Mapping physiology: biophysical mechanisms define scales of climate change impacts

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 × 2 cm 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.

Rocks of different mineralogy show different temperature characteristics: implications for biodiversity on rocky seashores

“…However, mineralogy is just one potential driver of the temperature differences identified here. Differences in colour (Raimondi, 1988;Marshall, McQuaid & Williams, 2010;Judge, Botton & Hamilton, 2011) and microtopography (Lathlean, Ayre & Minchinton, 2012;Choi et al, 2019) have been shown previously to affect rock temperature, with the six rock types investigated here also differing in these attributes. It is possible that the smooth, featureless surfaces of siltstone were able to reach hotter surface temperatures versus the coarse, textured surfaces of limestone and quartzite.…”

“…For example, temperature mosaics were generally observed on quartzite boulders only. These temperature mosaics may have developed because of the specific microtopographic features of quartzite (i.e., coarse, angular surfaces), with microtopography associated with temperature heterogeneity on rocky seashores elsewhere ( Lathlean, Ayre & Minchinton, 2012 ; Choi et al, 2019 ). Alternatively, these mosaics may reflect differences in long-wave emissivity not accounted for by our use of a generic emissivity value.…”

“…With predictions of hotter air temperatures and an increased frequency of extreme-heat events associated with global climate change ( IPCC, 2013 ), the survival and persistence of some intertidal species is likely to be challenged further. To predict the future fate of species and populations, ecologists have collected baseline rock temperature data ( Helmuth, 1999 ; Denny et al, 2011 ; Judge, Botton & Hamilton, 2011 ; Gunderson et al, 2019 ), created heat budget models ( Helmuth, 1999 ; Choi et al, 2019 ), investigated how x species is affected by y substrate temperature ( Raimondi, 1988 ; Lathlean, Ayre & Minchinton, 2012 ; Lathlean, Ayre & Minchinton, 2013 ; Lamb, Leslie & Shinen, 2014 ), or used biomimetic loggers to investigate how internal body temperatures can be variously affected by environmental temperature ( Helmuth & Hofmann, 2001 ; Seabra et al, 2011 ; Lathlean et al, 2015 ; Seuront et al, 2019 ). All of these studies confirm that temperature is an important driving force that can influence the distribution of species on rocky seashores.…”

“…The scale of these patterns of temperature difference can vary enormously, from many kilometres down to millimetres ( Helmuth et al, 2006 ; Denny et al, 2011 ; Judge, Botton & Hamilton, 2011 ; Lathlean, Ayre & Minchinton, 2012 ; Lathlean, Ayre & Minchinton, 2013 ). Identifying the sources of this temperature heterogeneity can be difficult, with rocky seashores not being equal in terms of their physical attributes, with variations in latitude ( Helmuth et al, 2006 ; Lathlean, Ayre & Minchinton, 2014 ), shore slope ( Helmuth & Hofmann, 2001 ; Harley, 2008 ), azimuth ( Helmuth & Hofmann, 2001 ; Harley, 2008 ; Chapperon, Studerus & Clavier, 2017 ), microhabitat features ( Chapperon & Seuront, 2011 ; Judge, Botton & Hamilton, 2011 ; Lathlean et al, 2015 ), rock type ( Raimondi, 1988 ; Marshall, McQuaid & Williams, 2010 ; Judge, Botton & Hamilton, 2011 ); relative humidity ( Lathlean, Ayre & Minchinton, 2014 ), wave splash ( Helmuth et al, 2006 ), the timing of low tide ( Helmuth et al, 2002 ; Helmuth et al, 2006 ) and microtopography ( Lathlean, Ayre & Minchinton, 2012 ; Choi et al, 2019 ) all contributing to temperature heterogeneity.…”

The challenge of novel abiotic conditions for species undergoing climate‐induced range shifts

“…Developing new models restarts the iterative process to test whether the additional physiological mechanisms better inform our understanding of what limits species distributions and how species are responding to novel abiotic pressures during climate‐induced shifts. Ultimately, physiological studies of non‐climatic abiotic factors can help shape ecological theory, as has proven particularly fruitful in intertidal communities (Choi et al 2019).…”