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Biogeographic history can lead to variation in biodiversity across regions, but it remains unclear how the degree of biogeographic isolation among communities may lead to differences in biodiversity. Biogeographic analyses generally treat regions as discrete units, but species assemblages differ in how much biogeographic history they share, just as species differ in how much evolutionary history they share. Here, we use a continuous measure of biogeographic distance, phylobetadiversity, to analyze the influence of biogeographic isolation on the taxonomic and functional diversity of global mammal and bird assemblages. On average, biodiversity is better predicted by environment than by isolation, especially for birds. However, mammals in deeply isolated regions are strongly influenced by isolation; mammal assemblages in Australia and Madagascar, for example, are much less diverse than predicted by environment alone and contain unique combinations of functional traits compared to other regions. Neotropical bat assemblages are far more functionally diverse than Paleotropical assemblages, reflecting the different trajectories of bat communities that have developed in isolation over tens of millions of years. Our results elucidate how long-lasting biogeographic barriers can lead to divergent diversity patterns, against the backdrop of environmental determinism that predominantly structures diversity across most of the world.

 

Niche theory predicts that ecologically similar species can coexist through multidimensional niche partitioning. However, owing to the challenges of accounting for both abiotic and biotic processes in ecological niche modelling, the underlying mechanisms that facilitate coexistence of competing species are poorly understood. In this study, we evaluated potential mechanisms underlying the coexistence of ecologically similar bird species in a biodiversity-rich transboundary montane forest in east-central Africa by computing niche overlap indices along an environmental elevation gradient, diet, forest strata, activity patterns and within-habitat segregation across horizontal space. We found strong support for abiotic environmental habitat niche partitioning, with 55% of species pairs having separate elevation niches. For the remaining species pairs that exhibited similar elevation niches, we found that within-habitat segregation across horizontal space and to a lesser extent vertical forest strata provided the most likely mechanisms of species coexistence. Coexistence of ecologically similar species within a highly diverse montane forest was determined primarily by abiotic factors (e.g. environmental elevation gradient) that characterize the Grinnellian niche and secondarily by biotic factors (e.g. vertical and horizontal segregation within habitats) that describe the Eltonian niche. Thus, partitioning across multiple levels of spatial organization is a key mechanism of coexistence in diverse communities. 

Forecasting the future state of a species is a tricky process, as there are numerous hidden factors that influence species trajectories in addition to the obvious unknowns about the future state of the planet. We echo the guidance of Clare et al. (2024) to use near‐term and long‐term forecasting in complementary ways. Near‐term forecasts can be used to guide specific management and conservation actions, which can be updated as new data and evidence are collected. Long‐term forecasts can be used to characterize uncertainty further into the future, which can help guide longstanding conservation planning and legislative actions that are based on such uncertainty in possible future outcomes.

 

Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets.

We developed thespAbundance Rpackage for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species.

We provide three vignettes and three case studies that highlightspAbundancefunctionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA.

spAbundanceprovides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities.

 

Environmental and anthropogenic factors affect the population dynamics of migratory species throughout their annual cycles. However, identifying the spatiotemporal drivers of migratory species' abundances is difficult because of extensive gaps in monitoring data. The collection of unstructured opportunistic data by volunteer (citizen science) networks provides a solution to address data gaps for locations and time periods during which structured, design‐based data are difficult or impossible to collect.

To estimate population abundance and distribution at broad spatiotemporal extents, we developed an integrated model that incorporates unstructured data during time periods and spatial locations when structured data are unavailable. We validated our approach through simulations and then applied the framework to the eastern North American migratory population of monarch butterflies during their spring breeding period in eastern Texas. Spring climate conditions have been identified as a key driver of monarch population sizes during subsequent summer and winter periods. However, low monarch densities during the spring combined with very few design‐based surveys in the region have limited the ability to isolate effects of spring weather variables on monarchs.

Simulation results confirmed the ability of our integrated model to accurately and precisely estimate abundance indices and the effects of covariates during locations and time periods in which structured sampling are lacking. In our case study, we combined opportunistic monarch observations during the spring migration and breeding period with structured data from the summer Midwestern breeding grounds. Our model revealed a nonstationary relationship between weather conditions and local monarch abundance during the spring, driven by spatially varying vegetation and temperature conditions.

Data for widespread and migratory species are often fragmented across multiple monitoring programs, potentially requiring the use of both structured and unstructured data sources to obtain complete geographic coverage. Our integrated model can estimate population abundance at broad spatiotemporal extents despite structured data gaps during the annual cycle by leveraging opportunistic data.