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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 Forest fragmentation is among the principal causes of global biodiversity loss, yet how it affects mutualistic interactions between plants and animals at large spatial scale is poorly understood. In particular, tropical forest regeneration depends on animal‐mediated seed dispersal, but the seed‐dispersing animals face rapid decline due to forest fragmentation and defaunation. Here, we assess how fragmentation influences the pairwise interactions between 407 seed disperser and 1,424 tree species in a highly fragmented biodiversity hotspot. Location Atlantic Forest, South America. Methods We predicted interaction networks in 912 sites covering the entire biome by combining verified interaction data with co‐occurrence probabilities obtained from a spatially explicit joint species distribution model. We identified keystone seed dispersers by computing a species‐specific keystone index and by selecting those species belonging to the top 5% quantile. Results We show that forest fragmentation affects seed dispersal interactions negatively, and the decreased area of functionally connected forest, rather than increased edge effects, is the main driver behind the loss of interactions. Both the seed disperser availability for the local tree communities and in particular the proportion of interactions provided by keystone seed dispersers decline with increasing degree of fragmentation. Importantly, just 21 keystone species provided >40% of all interactions. The numbers of interactions provided by keystone and non‐keystone species, however, were equally negatively affected by fragmentation, suggesting that seed dispersal interactions may not be rewired under strong fragmentation effects. Conclusions We highlight the importance of understanding the fragmentation‐induced compositional shifts in seed disperser communities as they may lead to lagged and multiplicative effects on tree communities. Our results illustrate the utility of model‐based prediction of interaction networks as well as model‐based identification of keystone species as a tool for prioritizing conservation efforts. Similar modelling approaches could be applied to other threatened ecosystems and interaction types globally.

Background Zoology’s dark matter comprises hyperdiverse, poorly known taxa that are numerically dominant but largely unstudied, even in temperate regions where charismatic taxa are well understood. Dark taxa are everywhere, but high diversity, abundance, and small size have historically stymied their study. We demonstrate how entomological dark matter can be elucidated using high-throughput DNA barcoding (“megabarcoding”). We reveal the high abundance and diversity of scuttle flies (Diptera: Phoridae) in Sweden using 31,800 specimens from 37 sites across four seasonal periods. We investigate the number of scuttle fly species in Sweden and the environmental factors driving community changes across time and space. Results Swedish scuttle fly diversity is much higher than previously known, with 549 putative species detected, compared to 374 previously recorded species. Hierarchical Modelling of Species Communities reveals that scuttle fly communities are highly structured by latitude and strongly driven by climatic factors. Large dissimilarities between sites and seasons are driven by turnover rather than nestedness. Climate change is predicted to significantly affect the 47% of species that show significant responses to mean annual temperature. Results were robust regardless of whether haplotype diversity or species-proxies were used as response variables. Additionally, species-level models of common taxa adequately predict overall species richness. Conclusions Understanding the bulk of the diversity around us is imperative during an era of biodiversity change. We show that dark insect taxa can be efficiently characterised and surveyed with megabarcoding. Undersampling of rare taxa and choice of operational taxonomic units do not alter the main ecological inferences, making it an opportune time to tackle zoology’s dark matter.

