DOI: 10.5061/dryad.b2rbnzsb7
title: Time of activity is a better predictor of the distribution of a tropical lizard than pure environmental temperatures

description: 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
 activty 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.
description: <i>Distribution data</i>. We used 359 distribution
 records from the literature and scientific collections spanning the range
 of <i>Tropidurus torquatus</i>. To minimize the effects of
 spatial autocorrelation and sampling bias, we used function ‘clean_points’
 from the R package Mapinguari to eliminate records within 40 km from each
 other, leaving us with 144 records. We empirically determined the size of
 the buffer area fitting Random Forest models under different buffers (1,
 5, 10, 20, 30, 40 and 50 km) and comparing Moran’s I index (Gittleman and
 Kot 1990) calculated from the models’ residuals. We selected buffer
 distance based on the smaller distance resulting in no spatial
 autocorrelation. We estimated Moran’s I using the ‘Moran.I’ function from
 R package ‘ape’ (Paradis et al. 2004). Thirty percent of the distribution
 data, 44 records, was set aside for model cross-validation.
 <i>Physiological data</i>. Between 2013 to 2017, we
 obtained physiological data from five populations of <i>T.
 torquatus</i> sampled during monitoring studies and field
 expeditions. Monitoring took place in Brasília, Distrito Federal
 (15.7998°S, 47.8645°W, 24 individuals) and Nova Xavantina, Mato Grosso
 (14.6644°S, 52.3585°W, 4 individuals). Short-term field sampling occurred
 at Gaúcha do Norte (12.9656°S, 53.5636°W, 13 individuals) and Alta
 Floresta, Mato Grosso (9.8765°S, 56.0855°W, 3 individuals); and Lagoa da
 Confusão, Tocantins (10.9201°S, 50.1833°W, 8 individuals). We captured
 animals using pitfall traps, lassos and by hand. We
 brought captured lizards to the laboratory, housed them individually and
 performed the thermal gradient experiments no longer than 24 h after
 capture. We measured the preferred temperature of each lizard using a
 thermal gradient, which consisted of a terrarium made of MDF plywood
 (Medium Density Fiberboard, 100 cm x 15 cm x 30 cm – L x W x H), open at
 the top and with 2 cm of substrate composed of sand and vermiculite. We
 generated a thermal gradient approximately between 15 °C – 50 °C by
 placing a 60-W incandescent lamp at one end and an ice pack on the other
 (Paranjpe et al. 2013). Lizards were placed in the gradient for one hour
 while their body temperature was recorded every minute by a 1 mm
 thermocouple attached with tape to their abdomen and connected to a data
 logger (Eltek® 1000 Series Squirrel Meter Data Logger 64K, 10 Channel
 1001WD). We allowed lizards to acclimate to the gradient for 10 min
 (Paranjpe et al. 2013) before recording body temperatures. We calculated
 for each individual and for the whole sample: (1) the range between the
 5<sup>th</sup> and 95<sup>th</sup> temperature
 percentile
 (<i>T</i><sub><i>90</i></sub>), (2)
 range between the 25<sup>th</sup> and
 75<sup>th</sup> temperature percentile
 (<i>T</i><sub><i>50</i></sub>) and (3)
 average temperature
 (<i>T</i><sub><i>mean</i></sub>).
 <i>T</i><sub><i>mean</i></sub> and
 <i>T</i><sub><i>50</i></sub> have been
 used in previous studies (Sinervo et al. 2010, Kubisch et al. 2016,
 Piantoni et al. 2016), and the broader range,
 <i>T</i><sub><i>90</i></sub>, was
 chosen under the hypothesis that lizards spend almost all of their time in
 the gradient at preferred temperatures. We obtained
 field-active body temperatures from lizards at Brasília, Distrito Federal,
 from natural populations occurring within the city’s Zoo (15.8512°S,
 47.9379°W, 1158 samples, 640 individuals, details in Wiederhecker et al.
 2002), which was visited weekly from March 1996 to September 1998, from 8
 am to 6 pm, and at Santa Terezinha, Mato Grosso (10.3705°S, 50.5145°W, 9
 samples, 9 individuals) in April 1999, from 12 pm to 2 pm. Active animals
 (i.e., those in the open, basking or moving) were captured, individually
 marked by toe-clipping, and had their cloacal temperature measured with a
 Miller & Weber T-6000 quick reading cloacal thermometer (0.02 ºC
 precision) immediately after capture. We then performed the same
 calculations for laboratory
 <i>T</i><sub><i>90</i></sub>,
 <i>T</i><sub><i>50</i></sub> and
 <i>T</i><sub><i>mean</i></sub> on the
 aggregated field body temperatures. The different
 methods of collecting body temperatures result in very different data
 structures. While the laboratory experiments allow extensive sampling of
 fewer individuals, field sampling allows the collection of many
 individuals, but fewer replicates per individual. In the laboratory, we
 sampled 52 individuals with a median of 65 samples per individual
 (standard deviation = 9.48), whereas in the field we sampled 649
 individuals with a median of 1 sample per individual (standard deviation =
 1.58). This presents a challenge when comparing data from the two sources,
 because we could calculate temperature ranges for each individual from the
 thermal gradient, but not from individuals in the field. Therefore, for
 the thermal measurements collected in the wild, we pooled the data and
 assumed the estimated thermal tolerances characterized the individuals
 from the entire sample. For laboratory data, we estimated temperature
 ranges as both averages of individual values or from the data aggregated
 from the whole sample, and then assessed which choice generated better
 predictors of distribution. We performed an analysis of variance with
 repeated measures to evaluate whether the body temperatures measured in
 the thermal gradient differed among individuals between populations and
 analysis of variance to see if there were differences between individuals
 in the same population. <i>Operative
 Environmental Temperatures</i>. We recorded operative temperatures
 using dataloggers (HOBO<sup>®</sup> U23 Pro v2 2x External
 Temperature Data Logger U23-003) with sensors attached to PVC models of
 equivalent size and color as <i>Tropidurus torquatus</i>. This
 methodology has been validated by previous studies with small ectotherms
 (Adolph 1990, Lara-Reséndiz et al. 2015, Kubisch et al. 2016, Kirchhof et
 al. 2017). We placed models adjacent to pitfall trap arrays, in the
 locations where lizards were captured for the physiological trials,
 uniformly distributed in microhabitats where they were observed in
 activity—shaded and open spots on the ground, on termite mounds, and at
 the base of trees. Data loggers were deployed during months of August 2013
 and April to July 2014 in Brasília, August 2015 to August 2016 in Nova
 Xavantina, August 2015 in Gaúcha do Norte, July to August 2016 in Lagoa da
 Confusão and July to August 2017 in Alta Floresta. Data loggers recorded
 temperatures every 10 min during the trapping period at each location.
 Variation in air temperature was also measured at the same time and
 locations, using another data logger without a PVC model attached to
 sensors (HOBO<sup>®</sup> U23 Pro v2 Temperature/Relative
 Humidity Data Logger U23-001), which was protected from rain and solar
 radiation by a PVC case suspended about 30 cm from the ground and open at
 the bottom to expose the sensor to the air.