Projecting global mariculture diversity under climate change

Previous studies have focused on changes in the geographical distribution of terrestrial biomes and species targeted by marine capture fisheries due to climate change impacts. Given mariculture's substantial contribution to global seafood production and its growing significance in recent decades, it is essential to evaluate the effects of climate change on mariculture and their socio‐economic consequences. Here, we projected climate change impacts on the marine aquaculture diversity for 85 of the currently most commonly farmed fish and invertebrate species in the world's coastal and/or open ocean areas. Results of ensemble projections from three Earth system models and three species distribution models show that climate change may lead to a substantial redistribution of mariculture species richness potential, with an average of 10%–40% decline in the number of species being potentially suitable to be farmed in tropical to subtropical regions. In contrast, mariculture species richness potential is projected to increase by about 40% at higher latitudes under the ‘no mitigation policy’ scenario (RCP 8.5) by the mid‐21st century. In Exclusive Economic Zones where mariculture is currently undertaken, we projected an average future decline of 1.3% and 5% in mariculture species richness potential under RCP 2.6 (‘strong mitigation’) and RCP 8.5 scenarios, respectively, by the 2050s relative to the 2000s. Our findings highlight the opportunities and challenges for climate adaptation in the mariculture sector through the redistribution of farmed species and expansion of mariculture locations. Our results can help inform adaptation planning and governance mechanisms to minimize local environmental impacts and potential conflicts with other marine and coastal sectors in the future.     

A cross-scale framework to support a mechanistic understanding and modelling of marine climate-driven species redistribution,from individuals to communities

Climate-driven species redistribution is pervasive and accelerating, yet the complex mechanisms at play remain poorly understood. The implications of large-scale species redistribution for natural systems and human societies have resulted in a large number of studies exploring the effects on individual species and ecological communities worldwide. Whilst many studies have investigated discrete components of species redistribution, the integration required for a more complete mechanistic understanding is lacking. In this paper, we provide a framework for synthesising approaches to more robustly understand and predict marine species redistributions. We conceptualise the stages and processes involved in climate-driven species redistribution at increasing levels of biological organisation, and synthesize the laboratory, field and modelling approaches used to study redistribution related processes at individual, population and community levels. We then summarise links between scales of biological organisation and methodological approaches in a hierarchical framework that represents an integrated mechanistic assessment of climate-driven species redistributions. In a rapidly expanding field of research, this framework provides direction for: 1) guiding future research, 2) highlighting key knowledge gaps, 3) fostering data exchange and collaboration between disciplines and 4) improving shared capacity to predict and therefore, inform the proactive management of climate impacts on natural systems.     

Predicting impacts of climate change on biodiversity: a role for semi‐mechanistic community‐level modelling

Aim Robust and reliable predictions of the effects of climate change on biodiversity are required in formulating conservation and management strategies that best retain biodiversity into the future. Significant challenges in modelling climate change impacts arise from limitations in our current knowledge of biodiversity. Community‐level modelling can complement species‐level approaches in overcoming these limitations and predicting climate change impacts on biodiversity as a whole. However, the community‐level approaches applied to date have been largely correlative, ignoring the key processes that influence change in biodiversity over space and time. Here, we suggest that the development of new ‘semi‐mechanistic’ community‐level models would substantially increase our capacity to predict climate change impacts on biodiversity. Location Global. Methods Drawing on an expansive review of biodiversity modelling approaches and recent advances in semi‐mechanistic modelling at the species level, we outline the main elements of a new semi‐mechanistic community‐level modelling approach. Results Our quantitative review revealed a sharp divide between mechanistic and non‐mechanistic biodiversity modelling approaches, with very few semi‐mechanistic models developed to date. Main conclusions We suggest that the conceptual framework presented here for combining mechanistic and non‐mechanistic community‐level approaches offers a promising means of incorporating key processes into predictions of climate change impacts on biodiversity whilst working within the limits of our current knowledge.     