Aim To characterize the functional diversity and selected ecological functions of marine epibenthic invertebrate communities at the ecosystem scale and to evaluate the relative contributions of environmental filtering, including bottom‐contact fishing, and competitive interactions to benthic community assembly. Location Flemish Cap, an ecosystem production unit and fishing bank in the high seas of the north‐west Atlantic Ocean. Methods Through the use of Hierarchical Modelling of Species Communities (HMSC), we have explored seven community response traits to the environment applied to 105 epibenthic species and evaluated the influence of such traits on the community assembly processes. Assumed bioturbation, nutrient cycling and habitat provision functions, linked to individual or a combination of biological traits, were mapped using random forest modelling. Results Functional richness within benthic communities reached an asymptote for trawl sets with roughly more than 30 species. Assemblages on top of the Flemish Cap (<500 m depth) were characterized by higher biomass of small‐ and medium‐sized species with short life spans, whereas large species with longer life spans and broadcast spawners were dominant in the deeper assemblages (500–1,500 m depth). The amount of variation explained by the species’ responses to the covariates mediated by the traits was relatively high (25%) indicating their relevance to community assembly. Community‐weighted mean trait values changed with depth and physical oceanographic variables, indicating that environmental filtering was occurring. Interspecific interactions, as inferred from the random effect at the sample level, accounted for 16.3% of the variance in the model, while fishing effort explained only 5.2% of the variance but conferred strong negative impacts for most species. Main conclusions Our results suggest that while bottom‐contact fishing impacts have an effect on functional diversity, changes to the physical oceanography of the system are likely to have more profound impacts. The maps of benthic functioning can aid assessments of ecosystem impacts of fishing.

Ecological communities are jointly structured by dispersal, density-independent responses to environmental conditions, and density-dependent biotic interactions. Metacommunity ecology provides a framework for understanding how these processes combine to determine community seagrass meadows along the British Columbia coast. We tested the hypothesis that eelgrass Zostera marina L. epifaunal invertebrate assemblages are influenced by local environmental conditions but that high dispersal rates at larger spatial scales dampen the effects of environmental differences. We used hierarchical joint species distribution modelling to understand the contribution of environmental conditions, spatial distance between meadows, and species co-occurrences to epifaunal invertebrate abundance and distribution across the region. We found that patterns of taxonomic compositional similarity among meadows were inconsistent with dispersal limitation, and meadows in the same region were often no more similar to each other than meadows over 1000 km away. Abiotic environmental conditions (temperature, dissolved oxygen) explained a small fraction of variation in taxonomic abundance patterns across the region. We found novel co-occurrence patterns among taxa that could not be explained by shared responses to environmental gradients, suggesting the possibility that interspecific interactions influence seagrass invertebrate abundance and distribution. Our results suggest that biodiversity and ecosystem functions provided by seagrass meadows reflect ecological processes occurring both within meadows and across seascapes and that management of eelgrass habitat for biodiversity may be most effective when both local and regional processes are considered.

Background Symbiotic microorganisms can profoundly impact insect biology, including their life history traits, population dynamics, and evolutionary trajectories. However, microbiota remain poorly understood in natural insect communities, especially in ‘dark taxa’—hyperdiverse yet understudied clades. Results Here, we implemented a novel multi-target amplicon sequencing approach to study microbiota in complex, species-rich communities. It combines four methodological innovations: (1) To establish a host taxonomic framework, we sequenced amplicons of the host marker gene (COI) and reconstructed barcodes alongside microbiota characterisation using 16S-V4 rRNA bacterial gene amplicons. (2) To assess microbiota abundance, we incorporated spike-in-based quantification. (3) To improve the phylogenetic resolution for the dominant endosymbiont, Wolbachia , we analysed bycatch data from the COI amplicon sequencing. (4) To investigate the primary drivers of host-microbe associations in massive multi-dimensional datasets, we performed Hierarchical Modelling of Species Communities (HMSC). Applying this approach to 1842 wild-caught scuttle flies (Diptera: Phoridae) from northern Sweden, we organised them into 480 genotypes and 186 species and gained unprecedented insights into their microbiota. We found orders-of-magnitude differences in bacterial abundance and massive within-population variation in microbiota composition. Patterns and drivers differed among microbial functional categories: the distribution and abundance of facultative endosymbionts ( Wolbachia , Rickettsia , Spiroplasma ) were shaped by host species, genotype, and sex. In contrast, many other bacterial taxa were broadly distributed across species and sites. Conclusions This study highlights facultative endosymbionts as key players in insect microbiota and reveals striking variations in distributional patterns of microbial clades. It also demonstrates the power of integrative sequencing approaches in uncovering the ecological complexity and significance of symbiotic microorganisms in multi-species natural communities. 1NruC7virMW6yK4puBds42 Video Abstract

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.

