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Non‐native species can dominate plant communities by competitively displacing native species, or because environmental change creates conditions favourable to non‐native species but unfavourable to native species. We need to disentangle these mechanisms so that management can target competitively dominant species and reduce their impacts. Joint‐species distribution models (JSDMs) can potentially quantify competitive impacts by simultaneously modelling how species respond to environmental variation and to changes in community composition. We describe a JSDM to model variation in plant cover and show how this can be applied to compositional data to detect dominant competitors that cause other species to decline in abundance. We applied the model to an experiment in an invaded grassy‐woodland community in Australia where we manipulated biomass removal (through slashing and fencing to prevent grazing by kangaroos) along a fertility gradient. Non‐native species dominated plant cover at high fertility sites in the absence of biomass removal. Results from the JSDM identified three of the 72 non‐native plant species (Bromus diandrus, Acetosella vulgaris and especially Avena fatua) as having a strong competitive impact on the community, driving changes in composition and reducing the cover of both native and non‐native species, particularly in the absence of grazing. The dominant non‐native grasses Bromus diandrus and Avena fatua were among the tallest species in the community and had the greatest impact on shorter‐statured species, most likely through competition for light under conditions of high fertility and low grazing. Synthesis. We demonstrate a method to measure competitive impact using a joint‐species distribution model, which allowed us to identify the species driving compositional change through competitive displacement, and where on the landscape competitive impacts were greatest. This information is central to managing plant invasions: by targeting dominant non‐native species with large competitive impacts, management can reduce impacts where they are greatest. We provide details of the modelling procedure and reproducible code to encourage further application. We demonstrate a method to measure competitive impact using a joint‐species distribution model, which allowed us to identify the species driving compositional change through competitive displacement, and where on the landscape competitive impacts were greatest. This information is central to managing plant invasions: by targeting dominant non‐native species with large competitive impacts, management can reduce impacts where they are greatest. We provide details of the modelling procedure and reproducible code to encourage further application.

© 2019 The Authors. Ecological Monographs published by Wiley Periodicals, Inc. on behalf of Ecological Society of America This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The difficulty of experimentally quantifying non-trophic species interactions has long troubled ecologists. Increasingly, a new application of the classic “checkerboard distribution” approach is used to infer interactions by examining the pairwise frequency at which species are found to spatially co-occur. However, the link between spatial associations, as estimated from observational co-occurrence, and species interactions has not been tested. Here we used nine common statistical methods to estimate associations from surveys of rocky intertidal communities in the Northeast Pacific Ocean. We compared those inferred associations with a new data set of experimentally determined net and direct species interactions. Although association methods generated networks with aggregate structure similar to previously published interaction networks, each method detected a different set of species associations from the same data set. Moreover, although association methods generally performed better than a random model, associations rarely matched empirical net or direct species interactions, with high rates of false positives and true positives, and many false negatives. Our findings cast doubt on studies that equate species co-occurrences to species interactions and highlight a persistent, unanswered question: how do we interpret spatial patterns in communities? We suggest future research directions to unify the observational and experimental study of species interactions, and discuss the need for community standards and best practices in association analysis.

The relative importance of the different processes that determine the distribution of species and the assembly of communities is a key question in ecology. The distribution of any individual species is affected by a wide range of environmental variables as well as through interactions with other species; the resulting distributions determine the pool of species available to form local communities at fine spatial scales. A challenge in community ecology is that these interactions (e.g. competition, facilitation, etc.) often are not directly measurable. Here, we used hierarchical modelling of species communities (HMSC), a recently developed framework for joint species distribution modelling, to estimate the role of biotic effects alongside environmental factors using latent variables. We investigate the role of these factors determining species distributions in communities of Artiodactyla, Perissodactyla and Proboscidea in the Afrotropics, an area of peak species richness for hoofed mammals. We also calculate pairwise trait dissimilarity between these species, from a mixture of morphological and behavioural traits, and investigate the relationship between dissimilarity and estimated residual co‐occurrence in the model. We find that while ungulate distributions appear to be predominantly determined (~ 70%) by climatic variables, such as precipitation, a substantial proportion of the variance in ungulate species distributions (~ 30%) can also be attributed to modelled latent variables that likely represent a combination of dispersal barriers and biotic factors. Although we find only a weak relationship between residual co‐occurrence and trait dissimilarity, we suggest that our results may show evidence that biotic factors, likely influenced by historical barriers to species dispersal, are important in determining species communities over a continental area. The HMSC framework can be used to provide insight into factors affecting community assembly at broad scales, and to make more powerful predictions about future species distributions as we enter an era of increasing impacts from anthropogenic change.

