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Foraging ecology of gentoo penguins (Pygoscelis papua) at the Falkland Islands
Abstract and Figures
Marine top predators often occupy broad geographical ranges that encompass varied habitats. Therefore, a pre-requisite towards conserving these animals is to determine the components of their realized niche, and investigate whether a species is a specialist or a generalist. For generalist species, it is also necessary to understand if local specialisation occurs. Uncovering these components can allow us to build models of a species realized niche that may then be used to infer habitat use in unsampled locations. However, fully understanding the components of a marine top predators realized niche is challenging owing to the limited opportunity for in situ observations. Overcoming these limitations is a key step in marine top predator research. It will enhance our understanding of trophic coupling in marine systems, and aid in the development of tools to better study these predators in their dynamic environment.
Seabirds, penguins (Spheniscids) in particular, are a group of animals for which investigating their realized niche is of vital importance. This is because numerous species face growing uncertainty in the Anthropocene, and in a time of rapid environmental change there is furthermore a need to better understand the potential use of these birds as indicators of ecosystem health. The aim of this thesis, therefore, is to investigate the foraging ecology of gentoo penguins (Pygoscelis papua) at the Falkland Islands. At the Falkland Islands, limited historical information exists regarding this species foraging ecology, with most information coming from a single location at the Falklands. As the Falkland Islands have the world’s largest population of gentoo penguins, elucidating factors influencing this population will have global relevance. Furthermore, historical information indicated potential competition with fisheries, and with prospecting for hydrocarbons and an inshore fishery, there is a need to understand the distribution of these birds across the islands. Penguins are also well suited to carry biologging devices allowing for in situ observations of inter and intraspecific interactions, as well as habitat specific interactions.
In this study, I sampled birds over three breeding seasons, from four breeding colonies - chosen for their varied surrounding at sea habitat - across the Falkland Islands. I investigated the diet with stomach content and stable isotope analysis, the at-sea distribution with GPS and time depth recorders, and how these birds behaved at sea using custom made animal-borne camera loggers. Furthermore, I developed a method to recognise prey encounter events from back mounted accelerometers, using a supervised machine learning approach.
As part of the first species specific description of diet at this scale for the Falklands, I revealed six key prey items for the birds: rock cod (Patagonotothen spp.), lobster krill (Munida spp.), Falkland herring (Sprattus fuegensis), Patagonian squid (Doryteuthis gahi), juvenile fish (likely all nototheniids), and southern blue whiting (Micromesistius australis). The use of animal-borne camera loggers verified that not only do gentoo penguins consume a diverse array of prey items, but they adopted various methods to capture and pursue prey, with evidence of birds following optimal foraging theory.
Prey composition varied significantly between study sites with the at-sea distribution and habitat use of penguins reflecting that of local prey. Birds from colonies close to gently sloping, shallow waters, foraged primarily in a benthic manner and had larger niche widths. However, those at a colony surrounded by steeply sloping, deeper waters, typically foraged in a pelagic manner. Contrasting diet patterns were also prevalent from stable isotope data, and the niche widths of birds relating to both stomach content and stable isotope data were larger at colonies where benthic foraging was prevalent. Therefore, it was clear that surrounding bathymetry played a key role in shaping this species’ foraging ecology, and that at the population level at the Falkland Islands birds are generalists. However, at individual colonies some specialisation occurs to take advantage of locally available prey.
I developed habitat distribution models - via boosted regression trees – which transferred well in time but poorly across space. Reasons for poor model transfer might relate to the generalist foraging nature of these birds and the reduced availability of environmental predictors owing to the limited range of these birds. I furthermore developed a method to identify prey encounter events that can also, to a degree, distinguish between prey items. This method will be a promising approach to refine habitat distribution models in future. These habitat distribution models could potentially contribute to marine spatial planning at the Falkland Islands.
Footage from animal-borne camera loggers clearly showed that prey behaviour can significantly influence trophic coupling in marine systems and should be accounted for in studies using marine top predators as samplers of mid to lower trophic level species. Ultimately, flexibility in foraging strategies and inter-colony variation will play a critical role when assessing factors such as inter-specific competition or overlap with anthropogenic activities.
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... Gentoo penguins Pygoscelis papua are distributed along the Antarctic Peninsula and on several Subantarctic islands and coexist with other penguin species at most of their breeding sites (Pütz et al. 2001;Clausen and Pütz 2003;Masello et al. 2010;Lynch 2013, a). Gentoos are also generalists as they can feed on benthic and pelagic prey depending on the region (Croxall and Prince 1980;Adams and Klages 1989;Pütz et al. 2001;Clausen and Pütz 2003;Miller et al. 2009;Handley 2017). Segregation in foraging areas and depth ranges with other sympatric species has been described (Wilson 2010;Pickett et al. 2018). ...
