10.5061/DRYAD.B2RBNZSB7
de Oliveira Caetano, Gabriel Henrique
0000-0003-4472-5663
University of Brasília
Santos, Juan Carlos
St. John's University
Godinho, Leandro
Universidade do Estado de Mato Grosso
Cavalcante, Vitor
Instituto Federal do Piauí
Viegas, Luisa
0000-0002-9225-4678
Rio de Janeiro State University
Campelo, Pedro
University of Brasília
Martins, Lidia
National Institute of Amazonian Research
de Oliveira, Alan
National Institute of Amazonian Research
Alvarenga, Júlio
Universidade do Estado do Mato Grosso
Wiederhecker, Helga
Universidade Católica de Brasília
de Novaes e Silva, Verônica
Instituto Chico Mendes de Conservação da Biodiversidade
Werneck, Fernanda
National Institute of Amazonian Research
Miles, Donald
0000-0001-5768-179X
Ohio University
Colli, Guarino
University of Brasília
Sinervo, Barry
University of California Santa Cruz
Time of activity is a better predictor of the distribution of a tropical
lizard than pure environmental temperatures
Dryad
dataset
2020
Bioinformatics
mechanistic models
methodology evaluation
thermoregulatory behavior
Coordenação de Aperfeicoamento de Pessoal de Nível Superior
https://ror.org/00x0ma614
99999.013716/2013-01
United States Agency for International Development
https://ror.org/01n6e6j62
AID-OAA-A-11-00012
National Science Foundation
https://ror.org/021nxhr62
EF-1241848
2020-10-21T00:00:00Z
2020-10-21T00:00:00Z
en
https://doi.org/10.1111/oik.07123
834621 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
Distribution data. We used 359 distribution records from the literature
and scientific collections spanning the range of Tropidurus torquatus. 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.
Physiological data. Between 2013 to 2017, we obtained physiological data
from five populations of T. torquatus 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 5th and 95th temperature
percentile (T90), (2) range between the 25th and 75th temperature
percentile (T50) and (3) average temperature (Tmean). Tmean and T50 have
been used in previous studies (Sinervo et al. 2010, Kubisch et al. 2016,
Piantoni et al. 2016), and the broader range, T90, 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 T90,
T50 and Tmean 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. Operative Environmental Temperatures. We recorded
operative temperatures using dataloggers (HOBO® U23 Pro v2 2x External
Temperature Data Logger U23-003) with sensors attached to PVC models of
equivalent size and color as Tropidurus torquatus. 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® 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.