There are several mechanisms by which the movement of individual animals influences species coexistence. These mechanisms span all types of movement, including dispersal, migration and daily foraging (Jeltsch et al. 2013; Schlägel et al. 2020). For instance, dispersal not only facilitates turnover and spatial connectivity in metacommunities (e.g., Logue et al. 2011) but also directly links to coexistence through the colonization–competition trade-off, where species differ in dispersal ability and competitive strength (e.g. Cadotte et al. 2006). Migration can induce competition both among migrants and between migrants and residents (Holdo et al. 2011), while daily foraging movements mediate competition for resources that are spatio-temporally heterogeneous (e.g. Wilson et al. 1999; Buchmann et al. 2012). Generally, the spatial distribution of individuals influences encounter rates, aggregation patterns and interaction strengths, including competition and predator-prey dynamics, making individual movement a key process structuring communities (Schlägel et al. 2020).

Beyond these direct effects, movement can also have mobile link effects, where animals transport nutrients, other organisms, propagules or genes (Lundberg and Moberg 2003). For example, hares have been shown to act as effective mobile linkers in dispersing seeds via endozoochory in agricultural landscapes thus contributing to preserving plant species coexistence and biodiversity (Stiegler et al. 2021). Unlike abiotic vectors, animals can move nutrients against environmental gradients, for example, uphill during salmon runs or from deep waters to the surface via the “whale pump”, enhancing productivity and shaping food web dynamics (McInturf et al. 2019). By responding to the nutrients they redistribute, animals also generate feedback loops and nutrient hotspots that influence local productivity and competitive interactions. For a detailed review of the mechanisms linking movement and coexistence see Schlägel et al., (2020).

Recent research has further expanded our understanding of movement-coexistence linkages, revealing insights such as the role of individual personalities in mediating species coexistence (Milles et al. 2020; Rohwäder et al. 2024), the impact of variations in dispersal strategies on local community composition (Parry et al. 2024, see Glossary), the effect of species mobility in fragmented landscapes on community assembly (Teckentrup et al. 2019; Szangolies et al. 2022), and the role of individual movement decisions in driving disease transmission and community persistence (Scherer et al. 2020). While these examples highlight a growing interest in integrating movement processes into research on coexistence and biodiversity, they often focus on movement paths in an aggregated, descriptive way, lacking a more mechanistic basis of movement motivations and trade-offs. Digging into more mechanistic causes and consequences of individual behaviour will further enhance research on coexistence processes.

Animal coexistence is fundamentally shaped by individual physiology and energetics (Hall et al. 1992; Ricklefs and Wikelski 2002; Brown et al. 2004). Energetic demands shape species’ home ranges, geographic distributions, and performance (Boratyński 2020; Claunch et al. 2023), affecting their potential to co-occur (see Glossary). Energy availability limits how much individuals can invest in maintenance and reproduction, directly influencing fitness and competitive dynamics (Lotka 1922; Hall et al. 1992). In turn, an organism’s internal energy status shapes its behaviour and movement patterns, with these individual-level processes scaling up to influence broader community dynamics and patterns of coexistence (Goossens et al. 2020). As Lotka (1922) suggested, “where the supply of available energy is limited, the advantage will go to that organism which is most efficient, most economical, in applying to preservative uses such energy as it captures”, highlighting the connection between energetics, behaviour and species interactions.

The two key mechanisms that govern species coexistence identified by modern coexistence theory can both be contextualized through the lens of animal energetics (Fig. 2: lower panel, Fig. 3). First, stabilizing mechanisms arise from niche differentiation, which reduces interspecific competition (Chesson 2000). Because niches reflect the energetic and physiological needs of organisms (Tschapka 2004), species may be seen as occupying ‘energetic niches’, which represent adaptations to particular energy requirements or energy distributions in the environment (McClain et al. 2020). Adaptation to a specific niche often results in negative density dependence, where intraspecific competition reduces energy balance and prevents any one species from becoming overly dominant.

Second, equalizing mechanisms reduce fitness differences among different species (Chesson 2000). Since survival and reproduction depend on covering energy costs (Brown et al. 1993; Tomlinson et al. 2014), species with similar energetic states may persist longer together. This energetic similarity can thus slow down competitive exclusion and enhance species coviability (Jeltsch et al. 2019). These dynamics are summarized in Fig. 3.

