https://orcid.org/0000-0003-0121-8485
Figures
Abstract
A unique characteristic of the food web along the Western Antarctic Peninsula (WAP), one of the fastest-warming regions globally, is that avian tertiary predators seasonally rely on avian secondary predators for their subsistence. We conducted a review to 1.) summarize research on Antarctic avian predator-prey relationships, 2.) investigate avian predator-prey relationships and trends with the environment, and 3.) highlight research gaps and provide recommendations for future research. We searched the Web of Science and Google Scholar for publications in English during any year. For our first aim, we used the terms “predator-prey dynamics” AND “Antarctica.” We kept only results that included both avian predators and prey, which resulted in seven Southern Ocean publications and one on the WAP. For our second aim, we searched using each species’ common and scientific names (gentoo penguin, Pygoscelis papua, Adélie penguin, P. adeliae, chinstrap penguin, P. antarcticus, southern giant petrel, Macronectes giganteus, south polar skua, Stercorarius maccormicki, brown skua, S. antarcticus) AND “population” AND “Antarctic Peninsula.” We refined our results (N = 59) to publications with data on at least one prey and one predator species of all papers on Web of Science, and the first 100 on Google Scholar. We selected five locations that had data spanning over 10 years from the northern WAP. Further, we compared predator-prey species temporal trends with sea surface and air temperature. We found that relationships between avian secondary and tertiary predators have had limited investigations in Antarctica. Along the WAP, the relationship between different penguin species and avian tertiary predators are highly variable and many population trends are decoupled from local temperature change. We recommend that in addition to continued and expanded data collection, more complex analyses are required and these dynamics should be incorporated into food web and ecosystem modelling to better inform current trends and future projections.
Citation: Russell TM, Hermanson VR (2025) Avian predator-prey dynamics in a changing climate along the Western Antarctic Peninsula: A scoping review. PLOS Clim 4(7): e0000347. https://doi.org/10.1371/journal.pclm.0000347
Editor: André Chiaradia, Phillip Island Nature Parks, AUSTRALIA
Received: December 30, 2023; Accepted: June 18, 2025; Published: July 8, 2025
Copyright: © 2025 Russell, Hermanson. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data used in this study are already publicly available. All sources of data are identified, and summaries are within publication manuscript and Supporting information.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Anthropogenic-driven warming affects marine ecosystems through multiple processes, including direct ocean warming, increased stratification, and decreases in sea ice formation [1,2]. These environmental changes may affect marine organisms through multiple processes, including through impacts on habitat suitability and prey availability [1,3], and can result in shifts in the phenology, distribution, and predation intensity of species [2,4,5]. Climate change driven decreases and/or range shifts of prey species can negatively affect predator populations, alternatively, decreases of primary prey species can result in disproportionate predation on other prey populations and reproductive success [3]. Such shifts in predator-prey interactions may have ecosystem-level consequences, therefore understanding these dynamics and their relationships to environmental change is critical for conservation and management of marine systems [6,7].
Polar ecosystems are already experiencing the effects of climate change [2,8]. In the Southern Ocean, the Western Antarctic Peninsula (WAP) is one of the fastest warming regions in the world and rapidly experiencing effects of climate change, including increasing ocean and air temperatures, changes in wind patterns, and the extent, thickness, and seasonality of sea ice, a critical characteristic of this habitat [9–11]. Along the WAP, the sea ice duration has already shortened by approximately 3 days per year and the sea ice extent has decreased by 5–6% per decade [10]. Sea ice characteristics are critical to this ecosystem, as it provides habitat, food through algal growth, breeding platforms, and can affect the success and survival of many Antarctic species [11–13]. Because of the magnitude of change and species-specific adaptations to this environment, this region is projected to be a hotspot for changes in species composition and diversity, including local extinctions and invasions [14–16].
