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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. 

Monitoring biodiversity change is key to effective conservation policy. While it is difficult to establish in situ biodiversity monitoring programs at broad geographical scales, remote sensing advances allow for near-real time Earth observations that may help with this goal. We combine periodical and freely available remote sensing information describing temperature and precipitation with curated biological information from several groups of animals and plants in the Brazilian Atlantic rainforest to design an indirect remote sensing framework that monitors potential loss and gain of biodiversity in near-real time. Using data from biological collections and information from repeated field inventories, we demonstrate that this framework has the potential to accurately predict trends of biodiversity change for both taxonomic and phylogenetic diversity. The framework identifies areas of potential diversity loss more accurately than areas of species gain, and performs best when applied to broadly distributed groups of animals and plants. 

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.