Predator–prey interactions in natural communities are complex, with predators often exploiting multiple prey types and generating indirect interactions among them. Ecological theory has traditionally modelled these interactions using functional response models which are based on foraging rates, not energy transfers. This approach overlooks how the energy acquisition rate of a predator can alter its behaviour and, in turn, the strength of species interactions. Here, we integrate predator energetics into a functional response model to represent trade‐offs predators face when foraging on prey that vary in risk and abundance across heterogeneous landscapes. We compared model predictions to 20 years of prey species density and reproductive success data. The mechanistic model was parameterized for an Arctic tundra vertebrate community, where the Arctic fox feeds on cyclic lemmings and eggs of sandpipers (non‐risky prey) and gulls (risky prey that often nest in partial refuge like islands). In this system, predator‐mediated interactions generate apparent mutualism between lemmings and birds, but their strength varies between species, and the mechanisms underlying this interaction remain unclear. We found that fox energetic balance was highly related to lemming density, with a threshold of 89 lemmings km −2 required for a positive energetic balance. Model‐predicted gull nest acquisition rates were lowest on islands when the energetic balance of foxes was positive, and highest for nests on the shore when foxes were in deficit. The model that incorporated predator risk‐taking behaviour and energetic balance produced variation in gull hatching success that most closely matched empirical observations. We documented for the first time that a shift in predator energetic balance, triggering changes in attack and capture probabilities on a risky prey, can be a key mechanism underlying the apparent mutualism between lemmings and gulls. In contrast, for non‐risky prey, the indirect effect can be essentially driven by changes in predator movement. These findings highlight how prey characteristics can lead to different mechanisms behind similar indirect interactions. Taken together, our results indicate that mechanistic models integrating species traits, landscape features and energy‐dependent behavioural adjustments can improve our ability to quantify interaction strengths in natural communities. Read the free Plain Language Summary for this article on the Journal blog.

Understanding how plants influence each other's spatial distribution is pivotal not only for interpreting current communities, but also for anticipating their responses to global changes. The combination of high-resolution, multi-scale sampling and novel statistical frameworks now enables us to identify species aggregations and segregations within their local co-occurrences. By applying this approach to approximately 800 plant species and their communities across the French Alps, we discovered that local species associations are dependent on soil acidity and nitrogen rather than climate. By building a regional network from these associations, we identified a centralised core comprising a few dominant, stress-tolerant graminoids and shrubs with high leaf dry matter content and no unique functional roles. Our findings demonstrate that plant community assembly is less dependent on random co-occurrence and more dependent on segregation around a few dominant, stress-tolerant species, with soil conditions modulating the outcome of local associations.

The Convention on Biological Diversity's Kunming-Montreal Global Biodiversity Framework (GBF) sets ambitious goals to protect and restore biodiversity. It includes a monitoring framework that mandates countries to track progress toward these goals using indicators that summarize biodiversity trends. Calculating indicators is challenging for countries because of fragmented biodiversity monitoring efforts, technical barriers, a lack of available data and tools, and capacity bottlenecks. The BON in a Box platform for biodiversity monitoring and indicator calculation, developed by the Group on Earth Observations Biodiversity Observation Network, was created to address these challenges by providing open, transparent, and reproducible analysis pipelines that convert data into essential biodiversity variables and indicators. These pipelines are built by experts and contributed by the community, follow FAIR principles, and help scientists apply their research to coordinate biodiversity monitoring efforts, build capacity to track progress toward the GBF, and affect policy change.

Taking the pulse of biodiversity is no small feat, and abundance time series are some of the most valuable data we have to measure change. Biodiversity changes are often complex, with some species declining while others grow, leading to a reorganisation of the network of interactions between species. The infamous decline of Atlantic cod in the Newfoundland shelf groundfish community between 1981 and 2013 is an example of such complexity. Here, we develop a new approach to track the multispecies signals before and after the cod decline, offering a community-oriented alternative to species abundance indicators like the Living Planet Index. We derive theory-based indicators and build a model to quantify the short-term stability and long-term reorganisation of communities with an explicit estimation of uncertainty. These indicators together reveal how severe declines in dominant species like Atlantic cod, combined with community-wide biomass changes, generated periods of instability and recovery that reshuffled the community more profoundly than previously thought. We find that the community was already facing reorganisation before the collapse and only recently recovered towards a desired state. By grounding indicators in ecological theory, we are better equipped to understand the process, and not just the pattern, of change.