There is an abundance of avian marine predators along the WAP, including secondary predators, such as brush-tailed penguins (Pygocelis) that primarily feed on Antarctic krill (Euphausia superba), and tertiary predators, such as south polar (Stercorarius maccormicki) and brown skuas (S. antarcticus), and southern giant petrels (Macronectes giganteus). These tertiary predators feed on a variety of foods, including penguins (Fig 1). Three species of Pygocelids breed and feed along the WAP: chinstrap (Pygoscelis antarcticus), Adélie (P. adeliae), and gentoo (P. papua) penguins. Pygocelids primarily feed on krill, although gentoo penguins are more generalist and occasionally consume fish and squid [17–19]. Both the skuas and giant petrels predate upon penguin resources (eggs, chicks, and even adults by the giant petrels), but also rely heavily upon scavenging and at-sea surface feeding of fishes, cephalopods, and invertebrates [20]. Giant petrels are a fierce predator in this ecosystem, and occupy the highest trophic level among birds in the WAP [21]. Skuas are also known for inducing penguins to regurgitate krill or will scavenge on krill that failed to make it into a chick’s throat (i.e., krill spills; [22]). Brown skuas have a predominantly predatory lifestyle, focusing on penguin resources, placentae, and carrion [23], with limited offshore foraging [24]. While, south polar skuas consume a high proportion of fish [25], and are regularly seen foraging at sea [26,27].
Sea ice algae and pelagic phytoplankton are displayed as separate components, although there may be overlap in species. This is to highlight the importance of sea ice in this ecosystem, as increases in sea surface and air temperatures increase there are impacts on sea ice along the WAP.
Much of the long-term monitoring and avian research in the WAP has focused on the brush-tailed penguin species, and changes in their population and distribution have been documented in association with ocean warming and sea ice conditions [28–30]. In this region, Adélie penguins are declining, chinstrap penguins show variable responses but overall declines. Gentoo penguins, a generalist that needs ice free conditions, have been increasing [28,31–33]. However, the population trends of avian tertiary predators remain under-studied, and these energy pathways are typically left out of food web models (e.g., [34,35]). It is unknown whether shifts in the environment can also influence penguin species indirectly, through changes in interspecies interactions with predatory seabirds. With colonies of large penguin populations, these interactions may not be readily apparent or may have an insignificant impact on penguin numbers or reproductive success. As species decline, as is the case for many chinstrap and Adélie populations along the WAP, the role of avian predators may play a larger role in their population dynamics.
Because ecosystem-based management is the primary driver in Antarctic resource management [36,37], it is important to consider ecosystem interactions that are not currently represented. While predator-prey interactions between upper trophic level predators and lower trophic level prey [38–40] are at the forefront of ecosystem-based management research, interactions between upper trophic level predator-prey can provide critical insight into how the Antarctic food web is evolving with a changing climate.
In this paper, we investigate Antarctic avian predator dynamics by addressing these three aims: 1) review published literature on Antarctic avian predator-prey dynamics; 2) investigate these species population trends and relationships with environmental conditions; and 3) provide recommendation for future research priorities.
Materials and methods
Protocol and search strategy
To conduct these reviews, we used Web of Science, and then a supplemental search with Google Scholar using the same terms. Both authors worked independently on different search terms and tracked results in a shared Google Sheet, and followed the same protocol to reduce bias. Our scoping review protocol included searching for articles in English across all years and the combination of search terms could appear anywhere in the article; searches began on 07 July 2023 and ended on 23 September 2023. After conducting our literature searches, we refined results to address two aims; (1) provide an overview of research on Antarctic predator-prey dynamics and (2) identify time series for comparisons between predators and prey along the WAP. We refined our results by manually evaluating the title, keywords and/or abstracts of all papers found in Web of Science, and the first 100 records of Google Scholar searches.
Aim 1
To address the first aim of this study, to review current knowledge of Antarctic avian predator-prey dynamics, we searched for predator-prey information, including the terms “predator-prey dynamics” and “Antarctic Peninsula.” Author T.M.R. selected publications if they compared an avian predator and avian prey, and if it was research from Antarctica. Our goal was to find research within the Antarctic Peninsula, however, broader Antarctic research was included in our results.
