Fine‐scale hydrological niche differentiation through the lens of multi‐species co‐occurrence models

Abstract

Summary1. Theory suggests spatial heterogeneity can facilitate species co-occurrence at fine scales, but environmental data are rarely collected at sufficiently high resolution to test this empirically. Whilst there is emerging evidence that subtle variation in soil hydrology represents a fundamental fine-scale niche axis within plant communities, this is largely derived from studies of soil hydrology in isolation from other environmental factors. 2. We assessed the comparative importance of fine-scale hydrological niche differentiation for species co-occurrence using a high-resolution study of soil hydrology and other edaphic variables, coupled with a long-term (24 years) data set of herbaceous plant plots in a heathland community in south-east Australia.3. For the analysis, we employed novel latent variable models (LVMs), which offer an explicit, model-based approach to partitioning out the different drivers of species co-occurrence patterns. Whilst the regression component of an LVM models the species-specific environmental responses, the latent variable component can be used to identify residual patterns of co-occurrence, which may be attributable to unmeasured factors and/or biotic interactions. 4. Relative to a host of plant resources, non-resource factors and 'unmeasured' latent variables, soil hydrology emerged as the best predictor of negative co-occurrences within the community, with the dominant species exhibiting strongly differentiated responses across a comparatively narrow moisture gradient. Nevertheless, strong species-specific responses to environmental variability only emerged at scales greater than those at which plants may be expected to compete for resources, throwing doubt on the direct role of spatial heterogeneity as a mechanism for local-scale coexistence. 5. Synthesis. This study confirms the vital role of hydrological niches for the maintenance of within-community plant diversity, but also highlights the need for more rigorous analysis of scale dependencies to better understand the underlying coexistence mechanisms at play. In addition, it illustrates the inferential gains made possible with model-based approaches to the analysis of species co-occurrence. R code illustrating model fitting and inference is provided as a supplement.

Joint species distribution models with species correlations and imperfect detection

“…Interestingly, for the LV model we found that the number of LVs needed to estimate the species correlation matrix adequately was substantially larger than previously suggested (Letten et al. , Warton et al. ), regardless of whether the model did or did not contain a detection component.…”

Networks Depicting the Fine-Scale Co-Occurrences of Fungi in Soil Horizons

“…One possible way for evaluating potential effects of niche sharing is to use new lines of statistical methods, which divide effects of direct interspecific interactions from those of shared environmental preferences [76–78]. Although these methods have been used to analyze data collected in observational community ecological studies, our preliminary analysis on human gut microbiome datasets [79] has shown that the new statistical approach is applicable to high-throughput sequencing-based datasets if data of physical/chemical environmental conditions are available (Toju et al, in review).…”

Sampling grain influences trends in vegetation composition and diversity with time since fire in Australian heathland

“…Despite the recognized importance of scale, studies determining its effects on plant community heterogeneity are uncommon (Steinberg & Kareiva 1997). Although the focus of our study was the effect of time since fire on vegetation variables, it should also be noted that environmental heterogeneity can promote niche partitioning and provide opportunities for local-scale species coexistence; however, this is conditional upon the scale at which the community is sampled (Letten et al 2015). This pattern is predominantly due to the fact that larger samples are more likely to include a greater subset of species from a given community, thus, increasing the level of similarity among quadrats, compared with smaller quadrats (Collins 1992).…”