While similarity in energy balance can foster coexistence, dissimilarity can disrupt it. Species with higher energy intake or greater efficiency may gain a competitive advantage, potentially outcompeting others. In systems initially in balance, shifts in energy dynamics, such as the introduction of new resources, can reduce opportunities for coexistence. For example, human-made garbage dumps provide abundant food for some species, boosting their energy intake leading to larger group sizes. These species often continue to exploit other resources, increasing competition with other species that do not consume garbage and intensifying predation pressure on prey, ultimately threatening coexistence (Plaza and Lambertucci 2017).

Despite these disruptive effects, energetics offer a powerful lens for understanding and fostering coexistence. In the following sections, we explore empirical examples and propose energetically mediated pathways that may support species coexistence.

The relationship between energetics and movement (Fig. 2: top panel, Fig. 4) offers a mechanistic lens into how individual activity scales up to influence communities and coexistence. Energy from metabolic processes fuels locomotion (Fig. 4A), and while movement can substantially raise daily energy expenditure (Halsey 2016), mobility enables access to remote or ephemeral resources, shaping how animals interact with their environments and one another. Scientific interest in the links between locomotion and energy use dates back over 125 years (e.g., Zuntz 1897) and advanced rapidly with the development of respirometers in the 1970s (Taylor et al. 1970; Schmidt-Nielsen 1972; Garland 1983). Today, animal-attached bio-logging devices can provide precise measurements of proxies of activity and energy expenditure in wild animals (Wilmers et al. 2015; Fahlman et al. 2021). However, there has been much focus on measuring locomotion costs, with relatively less attention given to the physiological mechanisms driving movement behavior (but see Hetem et al. 2025). Recent studies have begun to explore the connections between physiology, short-term activity, exploratory behavior and dispersal, revealing an intricate interplay between an animal’s internal state and its movement strategies (Wu and Seebacher 2022). We here highlight the drivers and consequences of animal decision-making around the fundamental questions of why to move, how to move, and when and where to move (Nathan et al. 2008, see Glossary: “Movement ecology paradigm”), describing how these decisions reverberate across ecological scales.

Operationalizing this framework requires linking individual energetics to behavior and interactions of individuals and species under realistic conditions. While tools for measuring movement, physiology, and community structure exist, integrating them remains a challenge. In the following we outline how three complementary approaches—controlled experiments, field-based methods, and mechanistic simulation modeling—can complement one another to test predictions and refine the framework.

Controlled experiments can be used to quantify the energy costs of movement or test fitness outcomes under standardized conditions. Treadmill studies, for example, can incorporate environmental complexity (e.g., slope, temperature) and biotic interactions (e.g., predators or competitors) using visual or other stimuli (e.g., Lees et al. 2013; Shemery et al. 2023). Mesocosms offer a flexible platform to measure metabolic traits (e.g., basal metabolic rate, aerobic scope) under varying resource regimes and community structures, allowing researchers to examine how physiological diversity shapes movement and interaction outcomes, as well as how social dynamics influence energy use and behavior (Levy et al. 2011; Nespolo et al. 2022).

Field-based methods are essential for capturing energy use in ecologically realistic contexts. Multi-sensor technologies recording accelerometry, heart rate and temperature, among other metrics, allow estimation of field metabolic rates (Halsey et al. 2009; Hawkes et al. 2021), though these often require calibration in laboratory settings using respirometer studies or doubly labelled water (e.g., Green et al. 2009). Although often restricted to small subsets of individuals, these approaches are becoming more scalable through innovations in tagging technologies. When combined with measures of resource availability, such as from remote sensing, they support the creation of energy landscapes (e.g., Berti et al. 2025). Machine learning applied to field data can further infer energy expenditure from movement patterns and uncover latent interaction dynamics within complex, high-dimensional ecological datasets (Pichler et al. 2020; Williams et al. 2020).

Mechanistic simulation models are essential for bridging biological scales, from individual energetics and behavior to population and community dynamics (Jeltsch et al. 2025). These models provide a powerful means to simulate how internal state variables, movement rules and energy budgets interact to shape species interactions and coexistence under changing environmental conditions (e.g., Milles et al. 2020; Szangolies et al. 2024). Unlike correlative approaches, mechanistic simulation modeling allows researchers to explore causal pathways and emergent outcomes that are otherwise difficult or impossible to observe directly. Such models are particularly valuable for forecasting responses to novel or extreme scenarios, such as climate-driven shifts in resource availability or species introductions (Gallagher et al. 2022; Daniels et al. 2023). However, their predictive power depends on high-quality empirical data for parameterization and validation, reinforcing the need for coordinated, cross-disciplinary efforts that integrate experimental, field, and modeling approaches.