Aim 2
To address our second research aim, to investigate species trends and relationships with the environment, we conducted a literature search for avian predator and prey time-series data that extended across at least 10 years, during any time-period, collected from colonies along the WAP. The search terms included both the species’ common and scientific names of the six species (Fig 1), “population” AND “Antarctic Peninsula.” We filtered our results by evaluating article titles and abstracts to decide whether it contained Antarctic seabird time series. From these results, we then extracted data including year, species, and data types. As many of our selected papers utilized the Oceanites Antarctic Site Inventory (OASI), we downloaded data from the OASI (https://www.penguinmap.com/mapppd, [41,42]). Locations were additionally filtered by evaluating whether the data spanned more than 10 years, and if there were data on both avian prey (i.e., chinstrap, gentoo, or Adélie penguins) and avian predators (i.e., southern giant petrels, brown or south polar skuas). We then selected our final locations for analysis based on their latitudinal spread and extracted the data for each year, for each species, and data type. The goal for this second aim was to ensure the data found was robust enough to provide a minimum duration for an adequate timeframe for analyses.
Data synthesis and characteristics
Seabird predator-prey data.
The selected data used to investigate trends all came from locations within the South Shetland Islands, which are located along the northwestern Antarctic Peninsula (within 60–63°S, 57–62°W; Fig 2). The South Shetland Islands are situated between the Bransfield Strait to the south, the Bellingshausen Sea to the southwest, and the Drake Passage and Southern Ocean in the north.
Air temperature data (A) were derived from monthly measurements from five stations around the South Shetland Islands [43]. Sea surface temperature (SST) data (B) were extracted from compiled data (HadISST 1˚ daily; [44]) from the surrounding region (60° to 64° S, 55° to 66° W). The annual air temperatures have increased in this region (R2 = 0.11, F(1,45)=5.71, p value = 0.021), while there is no significant trend in SST in the broader foraging region (R2= < 0.001, F(1,45)=0.028, p-value = 0.87).
Data types varied among locations and included the number of breeding pairs, number of active nests, and number of chicks. If more than one datum was collected per year, we used the average in analysis. As Antarctic data are typically collected in the austral summer, it spans across the new year (e.g., Dec 1995 to Jan 1996). We used the later year in our analysis, for example, data collected during the 1995/1996 season is 1996. We used only species-level data in our analysis and discarded grouped data; which was only available for skuas. A grouped ‘SKUA’ term in our selected datasets included records of birds that either could not be identified or were not attempted at being identified to species. As brown and south polar skua have different diets, we wanted to test for potential differences, therefore only using species-level data.
Environmental data.
We used two environmental variables to test for relationships between species abundance and local and regional conditions between 1972 and 2015. As a proxy for local conditions during the breeding season, we used air temperature data obtained from the READER project ([43]; https://legacy.bas.ac.uk/met/READER). We selected five stations around the South Shetland Islands (Captain Arturo Prat Base, Comandante Ferraz Antarctic Station, Deception Station, Great Wall Station, Carlini Base, King Sejong Station, and Teniente R. Marsh Airport), and took the average summer (December-February) temperature among all stations (Fig 2). For regional, summer conditions, we used sea surface temperature (SST) data from the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST, [44]) obtained from National Center for Atmospheric Research (https://rda.ucar.edu/datasets/ds277.3). The HadISST data is 1˚ daily means that are constructed by combining observations from different sources (ships, satellites, and buoys), along with reconstruction of historical conditions [44]. SST data were extracted within the bounding box that captured the South Shetland Islands and potential foraging habitat for seabirds that nest there (60° to 64° S, 55° to 66° W). We calculated the average summer (December-February) SST within these gridded data as an estimate of the foraging conditions experienced by these species during the breeding season (Fig 2).
