Abstract
The rocky intertidal zone is a highly dynamic and thermally variable ecosystem, where the combined influences of solar radiation, air temperature and topography can lead to differences greater than 15°C over the scale of centimetres during aerial exposure at low tide. For most intertidal organisms this small-scale heterogeneity in microclimates can have enormous influences on survival and physiological performance. However, the potential ecological importance of environmental heterogeneity in determining ecological responses to climate change remains poorly understood. We present a novel framework for generating spatially explicit models of microclimate heterogeneity and patterns of thermal physiology among interacting organisms. We used drone photogrammetry to create a topographic map (digital elevation model) at a resolution of 2 × 2cm from an intertidal site in Massachusetts, which was then fed into to a model of incident solar radiation based on sky view factor and solar position. These data were in turn used to drive a heat budget model that estimated hourly surface temperatures over the course of a year (2017). Body temperature layers were then converted to thermal performance layers for organisms, using thermal performance curves, creating 'physiological landscapes' that display spatially and temporally explicit patterns of 'microrefugia'. Our framework shows how non-linear interactions between these layers lead to predictions about organismal performance and survivorship that are distinct from those made using any individual layer (e.g. topography, temperature) alone. We propose a new metric for quantifying the 'thermal roughness' of a site (RqT, the root mean square of spatial deviations in temperature), which can be used to quantify spatial and temporal variability in temperature and performance at the site level. These methods facilitate an exploration of the role of micro-topographic variability in driving organismal vulnerability to environmental change using both spatially explicit and frequency-based approaches.
Highlights
Ongoing climate change is having clear impacts on the abundance, health and distribution of organisms and subsequently on patterns of biodiversity and ecosystem function (Doney et al, 2012; Bonebrake et al, 2018)
In an effort to forecast and potentially mitigate some of the worst impacts of these changes, conservation biologists are increasingly turning to forecasting approaches to predict which populations and species are most vulnerable to accelerating climate change (Dong et al, 2017; Sará et al, 2018; Rilov et al, 2019), where environmental change is occurring most rapidly (Sunday et al, 2015; Brito-Morales et al, 2018) and what measures might be enacted to protect threatened species and ecosystems by either reducing the effects of non-climatic stressors such as development and overharvesting (Przeslawski et al, 2005; Sará et al, 2018) or by prioritizing the protection of refugia (Morelli et al, 2017)
Projections of vulnerability based on climatic means have little hope of forecasting the effects of much higher frequency variability in environmental conditions such as heat waves and cold snaps (Wethey et al, 2011a; Roitberg and Mangel, 2016), which are themselves becoming more frequent under anthropogenic climate change
Summary
Ongoing climate change is having clear impacts on the abundance, health and distribution of organisms and subsequently on patterns of biodiversity and ecosystem function (Doney et al, 2012; Bonebrake et al, 2018).
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