Global change is reshaping the distribution of biodiversity and the functioning of ecosystems. Predicting the long-term consequences of such changes remains a challenge due to a need for a clear understanding of the mechanisms underpinning ecosystem-level responses, as well as the role of geographical and environmental contingencies. We propose that these gaps can be addressed for freshwater ecosystems using a globally distributed experiment with standardized observations and disturbances. Specifically, this paper outlines the structure of PondNet - a globally distributed network of pond mesocosm experiments - designed to investigate how aquatic food webs respond to environmental change across broad geographical gradients. Pond mesocosms are affordable, low maintenance and easily replicated model ecosystems, with broadly predictable trophic architectures and community size-structure. PondNet would implement state-of-the-art environmental DNA biodiversity assessments for standardized taxonomic identification across biogeographical regions. A major operational bottleneck for developing a global understanding of environmental change effects on ecosystem functioning is the current lack of standardized experiments across coordinated infrastructures. We propose that by building on existing distributed experiments, we can assemble a modular participation scheme that ensures broad biogeographical coverage whilst accounting for varying levels of resource commitments from local hosts. PondNet aims to answer two overarching questions: 1) how general are community, food web and ecosystem-level responses to climate change across scales (i.e. local environmental gradients to biogeographical regions)?, and 2) to what extent are such responses contingent on local climate, environment, and regional species pools? PondNet will contribute to developing predictive models that can be adaptively improved from testing with data from globally replicated experiments and monitoring programmes.

Adaptation is critical for biodiversity to persist under global change. Within ecological communities, species often face trade-offs between adapting to shifting abiotic conditions and navigating the complex selective pressures imposed by interaction networks. We hypothesize that network architectures characterized by high interaction diversity and overlap constrain coevolutionary dynamics, with asymmetric outcomes for exploiters and victims. Specifically, we predict that exploiters, subject to spread and conflicting selection imposed by their victims, will evolve more slowly and show reduced capacity to track victims' evolutionary responses, with these constraints strongest for generalist exploiters. In contrast, victims will show more variable dynamics depending on the coherence of selection (i.e., whether pressures from different exploiters push the victim's trait in the same vs. different directions). To test this, we simulated trait evolution in coevolving communities of exploiters and victims across 91 empirical networks, and in artificial networks designed to isolate specific structural effects. Our results show that higher connectance, species richness, nestedness, and centrality homogenize biotic effects and increase fluctuations in trait matching, ultimately weakening coevolutionary coupling. Under these conditions, exploiters face conflicting selection that slows evolution, whereas victims either benefit from aligned selection that accelerates evolution or are constrained by multiple pressures. Together, our findings suggest that network architecture plays a fundamental role in shaping coevolution and adaptation, and raises broader questions about its influence on eco-evolutionary processes in more complex and environmentally variable systems.

Predator-prey interactions are a fundamental aspect of ecology that has generated sustained research interests. Progress in the field stems from a diverse range of approaches, from highly controlled yet simplified mathematical and agent-based models, to grounded but data-limited field studies. As a compromise between mathematical and observation-oriented methods, we introduce an original approach based on an outdoor game. In this game, biologged human players follow simple rules to impersonate predators and prey in a natural landscape augmented with synthetic resource patches and refuges. We investigated the behaviour, movement, functional response and spatial organization of over 25 players simultaneously monitored during nine simulations to determine whether the game could replicate realistic predator-prey dynamics. Results derived from our real-life simulations were consistent with ecological patterns expected in natural systems. We found that (a) predator and prey movements were driven by risk and reward trade-offs, (b) predators took advantage of linear features to travel at higher speed, making these areas risky for prey, (c) prey had nonlinear and risk-sensitive functional responses and (d) consumer-resource interactions were spatially modular and defined by players' movement rates and landscape features. Moreover, the comprehensive dataset generated through the game allowed for the exploration of phenomena that are challenging to study in natural settings, such as spatial memory and the influence of satiety on resource acquisition rates. The approach offers a simple, computationally accessible and genuinely amusing way to explore the complex ramifications of predator-prey interactions and test otherwise data-deficient hypotheses. The strength and originality of the method lies in the use of living agents-players-making decisions in a real-world setting. This aspect alleviates the computational and empirical burden of defining and estimating decision-related parameters needed to build simulators, while generating extensive datasets in a flexible experimental framework that is generally out of reach for empirical studies. It also offers immersive insights into predator-prey interactions, making it an engaging pedagogical tool that encourages creative thinking. The numerous possible scenarios that can be explored are only constrained by the investigator's creativity in adapting game rules and the players' desire to win.

