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In recent years, new technologies have emerged that can generate rapid and standardized biodiversity inventories without explicit human guidance (novel community data). The benefits as well as technical challenges of these technologies have been extensively reviewed, and ecologists are currently in the process of incorporating them into their observational studies. So far, however, large novel community datasets are still rare. Consequently, there are still many open questions about how these new data should be optimally used to address fundamental questions in community ecology, macroecology, and conservation. We review the state of the field, highlight the opportunities and analytical tools for advancing ecological research with novel community data, and discuss the implications of these emerging technologies for ecological theory, ecological study design, and environmental management. New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce 'novel community data' as an umbrella term for these data. We review the emerging field around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning. New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce 'novel community data' as an umbrella term for these data. We review the emerging fields around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning. [ABSTRACT FROM AUTHOR]

Estimating organisms' responses to environmental variables and taxon associations across broad spatial scales is vital for predicting their responses to climate change. Macroinvertebrates play a major role in wetland processes, but studies simultaneously exploring both community structure and community trait responses to environmental gradients are still lacking. We compiled a global dataset (six continents) from 756 depressional wetlands, including the occurrence of 96 macroinvertebrate families, their phylogenetic tree, and 19 biological traits. Using Bayesian hierarchical joint species distribution models (JSDMs), we estimated macroinvertebrate associations and compared the influences of local and climatic predictors on both individual macroinvertebrate families and their traits. While macroinvertebrate families were mainly related to broad‐scale factors (maximum temperature and precipitation seasonality), macroinvertebrate traits were strongly related to local wetland hydroperiod. Interestingly, macroinvertebrate families and traits both showed positive and negative associations to the same environmental variables. As expected, many macroinvertebrate family occurrences were positively associated with temperature, but a few showed the opposite pattern and were found in cooler or montane regions. We also found that wetland macroinvertebrate communities would likely be affected by changing climates through alterations in traits related to precipitation seasonality, temperature seasonality, and wetland area. Temperature increases may negatively affect collector and shredder functional groups. A decrease in precipitation could lead to reductions in wetland area benefiting drought‐tolerant macroinvertebrates, but it may negatively affect macroinvertebrates lacking those adaptations. Wetland processes may be compromised through broad‐scale environmental changes altering macroinvertebrate family distributions and local hydroperiod shifts altering organism traits. Our complementary family‐based and trait‐based approaches elucidate the complex effects that climate change may produce on wetland ecosystems. [ABSTRACT FROM AUTHOR]

To preserve natural ecosystems and their biodiversity, there is a need to anticipate future ecosystem changes through better understanding of underlying drivers and assembly processes determining community composition. Assembly processes can be understood as a set of filters acting at different spatio‐temporal scales that jointly define the structure and composition of communities. Here, we explore the underlying assembly processes shaping marine fish species distribution and composition, using the heavily exploited North Sea. Our aims are to study 1) the relative importance of the different assembly processes shaping marine fish communities, 2) the key environmental drivers determining species distributions and composition, and to 3) quantify the role of traits in determining species niches and responses to the environment. Specifically, we fit a joint species distribution model (JSDM) using 31 years of standardized scientific bottom trawl survey data for 67 fish species. We use a set of environmental variables and species' traits representing morphology, life history, reproduction and diet, while also accounting for phylogenetic relationships of species. Environmental variables, primarily related to temperature, explained over one third of the variance in species occurrence, while spatial effects explained half of the variability across species. This shows that environmental filtering and spatially‐structured processes are the main drivers shaping the community assembly. Furthermore, among the total variance of individual species occurrences, 12.5% could be explained by traits, which improve the mechanistic understanding on species responses to environmental change. Hence, model predictions from JSDMs accounting for traits, environmental niches and potential interactions among multiple species can provide relevant simulations and forecasts with the potential to inform spatial management and conservation efforts aiming to preserve biodiversity and its associated services vital for human well‐being. [ABSTRACT FROM AUTHOR]

