Forum: Linking Pattern to Process in Metacommunities: Challenges and Opportunities

Forum: Linking Pattern to Process in Metacommunities: Challenges and Opportunities | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 22 September 2025 V1 Latest version Share on Forum: Linking Pattern to Process in Metacommunities: Challenges and Opportunities Authors : Mathew Leibold 0000-0003-3954-3187 [email protected] , Matthieu Barbier 0000-0002-0669-8927 , Leonora Bittlestone , Adam Clark 0000-0002-8843-3278 , Catalina Cuellar-Gempeler , Rafael D'Andrea 0000-0001-9687-6178 , Veronica Frans 0000-0002-5634-3956 , Gabriel Khattar 0000-0002-1703-4210 , Zachary Miller , Pedro Peres-Neto , and Nathan Wisnoski 0000-0002-2929-5231 Authors Info & Affiliations https://doi.org/10.22541/au.175855717.73873060/v1 480 views 175 downloads Contents Abstract Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Ecological communities, and especially metacommunities, are complex and dynamic entities. Resolving the processes and mechanisms that shape these systems remains a central challenge in ecology. This challenge is compounded by the increasing entanglement of mechanisms, processes, and emergent patterns of biodiversity as scales of space, time, and biological organization expand. Here, we define and contextualize key issues, describe recent progress, and identify remaining challenges in interpreting basic metacommunity data and using predictive models to link processes to patterns. We identify two contrasting modeling strategies for complex metacommunities – top-down and bottom-up – and consider how they guide different approaches to pattern-to-process inference. We find substantial progress in connecting pattern and process through improved data repeatability and scaling, enhanced analytical tools to quantify patterns, and increasingly sophisticated theoretical models that address ecological complexity. However, accurately matching observable patterns with process-oriented theory remains a persistent challenge. Finally, we identify potential pipelines connecting process and pattern and highlight areas for future progress. Abstract Ecological communities, and especially metacommunities, are complex and dynamic entities. Resolving the processes and mechanisms that shape these systems remains a central challenge in ecology. This challenge is compounded by the increasing entanglement of mechanisms, processes, and emergent patterns of biodiversity as scales of space, time, and biological organization expand. Here, we define and contextualize key issues, describe recent progress, and identify remaining challenges in interpreting basic metacommunity data and using predictive models to link processes to patterns. We identify two contrasting modeling strategies for complex metacommunities – top-down and bottom-up – and consider how they guide different approaches to pattern-to-process inference. We find substantial progress in connecting pattern and process through improved data repeatability and scaling, enhanced analytical tools to quantify patterns, and increasingly sophisticated theoretical models that address ecological complexity. However, accurately matching observable patterns with process-oriented theory remains a persistent challenge. Finally, we identify potential pipelines connecting process and pattern and highlight areas for future progress. Key words: Communities, metacommunities, patterns, metrics, processes, mechanisms, disordered systems models, joint species distribution models. Introduction: Ecological communities are shaped by a complex interplay of a limited number of basic ecological processes—or ’forces’—that influence species colonization, growth when rare, and persistence after establishment. While the number of fundamental processes is considered small (Vellend 2010, 2015), their combinations generate a wide variety of recognized mechanisms that organize community dynamics and structure (a glossary of terms in bold is provided in Table S1). This complexity is especially pronounced in systems with many species and when local communities interact across space as metacommunities. The interplay among ecological processes (see below) gives rise to the patterns observed in metacommunity data . Recent advances in data collection and analytical methods have significantly improved our ability to detect, describe, and quantify metacommunity patterns . Yet, inferring the underlying processes and their relative importance from these patterns remains a major challenge in community ecology (Schaffer 1981; Sanderson and Pimm 2015; Leibold et al. 2022). Vellend (2010, 2015), drawing an analogy with evolutionary theory, proposed that community dynamics are shaped by a handful of basic processes that shape community assembly and long-term dynamics . As in Thompson et al. (2020) we reframe Vellend’s original processes to better align with contemporary ecological theory: a) Density-independent selection : Species growth and persistence depend on their responses to environmental conditions, often (though not exclusively) abiotic. We assume no feedback from the biota to these environmental factors over time scales of interest. b) Density-dependent selection : Biotic interactions with conspecifics or heterospecifics, including direct interactions and indirect effects mediated by other species that can provide feedback mechanisms that aren’t accounted for by density-independent selection. c) Dispersal : Movement of organisms among local communities, influencing colonization ability and potentially leading to source–sink dynamics. Dispersal limitation restricts species to subsets of suitable sites, while dispersal excess allows persistence in otherwise unsuitable habitats. This could also include various forms of dormancy, which can be thought of as dispersal in time. d) Novelty and trait diversification : Includes speciation, evolutionary change, and species introductions due to biogeographic shifts or anthropogenic influence (e.g., introductions). This component remains underexplored in metacommunity studies (but see Borregaard et al. 2014; Germain et al. 2021; Leibold et al. 2023). e) Stochasticity : While stochasticity permeates all the above processes, we specifically highlight demographic stochasticity (random birth and death events, especially in small populations) and temporal environmental variation. These sources of variability are often conflated with measurement error (Shoemaker et al. 2020). Grouping them as variance components provides a practical way to account for their effects in ecological analyses of metacommunity data without the need for explicit mechanistic models. In nature, these processes interact to generate various mechanisms (e.g., resource partitioning, species sorting, mass effects, trophic cascades) with distinct effects on community patterns. Models often assume specific relationships among processes—e.g., trade-offs or context dependence—to represent ecological mechanisms, typically in simplified systems involving few species. Classic models like Lotka–Volterra (Lotka 1925; Volterra 1927) or resource competition models (MacArthur 1974; Tilman 1982) illustrate how abiotic trade-offs and species interactions shape coexistence. Experimental validation of such models has occurred in simple plant, animal, and microbial communities (e.g., Gause 1932, 1934; Crombie 1944; Vandermeer 1965; Tilman 1980; see Kneitel and Chase 2004). However, as the number of species increases, the potential combinations of parameters grow exponentially (and sometimes even factorially!), and multiple mechanisms often operate simultaneously. Most natural patterns emerge not from isolated species interactions but from the intertwined web of biotic interactions and environmental effects—Darwin’s “entangled bank” (Darwin 1859; Schaffer 1981; Kéfi et al. 2016). While we understand how specific processes can generate different distributional patterns in metacommunities, reliably inferring the underlying processes and mechanisms from observed patterns remains a major challenge. Multiple distinct process-based models can predict similar patterns (e.g., Barbier et al. 2018). This many-to-one mapping is both an opportunity and a challenge for ecology. It means that our models may effectively forecast responses to change (e.g., under climate change scenarios) without explaining the causal mechanisms involved. Such predictive capacity is highly valuable for ecological applications, including policy and management. Yet, as René Thom noted, “To predict is not to explain” (Thom et al. 2016). As environmental changes push systems beyond the conditions that informed past predictions, explanatory understanding becomes essential. Identifying the processes underlying observed patterns is therefore critical for addressing contemporary environmental issues, grounding empirical ecology, and advancing ecological theory. Our goal is to evaluate the degree to which current methods make the link between pattern and process and thereby facilitate understanding of the entangled web of ecological dynamics in biodiverse metacommunities. We structure this essay as follows: 1. We begin by defining “patterns” in metacommunities, contrasting those found in individual communities with those specific to metacommunities. 