Disentangling how climate change can affect an aquatic food web by combining multiple experimental approaches

Predicting the biological effects of climate change presents major challenges due to the interplay of potential biotic and abiotic mechanisms. Climate change can create unexpected outcomes by altering species interactions, and uncertainty over the ability of species to develop in situ tolerance or track environmental change further hampers meaningful predictions. As multiple climatic variables shift in concert, their potential interactions further complicate ecosystem responses. Despite awareness of these complexities, we still lack controlled experiments that manipulate multiple climatic stressors, species interactions, and prior exposure of species to future climatic conditions. Particularly studies that address how changes in water availability interact with other climatic stressors to affect aquatic ecosystems are still rare. Using aquatic insect communities of Neotropical tank bromeliads, we combined controlled manipulations of drought length and species interactions with a space‐for‐time transplant (lower elevations represent future climate) and a common garden approach. Manipulating drought length and experiment elevation revealed that adverse effects of drought were amplified at the warmer location, highlighting the potential of climatic stressors to synergistically affect communities. Manipulating the presence of omnivorous tipulid larvae showed that negative interactions from tipulids, presumably from predation, arose under drought, and were stronger at the warmer location, stressing the importance of species interactions in mediating community responses to climate change. The common garden treatments revealed that prior community exposure to potential future climatic conditions did not affect the outcome. In this powerful experiment, we demonstrated how complexities arise from the interplay of biotic and abiotic mechanisms of climate change. We stress that single species can steer ecological outcomes, and suggest that focusing on such disproportionately influential species may improve attempts at making meaningful predictions of climate change impacts on food webs.     

North Sea cod and climate change – modelling the effects of temperature on population dynamics

In order to examine the likely impacts of climate change on fish stocks, it is necessary to couple the output from large‐scale climate models to fisheries population simulations. Using projections of future North Sea surface temperatures for the period 2000–2050 from the Hadley General Circulation Model, we estimate the likely effects of climate change on the North Sea cod population. Output from the model suggests that increasing temperatures will lead to an increased rate of decline in the North Sea cod population compared with simulations that ignore environmental change. Although the simulation developed here is relatively simplistic, we demonstrate that inclusion of environmental factors in population models can markedly alter one's perception of how the population will behave. The development of simulations incorporating environment effects will become increasingly important as the impacts of climate change on the marine ecosystem become more pronounced.     

A framework integrating physiology,dispersal and land‐use to project species ranges under climate change

To study the potential effects of climate change on species, one of the most popular approaches are species distribution models (SDMs). However, they usually fail to consider important species‐specific biological traits, such as species’ physiological capacities or dispersal ability. Furthermore, there is consensus that climate change does not influence species distributions in isolation, but together with other anthropogenic impacts such as land‐use change, even though studies investigating the relative impacts of different threats on species and their geographic ranges are still rare. Here we propose a novel integrative approach which produces refined future range projections by combining SDMs based on distribution, climate, and physiological tolerance data with empirical data on dispersal ability as well as current and future land‐use. Range projections based on different combinations of these factors show strong variation in projected range size for our study species Emberiza hortulana. Using climate and physiological data alone, strong range gains are projected. However, when we account for land‐use change and dispersal ability, future range‐gain may even turn into a future range loss. Our study highlights the importance of accounting for biological traits and processes in species distribution models and of considering the additive effects of climate and land‐use change to achieve more reliable range projections. Furthermore, with our approach we present a new tool to assess species’ vulnerability to climate change which can be easily applied to multiple species.     

Benchmarking novel approaches for modelling species range dynamics

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process‐based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process‐based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species.     

Size‐based ecological interactions drive food web responses to climate warming

Predicting climate change impacts on animal communities requires knowledge of how physiological effects are mediated by ecological interactions. Food‐dependent growth and within‐species size variation depend on temperature and affect community dynamics through feedbacks between individual performance and population size structure. Still, we know little about how warming affects these feedbacks. Using a dynamic stage‐structured biomass model with food‐, size‐ and temperature‐dependent life history processes, we analyse how temperature affects coexistence, stability and size structure in a tri‐trophic food chain, and find that warming effects on community stability depend on ecological interactions. Predator biomass densities generally decline with warming – gradually or through collapses – depending on which consumer life stage predators feed on. Collapses occur when warming induces alternative stable states via Allee effects. This suggests that predator persistence in warmer climates may be lower than previously acknowledged and that effects of warming on food web stability largely depend on species interactions.     