... Optimal foraging theory states that, if possible, individuals will maximize the benefits over the cost of their behavior (Pyke 1984 given their oxygen stores, and based on diving efficiency models, gentoo penguins were more efficient than smaller sized penguin species at depths beyond 40-60 m (Elliott et al. 2010;Wilson 2010). In addition, gentoo penguins throughout their distribution live in relatively small colonies and feed close to their breeding grounds during the breeding season, usually benthically if surrounded by shallow waters or pelagically if surrounding waters slope rapidly to greater depth (Wilson et al. 1998;Handley 2017). Both pair members take turns to make daily foraging trips and take care of offspring, reducing the time available for each parent to forage every day. ...
Marine top predators living in sympatry tend to use their environment differentially, thereby reducing niche overlap. Magellanic penguins (Spheniscus magellanicus) and gentoo penguins (Pygoscelis papua) coexist at Martillo Is., Argentina. During the 2014, 2015 and 2017 breeding seasons a total of thirty-one Magellanic and eleven gentoo penguins were equipped with GPS-TDR loggers to record foraging trips. Both species dove to similar maximum depths with analogous effort (dives per hour). Magellanics, however, undertook longer trips and travelled further away from the colony (25 h and 43 km vs. 10 h and 20 km on average, respectively). Gentoos on the other hand, performed longer dives, had a higher percentage of “U” shaped dives and spent more time during the bottom phase of the dive. Gentoos foraged exclusively within an area close to the colony and in waters not deeper than 80 m. Magellanics had more variability in their behaviors and were not associated to a specific depth or distance from the colony. The ecological circumstances of both species (targeted prey and surrounding environment) suffice to explain the particular behaviors of both species independently from the presence of the other. Gentoos seem to focus on benthic prey which remains stable in this environment throughout the year, and Magellanics behave more opportunistically searching for pelagic prey during early chick rearing. Competition may not be always shaping coexistence of species of similar trophic positions and behaviors. Particularly, if resources are not limited, both species can behave in ways they find optimal without factoring the coexistence of the other species.
... Hence, the intrinsic and extrinsic (environmental) factors that influence Gentoo Penguin movements during the non-breeding period are likely subtle and complex, but the effect of age, prior breeding success and sex (through a more balanced design) could be further explored in future studies. Maximum foraging trip distance and duration from deployment location was over seven times greater than the 79 km and 3.1 days recorded during the breeding period (Handley 2017, Baylis et al. 2019. Although foraging trip metrics were biased toward longer foraging trips, extended foraging trips during the non-breeding period are broadly consistent with the movement patterns described for Gentoo Penguins at other breeding locations, and other colonial breeding seabird species that are resident year-round (Wilson et al. 1998, Daunt et al. 2006, Baylis et al. 2019. ...
Although many penguin species migrate during the non-breeding period, Gentoo Pen- guins Pygoscelis papua are year-round residents. Despite being characterized as inshore feeders, the at-sea spatial usage of Gentoo Penguins during the non-breeding period, when central place foraging constraints are relaxed, is poorly understood. Here, we tracked the movements of Gentoo Penguins from five breeding colonies at the Falkland Islands, globally one of the largest Gentoo Penguin breeding populations. Gentoo Pen- guin movement patterns during the non-breeding period were complex, which likely reflects a high degree of foraging plasticity. Specifically, considerable individual variation existed in foraging trip distance, duration and fidelity to deployment location. For exam- ple, maximum foraging trip distance for individual penguins ranged from 64 to 600 km from the colony location (or 480 km from the nearest point on land), and maximum for- aging trip duration ranged from 5.7 to 24.8 days. Gentoo Penguin foraging trip distance and duration at the Falkland Islands far exceeded those reported at other locations during the non-breeding period, and challenge the inshore, diurnal feeding stereotype. Gentoo Penguins also frequently moved between breeding colonies within the Falkland Islands archipelago, but typically returned to their respective colonies, although not necessarily on consecutive foraging trips. Extended movements highlight Gentoo Penguin breeding dispersal capability, which might play a crucial role in population dynamics and gene flow.
Camera loggers are increasingly used to examine behavioural aspects of free-ranging animals. However, often video loggers are deployed with a focus on specific behavioural traits utilizing small cameras with a limited field of view, poor light performance and video quality. Yet rapid developments in consumer electronics provide new devices with much improved visual data allowing a wider scope for studies employing this novel methodology. We developed a camera logger that records full HD video through a wide-angle lens, providing high resolution footage with a greater field of view than other camera loggers. The main goal was to assess the suitability of this type of camera for the analysis of various aspects of the foraging ecology of a marine predator, the yellow-eyed penguin in New Zealand. Frame-by-frame analysis allowed accurate timing of prey pursuits and time spent over certain seafloor types. The recorded video footage showed that prey species were associated with certain seafloor types, revealed different predator evasion strategies by benthic fishes, and highlighted varying energetic consequences for penguins pursuing certain types of prey. Other aspects that could be analysed were the timing of breathing intervals between dives and observe exhalation events during prey pursuits, a previously undescribed behaviour. Screen overlays facilitated analysis of flipper angles and beat frequencies throughout various stages of the dive cycle. Flipper movement analysis confirmed decreasing effort during descent phases as the bird gained depth, and that ascent was principally passive. Breathing episodes between dives were short (
Background: Foraging efficiency determines whether animals will be able to raise healthy broods, maintain their
own condition, avoid predators and ultimately increase their fitness. Using accelerometers and GPS loggers,
features of the habitat and the way animals deal with variable conditions can be translated into energetic costs
of movement, which, in turn, can be translated to energy landscapes.We investigated energy landscapes in
Gentoo Penguins Pygoscelis papua from two colonies at New Island, Falkland/Malvinas Islands.
