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ABSTRACT The Importance of the Regional Species PoolThe regional species pool—the set of species capable of entering a local community—is a foundational concept for understanding ecological processes that occur between local and extensive (biogeographic) spatial scales. However, the lack of precise definitions for the regional species pool, coupled with limited research into the dynamics of regional biodiversity, has impeded the development of a comprehensive framework to explain the mechanisms shaping these pools. Processes Governing Regional Species PoolsAlthough ecological processes at local and extensive scales are relatively well understood, the mechanisms shaping regional biota remain less clear. Regional species pools are likely shaped by a unique set of processes that often overlap minimally with those operating at local or extensive scales. Despite their significance, our understanding of the specific mechanisms driving the dynamics of regional species pools remains incomplete. The Need for a Theory of Regional Species PoolsWe argue that it is essential to prioritise the study of the regional species pool for two reasons. First, the regional species pool bridges spatial and temporal scales from ecological dynamics in landscapes to the long‐term processes shaping the biotas of entire biogeographic provinces. As such, understanding the dynamics of species pools addresses fundamental questions about the origin, maintenance, and dynamics of biodiversity. Second, effective biodiversity conservation in the Anthropocene hinges on understanding the processes that operate at regional scales. 

ABSTRACT Ecological differences among species, particularly dispersal capacity and life history strategies, influence population response to environmental changes. Genetic simulations now allow us to directly incorporate this variation into models of past demographic changes. However, the impact of life history strategies in demographic inference has been far less explored relative to that of dispersal capacity. Here, we utilise individual‐based simulations of a non‐Wright‐Fisher population to ask whether differences in life history traits (the average age of first reproduction of individuals, the average adult mortality and the average number of mates per reproductive season) lead to consistent and predictable differences in the summary statistics of genetic diversity commonly used for simulation‐based parameter estimation and demographic inference. Using a Random Forest model, we also estimate three population parameters (variance in reproductive success, generation time and effective population size) from genome‐wide SNP variation for two bird species known to have distinct life history strategies. The results demonstrate that life history variation leads to predictable differences in patterns of genetic diversity: higher values of life history traits, representing extreme polygamy, long adult longevity and later onset of reproduction, are associated with higher variance in reproductive success, longer generation times, smaller effective population sizes and overall lower genetic diversity. Parameter estimates from empirical datasets also agree with the general expectation that polygamic species with later onset of reproduction and long adult longevity exhibit higher variance in reproductive success, longer generation times and smaller effective population sizes. Since the signal of life history differences is observed in the genetic summary statistics, we argue that simulation‐ and model‐based multi‐species demographic inference will gain from the incorporation of life history information. 

ABSTRACT In integrative distributional, demographic and coalescent (iDDC) modelling, a critical component is the statistical relationship between habitat suitability and local population sizes. This study explores this relationship in twoEnyaliuslizard species from the Brazilian Atlantic Forest: the high‐elevationE. iheringiiand low‐elevationE. catenatusand how this transformation affects spatiotemporal demographic inference. Most previous iDDC studies assumed a linear relationship, but this study hypothesises that the relationship may be nonlinear, especially for high‐elevation species with broader environmental tolerances. We test two key hypotheses: (1) The habitat suitability to population size relationship is nonlinear forE. iheringii(high‐elevation) and linear forE. catenatus(low‐elevation); and (2)E. iheringiiexhibits higher effective migration across populations thanE. catenatus. Our findings provide clear support for hypothesis (2), but mixed support for hypothesis (1), with strong model support for a nonlinear transformation in the high‐elevationE. iheringiiand some (albeit weak) support for a nonlinear transformation also inE. catenatus. The iDDC models allow us to generate landscape‐wide maps of predicted genetic diversity for both species, revealing that genetic diversity predictions for the high‐elevationE. iheringiialign with estimated patterns of historical range stability, whereas predictions for low‐elevationE. catenatusare distinct from range‐wide stability predictions. This research highlights the importance of accurately modelling the habitat suitability to population size relationship in iDDC studies, contributing to our understanding of species' demographic responses to environmental changes. 

Abstract Understanding global patterns of genetic diversity is essential for describing, monitoring, and preserving life on Earth. To date, efforts to map macrogenetic patterns have been restricted to vertebrates, which comprise only a small fraction of Earth’s biodiversity. Here, we construct a global map of predicted insect mitochondrial genetic diversity from cytochrome c oxidase subunit 1 sequences, derived from open data. We calculate the mitochondrial genetic diversity mean and genetic diversity evenness of insect assemblages across the globe, identify their environmental correlates, and make predictions of mitochondrial genetic diversity levels in unsampled areas based on environmental data. Using a large single-locus genetic dataset of over 2 million globally distributed and georeferenced mtDNA sequences, we find that mitochondrial genetic diversity evenness follows a quadratic latitudinal gradient peaking in the subtropics. Both mitochondrial genetic diversity mean and evenness positively correlate with seasonally hot temperatures, as well as climate stability since the last glacial maximum. Our models explain 27.9% and 24.0% of the observed variation in mitochondrial genetic diversity mean and evenness in insects, respectively, making an important step towards understanding global biodiversity patterns in the most diverse animal taxon. 

Abstract Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Within ecological communities drift, dispersal, speciation, and selection operate simultaneously to shape patterns of biodiversity. Reconciling the relative importance of these is hindered by current models and inference methods, which tend to focus on a subset of processes and their resulting predictions. Here we introduce massive ecoevolutionary synthesis simulations (MESS), a unified mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: (i) species richness and abundances, (ii) population genetic diversities, and (iii) trait variation in a phylogenetic context. Using simulations we demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. MESS is unique in generating predictions of community‐scale genetic diversity, and in characterizing joint patterns of genetic diversity, abundance, and trait values. MESS unlocks the full potential for investigation of biodiversity processes using multidimensional community data including a genetic component, such as might be produced by contemporary eDNA or metabarcoding studies. We combine MESS with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of data availability scenarios, and spatial and taxonomic scales.