity
The final edited alignments comprised 464 bp of the mtDNA control region from 478 individuals. Control region sequences contained 21 polymorphic sites of which 19 were informative under parsimony. We recovered 21 individual haplotypes of which ten
(48%) were unique to a particular colony (Appendix 2 and Figure 1a) with one haplotype being shared between 11 colonies ranging from Dangerous Reef to Lounds. Haplotype
(h) and nucleotide ( π) diversities ranged from 0 to 0.86 (mean 0.53 ±0.239) and 0.0069
(mean 0.0028 ± 0.002) respectively with two colonies (Blefuscu and Lounds) being fixed for a single haplotype. We found neither a departure from neutrality nor evidence of population expansion (Tajima's D =-0.86 to 2.77; Fu's Fs >0.62, P>0.23 at all colonies in both cases).
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Chapter 4: Population structure
Strong Genetic Partitioning among Colonies
Overall fixation among colonies was high ( Φst = 0.75, P<0.001). Maximising significant between-group Φct variation clustered Australian sea lion breeding colonies into ten putative groups ( Φct =0.66, P<0.001). The Pages islands, Dangerous Reef and English
Island in the Spencer Gulf and the Nuyts Archipelago ( K=6) were clustered with all other colonies being characterised as isolated colonies (Figure 1a). The Pages (North Page and South Page Island) are considered one colony, given their close geographic proximity (<2km), two shared mtDNA haplotypes in similar proportions and high genetic similarity (~0 Φst ) (Appendix 3). Between-group corrected pairwise Φst comparisons generally supported clusters with few exceptions (Appendix 4). Peaks identified by
MMDA and genetic interpolation landscape plots qualitatively supported the clustering of colonies inferring restricted matrilineal gene-flow south-east of the Nuyts Archipelago, at the Thorny Passage (southern end of the Spencer Gulf) and at the eastern end of
Kangaroo Island (Figure 1b).
Alternative Foraging Behaviours
Maximum-value BIC models revealed up to three distinct foraging ecotype groups
(inshore, offshore and intermediate) at breeding colonies (Figure 3). LOOCV-QDA correctly assigned individuals to the same ecotype groups with high classification certainty (>80%) based on δ13 C and δ15 N values. Mean cluster δ13 C and δ15 N values showed congruence with existing data indicating the presence of alternate foraging ecotypes at eight colonies while adult females at seven colonies were characterised
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Chapter 4: Population structure almost exclusively as offshore foragers (Table 1; Figure 3). Foraging ecotypes at North
Pages Is. were indistinguishable using δ13 C (Students T-test t=0.77, P=0.45) and no trophic difference in ecotypes could be detected at West Waldegrave (Students T-test, t=1.95, P=0.07). At all other locations, foraging ecotypes were characterised by significant differences in the clustering of δ13 C and δ15 N values (Table 1).
Environmental Determinants of Genetic Structure and Foraging Behaviour
Foraging ecotypes are not associated with patterns of genetic structure. Although significant IBD was apparent (Mantel R=0.39 P<0.001; Figure 2), there was no propensity for individuals sharing mtDNA haplotypes to share foraging ecotypes (Mantel
R= -0.03, P=0.3). Rather, genetic structure became significantly stronger with both proximity of individuals to deep water and breeding asynchrony (Mantel R=0.29 and
0.22 respectively, P<0.001 in both cases). These relationships weakened though remained significant after partialling out the effects of geographic separation between individuals (partial Mantel R=0.16 and 0.1 respectively, P<0.001 in both cases).
However, the tendency for individuals to be offshore foragers was not influenced by their proximity to deep water (Mantel R> -0.1, P≈1). Variation in ecotype membership was weakly related to geographic distance separating individuals (Mantel R=0.08,
P<0.001) and this remained significant after controlling for breeding asynchrony between colonies (partial Mantel R=0.06, P<0.001; Appendix 1). A similar relationship was found between breeding asynchrony and foraging ecotype membership (Mantel R
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Chapter 4: Population structure
= 0.13, P<0.05) however this relationship disappeared when geographic distance was controlled (partial Mantel R=-0.1, P=0.93).
Discussion
Understanding the behavioural, environmental and ecological features that underpin population substructure is an essential prerequisite to the effective use of genetic data in assessing population status and connectivity. We find a degree of population structure in the Australian sea lion substantially more complex than that previously described in Campbell et al. (2008). There is considerable evidence of haplotype sharing, with two haplotypes spanning a large portion of the South Australian range and only two colonies displaying fixation for a single control region haplotype.
Commensurate with this, we report a lower degree of population subdivision than previously described ( Φst 0.93 cf. 0.75 this study). Nonetheless this more comprehensive assessment seems to support the earlier contention that Australian sea lions exhibit the most restricted effective migratory movement of any marine mammal
(Campbell et al. 2008).
Matrilineal Population Structure is unrelated to Broad-scale Ecological Processes
The scale over which isolation-by-distance influences the matrilineal population structure of the Australian sea lion is remarkable. Adult female Australian sea lions are capable of travelling much further distances than the fine spatial scale (<40km) at which significant matrilineal clustering of breeding colonies was detected in the current study
(Lowther et al. 2011). The reliance on suitable habitat or resource for breeding by adult
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Chapter 4: Population structure female mammals typically results in lower dispersal than conspecific males (Greenwood
1980). In central-place foraging otariids, breeding colonies tend to be situated in proximity to suitable foraging grounds to facilitate offspring survival with regular delivery of nutrition to dependent offspring (Boyd 1998). Intuitively, adult females should be free to move between colonies that are within range of a common habitat. This would be expressed as matrilineal panmixia among colonies that share, or are in close proximity to the same foraging habitat. At scales where alternative foraging locations become more cost-effective to access, adult females should be more restricted in their dispersive abilities and genetic structure should then become evident. The range over which IBD begins to shape matrilineal population structure in Steller, California and
South American sea lions typically encompass several breeding colonies (Maldonado et al. 1995, Hoffman et al. 2006, Tunez et al. 2007, Gonzalez-Suarez et al. 2009), and
Australian fur seals exhibit complete matrilineal panmixia with most extant breeding colonies being within travelling distance of known feeding grounds in central Bass Strait
(Arnould and Hindell 2001a, Lancaster et al. 2010).
Ecological barriers that influence prey distributions may result in highly-mobile predators moving well within their theoretical maximum limits (Geffen et al. 2004, McRae et al.
2005), leading to genetic partitioning of populations. Indeed, within the region, a trans-
Spencer Gulf genetic barrier appears to influence population structure in another highly- vagile marine mammal the common dolphin Tursiops spp . (Bilgmann et al. 2007). The authors speculate that Spencer Gulf bottlenose dolphin population structure may be
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Chapter 4: Population structure shaped by the presence of an oceanographic front across the Spencer Gulf significantly influencing the distribution of important prey for bottlenose dolphins.
At face value our data appeared to support restricted gene-flow at the same location with an almost complete lack of haplotype sharing or effective migration of adult females between colonies within the Spencer Gulf and those outside. Other putative matrilineal gene-flow restrictions were identified near the Nuyts Archipelago and near Kangaroo
Island. However the detection of alternate foraging ecotypes in almost every colony indicates there are no regional-scale ecological influences on feeding behaviour of adult female Australian sea lions. Given the degree of individual specialisation in their benthic foraging behaviour (Baylis et al. 2009, Lowther and Goldsworthy 2010, Lowther et al. 2011), dynamic broad-scale oceanographic features influencing the distribution of epipelagic, seasonal prey are unlikely to be an important factor in shaping matrilineal population structure.
Is Individual Specialisation driving Matrilineal Population Structure ?
Our data shows within-colony specialisation in foraging behaviour occurs repeatedly across the range of the species despite extensive subpopulation subdivisions. The contrast in haplotype composition and breeding synchronicity between clustered and singleton colonies suggest that effective migration away from a preferred foraging area is very uncommon, leading to conditions favourable to genetic drift and isolation. We propose a stepping-stone model of gene flow for adult female Australian sea lion
(Kimura and Weiss 1964) where genetic interchange between colonies within a cluster
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Chapter 4: Population structure occurs yet is negligible or completely absent between singletons. Examples of this may include restrictions to gene-flow at the Nuyts Archipelago, the southern end of the
Spencer Gulf and east of Kangaroo Island. Similar patterns of restricted gene-flow have been detected in California sea lions and harbour seals (Lehman et al. 1993, Lamont et al. 1996, Tunez et al. 2007) though the spatial scales over which this occurs in
Australian sea lions may reflect the scale at which foraging site fidelity occurs. The accessibility of multiple suitable habitats may require an animal to develop specialist foraging techniques to successfully exploit one, which may preclude exploitation of others (McLaughlin et al. 1999, Bolnick et al. 2003, Knudsen et al. 2011). Selection for foraging specialisation within discrete fine-scale foraging areas and habitats at the individual level may provide a mechanism to drive observed population structuring and associated breeding asynchrony. Evidence for the effects of habitat-specialisation on population substructuring is growing (Pichler et al. 1998, Geffen et al. 2004, Pilot et al.
2006, Clark et al. 2008). Indeed a positive relationship between population substructure and bathymetry suggests foragers who specialise in deep-water habitat may be the most restricted in their ability to disperse.
Assuming niche-width specialisation in Australian sea lions observed in this study is maintained over generations the manner in which specialisation persists is intriguing, particularly given the cryptic nature of foraging habitat. Active foraging is a stepwise process involving decisions as to when, where, what and how to forage and each step has the potential to be positively influenced by information gained from conspecfics
(Galef 2001). Social learning should be selectively advantageous at intermediate levels
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Chapter 4: Population structure as very slow environmental change favours genetically-based adaptation. Social learning may occur vertically when both parent and offspring are exposed to the same environment, or horizontally if changes in the environment are faster than generation time. When environmental change is very rapid, social learning may result in the acquisition of out-dated information and individual ‘trial and error’ learning should be selected for (Galef 2001, Galef and Laland 2005).