Abstract Theory predicts that demographic performance should peak at the core of species ranges and decrease toward their limits. Yet, empirical correlations between population growth rate and species distribution remain weak for most tree species. Part of the problem may arise from the difficulty of integrating multiple demographic processes across the complex life cycle of a forest, and from the significant variability among individuals and locations. It remains unclear if the mismatch between performance and distribution arises from modelling limitations or if climate is simply a poor predictor of species performance across distributions. Here, rather than asking whether demographic performance correlates with species distributions, we ask how climate and competition jointly shape population growth rate for 31 tree species across eastern North America. By combining flexible nonlinear hierarchical models for growth, survival, and recruitment with explicit uncertainty propagation, we use Integral Projection Models to address key gaps in previous studies. Perturbation analyses revealed that population growth rate was consistently more sensitive to mean annual temperature than to conspecific or heterospecific competition across all species. We further examined how sensitivities to climate and competition varied across species’ thermal ranges. The dominance of climate over competition increased toward both cold and hot range limits, while sensitivity to competition generally declined from cold to hot limits. Notably, these patterns emerged along the continental thermal gradient shared across species rather than within each species’ individual range, suggesting that range-edge demographic responses may arise as a community-level phenomenon. Across species, the largest source of variability remained the local plot conditions captured by random effects, likely reflecting differences in soil conditions, drainage, and disturbance history. Together, these results may provide a mechanistic pathway underlying the performance declines predicted by range-limit theories, and offer a basis for understanding how forest populations and communities may reorganize in response to ongoing climate change and shifting disturbance regimes.

ABSTRACT Maple syrup production is strongly influenced by spring weather conditions, particularly the frequency and intensity of freeze–thaw cycles. However, marked individual differences in sap and sugar yields persist among trees growing under similar stand conditions, indicating additional tree-level sources of variation. This study aimed to explain inter-individual variability in maple yields in commercial high-vacuum syrup production using structural, morphological, and growth characteristics. We used terrestrial light detection and ranging to derive variables describing crown, stem, and whole-tree size, biomass, and structure in 38 mature sugar maples. We related these variables to individual yields, including sap volume, sugar content, and syrup production. Our models explained substantial inter-individual variability: 52% in sap sugar content, 44% in sap volume, and 47% in syrup production. Laterally expanded crowns were associated with higher sap sugar content, as were lower growth rates in the first 25 mm of wood. Sap volume was highest in large, heavily branched trees with crowns extending vertically along the stem. Projected crown surface area was the strongest predictor of syrup yield, with an estimated increase of 250 mL per 5.6 m 2 . These findings highlight the importance of multidimensional crown development in maximizing individual yields in maple syrup production.

AbstractLife has evolved different strategies to take advantage of seasonal changes in the environment that are emblematic of boreal and arctic biomes. However, ecological theories often ignore seasonal changes for tractability or simplicity. Understanding the effect of seasonality may prove crucial as the changing climate puts more pressure on ecosystems. Hybrid dynamical models are an efficient way to represent seasonal adaptations where switches in food web compositions account for species migrations and predator movements. We use the highly seasonal and cyclic dynamics of an Arctic food web to showcase the utility of hybrid models. The simplified representation of community dynamics provided by the hybrid framework eases the study of conditions leading to lemming cycles and facilitates parameterization with empirical data. We corroborate that seasonal switches, accounting for the onset of reproduction of resident predators and the migration of mobile predators, likely drive cyclic fluctuations in lemming abundance. Our empirical investigation reveals that each predator alone does not reduce lemming growth rate enough to generate population cycles, which reinforces the idea that the predator community as a whole is responsible for the cyclic dynamics. This situation arises because each predator has unique adaptations to seasonality and impacts the dynamics in different but complementary ways. Our results have implications for community ecology, as they show how hybrid models can help understand complex dynamics in highly seasonal ecosystems. This is especially relevant in the Arctic, considering that rapid warming has the potential to disrupt lemming population cycles and negatively affect their predators.