Impacts of climate change on avian populations

This review focuses on the impacts of climate change on population dynamics. I introduce the MUP (Measuring, Understanding, and Predicting) approach, which provides a general framework where an enhanced understanding of climate‐population processes, along with improved long‐term data, are merged into coherent projections of future population responses to climate change. This approach can be applied to any species, but this review illustrates its benefit using birds as examples. Birds are one of the best‐studied groups and a large number of studies have detected climate impacts on vital rates (i.e., life history traits, such as survival, maturation, or breeding, affecting changes in population size and composition) and population abundance. These studies reveal multifaceted effects of climate with direct, indirect, time‐lagged, and nonlinear effects. However, few studies integrate these effects into a climate‐dependent population model to understand the respective role of climate variables and their components (mean state, variability, extreme) on population dynamics. To quantify how populations cope with climate change impacts, I introduce a new universal variable: the ‘population robustness to climate change.’ The comparison of such robustness, along with prospective and retrospective analysis may help to identify the major climate threats and characteristics of threatened avian species. Finally, studies projecting avian population responses to future climate change predicted by IPCC‐class climate models are rare. Population projections hinge on selecting a multiclimate model ensemble at the appropriate temporal and spatial scales and integrating both radiative forcing and internal variability in climate with fully specified uncertainties in both demographic and climate processes.     

Continental shelf-wide response of a fish assemblage to rapid warming of the sea

Climate change affects marine biological processes from genetic to ecosystem levels [1-3]. Recent warming in the northeast Atlantic [4, 5] has caused distributional shifts in some fish species along latitudinal and depth gradients [6,?7], but such changes, as predicted by climate envelope models [8], may often be prevented because population movement requires availability of suitable habitat. We assessed the full impacts of warming on the commercially important European continental shelf fish assemblage using a data-driven Eulerian (grid-based) approach that accommodates spatial heterogeneity in ecological and environmental conditions. We analyzed local associations of species abundance and community diversity with climatic variables, assessing trends in 172 cells from records of >100 million individuals sampled over 1.2 million km(2) from 1980-2008. We demonstrate responses to warming in 72% of common species, with three times more species increasing in abundance than declining, and find these trends reflected in international commercial landings. Profound reorganization of the relative abundance of species in local communities occurred despite decadal stability in the presence-absence of species. Our analysis highlights the importance of focusing on changes in species abundance in established local communities to assess the full consequences of climate change for commercial fisheries and food security.     

Integrating mechanistic and empirical model projections to assess climate impacts on tree species distributions in northwestern North America

Empirical and mechanistic models have both been used to assess the potential impacts of climate change on species distributions, and each modeling approach has its strengths and weaknesses. Here, we demonstrate an approach to projecting climate‐driven changes in species distributions that draws on both empirical and mechanistic models. We combined projections from a dynamic global vegetation model (DGVM) that simulates the distributions of biomes based on basic plant functional types with projections from empirical climatic niche models for six tree species in northwestern North America. These integrated model outputs incorporate important biological processes, such as competition, physiological responses of plants to changes in atmospheric CO₂ concentrations, and fire, as well as what are likely to be species‐specific climatic constraints. We compared the integrated projections to projections from the empirical climatic niche models alone. Overall, our integrated model outputs projected a greater climate‐driven loss of potentially suitable environmental space than did the empirical climatic niche model outputs alone for the majority of modeled species. Our results also show that refining species distributions with DGVM outputs had large effects on the geographic locations of suitable habitat. We demonstrate one approach to integrating the outputs of mechanistic and empirical niche models to produce bioclimatic projections. But perhaps more importantly, our study reveals the potential for empirical climatic niche models to over‐predict suitable environmental space under future climatic conditions.     