The vertical transmission of specialist behaviours to offspring along mtDNA lineages has been recorded in many species including Killer whales (Yurk et al. 2001), southern right whales (Valenzuela et al. 2009) and dolphins (Krutzen et al. 2005). This does not appear to occur in Australian sea lions; there was no detectable relationship between individual mtDNA haplotype and foraging ecotype. Furthermore, several colonies that were fixed for a single foraging ecotype were characterised by multiple mtDNA haplotypes, whilst adult females at the two monohaplotypic colonies expressed alternate foraging behaviours. For genetically-determined, stable behaviours to be visible along matrilines there would have to be a complete absence of paternal inheritance (Rendell and Whitehead 2001). This appears highly unlikely given that mating in the Australian sea lion is highly likely to transcend behavioural boundaries.
A more likely mechanism for maintaining niche-width specialisation is horizontal, within- generation social learning. Unequivocal evidence exists for social learning either from related non-parental adults or older siblings, or unrelated adults or juveniles in many mammal and bird species (Galef 1976, 2001, Galef and Laland 2005). A recent study
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Chapter 4: Population structure using network theory (Krause et al. 2007) explored the relationship between social networks and kinship in the Galapagos sea lion suggesting that, at least on-land, behavioural decisions had the potential to be influenced by the degree of kin- relatedness (Wolf et al. 2007). Additional genetic markers capable of resolving relatedness beyond immediate family could be incorporated into our technique and would be useful to explore the interaction between behavioural specialisation and relatedness across large numbers of individuals. This technique could be applied to any socially-cohesive species to determine whether behavioural specialisation occurs through social learning or independent trial and error.
Future Directions
Our study uncovered several intriguing aspects of matrilineal population structure of the
Australian sea lion. While our data support Campbell et al. (2008) findings of increased mtDNA haplotype diversity at larger colonies, we show that the second largest breeding colony for the species (The Pages islands) is one of the most depauperate. Restricted migratory dispersal of adult females is likely to be responsible for the difference in haplotype composition with the nearby colony on Kangaroo Island. However the recovery of only two haplotypes at The Pages islands is perplexing. Given one of these haplotypes is unique to The Pages Islands, the assessment of male-mediated gene-flow using biparentally-inherited microsatellite markers is essential to resolve the conservation status of the colony.
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Chapter 4: Population structure
Possibly the most interesting anomaly from a genetic standpoint is Jones Island.
Situated between two relatively large breeding colonies and typically producing less than 20 pups Jones Island was the fifth most diverse colony in the study. The prolonged duration of Australian sea lion breeding episodes (Higgins 1993) presents the opportunity of breeding at Jones Island overlapping with adjacent colonies. Thus adult females from adjacent colonies restricted in their dispersal only by the distance to their preferred foraging habitat could potentially undergo transient migration to Jones Island without compromising reproductive success.
Conversely it is possible that the unexpectedly high haplotype diversity observed at
Jones Island are caused by the transient movement of young pups from their natal colony, however the size of the pups sampled makes it highly unlikely they were capable of movement from nearby colonies. There are many smaller colonies, among the predominantly larger colonies that have not been sampled. The inclusion of these colonies will undoubtedly help resolve some of the anomalies identified in genetic population structure . Sampling of other small colonies (Figure 1a) is imperative to ascertain whether they represent unique (and therefore significant at-risk) entities, reproductive ‘intersection points’ between larger colonies or merely transient locations frequented by mothers and their pups from nearby colonies (Shaughnessy et al. 2005).
Additionally, three Australian sea lion breeding colonies between Liguanea and West
Waldegrave (Figure 1a) were not sampled in this study. Given there appears to be little or no genetic interchange between Liguanea and Lewis Island possibly due to a lack of
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Chapter 4: Population structure shared foraging habitat, sampling of these additional colonies should be a priority to establish the degree of connectivity westwards along the lower Western Eyre peninsula and resolve the isolated status of the Liguanea breeding colony.
Conclusion
The appropriate and synergistic use of biogeochemical and molecular markers is an effective technique to investigate the influence of behavioural specialisation on population structure. More precise estimates of the degree and significance of individual specialisation in key behaviours are likely to better inform on the processes influencing population structure. Our combination of techniques clearly demonstrates a way to increase sampling effort in cases where life-history constraints or prohibitive logistical costs restrict the numbers of animals from which data can be collected. This method can be applied across a wide range of marine and terrestrial predators, particularly those who travel large distances or feed in environments which are difficult to observe and whose offspring exhibit a period of nutritional-dependence. Our data underscores the inappropriateness of making broad-scale inferences regarding
Australian sea lion ecology. The complex interplay between individual niche-width specialisation and matrilineal population structure in the Australian sea lion is evident and presents a fascinating opportunity to study the selective factors that have shaped the unique reproductive and foraging strategies of Australian sea lions. Additional isotope screening of colonies is required to check our assumption that foraging specialisation occurs across generations. Of critical importance is information on adult male Australian sea lion foraging and reproductive ecology. Once the effects of male-
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Chapter 4: Population structure mediated gene flow are quantified, appropriate conservation management plans can be formulated.
Acknowledgements
Research was funded through the Department of Environment, Water, Heritage and the
Arts' Australian Marine Mammal Centre, Australian Antarctic Division and the
Commonwealth Environment Research Facilities (CERF) program, Sea World
Research and Rescue Foundation Inc and the Holsworth Widlife Research Endowment.
We thank Professor Stephen Donnellan at the South Australian Museum for providing feedback on early drafts, and acknowledge the logistical assistance of the South
Australian Department of Environment and Natural Resources rangers and the South
Australian State Emergency Services.
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Chapter 4: Population structure Table 1. Characterisation of fifteen Australian sea lion breeding colonies in South Australia by foraging ecotype using δ13 C and δ15 N present in pup whiskers. Optimal clustering models were selected using Bayesian Information Criteria. Clusters were assigned to 'inshore', 'intermediate' or 'offshore' foraging ecotypes based on congruence with mean δ13 C and δ15 N values described in previous studies (Lowther and Goldsworthy 2010, Lowther et al. 2011). Adult female Australian sea lions at three colonies in the Nuyts Archipelago and at Nicholas Baudin foraged exclusively offshore whilst 11 colonies were characterised by at least two significantly different alternate foraging strategies.
Ecotype representation (%) Mean δ13 C (‰) Mean δ15 N (‰) Students T test Colony n Inshore Intermediate Offshore Inshore Intermediate Offshore Inshore Intermediate Offshore δ13 C δ15 N NP 43 49 - 51 - 15.8 (±0.25) - - 15.9 (±0.52) 13.7 (±0.52) - 16.1 (±0.6) 0.77 ns 15.11 ** SP 50 48 - 52 - 15.6 (±0.71) - - 16 (±0.31) 13.6 (±0.52) - 15.7 (±0.69) 2.06 * 11.9 ** DR 32 3 - 97 -11.9 - - 15.8 (±0.41) 15 - 16 (±0.44) - - LI 26 4 - 96 -12.7 - - 15.6 (±0.55) 14.8 - 15.5 (±0.58) - - LIG 26 62 - 38 - 15.3 (±0.33) - - 15.9 (±0.44) 16.7 (±0.42) - 15.7 (±0.44) 3.63 ** 6.08 ** WW 33 70 - 30 - 15.2 (±0.29) - - 16.9 (±0.84) 16.9 (±0.32) - 16.6 (±0.47) 6.33 ** 1.95 NS JI 8 14 - 86 -15 - -15 16.8 - 16.2 (±0.06) - - NB 29 0 - 100 - - - 15.8 (±0.84) - - 15.8 (±0.41) - - OI 1,2 48 9 20 71 -12.6 (±0.27) -15.7 (±0.15) -15.5(±0.05) 12.7(±0.17) 13.7(±0.12) 15.5(±0.05) 74.1 **† 74.1 **† BL 1 26 29 - 71 -13.3(±0.38) - -16(±0.04) 14.8(±0.29) - 18(±0.12) 3.8 ** 3.6 ** LIL 1 31 49 39 12 -12.5 (±0.17) -15.3 (±0.21) -17.5 (±0.46) 14.5 (±0.17) 17.2 (±0.22) 15.6 (±0.32) 47 **† 47 **† WI 14 0 - 100 - - - 16.5 (±0.32) - - 16.7 (±0.39) - - FEN 5 0 - 100 - - - 16.6 (±0.08) - - 16.7 (±0.14) - - PU 29 0 - 100 - - - 16.5 (±0.33) - - 16.7 (±0.41) - - LO 13 38 - 62 - 12.5 (±0.94) - - 16.2 (±0.77) 14.4 (±0.42) - 16.3 (±0.31) 7.27 ** 8.84 ** ns Not Significant, * P<0.05, ** P<0.01, † Kruskal Wallis χ2
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Chapter 4: Population structure Appendix 1. Median breeding date of colonies sampled (below). Colonies clustered together using
SAMOVA are highlighted.
Colony NP & SP SB DR & EI LI LIG WW JI NB OI BL & LI WI & FEN PU & LO Median Breeding Date Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 May-09 Jun-09 Jul-09 Aug-09 Sep-09 Oct-09 Nov-09 Dec-09 Jan-10
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Chapter 4: Population structure Appendix 2. Haplotype frequency of colonies sampled throughout the South Australian range. Colonies are ordered East-West (left-right) and those clustered by SAMOVA are highlighted. Seven haplotypes were unique to a particular colony (D, J, P, Q,R, S, T and U). The Pages Islands (representing the second largest breeding colony of the species) shared only two haplotypes, whilst Blefuscu Is. and Lounds were fixed for a single halotype.