Results: In our study, the marine areas used by the penguins, parameters of dive depth and the proportion of
pelagic and benthic dives varied both between years and colonies. As a consequence, the energy landscapes also varied between the years, and we discuss how this was related to differences in food availability, which were also reflected in differences in carbon and nitrogen stable isotope values and isotopic niche metrics. In the second year, the energy landscape was characterized by lower foraging costs per energy gain, and breeding success was also higher in this year. Additionally, an area around three South American Fur Seal Arctocephalus australis colonies was never used.
Conclusions: These results confirm that energy landscapes vary in time and that the seabirds forage in areas of the energy landscapes that result in minimized energetic costs. Thus, our results support the view of energy landscapes and fear of predation as mechanisms underlying animal foraging behaviour. Furthermore, we show that energy landscapes are useful in linking energy gain and variable energy costs of foraging to breeding success.
Past research during the breeding season in the Antarctic Peninsula region indicates that gentoo penguins (Pygoscelis papua) are generalist foragers whereas Adélie (P. adeliae) and chinstrap (P. antarcticus) penguins tend to specialize on Antarctic krill (Euphausia superba). However, little is known about the degree of temporal consistency in the diets and foraging habitats of these three species, particularly at the individual level. Such year-round and inter-annual dietary understanding is important to help interpret contrasting trends in their populations. We used carbon and nitrogen stable isotope analysis of blood and feathers to evaluate seasonal shifts in diet and individual foraging consistency within Pygoscelis penguin species breeding in the South Shetland Islands as well as among three geographically distinct gentoo penguin populations in the western Antarctic Peninsula and South Shetland Islands. We found that gentoo penguins exhibited a generalist foraging strategy with individual consistency, Adélie penguins exhibited an intermediate generalist foraging strategy with little individual consistency, and chinstrap penguins exhibited a specialized diet with little inter-individual variation. Our results also indicated that all three species have greater variation in foraging habitat use during the post-breeding season compared to the breeding season. Finally, we observed differences in the degree of seasonal shifts in population level diet and consistency in foraging strategies at the individual level across the three gentoo penguin populations examined. This suggests that Pygoscelis penguins can differ in diets and foraging habitat use not only at the population level among species, sites, and seasons, but also in the level of variation within populations, and in the degree of seasonal consistency among individuals.
Analysis of radio signals from transmitters affixed to 7 Gentoo (Pygoscelis papua) and 6 Chinstrap (P. antarctica) penguins allowed us to track penguins at sea. Signal characteristics allowed us to distinguish among 5 foraging behaviors: porpoising, underwater swimming, horizontal diving, vertical diving, and resting or bathing. Gentoo Penguins spent a significantly greater portion of their foraging trips engaged in feeding behaviors than Chinstraps, which spent significantly more time traveling. Gentoos had significantly longer feeding dives than Chinstraps (128 s vs. 91 s) and significantly higher dive-pause ratios (3.4 vs. 2.6). These differences in foraging behavior suggest Gentoo and Chinstrap penguins may have different diving abilities and may forage at different depths.
Ten leopard cats (Prionailurus
bengalensis
borneoensis) were captured and radio tracked in an agricultural landscape in Sabah, Malaysia. Seventy-two leopard cat scats were analysed for diet while information on prey distribution and abundance was obtained from a concurrent study on small mammals. Mammals, namely murids, were the major prey with Whitehead's rat (Maxomys
whiteheadi) being the principal prey species. Leopard cats significantly preferred the relatively open oil palm habitat over both selectively logged dipterocarp forest and secondary forest fragments. Although relative murid abundance was highest in selectively logged dipterocarp forest, oil palm harboured a higher relative abundance of Maxomys
whiteheadi. Visibility and ease of movement for leopard cats was also better in oil palm, thereby possibly increasing their hunting success. We suggest that the significantly higher use of oil palm by leopard cats is related to their preference for areas with high prey ‘catchability’ rather than high prey density. Although secondary-forest fragments were least selected, they were important to leopard cats for resting and possibly breeding, highlighting the importance of forest fragments for the conservation of Bornean leopard cats in agricultural landscapes.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.