Species distribution models are commonly used in basic and applied ecological research to examine the factors driving the distribution and abundance of organisms. They are employed to quantify species' relationships with abiotic conditions, to predict species' response to land-use and climatic change, and to identify potential conservation areas. Biotic interactions have been rarely included in traditional species distribution models, wherein joint species distribution models (JSDMs) emerge as a feasible approach to simultaneously incorporate environmental factors and interspecific interactions, making it a powerful tool for analyzing the structure and assembly of biotic communities. Generally, the JSDMs are based on species distribution models (SDMs), with the abundance or occurrence of multiple species as response variables and environmental factors, species associations and species traits being incorporated in the modeling framework. These models commonly use generalized linear regression methods (GLM) to relate multivariate response to environmental variables, and capture species associations in the form of random effects. The limitation has been overcome by the introduction of latent variable models (LVMs). Typically, the model parameters are estimated using maximum likelihood estimation or Bayesian methods implemented by Laplace Approximation and Markov Chain Monte Carlo (MCMC) simulations, respectively. In this review, the generation and theoretical basis of JSDMs were summarized. The characteristics of different types of JSDMs and their applications in modern ecology were emphatically introduced. The problems and prospects of JSDMs were discussed. With the in-depth study of the relationship between environmental factors and multi-species interactions, JSDMs would be the focus of future studies of species distribution model. [ABSTRACT FROM AUTHOR]

• Many boreal forest saproxylic beetles strongly associated with a single tree species. • Oaks (Quercus sp.) in particular harbor many red-listed species. • Most saproxylic beetles are also associated with near-natural, vs. managed, forests. • Tree species is a better predictor of beetle communities than forest management. Wood-living beetles make up a large proportion of forest biodiversity and contribute to important ecosystem services, including decomposition. Beetle communities in managed southern boreal forests are less species rich than in natural and near-natural forest stands. In addition, many beetle species rely primarily on specific tree species. Yet, the associations between individual beetle species, forest management category, and tree species are seldom quantified, even for red-listed beetles. We compiled a beetle capture dataset from flight intercept traps placed on Norway spruce (Picea abies), oak (Quercus sp.), and Eurasian aspen (Populus tremulae) trees in 413 sites in mature managed forest, near-natural forest, and clear-cuts in southeastern Norway. We used joint species distribution models to estimate the strength of associations for 368 saproxylic beetle species (including 20 vulnerable, endangered, or critical red-listed species) for each forest management category and tree species. Tree species on which traps were mounted had the largest effect on beetle communities; oaks had the most highly associated beetle species, including most of the red-listed species, followed by Norway spruce and Eurasian aspen. Most beetle species were more likely to be captured in near-natural than in mature managed forest. Our estimated associations were compatible – for many species – with categorical classifications found in several existing databases of saproxylic beetle preferences. These quantitative beetle-habitat associations will improve future analyses that have typically relied on categorical classifications. Our results highlight the need to prioritize conservation of near-natural forests and oak trees in Scandinavia to protect the habitat of many red-listed species in particular. Furthermore, we underline the importance of carefully considering the species of trees on which traps are mounted in order to representatively sample beetle communities in forest stands. [ABSTRACT FROM AUTHOR]

Species distribution models are commonly used in basic and applied ecological research to examine the factors driving the distribution and abundance of organisms. They are employed to quantify species’ relationships with abiotic conditions, to predict species’ response to land-use and climatic change, and to identify potential conservation areas. Biotic interactions have been rarely included in traditional species distribution models, wherein joint species distribution models (JSDMs) emerge as a feasible approach to simultaneously incorporate environmental factors and interspecific interactions, making it a powerful tool for analyzing the structure and assembly of biotic communities. Generally, the JSDMs are based on species distribution models (SDMs), with the abundance or occurrence of multiple species as response variables and environmental factors, species associations and species traits being incorporated in the modeling framework. These models commonly use generalized linear regression methods (GLM) to relate multivariate response to environmental variables, and capture species associations in the form of random effects. The limitation has been overcome by the introduction of latent variable models (LVMs). Typically, the model parameters are estimated using maximum likelihood estimation or Bayesian methods implemented by Laplace Approximation and Markov Chain Monte Carlo (MCMC) simulations, respectively. In this review, the generation and theoretical basis of JSDMs were summarized. The characteristics of different types of JSDMs and their applications in modern ecology were emphatically introduced. The problems and prospects of JSDMs were discussed. With the in-depth study of the relationship between environmental factors and multi-species interactions, JSDMs would be the focus of future studies of species distribution model.