Colony
South Pages North Pages Seal Bay Dangerous Reef English I. Lewis I. Liguanea West Waldegrave Jones I. Nicholas Baudin Olive I. Blefuscu Lilliput I. West I. Fenellon Purdie Lounds
Abbreviations (SP) (NP) (SB) (DR) (EI) (LI) (LIG) (WW) (JI) (NB) (OI) (BL) (LIL) (WI) (FEN) (PU) (LO) n 50 45 50 32 10 27 27 33 10 29 48 26 31 14 5 29 14 Haplotypes A 0.52 0.556 0.22 0 0 0 0.296 0.0303 0 0.483 0.0833 0 0 0 0 0 0 B 0.48 0.444 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0.0625 0 0 0 0.333 0.5 0.103 0.354 1 0.808 0.643 0.6 0.517 1 D 0 0 0 0 0 0 0 0 0 0 0 0 0.0769 0 0 0 0 E 0 0 0 0 0 0 0 0.0303 0 0 0.229 0 0.115 0.357 0.4 0.31 0 F 0 0 0 0 0 0 0 0 0.3 0.414 0.292 0 0 0 0 0 0 G 0 0 0 0 0 0 0 0 0 0 0.0417 0 0 0 0 0 0 H 0 0 0 0 0 0 0 0.606 0.2 0 0 0 0 0 0 0.0345 0 I 0 0 0.04 0.0312 0 0 0.704 0 0 0 0 0 0 0 0 0 0 J 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.138 0 K 0 0 0 0.344 0.125 0.037 0 0 0 0 0 0 0 0 0 0 0 L 0 0 0 0.25 0.375 0.407 0 0 0 0 0 0 0 0 0 0 0 M 0 0 0 0.125 0 0.0741 0 0 0 0 0 0 0 0 0 0 0 N 0 0 0 0.156 0.25 0.0741 0 0 0 0 0 0 0 0 0 0 0 O 0 0 0 0.0312 0 0 0 0 0 0 0 0 0 0 0 0 0 P 0 0 0 0 0 0.37 0 0 0 0 0 0 0 0 0 0 0 Q 0 0 0 0 0 0.037 0 0 0 0 0 0 0 0 0 0 0 R 0 0 0.18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S 0 0 0.56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 0.125 0 0 0 0 0 0 0 0 0 0 0 0 U 0 0 0 0 0.125 0 0 0 0 0 0 0 0 0 0 0 0
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Chapter 4: Population structure
Appendix 3. Pairwise genetic differences between colonies.
BL LIL PU FEN WI LO SP NP SB LI OI WW LIG EI DR JI NB BL 0 LIL 0.10032 0 PU 0.03117 0.21275 0 FEN -0.05636 0.68259 -0.08504 0 WI 0.00307 0.44486 -0.02009 -0.15231 0 LO 0.04446 0 0.13917 0.4994 0.30769 0 SP 0.78908 0.85056 0.79843 0.75567 0.78159 0.81624 0 NP 0.78737 0.85364 0.79752 0.75254 0.78047 0.81708 -0.01897 0 SB 0.80519 0.88266 0.81157 0.79363 0.81104 0.85462 0.58922 0.5779 0 LI 0.76019 0.83939 0.76907 0.69063 0.74018 0.78307 0.58317 0.56947 0.6063 0 OI 0.16911 0.36653 0.16803 0.02404 0.10992 0.29845 0.70508 0.70024 0.70513 0.70019 0 WW 0.30708 0.45014 0.32817 0.33014 0.36621 0.36144 0.82297 0.82234 0.8368 0.79889 0.40766 0 LIG 0.80781 0.94377 0.8243 0.85176 0.85648 0.92101 0.51259 0.4978 0.42614 0.51966 0.64748 0.84624 0 EI 0.77158 0.8988 0.79146 0.66667 0.76674 0.82594 0.57606 0.56156 0.64433 0.08055 0.70346 0.81593 0.59932 0 DR 0.70141 0.77757 0.7068 0.61788 0.67175 0.71372 0.523 0.50558 0.54412 0.14128 0.64844 0.74488 0.43146 -0.05004 0 JI 0.0336 0.38856 0.07759 -0.03015 0.06233 0.23668 0.75773 0.75502 0.79809 0.71833 0.06795 0.22326 0.81756 0.70071 0.65495 0 NB 0.43536 0.59501 0.44425 0.29139 0.38104 0.50229 0.43269 0.41659 0.48442 0.50305 0.225 0.56019 0.32731 0.47178 0.4341 0.33406 0
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Chapter 4: Population structure
Appendix 4. Pairwise genetic difference P-values
BL LILLILLIL PUPUPU FEN WIWIWI LOLOLO SPSPSP NPNPNP SBSBSB LILILI OIOIOI WWWWWW LIGLIGLIG EIEIEI DRDRDR JIJIJI NBNBNB BL * 0.00000 LILLILLIL +-0.0000 * 0.10811 0.00000 PUPUPU +-0.0326 +-0.0000 * 0.65766 0.00000 0.93694 FEN +-0.0408 +-0.0000 +-0.0203 * 0.31532 0.00000 0.63964 0.99099 WIWIWI +-0.0434 +-0.0000 +-0.0394 +-0.0030 * 0.23423 0.99099 0.05405 0.09009 0.01802 LOLOLO +-0.0411 +-0.0030 +-0.0148 +-0.0192 +-0.0121 * 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 SPSPSP +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 * 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.81081 NPNPNP +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0359 * 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 SBSBSB +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 * 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 LILILI +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 * 0.00000 0.00000 0.00000 0.34234 0.01802 0.00000 0.00000 0.00000 0.00000 0.00000 OIOIOI +-0.0000 +-0.0000 +-0.0000 +-0.0379 +-0.0121 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 * 0.00000 0.00000 0.00000 0.01802 0.00901 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 WWWWWW +-0.0000 +-0.0000 +-0.0000 +-0.0121 +-0.0091 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 * 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 LIGLIGLIG +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 * 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.12613 0.00000 0.00000 0.00000 EIEIEI +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0337 +-0.0000 +-0.0000 +-0.0000 * 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.82883 DRDRDR +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0588 * 0.14414 0.00000 0.09910 0.57658 0.09910 0.00901 0.00000 0.00000 0.00000 0.00000 0.12613 0.00901 0.00000 0.00000 0.00000 JIJIJI +-0.0309 +-0.0000 +-0.0344 +-0.0667 +-0.0212 +-0.0091 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0242 +-0.0091 +-0.0000 +-0.0000 +-0.0000 * 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 NBNBNB +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 +-0.0000 *
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Chapter 4: Population structure
Figure 1. (a) Locations of sampled ( N=17, pie charts) and unsampled ( N=6, green circles) Australian sea lion breeding colonies along the South Australian coast.
Bathymetry shown in 50m increments. Mitochondrial DNA (tissue) and stable isotope
(whisker) samples were taken from newborn pups between January 2008 and October
2009. Pie charts depict relative frequency of haplotypes at sampled colonies (see legend). Dotted lines surround colonies ( N, h ± S.E) clustered together by SAMOVA.
(b) Genetic Landscape Surface (GLS) of individual pairwise genetic distances shown below. Peaks representing samples whose pairwise genetic distances exhibit maximal differences to other samples were congruent with colonies clustered by SAMOVA
(highlighted).
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Chapter 4: Population structure
Figure 2. Plot of Slatkins Linearised Φst on geographic distance. Overall there was evidence of significant Isolation By Distance (Mantel R=0.39 P<0.001).
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Chapter 4: Population structure
Figure 3. Isotope biplots of δ13 C and δ15 N values for 15 colonies sampled highlighting inter-colony variation in the expression of alternate foraging ecotypes. Optimal models from multivariate clustering analysis (selected using BIC) shown. Symbols represent cluster membership. Alternate foraging behaviours were identified across almost every sampled colony.
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Chapter 5: General Discussion ______CHAPTER 5
GENERAL DISCUSSION
______
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Chapter 5: General Discussion ______Understanding the ecological factors that shape Australian sea lion foraging ecology and population structure was the primary aim of this thesis. To achieve this, this project needed to develop techniques to characterise the foraging behaviour of large numbers of adult female Australian sea lions across multiple colonies to determine individual specialisation in foraging behaviour and habitat selection. Historically a combination of logistical difficulties, prohibitive costs and the biology of large, highly mobile predators such as otariids often results in niche-width utilisation being characterised by collecting geospatial and dietary data from a small number of individuals. While this may be appropriate in some species, (particularly those who demonstrate colony-specific reliance on broad-scale predictable habitats (sensu Baylis et al. 2008) the presence of substantial individual niche-width specialisation identified here conclusively demonstrates this is not the case for adult female Australian sea lions.
Defining the degree of habitat specialisation
When used appropriately, biogeochemical markers such as stable isotopes are powerful tools to investigate individual specialisation in apex predators. The upward trophic cascade of δ15 N values and predictable decrease of δ13 C values with distance from shore have provided a cost-effective means to qualitatively assess trophic level and foraging locations of many marine mammal and bird species. However, the assessment of individual foraging specialisation requires the behavioural characterisation of many animals. I used the isotope ratios present in the whiskers of wholly milk-dependent pups to make inferences on maternal foraging behaviour enabling me to screen over 60% of all reproductively-active adult female animals at
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Chapter 5: General Discussion ______each study colony. The technique of using offspring isotope ratios has been used as surrogate data to assess inter-colony variation in maternal foraging behaviour of
Galapagos sea lions (Z. wollebaeki ) (Wolf et al. 2008) and northern elephant seals ( M. angustirostris ) (Aurioles et al. 2006). In contrast to these studies my research is the first to confirm an important assumption when utilising stable isotope ratios in this manner, using telemetry data to validate differences in isotopic signals of carbon and nitrogen are reflected by spatial differences in foraging behaviour. The potential misinterpretation at The Pages islands of δ13 C homogeneity between inshore and offshore foraging ecotypes highlights the importance of validating this key assumption. In the absence of telemetry data, the isotope ratios at this colony could have been interpreted as adult female Australian sea lions expressing trophic partitioning while foraging in the same broad geographic location. Such a misinterpretation would result in a considerable underestimation of the degree of individual variation in foraging behaviour when characterising population-level foraging behaviour. Additionally, given that accidental bycatch from commercial fishing operations is a significant cause of mortality in
Australian sea lions (Goldsworthy and Page 2007, Goldsworthy et al. 2010), the non- identification of important foraging habitat would likely have important ramifications for estimating bycatch and informing appropriate mitigation measures.
