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Uncertainty in geographical estimates of performance and fitness
Author(s) -
Woods H. Arthur,
Kingsolver Joel G.,
Fey Samuel B.,
Vasseur David A.
Publication year - 2018
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13035
Subject(s) - ectotherm , statistics , bootstrapping (finance) , term (time) , contrast (vision) , ecology , econometrics , mathematics , computer science , biology , artificial intelligence , physics , quantum mechanics
Thermal performance curves ( TPC s) have become key tools for predicting geographical distributions of performance by ectotherms. Such TPC ‐based predictions, however, may be sensitive to errors arising from diverse sources. We analysed potential errors that arise from common choices faced by biologists integrating TPC s with climate data by constructing case studies focusing on experimental sets of TPC s and simulating geographical patterns of mean performance. We first analysed differences in geographical patterns of performance derived from two pairs of commonly used TPC s. Mean performance differed most (up to 30%) in regions with relatively constant mean temperatures similar to those at which the TPC s diverged the most. We also analysed the effects of thermal history by comparing geographical estimates derived from (a) a broad TPC based on short‐term measurements of insect larvae ( Manduca sexta ) with a history of exposure to thermal variation versus (b) a narrow TPC based on long‐term measurements of larvae held at constant temperatures. Estimated mean performance diverged by up to 40%, and differences were magnified in simulated future climates. Finally, to quantify geographical error arising from statistical error in fitted TPC s, we propose and illustrate a bootstrapping technique for establishing 95% prediction intervals on mean performance at each location (pixel). Collectively, our analyses indicate that error arising from several underappreciated sources can significantly affect the mean performance values derived from TPC s, and we suggest that the magnitudes of these errors should be estimated routinely in future studies.