AimSeasonally migratory species generate large movements of organisms and biomass between distant breeding and non-breeding grounds. However, our understanding of how migratory species shape global networks of interconnected communities (meta-communities) remains limited. Migratory links between communities can be measured in different ways (e.g., species occurrence, abundance or biomass), each providing complementary information by modulating the relative importance of species in meta-communities. We aim at investigating to what extent measuring migratory links using species occurrence, abundance or biomass can reveal alternative structures (i.e., topology) in a meta-community linking an Arctic breeding ground to remote non-breeding grounds.LocationWe use as a study case the High-Arctic vertebrate community of Bylot Island (Nunavut, Canada), along with ecoregions of North and South America, Europe and Africa.Time PeriodPresent.Major Taxa StudiedTerrestrial Arctic birds (30 species) and mammals (5 species).MethodsWe first consider species occurrence at the non-breeding grounds to define migratory links within the meta-community. Secondly, we measure the number of individuals and the amount of biomass travelling along those links. Finally, we compare the meta-community structure under each scenario using a migration network representation.ResultsPatterns of species occurrence, abundance and biomass reveal that temperate ecoregions of South and especially North America maintain strong ecological connections with the vertebrate community of Bylot Island. However, the structural role of species within the network can vary substantially depending on how migratory links are measured (i.e., contrasting topological anomalies). Using abundance or biomass to measure migratory links results in a finer partitioning of the network into modules compared to using species occurrence alone.Main ConclusionsWe highlight that using different metrics of migratory links reveals unique, yet complementary structural features of meta-communities. These findings contribute to assessing the vulnerability of communities to perturbations occurring in distant but connected environments through migration.

Understanding how ecological communities respond to environmental change remains a key challenge for biodiversity monitoring. To characterize such responses, we need tools that capture how coherently species respond across a community, and to predict their consequences, we must account for ecological interactions. We first introduce the Ecological Coherence (EC) framework, which describes how species’ co-responses are structured within a community. Building on this foundation, we extend it to Ecological Network Coherence (ENC), which embeds co-responses within the network of interactions by restricting them to interacting species. Both are expressed through two complementary representations: a response correlation matrix and the distribution of its values. The first can reveal aspects such as coherent or incoherent modules and the roles species play in shaping coherence, whereas the second provides a profile whose shape may serve as an early-warning indicator of instability. These can be applied to both intrinsic responses (environmental performance) and realized responses (abundance dynamics), derived from currently available monitoring data. We illustrate this approach in two empirical systems: a tropical pollination network, where interacting mutualists were more coherent in their temperature responses than the broader community, and a marine food web, where coherence in abundance trends shifted during collapse. Using a Lotka–Volterra model, we further show that ENC distributions with higher variance—reflecting stronger positive and negative co-responses—increase the risk of instability or amplification in dynamics. We also find that species influential in both the correlation matrix and the interaction matrix are key drivers of major dynamic shifts. These results point to the importance of further exploring ENC distributions as potential early-warning indicators of ecological disruption. ### Competing Interest Statement The authors have declared no competing interest. Financial support was provided by the NSERC - CREATE Training program in computational biodiversity science and the NSERC Discovery Grant to DG.

Canada has begun an ambitious project to build an observing system to monitor the changing state of its biodiversity and ecosystems. A Canada-wide Biodiversity Observation Network (CAN BON) can support the measurement, mapping, and modelling of biodiversity change—the losses and gains in the diversity of plant, animal, and microbial life—and ecosystem services. This initiative responds to eight challenges presently constraining Canada's capacity to deliver timely and robust knowledge to achieve its biodiversity goals. CAN BON is conceived as a network connecting diverse organizations to support sustained biodiversity monitoring by collaboration among universities, museums, governments, industries, NGOs, community groups, and Indigenous organizations. This inclusive network will “mobilize monitoring data” to (1) combine observation and computing infrastructures and traditional knowledge to track and understand biodiversity losses and gains across the country; and (2) link the accumulated data and knowledge to models to inform the detection and attribution of biodiversity change needed to support biodiversity policy with forecasts from local to national levels. We expect that CAN BON will foster the mainstreaming of biodiversity data and knowledge into other sectors of the economy and society, and thereby support the technical and social innovation in Canada's transition to a nature-positive future.