By combining the different approaches, we can overcome existing limitations and unlock a deeper understanding of how energetics shapes, and is shaped by, movement and community dynamics. For example, conducting parallel experimental or field studies alongside model development can allow for the targeted measurement of parameters needed to inform models and to test model-derived predictions in real-world scenarios. By encouraging collaboration between physiologists, movement ecologists, community ecologists, and ecological modellers, we aim to inspire innovative approaches that integrate physiological and behavioral measurements into multi-species analyses. Advancing this interdisciplinary effort will help answer pressing ecological questions and enhance our understanding of community biodiversity dynamics in the face of environmental perturbations and global change.

In the following, we highlight several illustrative testable predictions that emerge from our framework, describing how they are embedded within its structure and offering approaches for their evaluation. These predictions underscore the importance of jointly considering movement, energy and coexistence processes to understand how energy-mediated movement dynamics shape the assembly and persistence of communities in a warming, increasingly variable world.

Prediction 1. Under resource scarcity, individuals that flexibly switch between intensive foraging and energy-saving modes will persist longer than inflexible individuals, driving movement patterns that reshape competitive interactions and community composition.

In the presented framework, reduced energy intake leads to a negative energy balance, depleting reserves and altering the individual’s internal state. This shift can trigger a behavioral transition from high-effort foraging to energy-saving modes. Species with this flexibility may down-regulate motion capacity under scarcity, minimizing locomotion costs and preserving energy stores. In contrast, inflexible species that maintain high movement rates under low returns may quickly exhaust their reserves, leading to higher mortality or competitive disadvantage. When resources remain accessible but patchily distributed, such persistent movement may confer short-term advantages, but under ongoing scarcity, it becomes maladaptive. By modulating movement effort according to energetic state, flexible species reduce costly encounters in the biotic and abiotic filter, reducing competition and potentially stabilizing coexistence.

This prediction is supported by studies showing non-monotonic movement responses to hunger in species such as griffon vultures (Spiegel et al. 2013), which balance foraging motivation against starvation risk, and by research on small mammals that use torpor to conserve energy and buffer periods of competition or scarcity (Schubert et al. 2009; Levy et al. 2011; Nespolo et al. 2022). While the effects of energy-saving strategies on competitive outcomes remain underexplored, recent work suggests these behaviors may mediate coexistence by reducing direct competition during resource bottlenecks (Levy et al. 2011). However, studies directly linking such strategies to energetics or fitness outcomes are still lacking.

To test this prediction, controlled manipulations of resource availability in semi-natural environments could be paired with fine-scale monitoring of movement (e.g., GPS, accelerometry), metabolic rates (e.g., heart rate loggers, doubly labeled water) and fitness outcomes (e.g., survival, reproduction). Comparative studies across species or phenotypes with varying capacities for energy conservation would reveal how state-dependent movement plasticity shapes persistence and community dynamics under resource limitation. As droughts intensify and food pulses become more erratic, species capable of shifting between high-effort foraging and low-effort conservation modes may be better equipped to track ephemeral resources and survive prolonged lean periods.

Prediction 2. In environments with fluctuating energy availability, individuals with higher energy demands or lower tolerance to deficits will move away from depleted patches more rapidly, leading to shifts in community composition.

Individuals continuously assess local energy intake relative to their internal reserves. In our framework, as energy returns decline, an individual nearing an energetic threshold may abandon its current patch in search of better conditions. Moreover, consistent with Optimal Foraging Theory and the marginal value theorem (Charnov 1976), the net benefit of moving, defined as expected gains in a new patch minus the energetic and risk costs of travel, will modulate the energetic threshold for departure, so that longer distances or higher travel costs elevate the internal deficit at which individuals abandon depleted patches. Alternatively, individuals may attempt to build sufficient energy stores before performing costly dispersal, leading to a U-shaped relationship between body condition and dispersal probability (e.g., Barbraud et al. 2003). Importantly, species likely differ in the energetic thresholds that trigger departure. Those with higher metabolic demands, smaller energy buffers, or narrower physiological tolerances may leave depleted areas sooner than more energetically resilient competitors. This mechanism extends the classic R* rule, which suggests that in competing species the one which survives on a lower resource level will become dominant (Tilman 1982), into dynamic, heterogeneous systems, where persistence depends not only on minimum resource needs but on energy-buffering capacity and behavioral responses to resource dynamics. Even modest species-level differences in energetic thresholds could potentially drive divergent spatial responses to shared environmental change.