Separating direct and indirect effects of global change: a population dynamic modeling approach using readily available field data

Two sources of complexity make predicting plant community response to global change particularly challenging. First, realistic global change scenarios involve multiple drivers of environmental change that can interact with one another to produce non‐additive effects. Second, in addition to these direct effects, global change drivers can indirectly affect plants by modifying species interactions. In order to tackle both of these challenges, we propose a novel population modeling approach, requiring only measurements of abundance and climate over time. To demonstrate the applicability of this approach, we model population dynamics of eight abundant plant species in a multifactorial global change experiment in alpine tundra where we manipulated nitrogen, precipitation, and temperature over 7 years. We test whether indirect and interactive effects are important to population dynamics and whether explicitly incorporating species interactions can change predictions when models are forecast under future climate change scenarios. For three of the eight species, population dynamics were best explained by direct effect models, for one species neither direct nor indirect effects were important, and for the other four species indirect effects mattered. Overall, global change had negative effects on species population growth, although species responded to different global change drivers, and single‐factor effects were slightly more common than interactive direct effects. When the fitted population dynamic models were extrapolated under changing climatic conditions to the end of the century, forecasts of community dynamics and diversity loss were largely similar using direct effect models that do not explicitly incorporate species interactions or best‐fit models; however, inclusion of species interactions was important in refining the predictions for two of the species. The modeling approach proposed here is a powerful way of analyzing readily available datasets which should be added to our toolbox to tease apart complex drivers of global change.     

Competitive and demographic leverage points of community shifts under climate warming

Accelerating rates of climate change and a paucity of whole-community studies of climate impacts limit our ability to forecast shifts in ecosystem structure and dynamics, particularly because climate change can lead to idiosyncratic responses via both demographic effects and altered species interactions. We used a multispecies model to predict which processes and species'' responses are likely to drive shifts in the composition of a space-limited benthic marine community. Our model was parametrized from experimental manipulations of the community. Model simulations indicated shifts in species dominance patterns as temperatures increase, with projected shifts in composition primarily owing to the temperature dependence of growth, mortality and competition for three critical species. By contrast, warming impacts on two other species (rendering them weaker competitors for space) and recruitment rates of all species were of lesser importance in determining projected community changes. Our analysis reveals the importance of temperature-dependent competitive interactions for predicting effects of changing climate on such communities. Furthermore, by identifying processes and species that could disproportionately leverage shifts in community composition, our results contribute to a mechanistic understanding of climate change impacts, thereby allowing more insightful predictions of future biodiversity patterns.     

Species and community responses to short‐term climate manipulation: Microarthropods in the sub‐Antarctic

Abstract: Both species and community‐level investigations are important for understanding the biotic impacts of climate change, because current evidence suggests that individual species responses are idiosyncratic. However, few studies of climate change impacts have been conducted on entire terrestrial arthropod communities living in the same habitat in the southern Hemisphere, and the effects of precipitation changes on them are particularly poorly understood. Here we investigate the species‐ and community‐level responses of microarthropods inhabiting a keystone plant species, on sub‐Antarctic Marion Island, to experimental reduction in precipitation, warming and shading. These climate manipulations were chosen based on observed climate trends and predicted indirect climate change impacts on this system. The dry‐warm and shade inducing treatments that were imposed effected significant species‐ and community‐level responses after a single year. Although the strongest community‐level trends included a dramatic decline in springtail abundance and total biomass under the dry‐warm and shade treatments, species responses were generally individualistic, that is, springtails responded differently to mites, and particular mite and springtail species responded differently to each other. Our results therefore provide additional support for the dynamic rather than static model for community responses to climate change, in the first such experiment in the sub‐Antarctic. In conclusion, these results show that an ongoing decline in precipitation and increase in temperature is likely to have dramatic direct and indirect effects on this microarthropod community. Moreover, they indicate that while at a broad scale it may be possible to make generalizations regarding species responses to climate change, these generalizations are unlikely to translate into predictable effects at the community level.     

