Applicability of Dynamic Energy Budget (DEB) models across steep environmental gradients

Type Article
Date 2018-11
Language English
Author(s) Monaco CristianORCID1, 2, 3, McQuaid Christopher D.1
Affiliation(s) 1 : Rhodes Univ, Dept Zool & Entomol, Grahamstown, South Africa.
2 : Univ Adelaide, Sch Biol Sci, Southern Seas Ecol Labs, Adelaide, SA 5005, Australia.
3 : Univ Adelaide, Environm Inst, Adelaide, SA 5005, Australia.
Source Scientific Reports (2045-2322) (Nature Publishing Group), 2018-11 , Vol. 8 , P. 16384 (14p.)
DOI 10.1038/s41598-018-34786-w
WOS© Times Cited 21
Abstract Robust ecological forecasting requires accurate predictions of physiological responses to environmental drivers. Energy budget models facilitate this by mechanistically linking biology to abiotic drivers, but are usually ground-truthed under relatively stable physical conditions, omitting temporal/spatial environmental variability. Dynamic Energy Budget (DEB) theory is a powerful framework capable of linking individual fitness to environmental drivers and we tested its ability to accommodate variability by examining model predictions across the rocky shore, a steep ecotone characterized by wide fluctuations in temperature and food availability. We parameterized DEB models for co-existing mid/high-shore (Mytilus galloprovincialis) and mid/low-shore (Perna perna) mussels on the south coast of South Africa. First, we assumed permanently submerged conditions, and then incorporated metabolic depression under low tide conditions, using detailed data of tidal cycles, body temperature and variability in food over 12 months at three sites. Models provided good estimates of shell length for both species across the shore, but predictions of gonadosomatic index were consistently lower than observed. Model disagreement could reflect the effects of details of biology and/or difficulties in capturing environmental variability, emphasising the need to incorporate both. Our approach provides guidelines for incorporating environmental variability and long-term change into mechanistic models to improve ecological predictions.
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