The effects of predicted activity time on population-level measures of productivity in squamates a comparative analysis


SOCIETY FOR INTEGRATIVE AND COMPARATIVE BIOLOGY
2021 VIRTUAL ANNUAL MEETING (VAM)
January 3 – Febuary 28, 2021

Meeting Abstract


59-3  Sat Jan 2  The effects of predicted activity time on population-level measures of productivity in squamates: a comparative analysis Neel, LK*; Fornshell, D; Angilletta, MJ; Arizona State University lkneel@asu.edu

Ectotherm performance is highest within a relatively narrow range of body temperatures. As climates warm, organisms are expected to achieve their preferred body temperatures less frequently, constraining the time available for foraging, mate acquisition, territory defense, and thermoregulation. To understand how climate change will impact the persistence of ectotherms, it is important to understand how thermal constraints on activity impact the productivity of ectotherms in different environments However, the costs of restricted activity on population-level measures of productivity are difficult to quantify for myriad reasons. Here, we searched primary literature for studies that quantified any of three measures of productivity: 1) growth rates, 2) relative clutch mass, or 3) reproductive output. Data for relative clutch mass and reproductive output (mean ± se) were taken directly from the publications, while growth rate data were collected from growth trajectory figures using the freeware, WebPlotDigitizer. Then, we integrated estimates of heat flux from complex environments using the approach developed by Campbell and Norman (1988) and Bakken (1980), with downscaled microclimate data from NicheMapR, to model hourly body temperatures throughout the year for each population sampled. We use preferred temperatures to predict activity restrictions. We compare predicted annual activity times to population-level measures of growth and reproduction using computation modeling and pre-existing productivity data from n = 58 lizard species across n = 126 studies. Future work will incorporate phylogenetic relationships into our statistical models.

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