Data analyses
In addition to summarizing previous research on avian predators and prey in the WAP, we compared temporal trends among species at our selected colonies to investigate patterns in predator and prey populations and reproductive success, along with relationships with sea surface and air temperatures. These are a first approach in understanding how avian predator and prey population trends may be linked using the limited time series data available on both predator and prey in the WAP. For example, we might expect that at large penguin colonies, their trends in abundance may not affect predator populations, nor predators on them; whereas at small penguin colonies, small changes in predator or prey may have measurable impacts on populations.
All statistical analyses and plots were produced using R programming [45]. Multiple linear regression models were used to investigate trends in seabird species abundance (e.g., number of breeding pairs, nests, and/or chicks) overtime and relationships with summer environmental variable relationships. Data were truncated within the years 1972–2015, a period where both summer SST and air temperature were available and where there were species data that had over 4 observations spanning at least 10 years. We chose at least 10 years to prevent trends that could have resulted from interannual variability, although we recognize longer time series are needed to fully capture long-term impacts from climate change. We tested for collinearity between SST and air temperatures using variance inflation factors (VIF) using the vif fuction in the car package [46]. VIFs measure the amount of variance of a variable is inflated by its correlation with another variable. We used a conservative threshold of four, which all variables at all locations met, therefore year, SST, and air temperatures were included in all multiple regression models. We then conducted backward selection using the stepAIC function in the MASS package [47], which removes variables with the least significant test value and uses Akaike information criterion (AIC) to select the most parsimonious model of seabird trends.
Due to data limitations, such as low frequency and mismatched years between predator and prey species, there were restrictions on the analysis we could conduct to investigate avian predator and prey trends. We used Pearson’s correlation analysis to directly compare years where both predator and prey had data available and provide additional information on the strength and direction of relationships between predator-prey species. We only conducted the correlation analysis between predator and prey data that had data available for at least three of the same years. The Pearson correlation coefficient (r) measures how closely related two variables are, from -1 as a perfect negative linear relationship to +1 as a perfect positive linear relationship, and 0 indicates that there is no linear correlation between them. We classified r between 0.40 and 0.60 as moderate and over 0.60 as strong correlations [48]. We calculated and plotted Pearson’s correlation coefficients using the ‘ggpairs’ function in the GGally library in R [49].
Results
Aim 1
Our literature search on publications on Antarctic avian predator-prey dynamics initially yielded 31 results on Web of Science and 206 results on Google Scholar. After filtering our results, we identified seven publications that addressed the relationships of avian predators and prey around the Southern Ocean [50–56], and one from the WAP [22].
Aim 2
The initial results of our literature search on avian predator and prey time series data were 1,437 results on Web of Science and 6,070 results on Google Scholar. After evaluating all of the Web of Science results and the first 100 for each species on Google Scholar, we identified 59 articles with time series data on our species of interest. After further evaluation using our selection protocol, we selected 18 publications (Table 1, [57–74]) that included data from five locations from around the South Shetland Islands for analysis based on their latitudinal spread and extracted the data for each year, for each species, and data type (Fig 3).
Alongside each location, are the species that had data available and were used in this study. Due to lack of multi-species, publicly available data, our review did not extend beyond the South Shetland Islands (Illustrations by Freya Hammar).
