Time of activity is a better predictor of the distribution of a tropical lizard than pure environmental temperatures

Abstract

Environmental temperatures influence ectotherms’ physiology and capacity to perform activities necessary for survival and reproduction. Time available to perform those activities is determined by thermal tolerances and environmental temperatures. Estimates of activity time might enhance our ability to predict suitable areas for species’ persistence in face of climate warming, compared to the exclusive use of environmental temperatures, without considering thermal tolerances. We compare the ability of environmental temperatures and estimates of activity time to predict the geographic distribution of a tropical lizard, Tropidurus torquatus. We compared 105 estimates of activity time, resulting from the combination of four methodological decisions: 1) how to estimate daily environmental temperature variation (modeling a sinusoid wave ranging from monthly minimum to maximum temperature, extrapolating from operative temperatures measured in field or using biophysical projections of microclimate)? 2) In which temperature range are animals considered active? 3) Should these ranges be determined from body temperatures obtained in laboratory or in field? And 4) should thermoregulation simulations be included in estimations? We show that models using estimates of activity time made with the sinusoid and biophysical methods had higher predictive accuracy than those using environmental temperatures alone. Estimates made using the central 90% of temperatures measured in a thermal gradient as the temperature range for activity also ranked higher than environmental temperatures. Thermoregulation simulations did not improve model accuracy. Precipitation ranked higher than thermally related predictors. Activity time adds important information to distribution modeling and should be considered as a predictor in studies of the distribution of ectotherms. The distribution of T. torquatus is restricted by precipitation and by the effect of lower temperatures on their time of activity and climate warming could lead to range expansion. We provide an R package ‘Mapinguari’ with tools to generate spatial predictors based on the processes described herein.

The biogeography of warming tolerance in lizards

“…However, for forested‐region species, a reduction in vegetation cover might increase exposure to temperatures that exceed their (low) thermal optima and maxima (Laurent et al, 2020; Tuff, Tuff, & Davies, 2016). This signalises the high dependence of forested species on vegetation cover to avoid heat stress (Caetano et al, 2020; Logan et al, 2013; Pontes‐da‐Silva et al, 2018).…”

Dehydration Alters Behavioral Thermoregulation and the Geography of Climatic Vulnerability in Amazonian Lizards

“…The large effects that these changes had on the geographic models of thermal risk identify a new relevant facet to the problem of accounting for water availability (ex. rainfall) and thermohydroregulation when modelling climatic vulnerability 24,58 . The lack of congruence between the risk maps produced by both approaches is expected -apart from measuring relatively different things (Ex.…”

“…Other dehydrated herp species lower their mean body temperatures in laboratory thermal gradients 7,11,[20][21][22] , while dehydrated endotherms may sometimes allow their body temperatures to raise 17 . Preferences for lower temperatures is often associated to the use of sheltering microhabitats 11,22 , potentially making water a stronger determinant of habitat use and activity time than temperature 23,24 . Given the di culties of understanding the effects of dehydration on thermoregulation, studying species' responses to evade both, thermal and water stress should simplify the problem.…”

The firewall between Cerrado and Amazonia: Interaction of temperature and fire govern seed recruitment in a Neotropical savanna

“…We estimated TPCs with the function get_predict of package mApinguAri (Caetano et al, 2020). After preliminary tests (Appendix S2b), we estimated TPCs by habit (herb/shrub/trees) and physiognomy (rocky field/forest/savanna), for comparisons and to predict temperature effects on germination at the community level.…”

“…They produce an estimate of the fundamental niche using a substantial amount of data on physiology and behavior (Willi and Van Buskirk, 2019). Integration between correlative species distribution models and physiologically inspired models may, therefore, be useful to reduce uncertainties and make more accurate predictions of species' distributions and responses to climate change (Sinervo et al, 2010;Clusella-Trullas and Chown, 2011;Ceia-Hasse et al, 2014;Caetano et al, 2020). However, studies modeling plant species distribution have mostly (if not all) used vegetative traits and responses of yet established, adult individuals to environmental factors, such as photosynthesis, transpiration and biomass balance (Hoffmann et al, 2012;Moncrieff et al, 2016;Touboul et al, 2018).…”