Stability of individual foraging specialisation over time
In conjunction with the spatial variation in habitat use and trophic position, one must consider how foraging behaviour varies temporally before making inferences as to the proximal causes of population substructuring. In otariids the temporal window over
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Chapter 5: General Discussion ______which telemetry data can be collected is constrained by moult state. To characterise how foraging behaviour varies over longer timescales most studies tend to report telemetry data collected from a series of tracking events over time using different individuals at each event. The identification of significant individual variation within colonies of Australian sea lions renders this approach highly inappropriate as data from randomly selected individuals used to characterise foraging behaviour across seasons would likely result in the incorporation of alternate foraging ecotypes and be interpreted as marked seasonality in foraging behaviour. Agencies and managers responsible for mitigating accidental bycatch during commercial fishing operations will benefit from the appropriate characterisation of foraging behaviour within and between colonies.
Interaction rates between animals and vessels engaged in fishing operations have been shown to be dependent on the temporal and spatial utilisation of habitat (Goldsworthy et al. 2010) which will vary dependent on predator populations that are comprised of generalists or specialists.
Assessing the temporal variation of individual foraging behaviour is extremely challenging in otariids because most instrumentation is limited due to regular moulting and battery durability over long time frames (e.g. seasons and years). A recent study has utilised δ15 N and δ13 C ratios in the whiskers of adult male Antarctic fur seals to describe the seasonal cyclical variation in feeding and migratory behaviour of individuals across larger timescales than that afforded by telemetry data (Cherel et al.
2009). Using a similar approach, I characterised adult female Australian sea lions as individual foraging specialists, exhibiting long-term foraging habitat and trophic fidelity
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Chapter 5: General Discussion ______(Chapter 2). This is an important contrast to previous classification of adult female
Australian sea lions as generalist, seasonally-influenced predators (Costa and Gales
2003), and represents the first description of multi-seasonal stability in foraging behaviour of any otariid.
Individual specialisation as a mechanism to drive population structure
Many temperate otariid species utilise dynamic broad-scale oceanographic features that influence the distribution of epipelagic prey (Olson and Backus 1985, Schneider 1990).
Central place foraging theory (Orians and Pearson 1979) makes two predictions; firstly, breeding colonies should tend to aggregate in proximity to these features to facilitate optimal delivery of nutrition to offspring. Secondly, individuals that expend greater energy travelling further from a colony should be rewarded by either higher quality or more numerous prey items. Oceanic habitat tends to facilitate high levels of geneflow with matrilineal panmixia between colonies aggregated around a shared habitat has been reported in several otariid species (Maldonado et al. 1995, Arnould and Hindell
1999, Hoffman et al. 2006, Tunez et al. 2007, Gonzalez-Suarez et al. 2009, Lancaster et al. 2010). Results from these studies support the first prediction of central place foraging theory. When individual long-term specialisation in foraging behaviour of adult female Australian sea lions is placed into context with complex matrilineal population structure and an aseasonal, asynchronous breeding chronology there appears to an absence of any signal that would support seasonal variability in prey species consumed and foraging locations. In fact, results suggest individuals may develop specialist strategies to maximise foraging success in particular habitats. This specialisation may
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Chapter 5: General Discussion ______limit their ability to exploit other habitat types as effectively. Selection for individual foraging specialisation and reliance on a habitat type may provide strong selection for restricted dispersal and extreme philopatry in individual adult females.
In Chapter 2, I report adult females who move offshore to feed tend to forage at locations that have pronounced bathymetric features, while inshore foragers target either seagrass meadows or calcarite reefs. As stipulated under the ‘meeting point’ hypothesis, such features act as aggregation sites for benthic and demersal prey species (Fréon and Dagorn 2000). The distance an individual can move away from a preferred habitat may have an upper threshold, contingent upon that habitat type offering enough nutritional reward to compensate for increased travel time as outlined under the second prediction of central place foraging theory. Relocation to a different breeding colony would necessitate identifying and specialising in a new foraging environment which may not be possible given that the energetic demands of concurrent gestation and lactation must be met continuously. The presence of geneflow restrictions along the range of my study may reflect localised habitat heterogeneity which may present ecological barriers to regional gene flow. Such factors may be responsible for driving the complex population structure I describe (Chapter 3).
Do adult female Australian sea lions maintain individual foraging specialisation across generations ?
Given that suitable foraging habitat for adult female Australian sea lions is constrained to extremely fine spatial scales, the process by which offspring learn how to locate and successfully exploit foraging areas is intriguing. The vertical transmission of specialist
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Chapter 5: General Discussion ______behaviours and foraging locations from mother to offspring has been reported in several species. Southern right whale mothers teach the location of their foraging grounds to offspring (Valenzuela et al. 2009), the offspring of Killer Whales learn the vocal repertoire of their mothers (Yurk et al. 2001) and dolphins learn specialist foraging techniques such as ‘sponging’ along matrilines (Krutzen et al. 2005). Indeed,
Goldsworthy et al. (2009) proposed ‘The Family Farm’ hypothesis, suggesting that an extended lactation period may provide time for offspring to learn, through observation, key foraging habitats and prey capture techniques from their mother by following them to sea on foraging trips. My data seemingly does not support this as there was no proclivity for foraging ecotypes to share mtDNA lineages and several instances where colonies fixed for a single mtDNA haplotype expressed multiple foraging ecotypes.
However this does not preclude vertical transmission of specialist behaviour from occurring. It is highly likely that foraging ecotypes do not remain stable over the timescales that mtDNA mutation occurs. A foraging ecotype may only remain stable in a mtDNA lineage for several generations after which it may change for any number of reasons but become undetectable using mtDNA markers.
An alternate hypothesis could be that niche-width specialisation is maintained through independent trial-and-error. For example, prolonged lactation in Australian sea lions gives offspring an additional six months (relative to most otariids) to explore their marine environment and independently learn the skills required to be successful foragers
(Gales and Costa 1997). Although this hypothesis has some merit, one could suggest that the probability of an animal randomly adopting either inshore or offshore foraging
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Chapter 5: General Discussion ______habitat should be influenced by the proximity of its colony to deeper water which does not appear to be the case (Chapter 3).
Additionally, explicitly testing an independent trial-and-error hypothesis is difficult as it can only realistically be accepted after all others have been rejected. As such, there is a third means of maintaining individual specialisation that should be examined prior to accepting independent trial-and-error. Social learning is suggested to be optimal when changes in habitat are slow enough to be tracked and communicated (Galef 2001, Galef and Laland 2005). Evidence for this type of oblique transmission of information from related non-parental adults, older siblings or horizontal transmission from unrelated adults and juveniles has been well documented (Galef 1976, 2001, Galef and Laland
2005). Given that Australian sea lions are a socially-cohesive species it may be appropriate to employ a model of social learning, with prolonged breeding episodes providing ample opportunities for related or unrelated animals to impart foraging locations and feeding techniques to young pups.
The evolution of a unique life history
This body of work has added greatly to our understanding of contemporary adult female
Australian sea lion foraging behaviour and matrilineal population substructuring.
However what do these data mean in the context of selective forces that have driven such a unique life history ? And given the cessation of seal harvesting why has the population size of Australian sea lions remained apparently static over the last 25-30 years while over the same period geographically sympatric Australian and New Zealand
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Chapter 5: General Discussion ______fur seal populations have increased in size in some instances by an order of magnitude
(Littnan and Mitchell 2002, Lancaster et al. 2010) ?
The origins of fur seals and sea lions have been placed in the northern Pacific ocean at
6-10million years before present (yr BP) during the late Miocene and early Pliocene epochs. Environmental measurements such as gradients in sea surface temperatures
(SST) are regularly used to predict areas of oceanic productivity (Li et al. 2011) and show strong positive relationships with marine biodiversity including cetaceans (Li et al.
2006) and pinnipeds (Nakada and Lambeck 1988). The late Miocene period coincided with a ‘permanent El Nino state’ where SST was invariant across the Pacific characterised by the ‘western Pacific warm pool’ and almost no evidence of upwelling events (Ibaraki 1997, Li et al. 2006). The direction of dispersal of Australian sea lions is still strongly debated, with migration in either an easterly or westerly direction from
South America (Yonezawa et al. 2009). Irrespective of the direction of radiation, there appears to be growing consensus that the ancestral lineage of Australian sea lions was among the first to diverge (Arnason et al. 2006, Higdon et al. 2007) and radiate into the southern hemisphere at the end of the Miocene (Yonezawa et al. 2009). As such, the ancestors of Australian sea lions may have radiated at a time of aseasonal oceanic productivity.
Interestingly, towards the end of the Pliocene, the closure of the Central America
Seaway led to the separation of the Atlantic and Pacific oceans. The resulting changes in oceanic circulation led to a rise in oceanic productivity driven by regional coastal upwelling events (Ibaraki 1997). Conceivably, an increase in seasonal mesoscale
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Chapter 5: General Discussion ______primary productivity that progressed southwards down the eastern Pacific coast
(Ibaraki 1997, Li et al. 2011) may have been linked to a second more recent radiation of ancestral fur seals into the southern hemisphere (Yonezawa et al. 2009).