2. We then explore “processes” along with associated concepts such as mechanisms and models. We contrast two modeling approaches: a reductionist, “bottom-up” strategy and a “top-down” approach inspired by statistical mechanics. 3. We evaluate whether basic ecological processes can be robustly linked to patterns to test or validate models. 4. We assess the inverse—how patterns might be used to infer underlying processes. 5. Finally, we contextualize these discussions within current trends in community and metacommunity ecology, identifying opportunities to overcome outstanding challenges. Taken together, these points offer a roadmap for unifying process-based and pattern-based approaches in community and metacommunity ecology. Despite the difficulty of linking pattern and process, we argue that there are significant reasons for optimism. Progress in data generation, modeling, and theoretical frameworks positions community and metacommunity ecology for continued and exciting advancements. Data and Patterns We begin by clarifying what we mean by ” data ” in the context of metacommunities. Data are direct observations of community variation across localities within a region and are typically structured as site-by-species matrices. These matrices may represent presence/absence, abundance, relative abundance, biomass, or other descriptors of community composition. Data can originate from natural systems, experimental studies, or even simulations of an ecological model. From these matrices, we derive ”patterns”: simplified, often quantitative, summaries of ecological structure or variation. Patterns range from local community attributes—such as species richness, diversity indices, species abundance distributions, and compositional turnover—to aggregated summaries across sites, such as beta-diversity or variability and synchrony in temporal fluctuations. Comparisons among sites often incorporate ancillary variables like environmental gradients, spatial structure, or spatial isolation. Metacommunity-level patterns consider spatially-structured assemblages of multiple, interconnected communities. This broader focus enables exploration of how spatial dynamics, such as dispersal, interact with other structuring forces, such as internal patch dynamics and environmental differences among patches. For example, Leibold et al. (2002) proposed the ”Elements of Metacommunity Structure” to characterize coexistence patterns in spatially explicit metacommunities. Similarly, Cottenie (2005) applied variation partitioning methods (Borcard et al. 1992; Peres-Neto et al. 2006) to separate spatial and environmental components underlying community variation, aiming to diagnose dominant ecological processes. Together, such methods yield a diverse set of statistical models that yield pattern metrics that characterize communities and metacommunities (e.g., Ovaskainen et al. 2019; Thompson et al. 2020, Guzman et al. 2022). Each metric thus serves as a potential clue about the processes and mechanisms shaping communities, though few are diagnostic on their own. For example, researchers frequently examine how observed patterns deviate from null models—baseline expectations assuming the absence of particular ecological processes by randomization. While null models can become complex and contentious (e.g., Peres-Neto et al. 2001), most patterns deviate significantly from null expectations in at least some systems (Gotelli and McCabe 2002; Cottenie 2005). While there are an number of increasingly complex such statistical models such as beta-diversity partitioning (Si et al. 2017) and network models (e.g. Borthagarai et al. 2014), Joint Species Distribution Models (JSDMs; Ovaskainen et al. 2017), come closest to evaluating the major processes identified by Vellend (2010) described above. This is because they allow for explicit statistical modeling of co-occurrence structures that account for environmental and spatial variation. Additionally, JSDMs provide a powerful framework for inferring latent (unmeasured) influences beyond measured environmental and spatial variables or species trait variation, including potential effects of biotic interactions (although these cannot be separated from unmeasured environmental and spatial factors). However, such analyses alone do not identify the specific processes underlying metacommunity structure, as multiple models based on different mechanisms can produce convergent predictions. To improve inference, ecologists increasingly use cross-validation across multiple patterns (Holling and Allen 2002; Yanco et al. 2020). For instance, May et al. (2015) showed that while a neutral model (similar to Hubbell 2001) could individually match several empirical patterns from Barro Colorado Island, it could not do so with consistent parameter estimates. As a result, the neutral model was rejected due to inconsistencies across patterns. This strategy—testing models against suites of independent metrics—helps identify which mechanisms are more plausible. While not definitive, such multi-pattern approaches (e.g., Ovaskainen et al. 2019; Thompson et al. 2020; Guzman et al. 2021) represent a practical path toward more robust ecological inference. Processes, Models, and Mechanisms While patterns arise from data, we define ”processes” as the underlying forces and interactions that generate those patterns. Unlike patterns, processes are not easily quantified, and are instead inferred, often through mechanistic modeling . This distinction—between what is observed and what is hypothesized to underlie the observations—has long posed a challenge in ecology, and definitions of processes vary widely in the literature (see S2 in the Supplementary Information). Although Vellend’s five-process framework remains comprehensive (Vellend 2010, 2016), the ways in which these processes can interact to shape ecological patterns remain unclear. In theory, processes operate in a quasi-linear fashion in short-term models—such as metacommunity Lotka–Volterra models (Gravel et al. 2016) or colonization–extinction models (Leibold et al. 2022). Yet, over longer timescales or under more complex ecological dynamics, the effects of these processes become entangled in ways that obscure clear causal links. Understanding this entanglement is central to resolving how pattern and process are related in community and metacommunity ecology. Historically, ecologists have used mechanistic theories to explain patterns in ecological communities. One common strategy involves identifying a plausible mechanism—such as interspecific competition, predation, and/or mutualism—and modeling it with systems of differential (or difference) equations. For example, Lotka (1925) and Volterra (1927) modeled interspecific competition and predator–prey dynamics. These foundational models were empirically validated in microcosm experiments by Gause (1932, 1934) and others (e.g., Crombie 1945, 1946). Such experiments were instrumental in testing theories involving biotic interactions—processes that are density-dependent. However, they often overlooked density-independent processes such as environmental filtering (e.g., Choler et al. 2001; Qi et al. 2018) or the role of spatial