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions.

Selecting biodiversity indicators to report national and subnational progress towards the Kunming-Montreal Global Biodiversity Framework (GBF) is a major challenge, one made even more urgent by the fast-approaching 2030 targets. To identify appropriate indicators, the selection process must be streamlined, while remaining transparent, effective, and with the active engagement of stakeholders from the academic, public, and private sectors. We present guidelines for the selection of biodiversity indicators to track progress towards 2030 targets in the context of the GBF, with a case study of the province-level indicator recommendation process for Quebec's 2030 Nature Plan. We outline six steps to develop a shortlist of indicators that are relevant to targets, fulfill minimum criteria of scientific quality given available biodiversity data, and practical to inform decisions and on-the-ground conservation actions. We present the rationale and outcomes of this selection process, culminating in 15 biodiversity indicators that we recommended for Quebec's 2030 Nature Plan. Going forward, we recommend continuing to build trust across sectors, developing communication guidelines to standardise indicator reporting, and testing indicator performance at national and subnational scales. Overall, this case study demonstrates that with active engagement and cooperation, we can rapidly rise to the challenge of identifying the indicators we need to track biodiversity change.

Aim: Understanding the direct (e.g., on biological rates) and indirect (e.g., through changes in species richness) effects of temperature on food web properties, in the context of latitudinal gradients and climate warming. We focus on species interactions and predict variations in two metrics of food web properties: trophic control and temporal variability. Location: Global oceans. Time Period: 2001-2018.Major Taxa Studied Marine fish species. Methods: We use a modelling approach coupled with a global dataset of fish food webs. Species occurrences are obtained from data sources, while trophic interactions are predicted by a size-based niche model calibrated with a global interaction dataset. Interaction strengths are constrained by allometric scaling laws for predation and biomass. We investigate how predictors varying with latitude (temperature, species richness, productivity, food web structure) drive latitudinal variations in trophic regulation and variability. Results: Our results suggest a latitudinal gradient in two metrics of community dynamics, with both trophic feedback strength (underlying phenomena such as cycles and cascades) and temporal stability increasing with latitude. In our model, this variation is tied directly and indirectly to temperature, and we find that direct effects of temperature are weaker than (or at most equal to) indirect effects. The direct effect on interaction rates decreases trophic feedbacks yet increases variability. The organism-level temperature-size rule is found to increase both feedback and variability. Finally, community-level indirect effects (species richness and connectance) impact trophic control but not variability. Climate warming moderately affects trophic control, variability and total biomass, but more strongly alters individual species biomass. Main Conclusions: Our study improves understanding of the drivers of latitudinal variation in food web properties and helps disentangle the direct and indirect effects of temperature. Indirect effects are predicted to drive biogeographic variation in food web properties, while direct effects such as short-term warming could have stronger consequences at the species level.

Representing species interactions probabilistically as opposed to deterministically conveys uncertainties in our knowledge of interactions. The sources of uncertainty captured by interaction probabilities depend on the method used to evaluate them: uncertainty of predictive models, subjective assessment of experts, or empirical measurement of interaction spatiotemporal variability. However, guidelines for the estimation and documentation of probabilistic interaction data are lacking. This is concerning because our understanding of interaction probabilities depend on their sometimes elusive definition and uncertainty sources. We review how probabilistic interactions are defined at different spatial scales. These definitions are based on the distinction between the realisation of an interaction at a specific time and space (local networks) and its biological or ecological feasibility (metaweb). Using host-parasite interactions in Europe, we illustrate how these two network representations differ in their statistical properties, specifically: how local networks and metawebs differ in their spatial and temporal scaling of interactions. We present two approaches to inferring binary interactions from probabilistic ones that account for these differences and show that systematic biases arise when directly inferring local networks from metawebs. Our results underscore the importance of more rigorous descriptions of probabilistic species interactions that specify their conditional variables and uncertainty sources.