Time-series results within sites varied (Table 2, Fig 4). In the north, breeding pairs of both Adélie and chinstrap penguins on Penguin Island have significantly declined (Adjusted R2 = 0.63, p = 0.020 and Adj. R2 = 0.86, p = 0.005, respectively), while giant petrel breeding pairs on Penguin Island show no trend. Further south, at Potter’s Peninsula, there were declines in Adélie breeding pairs, nests, and chicks (Adj. R2 = 0.92, p < 0.0001, Adj. R2 = 0.86, p < 0.0001, and Adj. R2 = 0.75, p < 0.0001, respectively), whereas both gentoo breeding pairs and chicks increased, but not significantly (Adj. R2 = 0.29, p = 0.15 and Adj. R2 = 0.15, p = 0.16, respectively), and there were no trends in chinstrap penguin breeding pairs. There were non-significant increases in south polar skuas at Potter’s Peninsula (Adj. R2 = 0.12, p = 0.11), while brown skuas remained stable, and had significant positive relationship with air temperature (Adj. R2 = 0.49, p = 0.002). Continuing southeast to Ardley Island, both Adélie and chinstrap nests are declining and year explained most of the abundance trends for these two species (Adj. R2 = 0.61, p < 0.001 and Adj. R2 = 0.64, p < 0.001, respectively), with only air temperature having explanatory power with Adélie nests (p = 0.11). In contrast, gentoo penguin nest abundance increased and was significantly explained by year, air temperature, and SST (Adj. R2 = 0.49, p = 0.006). A small, but significant portion of the increasing south polar skua abundance at Ardley Island was explained by year and air temperature (Adj. R2 = 0.25, p = 0.013). While the brown skuas at Ardly Island showed no trend, but the variability in their breeding pairs significantly correlated with air temperature (Adj. R2 = 0.13, p = 0.037). On Barrientos Island in the south, giant petrel nests have significantly declined (Adj. R2 = 0.37, p = 0.006). Although there is an apparent increase in gentoo penguins on Barrientos (Fig 4), these trends are not significant over time or with environmental conditions, although the best-fit model selected SST and air temperature (p = 0.12 and p = 0.13, respectively). At Hannah Point, chinstrap penguin nests have decreased with both year and air temperature explaining a significant about of this trend (Adj. R2 = 0.97, p < 0.001). Additionally, southern giant petrels have also decreased at Hannah Point (Adj. R2 = 0.40, p = 0.054), but there were environmental variables did not improve model fit and trends were not significant.
Table 1) compared with the three independent variables used in analysis; year, sea surface temperature, and air temperature. Plot include points of the raw data, along with the line of best fit and the 95% confidence interval.
Pearson’s correlation coefficients varied between species and data types among sites (Table 3, Fig A in S1 Text). On Penguin Island, there was a strong, negative relationship between Adélie and giant petrel breeding pairs (−0.66). At Potter’s Peninsula, Adélie breeding pairs had a moderate negative relationship (−0.56) and Adélie chicks had a strong, negative relationship (−0.78) with south polar skua breeding pairs but neither with brown skuas. On Ardley Island, both Adélie and chinstrap penguin nests had a moderate to strong, negative relationship (−0.57 and −0.78, respectively) with south polar skua breeding pairs. However, gentoo penguin nests had a strong, positive relationship (0.61) with south polar skuas and a moderate, positive relationship (0.45) with brown skua breeding pairs. There was no relationship between gentoo penguin and southern giant petrel nests at Barrientos Island. At Hannah Point, chinstrap penguin nests had a strong, positive relationship with giant petrel nests (0.62).
Discussion
Potential relationships between avian predator and prey species in Antarctica are an understudied area of research in Antarctica. We found limited reports investigating these relationships, and few datasets that had both predator and prey data over common time periods. The data limitation on both predator and prey prevented more robust analyses into these dynamics. In addition to these limitations, data on phenology of both predator and prey species and detailed diet data of each overtime remains sparse and prohibits detailed insights into the role of avian predators in this unique ecosystem. We recommend that in addition to continued and expanded data collection, that more complex analyses are required, and that these dynamics should be incorporated into food web and ecosystem modelling to better inform current trends and projections into the future.
Aim 1
Avian predator-prey dynamics in Antarctica.
Research on predator-prey dynamics in Antarctica has focused on relationships between Antarctic krill and Pygocelis penguins, both abundant components of this unique food web (e.g., [32,75,76]. Although the diets of avian tertiary predators (e.g., Southern giant petrels and skuas) in this region are relatively well known, there has been limited investigations into the predator-prey dynamics between them and their Pygocelis prey. During the breeding season, penguin eggs, chicks, and sometimes adults can make up the majority of these tertiary avian predator’s diets [22,23,42]. Previous reports document that most of the known skua and giant petrel colonies are near or within Pygocelid colonies, providing easy access to this reliable food source during the breeding season [26,55,56,77]. We found limited investigations into avian predator-prey relationships within the Antarctic region, with most of the research focusing on skua-Adélie relationships around East Antarctica. In this region, there is a significant relationship between south polar skuas and Adélie penguin populations; locations where both are decreasing [50] or both are increasing [54]. At larger Adélie colonies, there are a larger number, but smaller proportion, of chick depredation by south polar skuas and the Adélie colonies that had larger numbers of skua territories had the lowest reproductive success [52,78]. Other research investigated multispecies occurrence including our species of interest, but with the focus of understanding niche overlap and mechanisms that prohibit competitive exclusion among these species [e.g., 79].