dynamics and dispersal (Amarasekare 2003; Peres-Neto et al. 2012, but see Huffaker 1958). Alternative models have focused on community patterns without assuming strong species interactions. These include spatial models assuming weak or no interspecific interactions (MacArthur and Wilson 1967; Connor and Simberloff 1979; Hart and Newman 2014) or neutral models in which all individuals are ecologically equivalent (Hubbell 2001). More comprehensive models that integrate species interactions, environmental filtering, and spatial dynamics are still relatively rare and often rely on complex simulations that are difficult to interpret, especially in species-rich or spatially structured systems. Bottom-Up versus Top-Down Perspectives As more species are included in models, their complexity increases dramatically (see Appendix 1). A more traditional bottom-up approach to managing this complexity is to model small sets of species using mechanistic modules (Holt and Hochberg 1999). These modules allow for detailed analysis of interactions, including indirect and higher-order effects, often in relation to environmental or spatial contexts (e.g., Tilman 1982; Holt et al. 1995; Leibold 1996). However, such models become analytically intractable with more than a few species—and even three-species systems can be difficult to fully analyze (e.g., Ranjan et al. 2024). While they serve as an informative starting point, these modules inevitably oversimplify the dynamics of natural communities, which involve complex webs of direct and indirect interactions (Schaffer 1981; Kéfi et al. 2016). An alternative is a top-down approach that seeks to explain patterns at the aggregate level using simplified assumptions about species interactions. As a classic example, May (1972) used random matrix theory to analyze stability in large communities. He assumed that interaction coefficients among species were randomly distributed, and showed that community stability occurs when the number of species (S), the connectance (c), and the standard deviation of interaction strengths (\(\sigma\)) obey the inequality: \begin{equation} \text{σ\ }\sqrt{c\ (S-1)}\ <\ m\nonumber \\ \end{equation} where \(m\) is the mean intraspecific self-limitation term. According to this inequality, communities with too many strongly connected species as compared with self-limitation are unlikely to be stable. Gravel et al. (2016) extended this model to metacommunities, showing that when dispersal and environmental heterogeneity are added, the stability condition becomes:\(\text{σ\ }\sqrt{c\ (S-1)/N}\ <\ m\)where N represents the effective number of uncorrelated local interaction matrices—a measure of environmental complexity that is always \(\geq\ 1\). This model suggests that metacommunities may support more species than local communities due to source–sink dynamics and spatial heterogeneity. These ”disordered systems” models rely on minimal assumptions and treat interaction networks as random objects, summarized by statistical moments (means, variances). However, many ecologists are uncomfortable with their oversimplified assumptions. For instance, Yodzis (1981) noted that trophic structure was a missing component in May’s original formulation. To address this, researchers have developed ”partially structured” models (Ahmadian et al. 2015; Barbier et al. 2018; Carugno et al. 2022; Servan et al. 2025) that incorporate limited structure into interaction matrices—such as distinct subgroups (guilds), body size scaling, trophic levels, or evolutionary relationships. Reviews by Akjouj et al. (2024) and Cui et al. (2024) explore how such partial structure can be introduced into community matrices to increase realism without full complexity. A clear question arises: how much structure should we impose? While including trophic structure might seem essential, further structure (e.g., spatial correlations or functional traits) risks reverting to highly constrained, bottom-up models. If over-structured, these models may become as complex and difficult to analyze as the bottom-up models they were intended to complement! In sum, we can approach ecological modeling from two strategic directions (Figure 1). The bottom-up approach starts with simple population models and incrementally adds complexity. Predicted patterns are closely linked to mechanisms, but this approach is limited to small systems. The top-down approach, inspired by statistical mechanics, uses randomized or partially structured models (e.g., interaction matrices) to make robust but coarse-grained predictions in large systems. Each strategy has limitations: bottom-up models lack scalability, while top-down models lack mechanistic detail. A promising direction may lie in hybrid approaches that blend these strategies, enabling cross-validation of predictions and mechanisms. For instance, Miller et al. (2024) showed that specific mechanistic models (in their case, patch colonization–extinction dynamics) can be embedded within disordered systems models to explore how pairwise processes scale up in complex, realistic communities. Linking Process to Patterns: Both modeling approaches can be used to explore how a limited set of processes act together to shape metacommunity patterns. One important application of metacommunity models is to identify patterns that are associated with specific sets of mechanisms, as a key step toward inferring relevant processes from metacommunity patterns. By comparing predicted patterns – linked to specific mechanisms – against empirical patterns, it may be possible to diagnose the mechanisms that are acting in the metacommunity. The bottom-up approach attempts to link process to pattern in a relatively direct way. Using bottom-up models to link processes to patterns is typically most successful when few species are involved, and when systems are relatively simple. Models of such systems frequently produce specific predictions that match empirical patterns both qualitatively and, in some cases, quantitatively (e.g., Vandermeer 1969; Friedman et al. 2017; Saavedra et al. 2017). As previously noted, it is difficult to extend such models to systems with many species Aside from the challenge of building and analyzing models with many species, increasing complexity also makes it hard to identify a simple mapping between mechanisms and diagnostic patterns. This is an issue of convergence between models: different process-based models, even those grounded in distinct mechanisms, can produce similar or indistinguishable patterns. For example, McGill (2010) demonstrated that six mechanistically-distinct models could generate similar predictions for five widely used community patterns. None of the patterns were uniquely diagnostic of any specific model. Given these challenges, one possible way to proceed is a ”brute force” approach: associating modeled mechanisms with a larger suite of metrics or patterns (an illustrative list is shown in Table 1) to find unique combinations of predictions.This approach makes it possible to extend the one-to-one mapping between processes and patterns further into complex settings. Holling and Allen (2002) and Yanco et al. (2020) argue that evaluating models in this way can help eliminate less plausible candidates. A more targeted variation on this approach would identify a smaller subset of particularly informative or complementary metrics, which might enhance diagnostic power and increase efficiency (ruling out more mechanisms with less data). Ultimately, the brute force approach functions as a model selection procedure: models that fail to explain the full pattern set are rejected. However, this approach does not necessarily confirm the remaining models, since new and untested alternatives may perform as well or better. For example, Ovaskainen et al. (2019), Thompson et al. (2020), and Guzman et al. (2021) applied sets of idealized and alternative models to test against a panel of pattern metrics. They found that while brute force filtering could efficiently rule out some models, it was often inconclusive in narrowing down to a single best-fit model. Including more or better metrics may help, but data will often become limiting in empirical systems. A complementary alternative is inspired