Empirical work already supports components of this mechanism. For instance, Teunissen et al., (2025) found that endangered purple-crowned fairy-wrens (Malurus coronatus) were more likely to disperse and travelled farther from low-quality or isolated patches, but also increased dispersal distance with food availability, indicating that favorable energy conditions enable investment in costly, long-range movement. Thus, both resource scarcity and energetic opportunity can shape dispersal decisions. While the importance of condition in dispersal behavior is generally widely acknowledged (e.g. Bonte and De La Peña 2009), interspecific variation in these processes due to differences in metabolic sensitivity and impacts on community assembly remain unexplored. Variation in metabolic demands, buffer capacity and dispersal thresholds could underlie shifts in dominance, coexistence and biogeographic sorting under environmental stress, helping explain why some species fragment, collapse or fail to recolonize a suitable habitat under climate change.

This prediction could be tested by tracking sympatric species with differing energetic demands in a shared, fluctuating landscape. In a semi-arid system with predictable dry-season declines, individuals from two or more species, which differ in metabolic rate, body size or physiological flexibility, could be fitted with GPS and energy-use sensors (e.g., accelerometers, heart rate loggers), while habitat monitoring (e.g., remote sensing) tracks resource availability. Linking movement with energy status and local conditions could reveal species-specific thresholds for patch departure. For example, if one species reliably departs sooner as conditions deteriorate it would indicate lower tolerance to declines and highlight how energetic traits shape patch abandonment. Long-term data could show how these differences influence community persistence and spatial sorting, particularly across years with contrasting rainfall. Semi-arid systems may be especially vulnerable to global change, as climate-driven drought and land-use-induced degradation make energy resources increasingly patchy and unpredictable. In response, species with high demands or narrow tolerances may face repeated, costly relocations, leading to fragmentation or local extinction, while more tolerant species may persist through bottlenecks and benefit from reduced competition. Understanding these dynamics can illuminate drivers of biodiversity shifts under increasing environmental volatility.

Prediction 3. Interference competition among sympatric species will intensify as their metabolic niches increasingly overlap.

Classic niche theory holds that the degree of resource-use overlap governs the intensity of interspecific competition, traditionally focusing on diet, habitat, and time (Chase and Leibold 2009). Expanding the niche concept to include energetic demands (e.g., metabolic rates, energy acquisition needs) and movement traits (e.g., foraging range, speed, timing) adds a critical dimension of spatial and temporal overlap that elevates direct interference competition. When species not only consume similar resources but also similarly invest their energy, direct encounters become more frequent, escalating stress, displacement, and injury, and reducing potential for coexistence. Energetics shape both access and outcomes of interactions: they determine how far and how often individuals can move, and how effectively they compete when encounters occur. Thus, even energetically efficient species may suffer under high overlap in metabolic niches due to frequent, costly interactions. By contrast, differences in energy budgets or activity timing can reduce encounters through spatial, temporal or energetic partitioning, weakening interference and facilitating coexistence.

Support for this prediction comes from studies showing that similarity in energetics can intensify antagonism, for instance, bacteria more strongly inhibit metabolically similar competitors (Russel et al. 2017). In ground beetles, warming experiments revealed that Pterostichus melanarius (with a weaker metabolic response to temperature) exhibited stronger interference at higher temperatures, while Poecilus versicolor (with greater metabolic sensitivity) prioritized foraging over aggression (Lang et al. 2012). These studies suggest that similarity in metabolic and movement traits can magnify interference, while differences may buffer it. However, empirical links between these dynamics and long-term fitness or coexistence outcomes are lacking.

To test this prediction, spatially explicit individual-based models (e.g., Szangolies et al. 2024) could assign to species or phenotypes distinct metabolic rates and corresponding movement rules (e.g., foraging speed, search radius, timing). When individuals co-occur in a patch, an interference event could be triggered, scaled by the similarity of metabolic rates and movement traits. By simulating gradients of metabolic–movement overlap (low to high), one could track encounter frequency, conflict duration, energy loss and resulting survival or reproduction. Comparing outcomes under stable versus variable resource regimes could clarify how energetic overlap drives interference and alters coexistence potential. Such models could also predict outcomes for novel assemblages, including invasive species, where increased niche overlap might intensify conflict under climate warming or resource scarcity.