Dynamic macroecology and the future for biodiversity

Reliable projections of climate‐change impacts on biodiversity are vital in formulating conservation and management strategies that best retain biodiversity into the future. While recent modelling has focussed largely on individual species, macroecology has the potential to add significant value to these efforts, by incorporating important community‐level constraints and processes. Here we show how a new dynamic macroecological approach can project climate‐change impacts collectively across all species in a diverse taxonomic group, overcoming shortfalls in our knowledge of biodiversity, while incorporating the key processes of dispersal and community assembly. Our approach applies a recently published technique (DynamicFOAM) to predict the present composition of every community, which form the initial conditions for a new metacommunity model (M‐SET) that projects changes in composition over time, under specified climate and habitat scenarios. Applying this approach at fine resolution to plant biodiversity in Tasmania (2,051 species; 1,157,587 communities), we project high average turnover in community composition from 2010 to 2100 (mean Sorensen's dissimilarity = 0.71 (±7.0 × 10^?5)), with major reductions in species richness (32.9 (±0.02) species lost per community) and no plant species benefitting from climate change in the long term. We also demonstrate how our modelling approach can identify habitat likely to be of high value for retaining rare and poorly reserved species under climate change. Our analyses highlight the potential value of this dynamic macroecological approach, that incorporates key ecological processes in projecting climate change impacts for all species simultaneously and uses simple macroecological inputs that can be derived even for highly diverse and poorly studied taxa.     

Wildfire–vegetation dynamics affect predictions of climate change impact on bird communities

Community‐level climate change indicators have been proposed to appraise the impact of global warming on community composition. However, non‐climate factors may also critically influence species distribution and biological community assembly. The aim of this paper was to study how fire–vegetation dynamics can modify our ability to predict the impact of climate change on bird communities, as described through a widely‐used climate change indicator: the community thermal index (CTI). Potential changes in bird species assemblage were predicted using the spatially‐explicit species assemblage modelling framework – SESAM – that applies successive filters to constrained predictions of richness and composition obtained by stacking species distribution models that hierarchically integrate climate change and wildfire–vegetation dynamics. We forecasted future values of CTI between current conditions and 2050, across a wide range of fire–vegetation and climate change scenarios. Fire–vegetation dynamics were simulated for Catalonia (Mediterranean basin) using a process‐based model that reproduces the spatial interaction between wildfire, vegetation dynamics and wildfire management under two IPCC climate scenarios. Net increases in CTI caused by the concomitant impact of climate warming and an increasingly severe wildfire regime were predicted. However, the overall increase in the CTI could be partially counterbalanced by forest expansion via land abandonment and efficient wildfire suppression policies. CTI is thus strongly dependent on complex interactions between climate change and fire–vegetation dynamics. The potential impacts on bird communities may be underestimated if an overestimation of richness is predicted but not constrained. Our findings highlight the need to explicitly incorporate these interactions when using indicators to interpret and forecast climate change impact in dynamic ecosystems. In fire‐prone systems, wildfire management and land‐use policies can potentially offset or heighten the effects of climate change on biological communities, offering an opportunity to address the impact of global climate change proactively.     

Climate change and bet-hedging: interactions between increased soil temperatures and seed bank persistence

In order to predict the long-term consequences of climate change, it is necessary to link future environmental changes to mechanisms that control plant population processes. This information can then be incorporated into strategies to more accurately model climate change impacts on species or to estimate future extinction risks. We examined the impact of increased temperatures on the longevity and dynamics of the persistent soil seed banks of eight ephemeral species from arid Australia. We found that the predicted global temperature increases under climate change will be reflected in increased soil temperatures, and that seeds in the soil seed bank will be exposed to long durations of high temperatures over the summer months. Three of the eight species studied had significantly greater levels of germination after exposure to predicted increased soil temperatures. Another species displayed a dramatic decrease in seed viability after such exposure. The capacity of such species to use the seed bank to bet hedge against rainfall events that cause germination but are insufficient to allow plant maturation, is compromised by increased germinability and subsequent loss or reduction of seed bank persistence. These predicted changes in the dynamics of soil seed banks increase the risk of local extinctions of these species, while the composition of the community may be altered by changes in species abundance. Our results show that the risk spreading mechanism provided by persistent seed banks could be compromised by the mechanistic impact of forecast temperature increases in arid habitats, and highlight the need to understand mechanisms that control population dynamics when attempting to address likely future impacts of climate change on biodiversity.     