by disordered systems (top-down) modeling. Instead of seeking a one-to-one correspondence between detailed process-based models and observed patterns by enlarging the set of patterns to keep pace with the larger and larger parameter space of models, this approach seeks specific features of models that remain uniquely associated with specific patterns even as the system of interest becomes very complex. For example, Barbier et al. (2021) used data from grassland plots to predict relative yield distributions based on mean and variance in interspecific interaction strengths— linking summary statistics of interactions to a specific pattern without attempting to parameterize specific pairwise interactions. Similarly, in another study, Barbier et al. (2023) found that pairwise correlations in species abundances could reflect interaction variances and carrying capacity heterogeneity, even when actual interaction coefficients were poorly known. The key to this approach is to identify high-level parameters or parameter combinations that characterize specific ecological mechanisms and relate them to specific, diagnostic patterns. In contrast to brute force approaches, this top-down strategy targets robust, emergent features of community structure as signatures of underlying processes. However, by design, this approach cannot resolve low-level mechanistic details of metacommunity dynamics. While this dichotomy between bottom-up and top-down approaches is simplified, it highlights contrasting philosophies in ecological modeling, and how both can be applied to link process to pattern. Hybrid strategies that integrate these perspectives—particularly through partially structured models—may offer another path forward. Appendix 1 further explores some nuances in combining these frameworks. Linking Patterns to Processes: In the section above, we considered how metacommunity models can be used to link processes to patterns, providing, in turn, a way to link observed patterns back to underlying processes. This approach to inference relies on first building out a map between processes and patterns, but this is complicated by the breakdown of a one-to-one mapping as the systems of interest become more complex. The bottom-up and top-down modeling strategies suggest two distinct approaches to coping with this issue: a brute force approach, where larger sets of patterns are used to maintain the one-to-one mapping, and a disordered systems approach where one seeks higher-level mappings between summary statistics or other emergent features of models and data. Both of these approaches are inherently limited – by data availability or by the ability to identify diagnostic patterns. Is there an alternative approach that avoids these limitations? One possibility is to use methods that more directly decompose the sources of variation in community patterns. Ovaskainen et al. (2017) proposed a method for doing this using Joint Species Distribution Models (JSDMs), which decompose observed variation in species distributions into components attributable to environment, space, species co-distribution (potentially indicative of interactions), and stochasticity. Following the logic of traditional species distribution models, “classic” JSDMs apply a sequential partitioning of variation: first accounting for environment, then for additional spatial effects, and finally for residual co-distribution among species. If environmental predictors are comprehensive, any remaining co-distribution may reflect biotic interactions. Figure 2a illustrates this sequential variation partitioning. However, JSDMs can also be used in a non-sequential framework, in which each component—environment, space, co-distribution—is estimated simultaneously. This leads to a more complex pattern of shared and unique contributions (Figure 2b), where the interpretation of overlaps becomes ambiguous. For instance, two species might be mutually exclusive along an environmental gradient because of direct environmental filtering, competitive exclusion, or both. Without further assumptions, these effects cannot be disentangled (Dormann et al., Blanchet et al. 2018, Poggiatto et al. 2020). Thus, while JSDMs offer powerful tools for detecting and predicting community patterns, they do not necessarily provide unambiguous inference about underlying ecological processes. Nevertheless, they represent one of the most promising methods currently available for linking pattern to process. A key direction for future progress may involve refining process models to better align with the data structures used in pattern-based inference. For example, Leibold et al. (2022) proposed analyzing the internal structure of metacommunities by parsing JSDM components across species and sites. This decomposition reveals how different species respond to environmental and spatial variation and how their distributions co-vary. Unlike traditional pattern metrics, which aggregate across species, this approach allows process inference to operate at the species level. In doing so, it opens the possibility of aligning species-specific effects with bottom-up models that operate on subsets of interacting taxa. This could eventually allow for a true integration of bottom-up and top-down perspectives, as outlined in Figure 3. Figure 3 illustrates potential inference pipelines that connect process-based models and data-based metrics. The top half of the figure emphasizes aggregated descriptors (e.g., means, variances), while the bottom half highlights parameter-specific models. Arrows show how models and metrics can reject or support hypotheses, and where gaps in inference still remain—particularly the persistent entanglement of processes such as dispersal, environmental filtering, and species interactions. In summary, the mapping from patterns to processes remains complex and often ambiguous. However, methodological advances—particularly JSDMs and species-level decomposition—offer a promising route forward. Further refinement of both models and inference tools may help to close the gap between observed community patterns and the underlying processes that generate them. Prospectus We have outlined the conceptual and methodological challenges involved in linking processes and patterns in ecological communities and metacommunities. These issues trace back to the origins of community ecology, yet recent decades have seen tremendous progress. The development of metacommunity theory has notably reshaped how we interpret spatial biodiversity patterns (Leibold and Chase 2018). On the analytical side, species distribution models have evolved from environmentally focused predictors to those incorporating spatial effects and species co-distributions, thereby often increasing predictive power from roughly 20–30% to 70–80% (Leibold and Peres-Neto, in prep). Despite these advancements, substantial challenges remain. Chief among them is the uncertainty of current inference approaches. This problem is especially clear in JSDMs, where the effects of density-independent environmental filtering and density-dependent biotic interactions are often conflated. Similar ambiguity arises in multi-metric brute force approaches, as illustrated by McGill (2010), and in simulation studies (Chave et al. 2002, Ovaskainen et al. 2019; Thompson et al. 2020; Guzman et al. 2021) that failed to uniquely support any single mechanistic model. It is important to acknowledge that many existing methods were not designed to infer mechanisms with high specificity. Often, the goal has been to reject null models or generate predictive models without mechanistic interpretation. While such approaches are valuable—particularly in applied contexts—the need for mechanistically grounded prediction grows as we face novel environmental conditions. Encouragingly, the field is evolving. Developments in data collection, modeling, and computation suggest a promising trajectory rather than a fixed endpoint. We anticipate major progress in the coming years, driven by: • Improved data streams that enhance sampling coverage, temporal resolution, and accuracy. • Greater computational capacity to analyze complex datasets and run more sophisticated models—including those incorporating machine learning or artificial intelligence. • Advances in process modeling , both in modular bottom-up approaches (e.g., for eco-evolutionary dynamics) and in statistical-mechanics-based top-down approaches, particularly through partially structured models. • Increased emphasis on validation and prediction , to better link model outputs with empirical data and strengthen the integration of metrics and models. To accelerate progress, we suggest several promising directions: 1. Integrate temporal dynamics and multi-scale data : Most metacommunity data remain spatial, but temporal metrics offer powerful insights (Holyoak et al. (2021, Castillo-Escriva et al. 2021, Record et al. 2021). For example, Jabot et al. (2020) and Guzman et al. (2022) showed how accounting for temporal effects resolution of metacommunity structure. Temporal data also facilitate causal inference via concepts like Granger causality, which can help infer directionality in species responses—something not possible from spatial data alone. Jackson et al. (2025) propose a novel and promising analytical method that exploits this aspect of metacommunity analysis called “maximum caliber”. 