The Pliocene – Pleistocene boundary (approximately 2-3million yr BP) brought the onset of several glaciation events approximately 41,000yr in duration interspersed with short (<10,000yr) interglacial periods (Morley and Dworetzky 1991). The Leeuwin
Current, which influences primary productivity by inhibiting coastal wind-stress-driven upwelling events along the western and southern shelf-break of Australia (Gaughan
2007), is thought to have weakened substantially during glacial events (Gordon et al.
2003) resulting in decreased water mass temperature stratification (Gedamke et al.
2011) and enhanced productivity that was not seasonally variable (Di Iorio and Clark
2010). The mid-Pleistocene transition (1million yr BP) was marked by a pronounced increase in the duration of glacial events to 100,000yr cycles (Zachos et al. 2001), presumably prolonging aseasonality in oceanographic productivity.
Given the proposed stable, aseasonal environmental conditions at the time of divergence and radiation I have outlined, some aspects of the proposal by Gales and
Costa (1997) that the species was adapted to a marine environment characterised by low productivity may be appropriate. Adult female Australian sea lion reproductive ecology, by breaking a phylogenetic constraint present in every other otariid (including its proposed ancestor, the ancestor of the northern fur seal), may be the result of a relaxation of selective pressure to maintain an exogenous breeding timetable linked to seasonality in food. (Fowler et al. 2006). Furthermore, an endogenously-set
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Chapter 5: General Discussion ______reproductive timetable has resulted in a reduction in per diem energetic cost of gestation and lactation (Gales et al. 1997).
However, recent study has revealed that the Great Australian Bight is replete with mesoscale areas of high primary productivity and seasonality (Maurer 1996) capable of supporting pelagic and benthic ecosystems rich enough to sustain large populations of predators and Australia’s largest commercial fishing fleet. The last glacial period ending approximately 10,000 yr BP at the start of the Holocene period, bringing a rapid rise in sea level reaching current levels approximately 6,000 yr BP (Nakada and Lambeck
1988, Estes et al. 2003). Concurrent with a rise in sea level through the Holocene was an increase in intensity of the Leeuwin Current and corresponding seasonality to upwelling events on biological productivity. Significant upwelling events in the eastern
Great Australian Bight attract large aggregations of Blue and Southern Right whales
(Matthews et al. 2011) and the highest density of New Zealand fur seals reflect the importance of the Bonney upwelling at the eastern end of the Great Australian Bight as a region of predictable foraging habitat (Baylis et al. 2008). It is also worth noting that the greatest density of Australian sea lion breeding colonies is at the eastern end of their range, in close proximity to upwelling events and a narrow continental shelf presumably due to the rich productivity of the area.
If Australian sea lions are adapted to an aseasonal environment, does this mean current environmental conditions are suboptimal for the species ? The shift in oceanographic conditions to more seasonally influenced, highly localised productivity has benefited predators such as New Zealand fur seals and may be responsible for
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Chapter 5: General Discussion ______facilitating their dramatic population recovery following the cessation of commercial harvesting. However, the weakening or negating of upwelling events by unpredictable environmentally stochastic events such as the El Nino Southern Oscillation (ENSO) has been linked to a significant increase in foraging trip duration of adult female South
American sea lions, Galapagos fur seals and Galapagos sea lions and mass starvation of their pups (Trillmich and Limberger 1985, Trillmich and Dellinger 1991, Maxwell et al.
2011). In contrast, Australian sea lions do not utilise the same broad scale oceanographic features having evolved successful strategies to feed in an aseasonal environment. By utilising static features that attract demersal and benthic prey items,
Australian sea lion populations may have the capacity to increase in numbers by adopting a foraging strategy that buffers against the effects of stochastic events such as
ENSO. Commercial fishers also identify the same benthic features as productive fishing grounds, with significant overlap in Australian sea lion foraging effort and marine fishing areas and modelled upper rates for accidental bycatch mortality estimated at up to 300 animals / year (Goldsworthy and Page 2007). The impact of bycatch mortality on breeding stock may explain no observable increase in numbers at almost every breeding colony in the species range, though estimates of the impact of sealing and bycatch on contemporary numbers are hampered by the absence of quantitative estimates of pre-European numbers.
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Chapter 5: General Discussion ______Future research and conclusions
The life history of the Australian sea lion marks the species as a behavioural and ecological outlier amongst otariids. While my work suggests that fine-scale foraging site fidelity may be a significant factor in driving matrilineal population substructure, additional research should focus on whether the stability of foraging ecotypes is trans- generational. Demographic studies involving Passive Inductive Transponder (PIT) tagging pups every season at Seal Bay (Kangaroo Island, South Australia) have been ongoing for almost a decade with PIT-tagged pups now returning to the colony as reproductively active adults. This breeding colony offers an excellent opportunity to examine the stability of foraging ecotypes across generations and examine the mechanisms by which foraging behaviour could be maintained, within a realistic timeframe.
The effect of different inshore foraging habitats on body condition suggested in Chapter
2 requires further exploration. As adult females revisit the same fine-scale foraging habitat repeatedly, swath-mapping and baited underwater remote video studies of foraging areas should be conducted to classify animals by habitat preference. Given that most colonies in South Australia have been screened for ecotype composition, additional intensive tracking and morphological studies can be targeted towards colonies that express predominantly inshore foraging ecotypes.
The complex matrilineal population structure I describe in Chapter 3 highlights several key colonies that must be sampled to better characterise the degree and significance of geneflow. The advent of software such as LAMARC that utilises coalescent theory as a
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Chapter 5: General Discussion ______tool for describing migration rates and effective population sizes require multiple genetic markers to make accurate inferences. Consequently, future work should incorporate microsatellite analyses. Of critical importance is the relative contribution of adult males to gene flow given the extreme levels of female natal site philopatry.
In conclusion, the synergistic use of tracking data, biogeochemical and molecular markers as demonstrated by this study can provide a powerful, cost-effective tool that can be used to determine the spatial and temporal utilisation of foraging habitat and its influence on population structure. The complexity of matrilineal population substructure in Australian sea lions and the spatial scales over which it occurs may reflect the degree of individual niche-width specialisation in foraging behaviour and the degree of habitat heterogeneity between adjacent colonies. Adult female Australian sea lions exhibit significant individual specialisation in foraging behaviour that is geographically fixed, temporally stable, invariant to lactation stage and probably influenced by the presence of fine-scale heterogeneity in habitat structure. Implicit to this is the inappropriateness of extrapolating colony-level processes from a small subset of animals, and we caution that the design of any behavioural or ecological study on this animal accounts for significant inter-individual variation. This work has uncovered more questions than it has answered; however it has generated several new testable hypotheses that should further inform on the unique life history of the Australian sea lion.
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APPENDIX
MATERNAL STRATEGIES OF THE AUSTRALIAN SEA
LION (NEOPHOCA CINEREA) AT DANGEROUS REEF,
SOUTH AUSTRALIA
______
Published as: Lowther, A.D. and Goldsworthy, S.D. (2011). Maternal strategies of the
Australian sea lion ( Neophoca cinerea ) at Dangerous Reef, South Australia. Australian
Journal of Zoology, 59 : 54-62.
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Abstract
Maternal strategies of otariid seals reflect the optimisation between resource exploitation and offspring provisioning driven across spatially separated foraging and nursing grounds. Inter-colony variation in the expression of maternal strategies may represent temporal and spatial differences in resource availability, intra-species competition or differences in life-history traits. The current study describes maternal strategies of the Australian sea lion at the largest breeding colony of the species
(Dangerous Reef) and a comparative analysis was performed with data collected sixteen years earlier at Seal Bay (Kangaroo Island). Significant differences in maternal strategies were characterised by lower milk lipid content (21.0 ~ 28.9%), abbreviated periods onshore (0.93 ~ 1.63d) and slower pup growth rates (0.09-0.12 kg d -1) at
Dangerous Reef. These data suggest flexibility in the expression of maternal investment between breeding sites and support the hypothesis of localised adaptation
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Introduction Phenotypic traits associated with parental care and provisioning strategies are expected to be under strong selection pressure (Arnold and Duvall 1994). As phenotypes are a product of genetic and environmental interaction, parental investment strategies are likely to be influenced by ecological pressures and phylogenetic constraints. The delivery of parental investment to offspring in mammals is almost exclusively the domain of mothers (Oftedal 1984, Gittleman and Thompson 1988, Boness and Bowen
1996). As such, maternal lifetime reproductive success can be considered as comprising of two components 1) facilitating offspring survival to reproductive age and
2) ensuring the success of future maternal reproductive events (Clutton-Brock 1991).
Thus, the temporal and spatial variability of resources important to reproduction and provisioning may influence the strategy of how maternal investment is delivered (Millar
1977, Rachlow and Bowyer 1994).
As central place foragers, otariids (fur seals and sea lions) represent a group of mammals amenable to the study the effects of environmental variability on maternal care because mothers must optimise their strategy to contend with the geographic decoupling of foraging and nursing grounds (Gentry and Kooyman 2000). The investment strategies employed by otariids have been termed foraging strategies, which reflect regular foraging trips interspersed with onshore bouts of nursing throughout the lactation period (Boness and Bowen 1996). If a mother is to successfully rear a pup, the duration of each foraging trip is constrained by how long a pup can fast for (Boness and
Bowen 1996, Gentry and Kooyman 2000).