Aim To analyse the occurrence and abundance of native versus alien fish species in relation to climate, land use, hydrologic alteration and habitat fragmentation in a heavily invaded and human‐impacted riverine ecosystem. To test whether co‐occurrence patterns of native versus alien species are structured by environmental filtering or biotic associations. Location Mediterranean, Iberian Peninsula, Ebro River catchment. Methods We modelled freshwater fish distributions and their association with environmental conditions using a hurdle model‐like approach involving boosted regression trees. Additionally, we applied a joint species distribution model to quantify the co‐occurrence of native versus alien fish species that can be attributed to shared environmental responses or potentially to biotic interactions. Results Our results point to environmental factors, rather than biotic associations, as major correlates of the increase of alien and the decline of native fishes in the Ebro River. We observed contrasting patterns of native versus alien species along the upstream‐downstream gradient. Alien species dominated in the lower reaches associated with warmer temperatures, higher shares of intensive land use and appeared facilitated by dams and river regulation. Native species richness was highest in the larger tributaries followed by a strong decline in the main stem which was related to the river network position and land use type. Fragmentation played a subordinate role in explaining fish richness and abundance patterns. Main conclusions Given the strong association with temperature, a further range expansion of alien fishes in the Ebro with future climate change may be expected. More local‐scale factors related to habitat degradation and hydrologic alteration will further exacerbate the invasion success of many alien fishes. Further multiple, independent species introductions might mask isolation and fragmentation effects of dams on the future spread and distribution of alien fish.

Our knowledge of the factors affecting species abundances is mainly based on time-series analyses of a few well-studied species at single or few localities, but we know little about whether results from such analyses can be extrapolated to the community level. We apply a joint species distribution model to long-term time-series data on British bird communities to examine the relative contribution of intra- and interspecific density dependence at different spatial scales, as well as the influence of environmental stochasticity, to spatiotemporal interspecific variation in abundance. Intraspecific density dependence has the major structuring effect on these bird communities. In addition, environmental fluctuations affect spatiotemporal differences in abundance. In contrast, species interactions had a minor impact on variation in abundance. Thus, important drivers of single-species dynamics are also strongly affecting dynamics of communities in time and space.

Aim We analysed the role of species interactions in wildlife community responses to urbanization. Specifically, we investigated non‐trophic associations within a bird community and the role of trophic interactions in the responses of bird species to the urbanization gradient. Location City‐state of Berlin, Central Europe. Methods Arthropod and bird abundances were sampled across the study area and analysed using hierarchical joint species distribution models (JSDMs). Urbanization gradient was defined by environmental predictors reflecting anthropogenic disturbances, for example noise level and human population density, as well as nature‐like features, for example tree cover and open green area. Relevant environmental predictors for each group and relevant spatial resolution were selected a priori using AICc. Arthropod abundances were modelled for the bird sampling transects and included as additional predictor variable in the bird community model. In this model, we used abundances and traits of 66 breeding bird species as response variables. Results Bird species responses to urbanization were captured by the interaction between invertebrate abundance and environmental predictors. We identified three groups of birds: the urban group (12 species) showed no decrease in abundance along the urbanization gradient and were not related to arthropods abundance; the woodland group (18 species) were positively related to tree cover and arthropod abundance, also in areas with high anthropogenic disturbance; and the nature group (36 species) were positively related to arthropod abundance, but the species abundance decreased sharply with increasing anthropogenic disturbance. All the non‐trophic associations found within the bird community were positive. Main conclusions Arthropod abundance clearly modulated birds’ responses to the urbanization gradient for most species. Especially at moderate levels of anthropogenic disturbance, the abundance of arthropods is key for the occurrence and abundance of bird species in urban areas. To maintain bird diversity in urban green areas, management measures should focus on maintaining and increasing invertebrate abundance.

