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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Paleoclimatic data are used in eco-evolutionary models to improve knowledge of biogeographical processes that drive patterns of biodiversity through time, opening unique windows into past climate–biodiversity dynamics. These models can strengthen projections of the future state of biodiversity if applied to harmonised simulations of past and future climatic change. StableClim provides harmonised continuous estimates of climate stability from the Last Glacial Maximum to the end of the 21st Century for ocean and terrestrial realms, measured as the trend and variability in air temperature and precipitation, and associated signal-to-noise ratios. Thresholds of natural variability in trends of annual, area-weighted regional- and global-mean temperature change allow periods in Earth’s history when climatic conditions were changing rapidly (and slowly) to be identified, permitting climate stability to be estimated locally during periods of accelerated warming and cooling. Model simulations are validated against independent paleoclimate and observational data. Continuous estimates of climatic stability, accessed through StableClim, will improve understanding of the roles of climate in shaping past, present-day and future patterns of biodiversity.

The European rabbit (Oryctolagus cuniculus) is a notorious economic and environmental pest species in its invasive range. To better understand the population and range dynamics of this species, 41 yr of abundance data have been collected from 116 unique sites across a broad range of climatic and environmental conditions in Australia. We analyzed this time series of abundance data to determine whether interannual variation in climatic conditions can be used to map historic, contemporary, and potential future fluctuations in rabbit abundance from regional to continental scales. We constructed a hierarchical Bayesian regression model of relative abundance that corrected for observation error and seasonal biases. The corrected abundances were regressed against environmental and disease variables in order to project high spatiotemporal resolution, continent‐wide rabbit abundances. We show that rabbit abundance in Australia is highly variable in space and time, being driven primarily by internnual variation in temperature and precipitation in concert with the prevalence of a non‐pathogenic virus. Moreover, we show that internnual variation in local spatial abundances can be mapped effectively at a continental scale using highly resolved spatiotemporal predictors, allowing “hot spots” of persistently high rabbit abundance to be identified. Importantly, cross‐validated model performance was fair to excellent within and across distinct climate zones. Long‐term monitoring data for invasive species can be used to map fine‐scale spatiotemporal fluctuations in abundance patterns when accurately accounting for inherent sampling biases. Our analysis provides ecologists and pest managers with a clearer understanding of the determinants of rabbit abundance in Australia, offering an important new approach for predicting spatial abundance patterns of invasive species at the near‐term temporal scales that are directly relevant to resource management.

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