2. Design and incorporate experimental manipulations : Experiments can validate observational inferences and provide stronger process-based insights (Werner 2001; Grace 2024). Effective experimental designs—such as manipulating species presence/absence across environments—could directly test candidate mechanisms identified in models. Excitingly, advances in sequencing and high-throughput culture methods have fostered an explosive growth in experimental microbial ecology; the unique tractability of microbial (meta)communities makes them a promising platform for such experimental manipulations. Approaches that combine experimental and observational approaches to address key conceptual issues are particularly compelling (e.g. Werner 2001, Abrego et al. 2025) 3. Bridge top-down and bottom-up approaches : Rather than treating these as oppositional approaches, hybrid strategies can combine mechanistic insights from modules with the robustness of statistical aggregation. Partially structured models are one way to integrate these perspectives. For instance, Barbier et al. (2018) showed how modest structure imposed on random matrices could shift predictions significantly, combining both realism and analytical tractability. Community ecology, and particularly metacommunity theory, is undergoing a methodological and conceptual transformation. Understanding the links between processes and patterns remains a central goal. Doing so not only enhances ecological theory but also improves prediction under environmental change. Purely predictive models will struggle as conditions diverge from historical baselines. Mechanistic understanding, while harder to achieve, provides a necessary complement to maintain predictive power. By reviewing current approaches and their limitations, while highlighting promising future directions, we aim to support and accelerate progress in this critical area. Ultimately, bridging the gap between pattern and process is not only a heuristic ambition but also an essential step toward predictive and causal ecology. By leveraging the complementary strengths of diverse approaches and fostering integration across empirical, theoretical, and computational fronts, we can move closer to a unified framework for understanding the dynamics of biodiversity. Acknowledgements [Redacted for peer-review] Literature Cited: Abrego, N., Saine, S., Penttila, R., Furneaux, B., Hytonen, T., Mettinen, O., Monkhouse, N., Makipaa, R., Pennanen, J. Zakharov, E.V., & Ovaskainen, O. (2025). The role of stochasticity in fungal community assembly: explaining apparent stochasticity with field experiments. Royal Society: Proceedings B, 292, 20242416. https://doi.org/10.1098/rspb.2024.2416. Abrego, N., Roslin, T., Huotari, T., Ji, Y., Schmidt, N. M., Wang, J., Yu, D. W., & Ovaskainen, O. (2021). Accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change. Ecography , 44 (6), 885–896. https://doi.org/10.1111/ecog.05547 Ahmadian, Y., Fumarola, F., & Miller, K. D. (2015). Properties of networks with partially structured and partially random connectivity. Physical Review E , 91 (1), 012820. https://doi.org/10.1103/PhysRevE.91.012820 Akjouj, I., Barbier, M., Clenet, M., Hachem, W., Maïda, M., Massol, F., Najim, J., & Tran, V. C. (2024). Complex systems in ecology: A guided tour with large Lotka–Volterra models and random matrices. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences , 480 (2285), 20230284. https://doi.org/10.1098/rspa.2023.0284 Amarasekare, P. (2003). Competitive coexistence in spatially structured environments: A synthesis. Ecology Letters , 6 (12), 1109–1122. https://doi.org/10.1046/j.1461-0248.2003.00530.x Amarasekare, P., & Nisbet, R. M. (2001). Spatial Heterogeneity, Source‐Sink Dynamics, and the Local Coexistence of Competing Species. The American Naturalist , 158 (6), 572–584. https://doi.org/10.1086/323586 Barbier, M., Arnoldi, J.-F., Bunin, G., & Loreau, M. (2018). Generic assembly patterns in complex ecological communities. Proceedings of the National Academy of Sciences , 115 (9), 2156–2161. Barbier, M., Bunin, G., & Leibold, M. A. (2023). Getting More by Asking for Less: Linking Species Interactions to Species Co-Distributions in Metacommunities [Preprint]. Ecology. https://doi.org/10.1101/2023.06.04.543606 Barbier, M., De Mazancourt, C., Loreau, M., & Bunin, G. (2021). Fingerprints of High-Dimensional Coexistence in Complex Ecosystems. Physical Review X , 11 (1), 011009. https://doi.org/10.1103/PhysRevX.11.011009 Blanchet, F. G., Cazelles, K., & Gravel, D. (2020). Co‐occurrence is not evidence of ecological interactions. Ecology Letters , 23 (7), 1050–1063. https://doi.org/10.1111/ele.13525 Borcard, D., Legendre, P., & Drapeau, P. (1992). Partialling out the Spatial Component of Ecological Variation. Ecology , 73 (3), 1045–1055. https://doi.org/10.2307/1940179 Borregaard, M. K., Rahbek, C., Fjeldså, J., Parra, J. L., Whittaker, R. J., & Graham, C. H. (2014). Node‐based analysis of species distributions. Methods in Ecology and Evolution , 5 (11), 1225–1235. https://doi.org/10.1111/2041-210X.12283 Borthagaray, A.I., Matias, A. and Marquet P.A. 2014. Inferring species roles in metacommunity structure from species co-occurence networks. Proceeding of the Royal Society B . 281:20141425. Carugno, G., Neri, I., & Vivo, P. (2022). Instabilities of complex fluids with partially structured and partially random interactions. Physical Biology , 19 (5), 056001. https://doi.org/10.1088/1478-3975/ac55f9 Castillo-Escriva, A., Mesquita-Joanes, F., Rueda, J. (2020). Frontiers in Ecology and Evolutio n 8:298. https://doi.org/10.3389/fevo.2020.561838 Chang, C.-Y., Bajić, D., Vila, J. C. C., Estrela, S., & Sanchez, A. (2023). Emergent coexistence in multispecies microbial communities. Science , 381 (6655), 343–348. https://doi.org/10.1126/science.adg0727 Chave, J., Muller-Landau, H.C. and Levin, S. (2002). Comparing classical community models: theoretical consequences for patterns of diversity. The American Naturalist 159, 1-23. Choler, P., Michalet, R., & Callaway, R. M. (2001). FACILITATION AND COMPETITION ON GRADIENTS IN ALPINE PLANT COMMUNITIES. Ecology , 82 (12), 3295–3308. https://doi.org/10.1890/0012-9658(2001)082[3295:FACOGI]2.0.CO;2 Connell, J. H. (1961). The Influence of Interspecific Competition and Other Factors on the Distribution of the Barnacle Chthamalus Stellatus. Ecology , 42 (4), 710–723. https://doi.org/10.2307/1933500 Connell, J. H. (1983). On the Prevalence and Relative Importance of Interspecific Competition: Evidence from Field Experiments. The American Naturalist , 122 (5), 661–696. https://doi.org/10.1086/284165 Connor, E. F., & Simberloff, D. (1979). The Assembly of Species Communities: Chance or Competition? Ecology , 60 (6), 1132–1140. https://doi.org/10.2307/1936961 Cottenie, K. (2005). Integrating environmental and spatial processes in ecological community dynamics. Ecology Letters , 8 (11), 1175–1182. https://doi.org/10.1111/j.1461-0248.2005.00820.x Crombie, A. C. (1945). On competition between different species of graminivorous insects. Proceedings of the Royal Society B: Biological Sciences . Crombie, A.C. (1946). Further experiments on insect competition. Proceedings of the Royal Society of London. Series B - Biological Sciences , 133 (870), 76–109. https://doi.org/10.1098/rspb.1946.0004 Cui, W., III, R. M., & Mehta, P. (2024). Les Houches Lectures on Community Ecology: From Niche Theory to Statistical Mechanics (arXiv:2403.05497). arXiv. https://doi.org/10.48550/arXiv.2403.05497 Darwin, C. (1859). On the Origin of Species . John Murray. Friedman, J., Higgins, L. M., & Gore, J. (2017). Community structure follows simple assembly rules in microbial microcosms. Nature Ecology & Evolution , 1 (5), 0109. https://doi.org/10.1038/s41559-017-0109 Gause, G. F. (1932). Experimental Studies on the Struggle for Existence In Mixed Population of Two Species of Yeast . 