Direct comparisons of multiple species on Elephant Island, located north of the WAP, found that all species of Pygocelids and brown skuas were declining, while southern giant petrel numbers were increasing [80]. The one study we found that directly compared these avian predator-prey species from the WAP was from King George Island, within the South Shetland Islands [22]. This study documented predation events by skuas and giant petrels on different-sized gentoo and Adélie penguin colonies. They found that smaller colonies were disproportionately affected by predation and that their reproductive success may be influenced by the size of the colony and the abundance and diversity of avian predators [22], which was similar to other findings from studies in the subantarctic islands [51,78].
Aim 2
Trends of avian predators and prey.
Within our five colonies used in these exploratory analyses, there were strong trends of penguins abundance, while tertiary predators were highly variable over time. In line with previous published results, at four locations either or both chinstrap and Adélie penguins have decreased, while gentoos are either increasing or stable (Fig 4). The results also varied across regions, for example, southern giant petrels are stable in the north at Penguin Island, where both penguin species are declining. Penguin Island is a heavily visited area by tourists, and impacts to avian species have been previously noted, including changing nest sites, which may mask actual population trends [61]. However, at both locations in the south (Hannah Point and Barrientos Island), giant petrel nests are declining alongside decreases in the number of chinstrap nests. These differences among sites indicate complex drivers of reproductive success, and potential differences in diets among predators at different sites. With the skua species, populations were stable among many of the sites, with the exception that south polar skua increased at Ardley Island, where Adélie and chinstrap have both declined and gentoo have increased. However, the skua data had high interannual variability at most sites, which has been previously report at Potter’s Peninsula colony in both breeding pairs and reproductive success [64].
For predator-prey data that we were able to directly compare per year, there were clear patterns among the relationships between secondary and tertiary avian predators (Table 3). Both ice-reliant species of Pygocelids (Adélie and chinstrap) had moderate to strong inverse relationships with south polar skuas. Whereas, the ice-avoiding, gentoo penguin had a positive relationship with the south polar skuas, and moderate, positive relationship with brown skuas. Although there may be correlations among these trends, south polar skuas are known to eat carrion and ectothermic, pelagic prey, rather than rely on taking live penguins as food as brown skuas do regularly. Therefore, the inverse trends among penguin species with skuas we detected may be due to similarities in the conditions that are good for both gentoo and south polar skuas, which have a negative effect on chinstrap and Adélie penguins. Relationships with southern giant petrels varied, with strong, inverse relationships with Adélie penguins in the north, where the petrel population is stable, and a strong, positive relationship with chinstraps in the south, where the population is in decline. On Potter’s Peninsula, the Adélie penguin data provided us the opportunity to compare data types. We found that Adélie breeding pairs and nests displayed the same trends in their relationships with brown and south polar skuas (Table 3), however, the nest data resulted in a stronger negative relationship with south polar skuas. To understand these complex relationships, collecting multiple data types may provide more clarity and should be a priority for continued and future monitoring.
The one publication on avian predator-prey relationships along the WAP highlighted the relationships between prey colony size and predators, with smaller colonies being more affected by predation. When we compare different sized colonies, we found variable patterns between small and large colonies with predator correlations. The chinstrap penguin results are in line with the previous report with the large colony on Hannah Point increasing alongside increases in southern giant petrels, while there was a decrease in the much smaller colony of chinstraps on Ardley Island, which was negatively correlated with south polar skuas. The two colonies with gentoo penguin data for comparisons had similar population numbers, and there were comparable correlations among small to large sized colonies of Adélie with skuas.
