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Abstract Body temperature is universally recognized as a dominant driver of biological performance. Although the critical distinction between the temperature of an organism and its surrounding habitat has long been recognized, it remains common practice to assume that trends in air temperature—collected via remote sensing or weather stations—are diagnostic of trends in animal temperature and thus of spatiotemporal patterns of physiological stress and mortality risk. Here, by analysing long-term trends recorded by biomimetic temperature sensors designed to emulate intertidal mussel temperature across the US Pacific Coast, we show that trends in maximal organismal temperature (‘organismal climatologies’) during aerial exposure can differ substantially from those exhibited by co-located environmental data products. Specifically, using linear regression to compare maximal organismal and environmental (air temperature) climatologies, we show that not only are the magnitudes of body and air temperature markedly different, as expected, but so are their temporal trends at both local and biogeographic scales, with some sites showing significant decadal-scale increases in organismal temperature despite reductions in air temperature, or vice versa. The idiosyncratic relationship between the spatiotemporal patterns of organismal and air temperatures suggests that environmental climatology cannot be statistically corrected to serve as an accurate proxy for organismal climatology. Finally, using quantile regression, we show that spatiotemporal trends vary across the distribution of organismal temperature, with extremes shifting in different directions and at different rates than average metrics. Overall, our results highlight the importance of quantifying changes in the entire distribution of temperature to better predict biological performance and dispel the notion that raw or ‘corrected’ environmental (and specially air temperature) climatologies can be used to predict organismal temperature trends. Hence, despite their widespread coverage and availability, the severe limitations of environmental climatologies suggest that their role in conservation and management policy should be carefully considered. 

Abstract Climate-mediated changes in thermal stress can destabilize animal populations and promote extinction risk. However, risk assessments often focus on changes in mean temperatures and thus ignore the role of temporal variability or structure. Using Earth System Model projections, we show that significant regional differences in the statistical distribution of temperature will emerge over time and give rise to shifts in the mean, variability and persistence of thermal stress. Integrating these trends into mathematical models that simulate the dynamical and cumulative effects of thermal stress on the performance of 38 globally distributed ectotherm species revealed complex regional changes in population stability over the twenty-first century, with temperate species facing higher risk. Yet despite their idiosyncratic effects on stability, projected temperatures universally increased extinction risk. Overall, these results show that the effects of climate change may be more extensive than previously predicted on the basis of the statistical relationship between biological performance and average temperature. 

Abstract Understanding the effects of climate-mediated environmental variation on the distribution of organisms is critically important in an era of global change. We used wavelet analysis to quantify the spatiotemporal (co)variation in daily water temperature for predicting the distribution of cryptic refugia across 16 intertidal sites that were characterized as ‘no’, ‘weak’ or ‘strong’ upwelling and spanned 2000 km of the European Atlantic Coast. Sites experiencing weak upwelling exhibited high synchrony in temperature but low levels of co-variability at monthly to weekly timescales, whereas the opposite was true for sites experiencing strong upwelling. This suggests upwelling generates temporal thermal refugia that can promote organismal performance by both supplying colder water that mitigates thermal stress during hot Summer months and ensuring high levels of fine-scale variation in temperature that reduce the duration of thermal extremes. Additionally, pairwise correlograms based on the Pearson-product moment correlation coefficient and wavelet coherence revealed scale dependent trends in temperature fluctuations across space, with a rapid decay in strong upwelling sites at monthly and weekly timescales. This suggests upwelling also generates spatial thermal refugia that can ‘rescue’ populations from unfavorable conditions at local and regional scales. Overall, this study highlights the importance of identifying cryptic spatiotemporal refugia that emerge from fine-scale environmental variation to map potential patterns of organismal performance in a rapidly changing world. 

Redressing global patterns of biodiversity loss requires quantitative frameworks that can predict ecosystem collapse and inform restoration strategies. By applying a network-based dynamical approach to synthetic and real-world mutualistic ecosystems, we show that biodiversity recovery following collapse is maximized when extirpated species are reintroduced based solely on their total number of connections in the original interaction network. More complex network-based strategies that prioritize the reintroduction of species that improve ‘higher order’ topological features such as compartmentalization do not provide meaningful performance improvements. These results suggest that it is possible to design nearly optimal restoration strategies that maximize biodiversity recovery for data-poor ecosystems in order to ensure the delivery of critical natural services that fuel economic development, food security, and human health around the globe.