9 , 389–402. Gause, G. F. (1934). The Struggle for Existence . Williams and Wilkins. Germain, R. M., Hart, S. P., Turcotte, M. M., Otto, S. P., Sakarchi, J., Rolland, J., Usui, T., Angert, A. L., Schluter, D., Bassar, R. D., Waters, M. T., Henao-Diaz, F., & Siepielski, A. M. (2021). On the Origin of Coexisting Species. Trends in Ecology & Evolution , 36 (4), 284–293. https://doi.org/10.1016/j.tree.2020.11.006 Gleason, H. A. (1926). The Individualistic Concept of the Plant Association. Bulletin of the Torrey Botanical Club , 53 (1), 7. https://doi.org/10.2307/2479933 Goh, B. S. (1979). Stability in models of mutualism. The American Naturalist , 113 , 261–275. Gotelli, N. J., & McCabe, D. J. (2002). SPECIES CO-OCCURRENCE: A META-ANALYSIS OF J. M. DIAMOND’S ASSEMBLY RULES MODEL. Ecology , 83 (8), 2091–2096. https://doi.org/10.1890/0012-9658(2002)083[2091:SCOAMA]2.0.CO;2 Gould, E., Fraser, H., Parker, T., Nakagawa, S., Griffith, S., Vesk, P., Fidler, F., Abbey-Lee, R., Abbott, J., Aguirre, L., Alcaraz, C., Altschul, D., Arekar, K., Atkins, J., Atkinson, J., Barrett, M., Bell, K., Bello, S., Berauer, B., … Tompkins, E. (2023). Same data, different analysts: Variation in effect sizes due to analytical decisions in ecology and evolutionary biology . https://doi.org/10.32942/X2GG62 Grace, J. B. (2024). An integrative paradigm for building causal knowledge. Ecological Monographs , 94 (4), e1628. https://doi.org/10.1002/ecm.1628 Grace, J. B., Harrison, S., & Damschen, E. I. (2011). Local richness along gradients in the Siskiyou herb flora: R. H. Whittaker revisited. Ecology , 92 (1), 108–120. https://doi.org/10.1890/09-2137.1 Gravel, D., Massol, F., & Leibold, M. A. (2016). Stability and complexity in model meta-ecosystems. Nature Communications , 7 (1), 12457. https://doi.org/10.1038/ncomms12457 Gurevitch, J., Morrow, L. L., Wallace, A., & Walsh, J. S. (1992). A Meta-Analysis of Competition in Field Experiments. The American Naturalist , 140 (4), 539–572. https://doi.org/10.1086/285428 Guzman, L. M., Thompson, P. L., Viana, D. S., Vanschoenwinkel, B., Horváth, Z., Ptacnik, R., Jeliazkov, A., Gascón, S., Lemmens, P., Anton‐Pardo, M., Langenheder, S., De Meester, L., & Chase, J. M. (2022). Accounting for temporal change in multiple biodiversity patterns improves the inference of metacommunity processes. Ecology , 103 (6), e3683. https://doi.org/10.1002/ecy.3683 Hart, S. P., Freckleton, R. P., & Levine, J. M. (2018). How to quantify competitive ability. Journal of Ecology , 106 (5), 1902–1909. https://doi.org/10.1111/1365-2745.12954 Harte, J., & Newman, E. A. (2014). Maximum information entropy: A foundation for ecological theory. Trends in Ecology & Evolution , 29 (7), 384–389. https://doi.org/10.1016/j.tree.2014.04.009 Holling, C. S., & Allen, C. R. (2002). Adaptive Inference for Distinguishing Credible from Incredible Patterns in Nature. Ecosystems , 5 (4), 319–328. https://doi.org/10.1007/s10021-001-0076-2 Holt, R. D., & Hochberg, M. E. (2001). Indirect interactions, community modules and biological control: A theoretical perspective. In E. Wajnberg, J. K. Scott, & P. C. Quimby (Eds.), Evaluating indirect ecological effects of biological control. Key papers from the symposium “Indirect ecological effects in biological control”, Montpellier, France, 17-20 October 1999 (1st ed., pp. 13–37). CABI Publishing. https://doi.org/10.1079/9780851994536.0013 Holt, Robert D. (1997). Community modules. In Multitrophic interactions in terrestrial ecosystems (pp. 333–350). Blackwell. Holyoak, M., Caspi T., Redosh, L.W. (2020). Integrating disturbance, seasonality, multi-year temporal dynamics, and dormancy into the dynamics and conservation of metacommunities. Frontiers in Ecology and Evolution 8:571130 https://doi.org/10.3389/fevo.2020.571130 Hubbell, S. P. (2001). The unified neutral theory of biodiversity and biogeography . Princeton University Press. Huffaker, C. B. (1958). Experimental studies on predation: Dispersion factors and predator-prey oscillations. Hilgardia 27, 795-835. Jabot, F., Laroche, F., Massol, F., Arthaud, F., Crabot, J., Bubart, M., Blanchet, S., Munoz, F., David, P., Datry, T. (2020). Assessing metacommunity processes through signatures in spatiotemporal turnover of community composition. Ecology Letters 23:1330-1339. https://doi.org/10.1111/ele.13523 Kéfi, S., Miele, V., Wieters, E. A., Navarrete, S. A., & Berlow, E. L. (2016). How Structured Is the Entangled Bank? The Surprisingly Simple Organization of Multiplex Ecological Networks Leads to Increased Persistence and Resilience. PLOS Biology , 14 (8), e1002527. https://doi.org/10.1371/journal.pbio.1002527 Khattar, G., & Peres-Neto, P. R. (2024). The Geography of Metacommunities: Landscape Characteristics Drive Geographic Variation in the Assembly Process through Selecting Species Pool Attributes. The American Naturalist , 203 (5), E142–E156. https://doi.org/10.1086/729423 Kneitel, J. M., & Chase, J. M. (2004). Trade‐offs in community ecology: Linking spatial scales and species coexistence. Ecology Letters , 7 (1), 69–80. https://doi.org/10.1046/j.1461-0248.2003.00551.x Leibold, M. A. (1996). A Graphical Model of Keystone Predators in Food Webs: Trophic Regulation of Abundance, Incidence, and Diversity Patterns in Communities. The American Naturalist , 147 (5), 784–812. https://doi.org/10.1086/285879 Leibold, M. A., & Chase, J. M. (2018). Metacommunity Ecology, Volume 59 . Princeton University Press. https://doi.org/10.1515/9781400889068 Leibold, M. A., Govaert, L., Loeuille, N., De Meester, L., & Urban, M. C. (2022). Evolution and Community Assembly Across Spatial Scales. Annual Review of Ecology, Evolution, and Systematics , 53 (1), 299–326. https://doi.org/10.1146/annurev-ecolsys-102220-024934 Leibold, M. A., Holyoak, M., Mouquet, N., Amarasekare, P., Chase, J. M., Hoopes, M. F., Holt, R. D., Shurin, J. B., Law, R., Tilman, D., Loreau, M., & Gonzalez, A. (2004). The metacommunity concept: A framework for multi-scale community ecology. Ecology Letters , 7 (7), 601–613. https://doi.org/10.1111/j.1461-0248.2004.00608.x Leibold, M. A., & Mikkelson, G. M. (2002). Coherence, species turnover, and boundary clumping: Elements of meta‐community structure. Oikos , 97 (2), 237–250. https://doi.org/10.1034/j.1600-0706.2002.970210.x Leibold, M. A., Rudolph, F. J., Blanchet, F. G., De Meester, L., Gravel, D., Hartig, F., Peres‐Neto, P., Shoemaker, L., & Chase, J. M. (2022a). The internal structure of metacommunities. Oikos , 2022 (1), oik.08618. https://doi.org/10.1111/oik.08618 Leibold, M. A., Rudolph, F. J., Blanchet, F. G., De Meester, L., Gravel, D., Hartig, F., Peres‐Neto, P., Shoemaker, L., & Chase, J. M. (2022b). The internal structure of metacommunities. Oikos , 2022 (1), oik.08618. https://doi.org/10.1111/oik.08618 Lerch, B. A., Rudrapatna, A., Rabi, N., Wickman, J., Koffel, T., & Klausmeier, C. A. (2023). Connecting local and regional scales with stochastic metacommunity models: Competition, ecological drift, and dispersal. Ecological Monographs , 93 (4), e1591. https://doi.org/10.1002/ecm.1591 Lotka, A. (1925). Elements of Physical Biology . Williams and Wilkins. MacArthur, R. H. (1974). Geographical Ecology: Patterns in the Distribution of Species . MacArthur, R. H., & Wilson, E. O. (1967). The theory of island biogeography . Princeton University Press. May, F., Huth, A., & Wiegand, T. (2015). Moving beyond abundance distributions: Neutral theory and spatial patterns in a tropical forest. Proceedings