The ongoing global change and the increased interest in macroecological processes call for the analysis of spatially extensive data on species communities to understand and forecast distributional changes of biodiversity. Recently developed joint species distribution models can deal with numerous species efficiently, while explicitly accounting for spatial structure in the data. However, their applicability is generally limited to relatively small spatial data sets because of their severe computational scaling as the number of spatial locations increases. In this work, we propose a practical alleviation of this scalability constraint for joint species modeling by exploiting two spatial-statistics techniques that facilitate the analysis of large spatial data sets: Gaussian predictive process and nearest-neighbor Gaussian process. We devised an efficient Gibbs posterior sampling algorithm for Bayesian model fitting that allows us to analyze community data sets consisting of hundreds of species sampled from up to hundreds of thousands of spatial units. The performance of these methods is demonstrated using an extensive plant data set of 30,955 spatial units as a case study. We provide an implementation of the presented methods as an extension to the hierarchical modeling of species communities framework.

The tree seed mycobiome has received little attention despite its potential role in forest regeneration and health. The aim of the present study was to analyze the processes shaping the composition of seed fungal communities in natural forests as seeds transition from the mother plant to the ground for establishment.We used metabarcoding approaches and confocal microscopy to analyze the fungal communities of seeds collected in the canopy and on the ground in four natural populations of sessile oak (Quercus petraea). Ecological processes shaping the seed mycobiome were inferred using joint species distribution models.Fungi were present in seed internal tissues, including the embryo. The seed mycobiome differed among oak populations and trees within the same population. Its composition was largely influenced by the mother, with weak significant environmental influences. The models also revealed several probable interactions among fungal pathogens and mycoparasites.Our results demonstrate that maternal effects, environmental filtering and biotic interactions all shape the seed mycobiome of sessile oak. They provide a starting point for future research aimed at understanding how maternal genes and environments interact to control the vertical transmission of fungal species that could then influence seed dispersal and germination, and seedling recruitment.

Anticipating the effects of global change on biodiversity has become a global challenge requiring new methods. Approaches like species distribution models have limitations which have fueled the development of joint species distribution models (JSDMs). However, JSDMs rely on systematic surveys community data, and no assessment has been made of their suitability with unstructured opportunistic databases data. We used hierarchical modeling of species communities (HMSC) to test JSDMs performance when using opportunistic databases. Using artificial data that mimic the limitations of such databases by subsampling complete co‐occurrence matrices (i.e. original data), we analysed how the completeness of opportunistic databases affects JSDMs regarding 1) the role of independent variables on species occurrence, 2) residual species co‐occurrence (as a proxy of biotic interactions) and 3) species distributions. Moreover, we illustrate how to evaluate completeness at the pixel level of real data with a study case of forest tree species in Europe, and evaluate the role of data completeness in model estimation. Our results with artificial data demonstrate that decreasing the completion percentage (the rate of original data presences represented in the subsampled matrices) increases false negatives and negative co‐occurrence probabilities, resulting in a loss of ecological information. However, HMSC tolerates different levels of degradation depending on the model aspect being considered. Models with 50% of missing data are valid for estimating species niches and distribution, but interaction matrices require databases with at least 75% of completion data. Furthermore, HMSC's predictions often resemble the original community data (without false negatives) even more than the subsampled data (with false negatives) in the training dataset. These findings were confirmed with the real study case. We conclude that opportunistic databases are a valuable resource for JSDMs, but require an analysis of data completeness for the target taxa in the study area at the spatial resolution of interest.