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Seasonal and Among‐Stream Variation in Predator Encounter Rates for Fish Prey
Author(s) -
Harvey Bret C.,
Nakamoto Rodney J.
Publication year - 2013
Publication title -
transactions of the american fisheries society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.696
H-Index - 86
eISSN - 1548-8659
pISSN - 0002-8487
DOI - 10.1080/00028487.2012.760485
Subject(s) - predation , streams , predator , rainbow trout , trout , population , biology , ecology , fish <actinopterygii> , fishery , environmental science , demography , computer network , computer science , sociology
Recognition that predators have indirect effects on prey populations that may exceed their direct consumptive effects highlights the need for a better understanding of spatiotemporal variation in predator–prey interactions. We used photographic monitoring of tethered Rainbow Trout Oncorhynchus mykiss and Cutthroat Trout O. clarkii to quantify predator encounter rates for fish in four streams of northwestern California during winter–spring and summer. To estimate maximum encounter rates, provide the clearest contrast among streams and seasons, and provide an empirical estimate of a key parameter in an individual‐based model of stream salmonids, we consistently placed fish in shallow microhabitats that lacked cover. Over 14‐d periods, predators captured fish at 66 of the 88 locations where fish were placed. Eight species of birds (including two species of owls) and mammals were documented as capturing fish. Thirty‐six percent of the predator encounters occurred at night. Predator encounter rates varied among streams and between seasons; the best‐fitting model of survival included a stream × season interaction. Encounter rates tended to be higher in larger streams than in smaller streams and higher in winter–spring than in summer. Conversion of predator encounter rates from this study to estimates of predation risk by using published information on capture success yielded values similar to an independent estimate of predation risk obtained from calibration of an individual‐based model of the trout population in one of the study streams. The multiple mechanisms linking predation risk to population dynamics argue for additional effort to identify patterns of spatiotemporal variation in predation risk.