Nerea Abrego

Using latent variable models to identify large networks of species-to-species associations at different spatial scales

Summary nWe present a hierarchical latent variable model that partitions variation in species occurrences and co-occurrences simultaneously at multiple spatial scales. We illustrate how the parameterized model can be used to predict the occurrences of a species by using as predictors not only the environmental covariates, but also the occurrences of all other species, at all spatial scales. nWe leverage recent progress in Bayesian latent variable models to implement a computationally effective algorithm that enables one to consider large communities and extensive sampling schemes. nWe exemplify the framework with a community of 98 fungal species sampled in c. 22xa0500 dead wood units in 230 plots in 29 beech forests. nThe networks identified by correlations and partial correlations were consistent, as were networks for natural and managed forests, but networks at different spatial scales were dissimilar. nAccounting for the occurrences of the other species roughly doubled the predictive powers of the models compared to accounting for environmental covariates only n n n.

Community Turnover of Wood-Inhabiting Fungi across Hierarchical Spatial Scales

For efficient use of conservation resources it is important to determine how species diversity changes across spatial scales. In many poorly known species groups little is known about at which spatial scales the conservation efforts should be focused. Here we examined how the community turnover of wood-inhabiting fungi is realised at three hierarchical levels, and how much of community variation is explained by variation in resource composition and spatial proximity. The hierarchical study design consisted of management type (fixed factor), forest site (random factor, nested within management type) and study plots (randomly placed plots within each study site). To examine how species richness varied across the three hierarchical scales, randomized species accumulation curves and additive partitioning of species richness were applied. To analyse variation in wood-inhabiting species and dead wood composition at each scale, linear and Permanova modelling approaches were used. Wood-inhabiting fungal communities were dominated by rare and infrequent species. The similarity of fungal communities was higher within sites and within management categories than among sites or between the two management categories, and it decreased with increasing distance among the sampling plots and with decreasing similarity of dead wood resources. However, only a small part of community variation could be explained by these factors. The species present in managed forests were in a large extent a subset of those species present in natural forests. Our results suggest that in particular the protection of rare species requires a large total area. As managed forests have only little additional value complementing the diversity of natural forests, the conservation of natural forests is the key to ecologically effective conservation. As the dissimilarity of fungal communities increases with distance, the conserved natural forest sites should be broadly distributed in space, yet the individual conserved areas should be large enough to ensure local persistence.

Using joint species distribution models for evaluating how species‐to‐species associations depend on the environmental context

Summary nJoint species distribution models (JSDM) are increasingly used to analyse community ecology data. Recent progress with JSDMs has provided ecologists with new tools for estimating species associations (residual co-occurrence patterns after accounting for environmental niches) from large data sets, as well as for increasing the predictive power of species distribution models (SDMs) by accounting for such associations. Yet, one critical limitation of JSDMs developed thus far is that they assume constant species associations. However, in real ecological communities, the direction and strength of interspecific interactions are likely to be different under different environmental conditions. nIn this paper, we overcome the shortcoming of present JSDMs by allowing species associations covary with measured environmental covariates. To estimate environmental-dependent species associations, we utilize a latent variable structure, where the factor loadings are modelled as a linear regression to environmental covariates. nWe illustrate the performance of the statistical framework with both simulated and real data. Our results show that JSDMs perform substantially better in inferring environmental-dependent species associations than single SDMs, especially with sparse data. Furthermore, JSDMs consistently overperform SDMs in terms of predictive power for generating predictions that account for environment-dependent biotic associations. nWe implemented the statistical framework as a MATLAB package, which includes tools both for model parameterization as well as for post-processing of results, particularly for addressing whether and how species associations depend on the environmental conditions. nOur statistical framework provides a new tool for ecologists who wish to investigate from non-manipulative observational community data the dependency of interspecific interactions on environmental context. Our method can be applied to answer the fundamental questions in community ecology about how species’ interactions shift in changing environmental conditions, as well as to predict future changes of species’ interactions in response to global change.

Measuring and predicting the influence of traits on the assembly processes of wood‐inhabiting fungi

Summary nThe identification of traits that influence the responses of the species to environmental variation provides a mechanistic perspective on the assembly processes of ecological communities. While much research linking functional ecology with assembly processes has been conducted with animals and plants, the development of predictive or even conceptual frameworks for fungal functional community ecology remains poorly explored. Particularly, little is known about the contribution of traits to the occurrences of fungal species under different environmental conditions. nWood-inhabiting fungi are known to strongly respond to habitat disturbance, and thus provide an interesting case study for investigating to what extent variation in occurrence patterns of fungi can be related to traits. We apply a trait-based joint species distribution model to a data set consisting of fruit-body occurrence data on 321 wood-inhabiting fungal species collected in 22xa0460 dead wood units from managed and natural forest sites. nOur results show that environmental filtering plays a big role on shaping wood-inhabiting fungal communities, as different environments held different communities in terms of species and trait compositions. Most importantly, forest management selected against species with large and long-lived fruit-bodies as well as late decayers, and promoted the occurrences of species with small fruit-bodies and early decayers. A strong phylogenetic signal in the data suggested the existence of also some other functionally important traits than the ones we considered. nWe found that those species groups that were more prevalent in natural conditions had more associations to other species than species groups that were tolerant to or benefitted from forest management. Therefore, the changes that forest management causes on wood-inhabiting fungal communities influence ecosystem functioning through simplification of interactive associations among the fungal species. nSynthesis. Our results show that functional traits are linked to the responses of wood-inhabiting fungi to variation in their environment, and thus environmental changes alter ecosystem functions via promoting or reducing species with different fruit-body types. However, further research is needed to identify other functional traits and to provide conclusive evidence for the adaptive nature of the links from traits to occurrence patterns found here.