Physiologically informed organismal climatologies reveal unexpected spatiotemporal trends in temperature.

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.

Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors.

At a proximal level, the physiological impacts of global climate change on ectothermic organisms are manifest as changes in body temperatures. Especially for plants and animals exposed to direct solar radiation, body temperatures can be substantially different from air temperatures. We deployed biomimetic sensors that approximate the thermal characteristics of intertidal mussels at 71 sites worldwide, from 1998-present. Loggers recorded temperatures at 10-30 min intervals nearly continuously at multiple intertidal elevations. Comparisons against direct measurements of mussel tissue temperature indicated errors of ~2.0-2.5 °C, during daily fluctuations that often exceeded 15°-20 °C. Geographic patterns in thermal stress based on biomimetic logger measurements were generally far more complex than anticipated based only on 'habitat-level' measurements of air or sea surface temperature. This unique data set provides an opportunity to link physiological measurements with spatially- and temporally-explicit field observations of body temperature.

Biomechanics meets the ecological niche: the importance of temporal data resolution.

The emerging field of mechanistic niche modelling aims to link the functional traits of organisms to their environments to predict survival, reproduction, distribution and abundance. This approach has great potential to increase our understanding of the impacts of environmental change on individuals, populations and communities by providing functional connections between physiological and ecological response to increasingly available spatial environmental data. By their nature, such mechanistic models are more data intensive in comparison with the more widely applied correlative approaches but can potentially provide more spatially and temporally explicit predictions, which are often needed by decision makers. A poorly explored issue in this context is the appropriate level of temporal resolution of input data required for these models, and specifically the error in predictions that can be incurred through the use of temporally averaged data. Here, we review how biomechanical principles from heat-transfer and metabolic theory are currently being used as foundations for mechanistic niche models and consider the consequences of different temporal resolutions of environmental data for modelling the niche of a behaviourally thermoregulating terrestrial lizard. We show that fine-scale temporal resolution (daily) data can be crucial for unbiased inference of climatic impacts on survival, growth and reproduction. This is especially so for species with little capacity for behavioural buffering, because of behavioural or habitat constraints, and for detecting temporal trends. However, coarser-resolution data (long-term monthly averages) can be appropriate for mechanistic studies of climatic constraints on distribution and abundance limits in thermoregulating species at broad spatial scales.