Changing environmental conditions are influencing the seasonal timing in life history events of organisms. Such shifts in phenology are often linked to increasing temperatures that stimulate faster developments or earlier arrivals. This phenomenon has been demonstrated in terrestrial and aquatic realms, but data and knowledge are limited on how early life stages of fish are affected over long-term and broad environmental scales. Here, we analyze 2 decades (1974–1996) of size class-specific Baltic herring Clupea harengus membras L. larval data along the whole coast of Finland to expose shifts in phenology linked to changes in environmental covariates. We use a novel Bayesian hierarchical spatio-temporal hurdle model that describes larval occurrence and abundance with separate processes. Abundances are modeled with the Ricker population growth model that enables us to predict size-specific larvae groups in relation to the environment while accounting for population density dependence. We quantify shifts in phenology at multiple life stages, based on first appearances of smallest larvae (<10 mm) and by detection of higher proportions of larger larvae (>15 mm) appearing earlier than they have done historically. Our results show a strong signal in shifting phenology of the larvae toward an earlier development of 7.7 d per decade. Increasing temperature had a positive effect on the earlier development of larger larvae. Additionally, we highlight that the survival of larvae becomes more density dependent as their size increases. Our modeling framework can reveal phenological shifts of early life stages in relation to environmental change for survey data that do not necessarily cover the onset of reproduction.

Community ecologists and conservation biologists often work with data that are too sparse for achieving reliable inference with species-specific approaches. Here we explore the idea of combining species-specific models into a single hierarchical model. The community component of the model seeks for shared patterns in how the species respond to environmental covariates. We illustrate the modeling framework in the context of logistic regression and presence-–absence data, but a similar hierarchical structure could also be used in many other types of applications. We first use simulated data to illustrate that the community component can improve parameterization of species-specific models especially for rare species, for which the data would be too sparse to be informative alone. We then apply the community model to real data on 500 diatom species to show that it has much greater predictive power than a collection of independent species-specific models. We use the modeling approach to show that roughly one-third of distance decay in community similarity can be explained by two variables characterizing water quality, rare species typically preferring nutrient-poor waters with high pH, and common species showing a more general pattern of resource use.

1. Species richness is often used as a tool for prioritizing conservation action. One method for predicting richness and other summaries of community structure is to develop species-specific models of occurrence probability based on habitat or landscape characteristics. However, this approach can be challenging for rare or elusive species for which survey data are often sparse. 2. Recent developments have allowed for improved inference about community structure based on species-specific models of occurrence probability, integrated within a hierarchical modelling framework. This framework offers advantages to inference about species richness over typical approaches by accounting for both species-level effects and the aggregated effects of landscape composition on a community as a whole, thus leading to increased precision in estimates of species richness by improving occupancy estimates for all species, including those that were observed infrequently. 3. We developed a hierarchical model to assess the community response of breeding birds in the Hudson River Valley, New York, to habitat fragmentation and analysed the model using a Bayesian approach. 4. The model was designed to estimate species-specific occurrence and the effects of fragment area and edge (as measured through the perimeter and the perimeter/area ratio, P/A), while accounting for imperfect detection of species. 5. We used the fitted model to make predictions of species richness within forest fragments of variable morphology. The model revealed that species richness of the observed bird community was maximized in small forest fragments with a high P/A. However, the number of forest interior species, a subset of the community with high conservation value, was maximized in large fragments with low P/A. 6. Synthesis and applications. Our results demonstrate the importance of understanding the responses of both individual, and groups of species, to environmental heterogeneity while illustrating the utility of hierarchical models for inference about species richness for conservation. This framework can be used to investigate the impacts of land-use change and fragmentation on species or assemblage richness, and to further understand trade-offs in species-specific occupancy probabilities associated with landscape variability.