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Aim: Distribution modelling is a useful approach to obtain knowledge about the spatial distribution of biodiversity, required for, for example, red-list assessments. While distribution modelling methods have been applied mostly to single species, modelling of communities and ecosystems (EDM; ecosystem-level distribution modelling) produces results that are more directly relevant for management and decision-making. Although the choice of predictors is a pivotal part of the modelling process, few studies have compared the suitability of different sets of predictors for EDM. In this study, we compare the performance of 50 single environmental variables with that of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The CLGs represent gradients in landscape element composition derived from multivariate analyses, for example “inner-outer coast” and “land use intensity.” Location: Norway. Methods: We used data from field-based ecosystem-type mapping of nine ecosystem types, and environmental variables with a resolution of 100 × 100 m. We built nine models for each ecosystem type with variables from different predictor sets. Logistic regression with forward selection of variables was used for EDM. Models were evaluated with independently collected data. Results: Most ecosystem types could be predicted reliably, although model performance differed among ecosystem types. We identified significant differences in predictive power and model parsimony across models built from different predictor sets. Climatic variables alone performed poorly, indicating that the current climate alone is not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs resulted in parsimonious models with relatively high predictive power. Used together with other variables, they consistently improved the models. Main conclusions: Our study highlights the importance of variable selection in EDM. We argue that the use of composite variables as proxies for complex environmental gradients has the potential to improve predictions from EDMs and thus to inform conservation planning as well as improve the precision and credibility of red lists and global change assessments.conservation planning, distribution modelling, ecosystem classification, ecosystem types, IUCN Red List of Ecosystems, landscape gradients, spatial prediction, species response curves

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2022 The Authors. Ecological Monographs published by Wiley Periodicals LLC on behalf of Ecological Society of America. Competition between apex predators can alter the strength of top-down forcing, yet we know little about the behavioral mechanisms that drive competition in multipredator ecosystems. Interactions between predators can be synergistic (facilitative) or antagonistic (inhibitive), both of which are widespread in nature, vary in strength between species and across space and time, and affect predation patterns and predator–prey dynamics. Recent research has suggested that gray wolf (Canis lupus) kill rates decrease where they are sympatric with brown bears (Ursus arctos), however, the mechanisms behind this pattern remain unknown. We used data from two long-term research projects in Scandinavia (Europe) and Yellowstone National Park (North America) to test the role of interference and exploitation competition from bears on wolf predatory behavior, where altered wolf handling and search time of prey in the presence of bears are indicative of interference and exploitation competition, respectively. Our results suggest the mechanisms driving competition between bears and wolves were dependent on the season and study system. During spring in Scandinavia, interference competition was the primary mechanism driving decreased kill rates for wolves sympatric with bears; handling time increased, but search time did not. In summer, however, when both bear and wolf predation focused on neonate moose, the behavioral mechanism switched to exploitation competition; search time increased, but handling time did not. Alternartively, interference competition did affect wolf predation dynamics in Yellowstone during summer, where wolves prey more evenly on neonate and adult ungulates. Here, bear presence at a carcass increased the amount of time wolves spent at carcasses of all sizes and wolf handling time for small prey, but decreased handling time for the largest prey. Wolves facilitate scavenging opportunities for bears, however, bears alter wolf predatory behavior via multiple pathways and are primarily antagonistic to wolves. Our study helps to clarify the behavioral mechanisms driving competition between apex predators, illustrating how interspecific interactions can manifest into population-level predation patterns.

The warming trend of the Arctic is punctuated by several record-breaking warm years with very low sea ice concentrations. The nature and reversibility of marine ecosystem responses to these multiple extreme climatic events (ECEs) are poorly understood. Here, we investigate the ecological signatures of three successive bottom temperature maxima concomitant with surface ECEs between 2004 and 2017 in the Barents Sea across spatial and organizational scales. We observed community-level redistributions of fish concurrent with ECEs at the scale of the whole Barents Sea. Three groups, characterized by different sets of traits describing their capacity to cope with short-term perturbations, reacted with different timing and intensity to each ECE. Arctic species co-occurred more frequently with large predators and incoming boreal taxa during ECEs, potentially affecting food web structures and functional diversity, accelerating the impacts of long-term climate change. On the species level, responses were highly diversified, with different ECEs impacting different species, and species responses (expansion, geographical shift) varying from one ECE to another, despite the environmental perturbations being similar. Past ECEs impacts, with potential legacy effects, lagged responses, thresholds, and interactions with the underlying warming pressure, could constantly set up new initial conditions that drive the unique ecological signature of each ECE. These results highlight the complexity of ecological reactions to multiple ECEs and give prominence to several sources of process uncertainty in the predictions of climate change impact and risk for ecosystem management. Long-term monitoring and studies to characterize the vertical extent of each ECE are necessary to statistically link demersal species and environmental spatial–temporal patterns. In the future, regular monitoring will be crucial to detect early signals of change and understand the determinism of ECEs, but we need to adapt our models and management to better integrate risk and stochasticity from the complex impacts of global change. Successive extreme climatic events lead to immediate, large-scale, and diverse responses from fish in the Arctic

The fasting endurance hypothesis (FEH) predicts strong selection for large body size in mammals living in environments where food supply is interrupted over prolonged periods of time. The Arctic is a highly seasonal and food restricted environment, but contrary to predictions from the FEH, empirical evidence shows that Arctic mammals are often smaller than their temperate conspecifics. Intraspecific studies integrating physiology and behaviour of different‐sized individuals, may shed light on this paradox. We tested the FEH in free‐living Svalbard reindeer (Rangifer tarandus platyrhynchus). We measured daily energy expenditure (DEE), subcutaneous body temperature (Tsc) and activity levels during the late winter in 14 adult females with body masses ranging from 46.3 to 57.8 kg. Winter energy expenditure (WEE) and fasting endurance (FE) were modelled dynamically by combining these data with body composition measurements of culled individuals at the onset of winter (14 years, n = 140) and variation in activity level throughout winter (10 years, n = 70). Mean DEE was 6.3±0.7 MJ day−1. Lean mass, Tsc and activity had significantly positive effects on DEE. Across all 140 individuals, mean FE was 85±17 days (range 48–137 days). In contrast to the predictions of the FEH, the dominant factor affecting FE was initial fat mass, while body mass and FE were not correlated. Furthermore, lean mass and fat mass were not correlated. FE was on average 80% (45 days) longer in fat than lean individuals of the same size. Reducing activity levels by ~16% or Tsc by ~5% increased FE by 7%, and 4%, respectively. Our results fail to support the FEH. Rather, we demonstrate that (i) the size of fat reserves can be independent of lean mass and body size within a species, (ii) ecological and environmental variation influence FE via their effects on body composition, and (iii) physiological and behavioural adjustments can improve FE within individuals. Altogether, our results suggest that there is a selection in Svalbard reindeer to accumulate body fat, rather than to grow structurally large. he Arctic, activity, biologging, daily energy expenditure, doubly labelled water, intraspecific scaling, subcutaneous body temperature, Svalbard reindeer to accumulate body fat, rather than to grow structurally large.