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Subpolar species such as Antarctic fur seals Arctocephalus gazella take advantage of seasonally predictable prey resource by undertaking long foraging trips (4-7d), providing milk of a high fat content (42%) and weaning their offspring after four months (Arnould and Boyd 1995, Lunn and Arnould 1997). Conversely, low latitude species such as the
Galapagos fur seal A. galapagoensis must contend with less predictable and less seasonal prey availability which is reflected in a more protracted lactation period (up to
2years), shorter foraging trips (~1.3d) and lower milk-lipid content (~26%) (Trillmich
1986, Trillmich and Lechner 1986).
In their seminal review, Gentry, Costa et al . (1986) predicted that temperate-zone otariids would exhibit a pattern of maternal strategies intermediate to observed latitudinal extremes. Recent studies indicate a much wider range of investment strategies that appear strongly influenced by the distances between colonies and foraging grounds, and the temporal variability of prey resources (Gentry et al. 1986,
Lunn et al. 1993, Boness and Bowen 1996). Two sympatrically breeding fur seal species (the Antarctic fur seal and the subantarctic fur seal A. tropicalis ) with large differences in lactation period length displayed no difference in foraging trip duration at
Macquarie Island (Goldsworthy 1999). Goldsworthy (1999) also noted the large intraspecific range of foraging trip duration found at several Antarctic fur seal breeding colonies at the same latitude suggested that local environmental factors largely dictate variation in foraging behaviour. The northern fur seal Callorhinus ursinus has flexibility in foraging trip duration dependent on environmental conditions providing further
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Appendix: Maternal strategies evidence that intraspecific differences are largely driven by local environmental variation
(Gentry and Holt 1986).
However, despite the flexibility of most aspects of maternal investment, almost every otariid species is phylogenetically constrained to an annual breeding cycle (Atkinson
1997). The notable exception is is Australia’s only endemic otariid the Australian sea lion ( Neophoca cinerea) as it represents an outlier in the behavioural range of temperate latitude otariids. Distributed from the Pages Islands in South Australia to the
Houtman Abrolhos Islands in Western Australia, a census estimate of ~13,000 animals makes the species one of the rarest otariids in the world. The Australian sea lion has a unique 17-18 month breeding cycle (Higgins and Gass 1993), that is asynchronous between geographically close colonies, with females exhibiting the highest natal site fidelity of any marine mammal (Campbell et al. 2008). This life history pattern is in stark contrast to that of geographically sympatric fur seal species (New Zealand and
Australian fur seals) and all other otariids that share annual and highly synchronous breeding seasons (Trillmich 1991, Shaughnessy et al. 1995, Ochoa-Acu-a et al. 1998,
Goldsworthy 1999, Arnould and Hindell 2001b).
The determinants of such a unique life history are unclear though two competing hypotheses have been suggested. One suggests regional variation in key resources may lead to considerable differences in maternal strategies between populations that relate to localised adaptations (Goldsworthy et al. 2010). Conversely, argue that prolonged maternal care in the species may be necessary to allow offspring time to develop sufficient foraging skills to survive in what has been characterised as an
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Appendix: Maternal strategies oligotrophic, aseasonal marine environment (Gales and Costa 1997, Costa and Gales
2003).
Only one other study has been conducted into maternal care and provisioning of
Australian sea lions. Observational data of maternal body size, attendance patterns, milk fat content and pup growth rates were collected from birth to three months during two winter and one summer breeding episodes from 1987 to 1990 at the fourth largest breeding colony of the species at Seal Bay on Kangaroo Island which produces approximately 260 pups per breeding episode (Kretzmann et al. 1991, Higgins 1993,
Higgins and Gass 1993, Gales et al. 1996).
Here we replicate that study at a second site, Dangerous Reef. The two sites are separated by approximately 200km and breed six months out of phase, with Dangerous
Reef representing the largest breeding colony for the species producing an estimated
700 pups (Goldsworthy et al. 2010). We employed pup growth rates, the duration of adult female foraging trips and onshore bouts and the proportional lipid content of milk as proximal measures of maternal strategy and perform a comparative analysis with data collected at Seal Bay.
Materials and methods
Field work was undertaken at Dangerous Reef (34°50’S 136°15’E) in Southern Spencer
Gulf, 30 kilometres east of Port Lincoln in South Australia (Fig. 1). The island is approximately 600m long (east-west) and 200m wide (north-south). Six field trips of 5-
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11d were made at near-monthly intervals between 28 August 2006 and 14 February
2007.
Maternal attendance, female body condition and milk collection
Thirteen adult female Australian sea lions and their known-age pups were used for gathering maternal attendance data. Adult females were captured, restrained and anaesthetised using isofluorane (5% induction, 3% maintenance) (Veterinary
Companies of Australia, Artarmon, New South Wales) delivered through a portable gas anaesthesia machine. Capillary refill, breathing rate and tactile response were used to monitored to provide an indication of the animals degree of anaesthesia. After anaesthetic induction the animal was secured on a stretcher and suspended from a spring balance that measured to 100±0.5kg (Salter Weigh-Tronix, Blackburn, Victoria,
Australia.). Standard length and axillary girth measurements were taken as for pups. A
20g two-stage VHF radio transmitter (150-151Mhz) (Sirtrack Ltd, Havelock North, New
Zealand) set at unique frequencies and transmitting every second was attached to each animal either to the pelage distal to the midpoint on the back using two-part Araldite ©
2014 epoxy resin (n=9) or to the trailing edge of the right fore-flipper of each animal
(n=4). Animals were tagged using uniquely numbered plastic tags (Dalton porcine
Supertag ®, Dalton Supplies Ltd, U.K.) applied to the trailing edge of each fore-flipper.
An intramuscular injection of Oxytocin (0.7ml, 10 UI mL -1, Syntocin-Sandoz) was given five minutes prior to collecting milk samples. Two 5ml vials of milk were collected per animal by manual expression of the teat, and stored in 5ml Eppendorf tubes. Milk could not be expressed from three females (ID# 562, 566 and 574). All samples were frozen
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Appendix: Maternal strategies at -20 oC until analysed. To ensure complete recovery from anaesthesia, each animal was continuously monitored until it lost all ataxia and had reunited with their pups.
Presence and absence of mothers at the colony was monitored using a DCC II scanning receiver connected to a programmable data logger (Model 2000B receiver, range 150-151MHz; Data collection Computer Model 5040, Advanced Telemetry
Systems Inc., Isanti, Minnesota 55040, U.S.A.). Frequencies were monitored sequentially every 60 seconds and the number of pulses received from each transmitter was recorded between 10 October 2006 and 31 January 2007.
Australian sea lions typically lactate for 17-18 months though lactation can continue for up to three years (Higgins and Gass 1993) though onshore attendance data available after 90 days post-partum in this study may have been distorted by the movement of pups away from the colony as they grew older (two 100-day old pups were seen on a visit to English Island 30km away). Thus, for each female, durations of foraging trips and onshore bouts were grouped into three equal bins of 30d to 90d after pup birth
(Goldsworthy 1995). Foraging trips or onshore bouts that crossed over a 30 day block were placed into the block in which more than 50% of the time was spent.
Milk composition
Individual milk samples were thawed for four hours and emptied onto separate petri- dishes and weighed (300-HG scales max=310g d=0.001g) then placed into an oven at
60 oC for 48hours (Department of Genetics, La Trobe University, Bundoora). On completion, the dishes and dried content were reweighed. Dried mass was subtracted
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Appendix: Maternal strategies from wet mass to estimate water content (nearest 0.01g), and expressed as a percentage. We employ the equation to estimate the lipid content of Australian sea lion milk from its water content (% wet weight (Milk-lipid) = 91.365 –1.054 x (%wet weight
Milk Water), r 2=0.95) derived by Kretzmann, Costa et al . (1991)
Pup growth
A total of thirty-six known-age pups (including thirteen sampled with their mothers) were captured shortly after birth and initially bleach-marked with a unique number on the pelage across the pelvic girdle (Clairol ®, Bristol-Myers Squibb Ltd, West Ryde, N.S.W,
Australia) as they were too small to tag. As pups grew, the bleach faded and to ensure ongoing individual recognition they were individual tagged (Dalton porcine Supertag ®,
Dalton Supplies Ltd, U.K.). Sex, standard length (straight-line length from tip-of-nose to tip-of-tail, to nearest 0.5cm) and axillary girth (exhaled, nearest 0.5cm) of each pup were recorded. The mass of pups was measured using a 25x0.1kg spring balance
(Salter Weigh-Tronix, Blackburn, Victoria, Australia.). On completion pups were released back to the area of capture and observed until mother and pup were reunited.
Statistical analysis
Statistical tests were performed using R Software (R Core Development Team 2006).
Unless otherwise stated, data are presented as mean ± Standard Error and results were considered significant at p<0.05. Proportions of total time spent ashore for each adult were arcsine transformed prior to analysis.
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The significance of individual changes in maternal attendance through lactation were analysed by ANOVA with sequential Bonferroni adjustment of p-values after the method of Goldsworthy (1999). Kruskal-Wallis rank sum testing was used in place of ANOVA when data were not normally distributed. Residuals from the linear regression of adult female Australian sea lion mass on length as a proximal measure of age (McIntosh
2007) were used to create a body condition index (BCI) independent of length (Trites
1991), with animals in poor condition (relative to the others) characterised by small or negative BIC values.
The influence of maternal attendance strategies, body size and condition and offspring sex on milk lipid content was examined using Pearson Chi square tests. Pup growth rates, birth masses and masses at 90 days were estimated from the linear regression of individual pup masses which consisted of three or more sampling points. Individual and multiple linear regression modelling of pup growth with maternal characteristics were conducted to identify which variables best explained variance in estimated pup birth masses, growth rates and masses at 90 days. Adult female mean mass, foraging cycle duration, milk lipid content, estimated pup birth masses and growth rate data for Seal
Bay are drawn from Higgins (1990), Kretzmann et al. (1991) and Higgins and Gass
(1993) were compared to data collected during this study using Pearson Chi square and students t-tests.
