Abstract
Summary Species are seldom distributed at random across a community, but instead show spatial structure that is determined by environmental gradients and/or biotic interactions. Analysis of the spatial co‐associations of species may therefore reveal information on the processes that helped to shape those patterns. We propose a multivariate approach that uses the spatial co‐associations between all pairs of species to find subcommunities of species whose distribution in the study area is positively correlated. Our method, which begins with the patterns of individuals, is particularly well‐suited for communities with large numbers of species and gives rare species an equal weight. We propose a method to quantify a maximum number of subcommunities that are significantly more correlated than expected under a null model of species independence. Using data on the distribution of tree and shrub species from a 50 ha forest plot on Barro Colorado Island (BCI), Panama, we show that our method can be used to construct biologically meaningful subcommunities that are linked to the spatial structure of the plant community. As an example, we construct spatial maps from the subcommunities that closely follow habitats based on environmental gradients (such as slope) as well as different biotic conditions (such as canopy gaps). We discuss extensions and adaptations to our method that might be appropriate for other types of spatially referenced data and for other ecological communities. We make suggestions for other ways to interpret the subcommunities using phylogenetic relationships, biological traits and environmental variables as covariates and note that subcommunities that are hard to interpret may suggest groups of species and/or regions of the landscape that merit further attention.
Highlights
Understanding the processes that underpin observed patterns of biodiversity and how functionally similar species coexist in close spatial proximity are among the primary challenges in ecology (Hardin 1960; Wright 2002)
Using data on the distribution of tree and shrub species from a 50 ha forest plot on Barro Colorado Island (BCI), Panama, we show that our method can be used to construct biologically meaningful subcommunities that are linked to the spatial structure of the plant community
Punchi-Manage et al (2013) use the Bray–Curtis dissimilarity measure combined with a multivariate regression tree (MRT) analysis to show five distinct habitat types emerge across all life-history stages of a mixed dipterocarp forest in Sri Lanka
Summary
Understanding the processes that underpin observed patterns of biodiversity and how functionally similar species coexist in close spatial proximity are among the primary challenges in ecology (Hardin 1960; Wright 2002). In a separate study, Baldeck et al (2013) used the Bray– Curtis dissimilarity analysis of species composition for quadrats at the 20 m scale, but instead used principal coordinates of neighbour matrices (PCNM) to model spatial structure in the variation of community composition among quadrats (see Borcard & Legendre 2002; Legendre et al 2009) This variation was partitioned into portions explained by soil, topographic and spatial variables. Similar to PunchiManage et al (2013), the results for eight separate mixed forests showed the soil and topographic covariates could explain 19–39% of the variation, but that spatial processes such as dispersal limitation and other unmeasured environmental variables could explain a further 19–37% of the variation Both studies highlight the importance of small-scale environmental variation in structuring species-rich plant communities, and that biological processes are likely to play an important role
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