From pairwise interactions to community structure: A trait‐based approach to inferring assembly mechanisms across spatial scales

One of the most fundamental goals of ecology is to understand the mechanisms that underpin and maintain variation in biological diversity in ecological communities (Pavoine & Bonsall, 2011; Scheiner & Willig, 2008). This is a challenging and daunting task, given that ecological communities are dynamic systems influenced by processes operating at various spatial and temporal scales (Gianuca et al., 2013). To identify and explain these mechanisms, ecologists have developed and employed a range of methodological approaches (Cornell & Lawton, 1992). These approaches can be positioned along a ‘conceptual continuum’, reflecting the varying importance attributed to ecological interactions in structuring communities (Cornell & Lawton, 1992). At one end of the spectrum, communities are primarily viewed as structured by local interactions among species, such as trophic interactions, facilitation and competition (Cottenie & De Meester, 2004; Gianuca et al., 2013; Godoy et al., 2024). At the opposite end, emphasis is placed on processes like dispersal limitation and environmental filtering, which affect community composition by determining which species from the regional pool can reach, establish themselves and persist in a particular habitat patch (Cottenie & De Meester, 2004; Gianuca et al., 2013). Taken together, these approaches suggest that ecological communities result from a series of concurrent, or sometimes complementary, processes that determine the co-occurrence of species (Cottenie & De Meester, 2004; Gianuca et al., 2013; Godoy et al., 2024; Pavoine & Bonsall, 2011). To date, most approaches to disentangle different mechanisms of community assembly have relied on whole-community data, taking a temporal ‘snapshot’ of the community composition and using the degree of trait and phylogenetic dispersion among species to infer underlying processes (e.g. Dias et al., 2020; Pausas & Verdú, 2010; Webb et al., 2002)—often without considering species interactions explicitly. Mechanisms such as limiting similarity and habitat filtering are tested by examining patterns of trait and phylogenetic similarity within communities relative to a regional species pool (Dias et al., 2020; Mittelbach & McGill, 2019; Pausas & Verdú, 2010). The basic assumption is that habitat filtering selects species with similar trait values suited to local environmental conditions, leading to trait clustering and, depending on whether the trait is phylogenetically conserved or convergent, either phylogenetic clustering (if traits are conserved) or variable phylogenetic structure (if traits are convergent). In contrast, limiting similarity is driven by competition and excludes ecologically similar species, resulting in trait overdispersion (and often phylogenetic overdispersion) when traits are conserved (Dias et al., 2020; Pausas & Verdú, 2010). However, as is often the case in ecology, multiple assembly processes can lead to similar patterns of trait dispersion, or the same process can generate different patterns depending on factors such as the evolutionary history of traits, environmental context or spatial scale (Herben & Goldberg, 2014). Consequently, it is challenging to draw definitive conclusions without integrating additional data and analytical approaches, or relying on fully mechanistic approaches (e.g. Gallien et al., 2014). In this issue of Functional Ecology, Beyns et al. (2025) propose a way forward to disentangle the effects of environmental filtering, hierarchical competition and limiting similarity by identifying the influence of concurrent processes of ecological community assembly. They accomplish this by integrating spatial point pattern analysis—a class of methods aimed at extracting information about the underlying process from individual spatial locations at multiple scales (Velázquez et al., 2015, 2016; Wiegand et al., 2007)—with species' functional traits (see also Velázquez et al., 2015; Yin et al., 2021), environmental preferences and colonization effects. They characterised all pairwise species spatial associations at the ‘Le Kauwberg’—a Belgian semi-natural area, comprising grasslands, fallows and forests—using bivariate spatial statistics to estimate the spatial associations among species. They achieved this by comparing observed spatial association statistics with trait and environmental dissimilarities while accounting for plant age, to focus on pre-colonization stages and their effects. To evaluate whether spatial patterns were non-random, they used homogeneous and inhomogeneous Poisson null models, which account for environmental heterogeneity by randomizing individual locations based on an estimated intensity function. This approach allowed the authors to identify significant species association/dissociation by comparing observed patterns against these randomized expectations at various spatial scales (Wiegand et al., 2007) and to use a classification scheme that categorizes species pairs exhibiting significant deviations from the null model into four quadrants in a bivariate space, each representing a distinct association pattern (Velázquez et al., 2015) and underlying mechanism. Additionally, the authors used mixed models of maximum likelihood population effects (Van Strien et al., 2012) to evaluate how differences in cluster density between species pairs influenced their spatial associations, controlling for biases from limited dispersal and clustering. As expected, Beyns et al. (2025) found that species association patterns are shaped by a combination of ecological processes—that is environmental filtering, hierarchical competition and limiting similarity. However, given the high proportion of non-significant species associations, they also argue that stochastic processes likely play a dominant role. Nonetheless, despite this strong stochastic component, Beyns et al. (2025) suggest that environmental filtering and its interaction with hierarchical competition provide the most compelling explanation for species association. They also found that colonization effects can mask both environmental filtering and hierarchical competition while reinforcing the apparent effect of limiting similarity. Consequently, the influence of limiting similarity might be minimal. Furthermore, Beyns et al. (2025) emphasize that when environmental variability is taken into consideration, positive spatial associations become more apparent, suggesting that environmental variation can obscure biotic interactions that promote positive co-occurrence. The novelty of the study by Beyns et al. (2025) lies in its integrative approach, which combines widely applied spatial point pattern analyses at multiple spatial scales with species traits, environmental preferences and the effects of colonization. This framework offers a comprehensive understanding of community assembly processes across temporal and spatial scales. Such an approach is invaluable for advancing our understanding of the long-standing ecological question of what drives and maintains species diversity in ecological communities, improving predictions of community assembly dynamics under environmental change, and informing conservation strategies by identifying key drivers of species coexistence and spatial structuring. I thank André L. Luza, Laurane Winandy and Rafael A. Dias for kindly reviewing the first draft of this manuscript. Vinicius Bastazini is an Associate Editor of Functional Ecology, but took no part in the peer review and decision-making processes for this paper. 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