of the Royal Society B: Biological Sciences , 282 (1802), 20141657. https://doi.org/10.1098/rspb.2014.1657 May, F., Wiegand, T., Lehmann, S., & Huth, A. (2016). Do abundance distributions and species aggregation correctly predict macroecological biodiversity patterns in tropical forests? Global Ecology and Biogeography , 25 (5), 575–585. https://doi.org/10.1111/geb.12438 May, R. M. (1972). Will a large complex system be stable? Nature , 238 , 413–414. May, R. M. (1973). Stability and Complexity in Model Ecosystems. . (Vol. 6). Princeton University Press. McGill, B. J. (2010). Towards a unification of unified theories of biodiversity. Ecology Letters , 13 (5), 627–642. https://doi.org/10.1111/j.1461-0248.2010.01449.x McPeek, M. A. (2022). Coexistence in ecology: A mechanistic perspective . Princeton University Press. Miller, Z. R., Clenet, M., Della Libera, K., Massol, F., & Allesina, S. (2024). Coexistence of many species under a random competition–colonization trade-off. Proceedings of the National Academy of Sciences , 121 (5), e2314215121. https://doi.org/10.1073/pnas.2314215121 Mühlbauer, L. K., Schulze, M., Harpole, W. S., & Clark, A. T. (2020). gauseR: Simple methods for fitting Lotka‐Volterra models describing Gause’s “Struggle for Existence.” Ecology and Evolution , 10 (23), 13275–13283. https://doi.org/10.1002/ece3.6926 Ovaskainen, O., & Abrego, N. (2020). Joint species distribution modelling: With applications in R . Cambridge university press. Ovaskainen, O., Rybicki, J., & Abrego, N. (2019). What can observational data reveal about metacommunity processes? Ecography , 42 (11), 1877–1886. https://doi.org/10.1111/ecog.04444 Pascual, M. A., & Kareiva, P. (1996). Predicting the outcome of competition using experimental data: Maximum likelihood and bayesian approaches. Ecology , 77 , 337–249. Peake, J., Leibold, M., Schrandt, M., & Stallings, C. (2024). Assembly processes in estuarine metacommunities are dependent on spatial scale . https://doi.org/10.22541/au.172506225.52119085/v1 Peres-Neto, P. R., Legendre, P., Dray, S., & Borcard, D. (2006). Variation Partitioning of Species Data Matrices: Estimation and Comparison of Fractions. Ecology , 87 (10), 2614–2625. https://doi.org/10.1890/0012-9658(2006)87[2614:VPOSDM]2.0.CO;2 Peres‐Neto, P. R., Olden, J. D., & Jackson, D. A. (2001). Environmentally constrained null models: Site suitability as occupancy criterion. Oikos , 93 (1), 110–120. https://doi.org/10.1034/j.1600-0706.2001.930112.x Poggiato, G., Münkemüller, T., Bystrova, D., Arbel, J., Clark, J. S., & Thuiller, W. (2021). On the Interpretations of Joint Modeling in Community Ecology. Trends in Ecology & Evolution , 36 (5), 391–401. https://doi.org/10.1016/j.tree.2021.01.002 Pollock, L. J., Tingley, R., Morris, W. K., Golding, N., O’Hara, R. B., Parris, K. M., Vesk, P. A., & McCarthy, M. A. (2014). Understanding co‐occurrence by modelling species simultaneously with a Joint Species Distribution Model ( JSDM ). Methods in Ecology and Evolution , 5 (5), 397–406. https://doi.org/10.1111/2041-210X.12180 Qi, M., Sun, T., Xue, S., Yang, W., Shao, D., & Martínez‐López, J. (2018). Competitive ability, stress tolerance and plant interactions along stress gradients. Ecology , 99 (4), 848–857. https://doi.org/10.1002/ecy.2147 Ranjan, R., Koffel, T., & Klausmeier, C. A. (2024). The three‐species problem: Incorporating competitive asymmetry and intransitivity in modern coexistence theory. Ecology Letters , 27 (4), e14426. https://doi.org/10.1111/ele.14426 Record, S., Voelker, N.M., Zarnetske, P.L., Wisnoski, N.I., Tonkin, J.D., Swan, C., Marazzi, L., Lany, N., Lamy, T., Complagnoni, A., Castorani, M.C.N., Andrade, R., Sokol, E.R. (2020). Novel insights to be gained from applying metacommunity theory to long-term, spatially replicated biodiversity data. Frontiers in Ecology and Evolution 8: 612794. https://doi.org/10.3389/fevo.2020.612794 Saavedra, S., Rohr, R. P., Bascompte, J., Godoy, O., Kraft, N. J. B., & Levine, J. M. (2017). A structural approach for understanding multispecies coexistence. Ecological Monographs , 87 (3), 470–486. https://doi.org/10.1002/ecm.1263 Sanderson, J. G., & Pimm, S. L. (2015). Patterns in Nature: The Analysis of Species Co-occurences (s). University of Chicago Press. Schaffer, W. M. (1981). Ecological Abstraction: The Consequences of Reduced Dimensionality in Ecological Models. Ecological Monographs , 51 (4), 383–401. https://doi.org/10.2307/2937321 Schoener, T. W. (1983). Field Experiments on Interspecific Competition. The American Naturalist , 122 (2), 240–285. https://doi.org/10.1086/284133 Serván, C. A., Capitán, J. A., Miller, Z. R., & Allesina, S. (2024). Effects of Phylogeny on Coexistence in Model Communities. The American Naturalist , E000–E000. https://doi.org/10.1086/733415 Shoemaker, L. G., Sullivan, L. L., Donohue, I., Cabral, J. S., Williams, R. J., Mayfield, M. M., Chase, J. M., Chu, C., Harpole, W. S., Huth, A., HilleRisLambers, J., James, A. R. M., Kraft, N. J. B., May, F., Muthukrishnan, R., Satterlee, S., Taubert, F., Wang, X., Wiegand, T., … Abbott, K. C. (2020). Integrating the underlying structure of stochasticity into community ecology. Ecology , 101 (2), e02922. https://doi.org/10.1002/ecy.2922 Shoemaker, W. R., Sánchez, Á., & Grilli, J. (2023). Macroecological patterns in experimental microbial communities . https://doi.org/10.1101/2023.07.24.550281 Si, X., Zhao, Y., Chen, C., Ren, P., Zeng, D., Wu, L., Ding, P. (2017). Beta-diversity partitioning: methods, applications and perspectives. Journal of Biodiversity Science J. 25: 464-480. Sih, A., Crowley, P., McPeek, M., Petranka, J., & Strohmeier, K. (1985). Predation, Competition, and Prey Communities: A Review of Field Experiments. Annual Review of Ecology and Systematics , 16 (1), 269–311. https://doi.org/10.1146/annurev.es.16.110185.001413 Ter Braak, C. J. F., & Prentice, I. C. (1988). A Theory of Gradient Analysis. In Advances in Ecological Research (Vol. 18, pp. 271–317). Elsevier. https://doi.org/10.1016/S0065-2504(08)60183-X Thom, R., Noël, E., Tsatsanis, S. P., & Lisker, R. (2016). To predict is not to explain: Conversations on mathematics, science, catastrophe theory, semiophysics, natural philosophy and morphogenesis . Thombooks Press. Thompson, P. L., Guzman, L. M., De Meester, L., Horváth, Z., Ptacnik, R., Vanschoenwinkel, B., Viana, D. S., & Chase, J. M. (2020). A process‐based metacommunity framework linking local and regional scale community ecology. Ecology Letters , 23 (9), 1314–1329. https://doi.org/10.1111/ele.13568 Tilman, D. (1980). Resources: A Graphical-Mechanistic Approach to Competition and Predation. The American Naturalist , 116 (3), 362–393. https://doi.org/10.1086/283633 Tilman, D. (1982). Resource Competition and Community Structure . Vandermeer, J. H. (1969). The Competitive Structure of Communities: An Experimental Approach with Protozoa. Ecology , 50 (3), 362–371. https://doi.org/10.2307/1933884 Vellend, M. (2010). Conceptual synthesis in community ecology. Quarterly Review of Biology , 85 , 183–206. Vellend, M. (2016). The Theory of Ecological Communities . Princeton University Press. Volterra, V. (1926). Variazioni e Fluttuazioni Del Numero d’individui in Specie Animali Conviventi . 