M
Michael J. Angilletta
Researcher at Arizona State University
Publications - 127
Citations - 13423
Michael J. Angilletta is an academic researcher from Arizona State University. The author has contributed to research in topics: Ectotherm & Population. The author has an hindex of 45, co-authored 118 publications receiving 11888 citations. Previous affiliations of Michael J. Angilletta include University of Wisconsin-Madison & Indiana State University.
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Book
Thermal Adaptation: A Theoretical and Empirical Synthesis
TL;DR: This Discussion focuses on the part of the history of thermal evolution and its role in climate change that has an impact on human well-being.
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The evolution of thermal physiology in ectotherms
TL;DR: This review applies classical models of thermal adaptation to predict variation in body temperature within and among populations of mammals and birds and relates these predictions to observations generated by comparative and experimental studies.
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Temperature, Growth Rate, and Body Size in Ectotherms: Fitting Pieces of a Life-History Puzzle
TL;DR: A multivariate theory that focuses on the coevolution of thermal reaction norms for growth rate and size at maturity is recommended, which should incorporate functional constraints on thermal Reaction norms, as well as the natural covariation between temperature and other environmental variables.
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The temperature-size rule in ectotherms: Simple evolutionary explanations may not be general
TL;DR: Little evidence is found that growth efficiency is negatively related to environmental temperature within the thermal range that is relevant to the temperature‐size rule, and growth efficiency was either positively related or insensitive to environmentalTemperature in the majority of cases.
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Can mechanism inform species’ distribution models?
Lauren B. Buckley,Mark C. Urban,Michael J. Angilletta,Lisa G. Crozier,Leslie J. Rissler,Michael W. Sears +5 more
TL;DR: It is compared how two correlative and three mechanistic models predicted the ranges of two species: a skipper butterfly and a fence lizard, to find out how these models performed similarly in predicting current distributions.