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Body Temperature
Climate Change
Environmental Gradient
In Situ Measurement
Land Surface Model
Marine Environment
Mortality Risk
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Predicting intertidal organism temperatures with modified land surface models
Predicting intertidal organism temperatures with modified land surface models,10.1016/j.ecolmodel.2011.08.019,Ecological Modelling,David S. Wethey,Lin
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Predicting intertidal organism temperatures with modified land surface models
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David S. Wethey
,
Lindsay D. Brin
,
Brian Helmuth
,
K. A. S. Mislan
Animals and plants in the marine intertidal zone live at the interface between terrestrial and marine environments. This zone is likely to be a sensitive indicator of the effects of
climate change
in coastal ecosystems, because of several key characteristics including steep environmental gradients, rapid temperature changes during tide transitions, fierce competition for limited space, and a community of mostly sessile organisms. Here we describe a modular modeling approach using modifications to a meteorological
land surface model
to determine body temperatures of the ecologically dominant
rocky intertidal
mussel Mytilus californianus, as a tool that can be used as a proxy for ecological performance. We validate model results against in situ measurements made with biomimetic
body temperature
sensors. Model predictions lie within the range of variability of biomimetic measurements, based on observations over a 4-year period at sites along 1700km of the US west coast from
southern California
(34.5°N) to northern Washington (48.4°N). Our modular approach can be easily applied to many situations in the intertidal zone, including bare rock, mussel, barnacle, and algal beds, salt-marsh grasses, and sand- and mud-flats, by modifying the “vegetation layer” in a standard meteorological land surface model. Biophysical models such as these, which link ecological processes to changing climates through predictions of body temperature, are essential for understanding biogeographic patterns of physiological stress and mortality risk.
Journal:
Ecological Modelling - ECOL MODEL
, vol. 222, no. 19, pp. 3568-3576, 2011
DOI:
10.1016/j.ecolmodel.2011.08.019
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