2 , 31–113. Werner, E. E. (2001). Ecological experiments and a research program in community ecology. In Experimental Ecology: Issues and Perspectives (pp. 3–26). Oxford University Press. Whittaker, R. H. (1960). Vegetation of the Siskiyou Mountains, Oregon and California. Ecological Monographs , 30 (3), 279–338. https://doi.org/10.2307/1943563 Yanco, S. W., McDevitt, A., Trueman, C. N., Hartley, L., & Wunder, M. B. (2020). A modern method of multiple working hypotheses to improve inference in ecology. Royal Society Open Science , 7 (6), 200231. https://doi.org/10.1098/rsos.200231 Yodzis, P. (1981). The stability of real ecosystems. Nature , 289 (5799), 674–676. https://doi.org/10.1038/289674a0 Table 1: A representative set of possible metrics and patterns that can be derived from the site-by-species data matrix. Illustrative examples adapted and extended from Guzman et al. (2022). These represent a limited selection of a very large number of statistics and derived patterns that ecologists have explored and are meant to assist in our narrative of linking patterns to processes. Metric or Pattern Examples Simple Descriptive Ecological Statistics Summaries Means or variances of abundance, biomass, relative and absolute density, functional traits. Distributions Of abundances, incidences, functional traits, biomass, spatial or temporal occupancy, species-abundance distributions. Diversity Alpha and gamma, Hill numbers, functional and phylogenetic diversity. Simple Derived Patterns Turnover Spatial or temporal beta diversity; distance-decay, environmental-decay (with ancillary data). Scaling relationships Species-area, Taylor power, rarefaction or sampling curves. Network structure Nestedness, co-occurrence, centrality. Second Order Derived Patterns Species network structure that varies as a function of environmental variation. Metacommunity model with landscape fragmentation; time-varying trophic interactions. Model Derived Outputs Variation partitioning only in space and environment Variation partitioning of space and environment. Latent variables for each and their interactions. Variation partitioning in time and space Same as above but also with time. Results from distribution models incorporating species covariances Variation partitioning of space, environment, time, and species co-distributions; e.g., from JSDMs and similar approaches. Figure 1: Two “strategic” approaches to modeling in community ecology. The more conventional “bottom-up” approach starts from the left and moves to the right. This is based on starting from single species population models, which can be arbitrarily complex to begin with, and adding species interactions in a structured way. For example, pairwise interactions may be combined to build up small sets of species as “modules” (Holt 1997). In principle, this could eventually lead to the analysis of realistically complex communities (center) that better represent those we find in nature, but progress seems to be exponentially harder as diversity increases. Alternatively, it is possible to think about communities as much more diffuse in nature. This approach is usually embodied by using random matrices to model species interactions (e.g., Random Lotka-Volterra Matrix or “RLVM” models), allowing the application of powerful mathematical methods. By progressively adding structural constraints on these models (e.g. distinct submatrices within the RLVM), they are converted into “partially structured models” that may approach realistic complex communities from another direction (right to left). Figure 2: Variation partitioning alternatives for JSDM represented by Venn diagrams. The total variation in community composition in the metacommunity is encompassed by the outer square and is equal to 1. The white part of the figure is the unexplained or residual variation that is not accounted for by any of the predictors in the model. a) Accounting for predictors in a sequential order: First the measured environmental predictors are used and their contribution to the community variation is quantified as represented by the green area. Then spatial predictors are used to further account for community variation and their marginal contribution is shown in blue. Finally, the non-random latent correlations among the species are described, and their marginal contribution is quantified by the orange area. b) A non-sequential variation partitioning approach. Here, unique contributions from environmental, spatial, and codistribution components are represented (i.e., fractions without overlaps). Additionally there are a number of components that account for community variation that cannot be uniquely described (fractions representing predictor intersections). An interesting possibility is to compare the results of JSDM or other related methods across different metacommunities (e.g. Khattar and Peres-Neto 2024, Peak et al. 2024) Figure 3: Pipelines between ‘Process’ (left) and ‘Pattern’ (right). We distinguish between approaches that aggregate parameters and/or data (upper pipeline in gold) and those that focus on maintaining detailed parameters and predictors (lower pipeline in violet). We define process-based approaches (starting from the left) as those that combine basic processes (i.e. density-independent selection, density dependent selection, dispersal, stochasticity) to make predictions about resulting patterns within landscapes that consider levels of heterogeneity and spatial structure (left side of the figure). The ‘top-down approach’ (1) uses ‘disordered’ or ‘partially structured’ models by aggregating parameters (typically, into mean, variances and covariances of these parameters) to predict robust patterns, but sacrifices predicting ‘fragile’ detailed components of those patterns. Alternatively, there is a ‘bottom-up’ approach (2) that specifies parameters, typically in models with relatively few species, and uses detailed model specifications (e.g., using individually based models (IBMS), Lotka-Volterra models with specified parameters, or other simulation models) to make detailed predictions. Such models can also predict some of the same general patterns as the top-down models but aim to be able to make more detailed predictions that may produce detailed fit to data. There is a parallel structure that derives patterns from data (typically the site-by-species matrix, along with ancillary data on species traits/interrelations and/or explicit landscape features). One approach derives aggregated pattern descriptors (gold arrow 2). These include a wide array of ‘metrics’ such as species area relations, diversity and related methods, etc. A more recent effort has been directed at identifying parameter values from data. These metrics provide means of rejecting particular hypotheses (5. and 6., magenta and blue lines) but do not normally parameterize the processes. An alternate approaches uses sophisticated statistical modeling to identify detailed features of mechanisms. This may involve either the inferences of statistical features (red arrow, 7) via e.g. JSDMs to infer metacommunity mechanisms (e.g. Ovaskainen et al. 2017, Leibold et al. 2022) or an attempt to derice actual parameter values (green dashed arrow, 8). These approaches, while desirable, have not yet been able to resolve the entanglement of the five processes to clearly parameterize mechanistic models. In the absence of such detailed inference of models. An intriguing point is to explore how bottom-up and top-down methods might be cross-validated (yellow arrow). Information & Authors Information Version history V1 Version 1 22 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords communities mechanisms metacommunities metrics patterns processes Authors Affiliations Mathew Leibold 0000-0003-3954-3187 [email protected] University of Florida View all articles by this author Matthieu Barbier 0000-0002-0669-8927 View all articles by this author Leonora Bittlestone Boise State University View all articles by this author Adam Clark 0000-0002-8843-3278 University of Graz View all articles by this author Catalina Cuellar-Gempeler California Polytechnic University, Humboldt View all articles by this author Rafael D'Andrea 0000-0001-9687-6178 University of Illinois View all articles by this author Veronica Frans 0000-0002-5634-3956 Michigan State University View all articles by this author Gabriel Khattar 0000-0002-1703-4210 Concordia University Faculty of Arts and Science View all articles by this author Zachary Miller Yale University View all articles by this author Pedro Peres-Neto Concordia University View all articles by this author Nathan Wisnoski 0000-0002-2929-5231 Mississippi State University View all articles by this author Metrics & Citations Metrics Article Usage 480 views 175 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mathew Leibold, Matthieu Barbier, Leonora Bittlestone, et al. Forum: Linking Pattern to Process in Metacommunities: Challenges and Opportunities. Authorea . 22 September 2025. DOI: https://doi.org/10.22541/au.175855717.73873060/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175855717.73873060/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fec5c677fb68650',t:'MTc3OTI5MDc5OQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();