As the data are limited, these correlations may be due to a variety of processes, such as similar environmental conditions that benefit generalists, or shifts in foraging locations that affect all species. Without diet data or direct observations of predation, we cannot verify proposed relationships. However, the variability in the relationships between penguins and skuas are likely more complex due to their heavy reliance on scavenging, compared to the frequency of direct predation that giant petrels exhibit.
Environmental relationships with predator-prey dynamics
In line with previous studies, we found air temperatures had increased around the South Shetland Islands [81], however the SST of the broader region had remained stable within our study period. Although there were significant increases in air temperatures, these data did not explain many of the species trends. The notable exceptions were with chinstrap penguins and skuas. At Hannah Point in the south, the decline of chinstrap nests were negatively correlated with the increase in air temperature, while declines at other locations were not significantly correlated with local and regional environmental conditions. Brown skuas had no significant trend overtime; however, their variability was explained by air temperature at both Potter’s Peninsula and Ardley Island. As these are less migratory than south polar skuas, the local conditions are more impactful for brown skuas. The lack of explanatory power of these environmental variables with other species may be due to the spatial scale we used and/or climatic variability masking overall trends (Fig 2), but may be due to other drivers, such as shifts in prey abundance and availability, which are either not tied directly to temperatures or may have lagged responses.
With the temperature increases that are occurring and/or are projected for this region there will be further impacts to these seabird populations and therefore shifts in the predator-prey relationships between them [82,83]. For example, Adélie and chinstrap penguins are decreasing in many of these colonies, and if gentoos are not establishing or increasing at those colonies, the predation pressure from skuas and giant petrels may increase disproportionately for declining species [22]. Because these tertiary predators are generalists and rely on a wide variety of foods, including scavenging, they may be less affected from the immediate pressures of climate change, at least for now. However, if there are decreases in oceanic prey (i.e., fishes, krill, and squid) it may affect the predation pressure on penguins.
Data limitations
The lack of fine-scale temporal data prevented more complex analyses and investigations into lagged relationships between predators and prey. More detailed and frequent data are necessary for conducting more robust analyses on these relationships. Our conclusions come from correlative analyses and without more data we cannot present substantiative claims from these analyses. Future efforts with more fine scale data should investigate lagged relationships between predator and prey, and analyses with more substantial data types would help determine the causal mechanisms behind such relationship. We also recognize that species population and reproductive success are complex, and not purely driven by predator controls. Our analyses are only correlations and not causes, but form a base for future investigations into these predator-prey dynamics.
We found limited previous research on avian predator-prey linkages along the WAP, and that tertiary predators (skuas and giant petrels) are often left out of food webs, or that energy pathways from penguins to avian tertiary predators are not incorporated. If flying seabirds are included, they are lumped into a general bird group ignoring the high diversity in foraging strategies and diets which make them incompatible for grouping. The lack of species or functional group resolution in these models results in an incomplete food web assessment. Penguin population data are used as a metric of prey availability, resource management, and ecosystem monitoring in the WAP, however, potential top-down controls on these penguins are not being incorporated. As certain colonies and/or species decline, these relationships may become more impactful and therefore models missing this component are not capturing the full picture of food web connectivity.
Excluding potential effects from top-down controls likely results in an incomplete understanding of population dynamics. As these predators are not entirely reliant on penguins as a resource, and are opportunistic, generalists, they may also reflect other food web changes. Just as generalist penguins (e.g., gentoo) are the ecological “winners” with warming temperatures, generalist, tertiary predators of the Antarctic are affected differently by fluctuations in specific species, although decreases are occurring in some colonies [84]. Monitoring both secondary and tertiary consumer data provides a deeper understanding of ecosystem structure and functioning, and important data for use in marine conservation.
Aim 3
Recommendations.