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Results Maternal attendance
To quantify foraging trip and attendance bout durations, VHF pulse recordings of less than five pulses per minute were disregarded as background noise (transmission drift, radio interference from passing vessels etc). Foraging trips of less than five hours (n=5 across the entire dataset) were assumed to represent females either in areas obstructing VHF signal (e.g. thermoregulating in pools of water) or swimming close onshore and were not included in analyses (Boyd et al. 1991). Of 13 units that were deployed, three failed on attachment or shortly after and were excluded from analysis.
Thus, telemetric data up to a maximum 102 days duration days was collected for ten lactating Australian sea lions, with individual longitudinal datasets varying considerably
(range 23-102 d, mean = 93.9 ±13.36 d). Across all animals, a mean 20.4 (±3.21; range
9-40) foraging trips and 18.8 (±2.49; range 4-39) attendance bouts were recorded.
Foraging trips lasted 1.91 ± 0.23d between attendance bouts of 0.83± 0.13d. Across all attendance data, female Australian sea lions spent approximately one third of their time on land (mean time ashore / total time = 0.32 ±0.03) (Table 1).
Individual adult females did not vary in the proportion of time spent ashore (Kruskal Χ2
<1.2, d.f=2, p>0.05 in all cases), foraging trip or attendance bout duration ( ANOVA
F[1,2] = 2.3, p>0.05 in all cases) over the three binned time periods covering the period of lactation observed in this study. Consequently movement and attendance data from all ten animals were pooled. There were no differences in the duration of foraging trips
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(ANOVA F [1,23] =3.78, P>0.05 Bonferroni Adjusted p-value) or attendance bouts (Kruskal
χ2=3.18, d.f=1, p>0.05) for mothers with sons or daughters throughout the study period.
To examine whether individual variation in maternal body condition and size translated into significantly different patterns of foraging or nursing, mass on length regression
2 residuals for adult females (r = 0.8, F [1,8] =47.5, p<0.001) were used as a body condition index (BCI) (Table 2). There was no relationship between maternal time spent at sea, ashore or the proportion of total time spent ashore and maternal body size or condition (F [1,8] <1.27, p>0.29 in all cases).
Milk-lipid content
Mean % milk-lipid showed a bimodal distribution and arcsine transformation was unsuccessful in normalising data.The estimated milk-lipid levels during the first two months of lactation of individual females was 21.0% (± 1.18% S.E) (Table 2) and did not vary with pup age, foraging trip length, shore attendance duration or proportion of total time spent ashore (Pearsons χ2 < 0.63, d.f=1, p>0.05 in all cases) Mothers with sons did not produce significantly different levels of milk-lipid to mothers with daughters
(Student’s t-test, t=0.23, d.f=8, p<0.05). Maternal length was negatively related to their milk-lipid content (Pearsons χ2 = 4.23, d.f = 1, p<0.05) (Figure 2a), with shorter females having higher lipid levels than longer females. Maternal body condition was unrelated to milk fat content (Pearsons χ2 = 0.39, d.f = 1, p>0.05) though the relationship with maternal length approached significance (Pearsons χ2 = 3.6, d.f = 1, p = 0.06) (Figure
2b) suggesting that milk composition is primarily influenced by the size and not the condition of females.
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Pup Growth 36 pups were measured between 27 August 2006 and 31 January 2007. Owing to the density of animals on the colony hindering accessibility to known-aged pups, only 18 individuals were recaptured more than twice. Birth mass (male n=13, female n=5) estimated from the y-intercept of age-on-mass regression ranged from 7.6-10.7kg with a mean of 9.1±0.3kg. Given considerable evidence for early-lactation linear growth rates observed in otariid species such as Australian, antarctic and subantarctic fur seals we assumed that type I error rates were extremely unlikely (Guinet et al. 1999, Guinet and
Georges 2000, Arnould and Hindell 2002, Goldsworthy 2006). Regressions showed high r 2 values and as only slope and intercept values were required, p-values were adjusted upwards to 0.12 (Quinn and Keough 2004). Growth rates (male n=13, female n=5) varied from 0.05- 0.11kg d -1 with a mean of 0.09±0.01kg day -1(Table 3).
There were no significant differences between male and female pups for the estimated masses at birth or at 90 days (16.9 ±0.01kg) or for their estimated growth rates
(Student’s t-test, t <1.25, d.f=16, p>0.05 in all cases). Estimated birth masses, masses at 90 days and growth rates for pups whose mothers were also measured (n=9) were unrelated to maternal length (F [1,8] <1.41, p>0.27 in all cases) though approached significance with maternal mass (mass at 90 days F [1,8] = 4.51, p=0.07; growth rates
F[1,8] =4.15, p=0.08).
Generalised linear modeling procedures were used to examine the importance of maternal parameters (length, mass, BCI, milk-lipid content, foraging trip duration, attendance bout duration and proportion of time spent ashore), both individually and
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Appendix: Maternal strategies interactively, in enhancing the ability to estimate mass of pups at birth and 90 days, and their growth rates. Where complete mother-and-pup paired datasets (including telemetric data) were available (n=7 pairs), individual linear modeling of maternal parameters and estimated pup birth masses, growth rates and masses at 90 days resulted in a positive relationship between proportion of time spent ashore by mothers
2 (P) and estimated pup growth rates (r = 0.56, F [1,6] =7.58, p<0.05). The regression equation was:
Pup growth rate (kg day -1) = 0.14 P – 0.04
No other single maternal parameter contributed significantly to pup growth rates or masses at 90 days (F [1,6] <4.14, p>0.05 in all cases). Similarly, no single maternal parameter significantly predicted estimated birth mass (F [1,6] < 0.56, p> 0.05 in all cases). The relationship of proportion of time spent ashore and estimated pup growth was strengthened only when maternal length (L) was considered (r 2=0.70) though evidence was weak (Bayesian posterior probability Bpp = 60.6%). The regression equation was:
Pup growth (kg day -1) = 0.111 P + 0.001 L (cm) - 0.1
Modelling of the same maternal variables provided weak supporting evidence for
2 estimated pup mass at 90 days (r =0.56, B pp =55.9%). The regression equation was:
Pup mass at 90 days (kg) = 14.65 P + 0.18 L (cm) - 17.77
No combination of maternal parameters significantly predicted estimated birth mass
(B pp <50% in all cases). Predicted pup growth rates and masses at 90 days fitted well
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Appendix: Maternal strategies
with observed data (growth rate: F [1,6] =14.13, p<0.01; mass at 90 days: F [1,6] =7.59, p<0.05) (r 2=0.70 and 0.56 respectively) (Figure 3) suggesting that maternal length
(proxy for age) and the proportion of time spent ashore positively influence pup growth rates and mass at 90 days.
There were no significant changes in foraging cycle through lactation, though mothers of male pups showed a propensity to remain ashore for a greater proportion of time than those with female pups after 30 days post-partum.
Comparison between Seal Bay and Dangerous Reef
There were no significant differences in the mean mass or foraging trip duration of adult female Australian sea lions at Seal Bay and Dangerous Reef however time spent onshore was significantly greater at Seal Bay (students t-test, t=4.3, d.f=15, p<0.001)
(Table 4). Milk contained significantly more lipid at Seal Bay (28.9% vs. 21%; students t-test, t=2.8, d.f=23, p<0.05). Estimated pup birth mass was significantly lower at Seal
Bay (7.8kg vs. 9.1kg), however pups grew faster to 100d (0.12kg/d vs. 0.09kg/d)
(students t-test, t<0.4, d.f=34, p<0.001 in both cases) (Table 4).
Discussion
Maternal strategy
Two studies of the maternal strategies employed by Australian sea lions have now been conducted, at Seal Bay in 1990 and Dangerous Reef in 2006. The sites are separated by 200km and the studies were conducted 16 years apart. The observed differences in maternal strategies between the two sites may result from both spatial variability in
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Appendix: Maternal strategies foraging habitat and temporal variability in oceanographic conditions. Satellite telemetry data collected on the foraging behavior of 34 adult female Australian sea lions at
Dangerous Reef between 2003-05 indicate all foraging occurs within the Spencer Gulf
(Goldsworthy et al. 2010). An oceanographic front at the mouth of the Spencer Gulf is postulated to be responsible for dispersal barriers to prey items important to the common bottlenose dolphin Tursiops spp. (Bilgmann et al. 2007). This same feature may be sufficient to generate localised differences in the distribution of prey items for
Australian sea lions between Dangerous Reef and Seal Bay, requiring the employment of significantly different foraging strategies by adult females. However we also note that the seasonal nature of this oceanographic front may be a confounding factor and we recommend additional sampling of maternal strategies at Dangerous Reef during winter breeding episodes to assess any temporal component to our observed differences.
Increasing the duration of foraging trips as lactation progresses has been observed in many otariids and may result from increased energy demands of pups and gestation as well as a seasonal reduction in prey abundance (Goldsworthy 1995, Ochoa-Acu-a et al.
1998, Goldsworthy 1999, Goldsworthy and Crowley 1999, Guinet and Georges 2000,
Arnould and Hindell 2001b, Goldsworthy 2006). This pattern was not observed during the first 90 days of the current study, nor at the study conducted at Seal Bay. Given that adult female Australian sea lions do not exhibit a breeding chronology tied to environmental seasonality these data support the hypothesis that maternal foraging and energetic requirements can be met across all seasons without significant variation to individual maternal strategy. The typical otariid model of breeding and lactation being
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Appendix: Maternal strategies closely tied to seasonal abundance and availability of prey is not apparent in the maternal strategies of Australian sea lions.