A general multi-trait-based framework for studying the effects of biodiversity on ecosystem functioning

Environmental change is as multifaceted as are the species and communities that respond to these changes. Current theoretical approaches to modeling ecosystem response to environmental change often deal only with single environmental drivers or single species traits, simple ecological interactions, and/or steady states, leading to concern about how accurately these approaches will capture future responses to environmental change in real biological systems. To begin addressing this issue, we generalize a previous trait-based framework to incorporate aspects of frequency dependence, functional complementarity, and the dynamics of systems composed of species that are defined by multiple traits that are tied to multiple environmental drivers. The framework is particularly well suited for analyzing the role of temporal environmental fluctuations in maintaining trait variability and the resultant effects on community response to environmental change. Using this framework, we construct simple models to investigate two ecological problems. First, we show how complementary resource use can significantly enhance the nutrient uptake of plant communities through two different mechanisms related to increased productivity (over-yielding) and larger trait variability. Over-yielding is a hallmark of complementarity and increases the total biomass of the community and, thus, the total rate at which nutrients are consumed. Trait variability also increases due to the lower levels of competition associated with complementarity, thus speeding up the rate at which more efficient species emerge as conditions change. Second, we study systems in which multiple environmental drivers act on species defined by multiple, correlated traits. We show that correlations in these systems can increase trait variability within the community and again lead to faster responses to environmental change. The methodological advances provided here will apply to almost any function that relates species traits and environmental drivers to growth, and should prove useful for studying the effects of climate change on the dynamics of biota.     

Distinct impacts of different mammalian herbivore assemblages on arctic tundra CO2 exchange during the peak of the growing season

Herbivores play a key role in the carbon (C) cycle of arctic ecosystems, but these effects are currently poorly represented within models predicting land–atmosphere interactions under future climate change. Although some studies have examined the influence of various individual species of herbivores on tundra C sequestration, few studies have directly compared the effects of different herbivore assemblages. We measured peak growing season instantaneous ecosystem carbon dioxide (CO₂) exchange (photosynthesis, respiration and net ecosystem exchange) on replicated plots in arctic tundra which, for 14 years, have excluded different portions of the herbivore population (grazed controls, large mammals excluded, both small and large mammals excluded). Herbivory suppressed photosynthetic CO₂ uptake, but caused little change in ecosystem respiration. Despite evidence that small mammals consume a greater portion of plant biomass in these ecosystems, the effect of excluding only large herbivores was indistinguishable from that of excluding both large and small mammals. The herbivory‐induced decline in photosynthesis was not entirely attributable to a decline in leaf area but also likely reflects shifts in plant community composition and/or species physiology. One shrub species – Betula nana – accounted for only around 13% of total aboveground vascular plant biomass but played a central role in controlling ecosystem CO₂ uptake and release, and was suppressed by herbivory. We conclude that herbivores can have large effects on ecosystem C cycling due to shifts in plant aboveground biomass and community composition. An improved understanding of the mechanisms underlying the distinct ecosystem impacts of different herbivore groups will help to more accurately predict the net impacts of diverse herbivore communities on arctic C fluxes.     

Ecosystem Oceanography of Seabird Hotspots: Environmental Determinants and Relationship with Antarctic Krill Within an Important Fishing Ground

The discipline of ecosystem oceanography provides a framework for assessing the role of mesoscale physical processes on the formation and occurrence of biological hotspots. We used shipboard surveys over nine years to investigate environmental determinants of seabird hotspots near the Antarctic Peninsula, a region experiencing rapid climate change and an expanding krill fishery. We hypothesize that seabird hotspots are structured by mesoscale ocean conditions that reflect differences in prey distribution within oceanic and coastal waters. We used generalized additive models to quantify functional relationships of seabird hotspots with krill biomass, and a suite of remotely sensed environmental variables, such as eddy kinetic energy. The spatial organization, changes in intensity, and distribution shifts of seabird hotspots indicate different environmental drivers within coastal and oceanic domains and reflect the seasonal variability of the ecosystem. Our results indicate at least eight mesoscale hotspot zones that represent ecologically important areas where significant krill and predator biomass may be concentrated. Our ecosystem assessment of seabird hotspots identified critical foraging habitat and provided reference points to benefit research on estimating their trophic impacts on Antarctic ecosystems and potential effects from the krill fishery. Our approach is generally applicable to other pelagic ecosystems that are structured by hydrographic fronts and eddies, and containing schooling forage species shared by multiple wide-ranging predators. Furthermore, identification of biological hotspots is useful for the designation of marine protected areas most critical to potentially endangered wildlife and fisheries resources.