After this evaluation of predator-prey interactions along with WAP and investigations between these species trends, we have developed a series of recommendations for future research. Firstly, the continuation and expansion of current monitoring programs. The WAP is one of the fastest warming regions on Earth, and as vulnerable secondary consumer species decrease, the proportional effect of their predation may increase [22]. Therefore, collecting useful data, including predation events, diets, and population data on the same time scale, to monitor the pressure of avian predators will become increasingly important. In addition, as the phenology of these populations change, mismatches between predator-prey interactions may occur [85]. These programs should not only collect data on penguin breeding pairs, but attempts should also be made to collect data on the number of nests and chicks so that a metric of reproductive success and phenology can be monitored. In addition to penguin data, data needs to be collected on any breeding predator species within or around the colonies. The limitations of these data collection are in the ability for researchers to remain at or revisit colonies throughout the breeding season. To confirm these relationships, studies on skua and giant petrel diets overtime within these monitored colonies should be conducted and compared with population data to provide information on how their diets change over time, species preferences of prey, and potential population level impacts on these species [86].
In addition to enhanced and expanded data collection, the use of predator-prey models such as Lotka-Volterra models should be used to further understand these relationships under changing environmental conditions. These tertiary predators and these predator-prey interactions should also be included and with more specificity within food web models to more thoroughly understand the food web dynamics in this region. For example, these species and energy pathways could be included into models such as Ecopath with Ecosim [87] or building specific predator-prey models (e.g., [3]). Finally, it would be prudent to evaluate the impact that HPAI has had upon the predator-prey dynamic between species in the Antarctic Peninsula region. There have been recorded cases of increased skua deaths in part of the Antarctic Peninsula, with a low impact to penguins [88,89]. This may have a significant impact in the reproductive progress of penguin populations during their breeding season. This work is a first-step in better understanding the complexities of avian predator interactions in this region, and we hope to inspire future work in order to better monitor and predict future changes within this vulnerable ecosystem.
Supporting information
S1 Text.
Fig A. Pearson’s correlation coefficients (r) between Adélie (ADPE) and southern giant petrel (SGPE) breeding pairs from Penguin Island. Fig B. Pearson’s correlation coefficients (r) between Adélie (ADPE), brown skua (BRSK) and south polar skua (SPSK) breeding pairs from Potter’s Peninsula. Fig C. Pearson’s correlation coefficients (r) between Adélie (ADPE), brown skua (BRSK) and south polar skua (SPSK) breeding pairs from Potter’s Peninsula. Fig D. Pearson’s correlation coefficients (r) between Adélie (ADPE) nests and brown skua (BRSK) and south polar skua (SPSK) breeding pairs from Ardley Island. Fig E. Pearson’s correlation coefficients (r) between chinstrap penguin (CHPE) nests, brown skua (BRSK) and south polar skua (SPSK) breeding pairs from Ardley Island. Fig F. Pearson’s correlation coefficients (r) between gentoo penguin (GEPE) nests, brown skua (BRSK) and south polar skua (SPSK) breeding pairs from Ardley Island. Fig G. Pearson’s correlation coefficients (r) between gentoo penguin (GEPE) and southern giant petrel (SGPE) nests from Barrientos Island. Fig H. Pearson’s correlation coefficients (r) between chinstrap penguin (CHPE) and southern giant petrel (SGPE) nests from Hannah Point.
https://doi.org/10.1371/journal.pclm.0000347.s001
(DOCX)
S1 Checklist.
Additional text and figures on the results from our Pearson’s correlation coefficient analysis between species at each site (FIgs S1-S8).
https://doi.org/10.1371/journal.pclm.0000347.s002
(PDF)
Acknowledgments
We would like to thank PLoS Climate for supporting early career researchers through this special issue, and the work of the Association of Polar Early Career Scientists for this opportunity. We would also like to thank all those involved in the data collection on marine birds that has occurred throughout the WAP, including tourists that serve as citizen scientists to collect meaningful data. We also thank B. Jack Pan for his assistance with the environmental data used in this research, and thank Stephanie A. Matthews and Allison M. Cusick for their feedback on this work. We sincerely thank the reviewers whose evaluation and advice greatly enhanced this work.
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