There appears to be a strong positive relationship between the duration of maternal foraging trips and the fat content of milk (Costa 1991). Milk lipid levels at Dangerous
Reef were significantly lower than those at Seal Bay, however given that we detected no significant differences in adult female Australian sea lion foraging trip duration between sites, we conclude that foraging trip duration is not responsible. Milk-fat levels of Australian sea lions are considerably lower than subantarctic fur seals (43%),
Antarctic fur seals (42%) and nearby Australian (42%) and New Zealand (47%) fur seals which spend proportionately less time with pups by extending foraging trips through lactation, with longer females producing higher milk-fat levels (Goldsworthy and Crowley
1999, Arnould and Hindell 2002, Baylis and Nichols 2009). Surprisingly, and contrary to data from Seal Bay, longer female Australian sea lions at Dangerous Reef produced lower milk-fat levels than shorter females. The discrepancy between milk-fat levels described in this study does not appear to conform to previously documented correlated variation in foraging ability and milk fat levels, with the diet of younger less experienced females negatively influencing milk composition (Georges et al. 2001). Additional sampling of milk from adult female Australian sea lions at different life history stages is recommended and would prove informative.
Maternal experience is likely to influence pup growth in two ways 1) by increasing foraging efficiency therefore reducing metabolic costs incurred through feeding (Bowen et al. 2001a) and 2) by optimising foraging and attendance durations to meet maternal
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Appendix: Maternal strategies energy budgets and limit the fasting requirements of pups(Lunn et al. 1993). Older
Harbour seal ( Phoca vitulina ) mothers wean pups significantly faster than younger mothers (Bowen et al. 2001b) and subantarctic fur seal pup growth is positively related to maternal foraging efficiency and age (Lunn et al. 1993, Guinet and Georges 2000).
Older northern elephant seal mothers impart more maternal energy to gestation
(resulting in the production of heavier pups at birth) and create a greater amount of milk than younger mothers for the same amount of maternal energy invested (Crocker et al.
2001b).
The influence of maternal experience on offspring birth masses and growth rates may also translate into increased post-weaning survival. Northern fur seal 2 nd year and southern elephant seal 1st year survival rates were significantly higher for heavier pups at birth and weaning respectively (Baker and Fowler 1993, McMahon et al. 2000).
We identify longer (presumably older) adult female Australian sea lions spend a significantly greater proportion of time ashore than shorter (younger) females, though maternal length or mass by themselves were unrelated to pup growth rates and masses at 90d. However we did discover weak evidence for female age in conjunction with the relative proportion of time spent onshore influencing pup growth rates and mass at 90d.
It is possible that longer (presumably older) Australian sea lions invest more energy in gestation and produce more milk (with a lower energy content) which can be delivered to pups during the proportionally longer nursing bouts relative to shorter (younger) females. The maternal experience of older Australian sea lion mothers may be responsible for producing faster growing, heavier pups and these pups may have higher
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Appendix: Maternal strategies survival rates than those pups of younger mothers. However we caution that further sampling of pups from adult females of different age classes is required to clarify if this effect is real.
Conclusions
Assuming that maternal strategy and pup growth data is representative for Seal Bay and Dangerous Reef, it appears that temporal and spatial variation in local foraging conditions between the two sites may be responsible for significant differences in milk- fat levels, offspring growth and some aspects of maternal strategy. Data indicates maternal experience plays a role in determining the partitioning of time spent at sea foraging and nursing pups onshore similar to other otariid species. Given the paucity of life-history data for both Dangerous Reef and Seal Bay, and the smaller representative sample size at Dangerous Reef, we acknowledge that other confounding variables such as an adult female age bias may be responsible for the observed inter-colony differences. We suggest additional sampling at Dangerous Reef across different seasons to ensure sufficient statistical power when characterizing colony-level processes. An updated study of maternal strategies at the Seal Bay colony would supply more recent comparable data to those generated in the current study. Using temporal comparisons of maternal strategies (as proximal indicators of foraging conditions) at both Seal Bay and Dangerous Reef may also prove useful in detecting any changes in environmental conditions over time.
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Acknowledgements
We would like to thank Brad Page, Derek Hamer, Alistair Baylis and Kris Peters of
SARDI Aquatic Sciences for their support of this project and their assistance in the field, and the Wildlife Conservation Fund (South Australia) which funded this study. All work was conducted under permits from La Trobe University Animal Ethics
(AEC06/24(BG)/V1) and the South Australian Department of Environment and Heritage
(Permit #A24684).
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Table 1. Summary of foraging trip duration, attendance bout duration and proportion of time spent ashore for individual lactating Australian sea lions in days.
Numbers of events in parentheses. Standard Errors shown.
Mean duration (days) Prop time Female ashore ID Foraging ±SE Attendance ±SE ±SE
564 1.98 (27) 0.18 1.08 (26) 0.14 0.38 0.03 562 2.28 (31) 0.14 0.39 (26) 0.05 0.18 0.02 566 2.32 (18) 0.13 1.29 (17) 0.12 0.34 0.02 561 1.89 (21) 0.15 1.00 (20) 0.14 0.36 0.03 553 1.22 (40) 0.08 0.02 (39) 0.09 0.33 0.03 574 2.16 (9) 0.54 0.57 (7) 0.12 0.27 0.05 560 1.18 (15) 0.19 1.09 (14) 0.26 0.56 0.05 559 2.28 (22) 0.15 1.09 (21) 0.58 0.34 0.02 572 1.95 (15) 0.21 0.46 (14) 0.06 0.23 0.03 581 1.86 (6) 0.55 0.56 (4) 0.31 0.18 0.04 _ X 1.91 0.23 0.83 0.13 0.32 0.03
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Table 2. Maternal body length, body condition index and milk-lipid levels of adult female
Australian sea lions during the first two months of lactation at Dangerous Reef. ‘-‘ indicate milk samples not obtained.
Female Length BCI Lipid levels ID (cm) (residuals) (%) ±SE
573 167 6.13 14.5 0.84 566 146 4.54 - - 572 150 4.39 24.8 4.57 561 156 2.92 16.7 0.82 574 161 2.11 - - 559 158 1.09 14.7 0.43 553 155 0.08 28.2 1.49 560 159 -0.57 11.8 0.63 564 143 -0.97 26.1 0.39 563 156 -1.08 26.3 1.39 562 157 -2.24 - - 558 170 -7.35 13.8 0.15 581 146 -9.04 33.3 1.06
_ X 155.7 21.0 1.18
.
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Table 3. Summary of linear regressions of mass on age for 18 individual pups weighed three or more times at Dangerous Reef during the 2006-07 breeding episode. Eleven regressions were significant at p<0.05 with the remainder p<0.12. Note all r 2 > 0.96.
Pup ID# Sex y-intercept Slope r2 p
2 M 9.8 0.100 1.00 0.01 3 F 8.6 0.103 0.98 0.08 5 M 10.7 0.099 1.00 <0.001 8 M 9.9 0.061 0.97 0.11 9 M 10.1 0.106 1.00 <0.001 10 M 10.7 0.119 0.97 0.11 11 M 8.5 0.064 1.00 <0.001 12 M 9.5 0.110 0.97 0.11 19 F 8.1 0.089 0.99 <0.001 22 F 9.3 0.050 0.99 0.05 23 M 10.2 0.109 1.00 0.01 26 M 9.4 0.110 0.99 0.06 28 M 7.8 0.075 0.98 0.01 30 M 7.6 0.105 0.99 0.01 31 M 6.3 0.059 1.00 0.02 32 F 8.9 0.090 0.98 0.10 34 F 8.8 0.080 0.97 0.11 35 M 8.7 0.076 0.99 0.01
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Table 4. Comparison of maternal strategy data between Seal Bay (Kangaroo Island) and Dangerous Reef colonies.
Data for Kangaroo Island were derived from Higgins (1990), Kretzmann et al. (1991) and Higgins and Gass (1993). Data were compared using students t-test. Proportions of time spent at sea were arcsin transformed and analysed using Chi Square test. * represents a non-significant result at p=0.05. Mean (SD, n) values displayed.
Maternal strategy factors Dangerous Reef Kangaroo Island d.f p
Adult female Mass (kg) 81.6 (10.4, 10) 79.8 (9.7, 15) 23 NS *
Foraging trip duration (hrs) 46.0 (8.2, 10) 43.7 (10.2, 7) 15 NS *
Shore bout duration (hrs) 22.3 (9.3, 10) 39.0 (4.8, 7) 15 p < 0.001
Percentage time onshore 32.1 (10.9, 10) 47.6 (6.1, 7) 15 p = 0.004
Estimated birth mass (kg) 9.1 (1.1, 18) 7.8 (0.9, 18) 34 p = 0.001
Growth to 100d (kg/d) 0.09 (0.021, 18) 0.12 (0.012, 18) 34 p < 0.001
Estimated milk lipid (%) 21.0 (7.5, 10) 28.9 (6.5, 15) 23 p = 0.010
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Figure 1. Location of the current study at Dangerous Reef in the Spencer Gulf, and
Seal Bay (Kangaroo Island) where a similar study was conducted 16 years earlier.
Bathymetry to 100m is shown. Depth increases rapidly towards 100m in the immediate vicinity of Seal Bay whereas potential foraging habitat within the Spencer Gulf rarely exceeds 40m.
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a.
b.
Figure 2. Plots of estimated milk-lipid levels among female Australian sea lions against adult female a) length and b) mass. Milk-lipid levels were significantly negatively related to maternal length and mass.
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a.
b.
Figure 3. Plots of (a) estimated pup growth rates and (b) mass at 90 days against predicted values generated by GLM modeling of maternal length and proportion of time spent ashore. Maternal length and the proportion of time spent ashore positively influence pup growth rates and masses at 90 days.
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"Do not follow where the path may lead. Go instead where there is no path and leave a trail ". H. McAlindon
...... NOW the journey begins.
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