Microclimates can be accurately predicted across ecologically important remote ecosystems

Microclimate information is often crucial for understanding ecological patterns and processes, including under climate change, but is typically absent from ecological and biogeographic studies owing to difficulties in obtaining microclimate data. Recent advances in microclimate modelling, however, suggest that microclimate conditions can now be predicted anywhere at any time using hybrid physically- and empirically-based models. Here, for the first time, we test the utility of this approach across a remote, inaccessible, and climate change threatened polar island ecosystem at ecologically relevant scales. Microclimate predictions were generated at a 100 x 100 m grain (at a height of 4 cm) across the island, with models parameterised using either meteorological observations from the islands weather station (AWS) or climate reanalysis data (CRA). AWS models had low error rates and were highly correlated with observed seasonal and daily temperatures (root mean squared error of predicted seasonal average Tmean [≤] 0.6 {degrees}C; Pearsons correlation coefficient (r) for the daily Tmean [≥] 0.86). By comparison, CRA models had a slight warm bias in all seasons and a smaller diurnal range in the late summer period than in situ observations. Despite these differences, the modelled relationship between the percentage cover of the threatened endemic cushion plant Azorella macquariensis and microclimate varied little with the source of microclimate data (r = 0.97), suggesting that both model parameterisations capture similar patterns of spatial variation in microclimate conditions across the island ecosystem. Here, we have shown that the accurate prediction of microclimate conditions at ecologically relevant spatial and temporal scales is now possible using hybrid physically- and empirically-based models across even the most remote and climatically extreme environments. These advances will help add the microclimate dimension to ecological and biogeographic studies, which could be critical for delivering climate change-resilient conservation planning in climate-change exposed ecosystems.