Trout reverse the effect of water temperature on the foraging of a mayfly
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Climate change is likely to increase the metabolisms of ectothermic animals living below their thermal optimum. While ectothermic top predators may compensate by increasing foraging, ectothermic prey may be unable to increase foraging because of increased predation risk from ectothermic predators. We examined how the diurnal drift behavior (i.e., the downstream movement associated with foraging) of the mayfly Baetis, an ectothermic herbivore, responds to changing temperature in the implied presence and absence of trout, an ectothermic predator. In an experiment replicated at the catchment scale, water temperature and trout presence strongly interacted to affect the diurnal drift of Baetis from artificial channels lacking periphyton over a water temperature range of 4.2–14.8 °C. In fishless streams, daytime drift increased with increasing water temperature, likely because of increased metabolic demand for food. However, in trout-bearing streams, daytime drift decreased with increasing water temperature. Our interpretation is that the perceived threat of trout rose with increasing water temperature, causing mayflies to reduce foraging despite heightened metabolic demand. These results suggest that anticipated increases in stream temperature due to climate change may further escalate divergence in structure and process between fishless and trout-bearing streams. Similar dynamics may occur in other ecosystems with ectothermic predators and prey living below their thermal optima.
KeywordsInvertebrate drift Metabolic demand Baetidae Climate change Invasive species Foraging
We thank the staff and scientists at SNARL for supporting this research with their time, expertise, and equipment. We also thank Sequoia-Kings Canyon National Park for permission to conduct the factorial experiment. We are grateful to Benjamin Hass, Roisin Murphy-Deak, Margaret O’Neil, Lassie Hammock, Nickilou Krigbaum, Bruce D. Hammock, Tom Hammock, and Rafael Rodriguez for their help in the field. Comments by David Herbst, William Wetzel, Roland Knapp, Sharon Lawler, Leon Barmuta and two anonymous reviewers greatly improved the manuscript. This research was funded by a Mildred E. Mathias grant and the Bob Wisecarver Scholarship from the Diablo Valley Fly Fishermen.
- Bates D, Maechler M, Bolker B (2011) lme4: Linear mixed-effects models using S4 classes.–R package ver. 0.999375-42. http://CRAN.R-project.org/package=lme4
- Bolker BM (2008) Ecological models and data in R. Princeton Univ Press, PrincetonGoogle Scholar
- Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New YorkGoogle Scholar
- Pachauri RK (2007) Climate change 2007: synthesis report. Contribution of working groups I, II and III to the fourth assessment report of the Intergovernmental Panel on Climate Change. IPCCGoogle Scholar
- R Development Core Team (2011) R: A language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar