http://orcid.org/0000-0002-9225-4678
Figures
Abstract
We summarize thermal-biology data of 69 species of Amazonian lizards, including mode of thermoregulation and field-active body temperatures (Tb). We also provide new data on preferred temperatures (Tpref), voluntary and thermal-tolerance ranges, and thermal-performance curves (TPC’s) for 27 species from nine sites in the Brazilian Amazonia. We tested for phylogenetic signal and pairwise correlations among thermal traits. We found that species generally categorized as thermoregulators have the highest mean values for all thermal traits, and broader ranges for Tb, critical thermal maximum (CTmax) and optimal (Topt) temperatures. Species generally categorized as thermoconformers have large ranges for Tpref, critical thermal minimum (CTmin), and minimum voluntary (VTmin) temperatures for performance. Despite these differences, our results show that all thermal characteristics overlap between both groups and suggest that Amazonian lizards do not fit into discrete thermoregulatory categories. The traits are all correlated, with the exceptions of (1) Topt, which does not correlate with CTmax, and (2) CTmin, and correlates only with Topt. Weak phylogenetic signals for Tb, Tpref and VTmin indicate that these characters may be shaped by local environmental conditions and influenced by phylogeny. We found that open-habitat species perform well under present environmental conditions, without experiencing detectable thermal stress from high environmental temperatures induced in lab experiments. For forest-dwelling lizards, we expect warming trends in Amazonia to induce thermal stress, as temperatures surpass the thermal tolerances for these species.
Citation: Diele-Viegas LM, Vitt LJ, Sinervo B, Colli GR, Werneck FP, Miles DB, et al. (2018) Thermal physiology of Amazonian lizards (Reptilia: Squamata). PLoS ONE 13(3): e0192834. https://doi.org/10.1371/journal.pone.0192834
Editor: Michael Sears, Clemson University, UNITED STATES
Received: March 31, 2017; Accepted: January 16, 2018; Published: March 7, 2018
Copyright: © 2018 Diele-Viegas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper, its Supporting Information files, and available at doi: 10.6084/m9.figshare.5293756.
Funding: This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - graduate fellowship to LDV and financial support to GRC (http://www.capes.gov.br/): Allowed the development of the research and the data collection. Conselho Nacional de Desenvolvimento Científico e Tecnológico - research fellowship to TAP and financial support to FPW (475559/2013-4) (http://cnpq.br/): Allowed the data collection. PVE (Pesquisador Visitante Especial) grant from CNPq (http://cnpq.br/) – BRS: Allowed the development of the research. Fundação de Amparo à Pesquisa do Amazonas – financial support to FPW and WM (FAPEAM, 062.00665/2015) (http://www.fapeam.am.gov.br/): Allowed the study design and decision to publish. Partnerships for Enhanced Engagement in Research from the U.S. National Academy of Sciences and U.S. Agency of International Development (PEER NAS/USAID PGA-2000005316) – financial support to GRC and FPW (http://sites.nationalacademies.org/pga/peer/index.htm): Allowed the development of the research and the decision to publish. Fundação de Amparo à Pesquisa do Pará -ICAAF number 011/2012 SISBIOTA Herpeto-Helminto – financial support to LDV and TAP (http://www.fapespa.pa.gov.br/): Allowed the data collection. *Fundação deAmparo à Pesquisa do Distrito Federal (FAPDF)- financial support to GRC (http://www.fap.df.gov.br/ <http://www.fap.df.gov.br/>)* - Allowed the data collection. National Science Foundation – Emerging Frontiers [grant number 1241885, Collaborative Research: “Quantifying Climate-forced Extinction Risks for Lizards, Amphibians, Fishes, and Plants”] – LDV, BRS, GRC and FPW (https://www.nsf.gov/): Allowed the data collection, analysis and reparation of the manuscript. National Science Foundation – Emerging Frontiers [grant number 241848] – BRS and DBM: Allowed the data collection, analysis and preparation of the manuscript. Research Council via a George Lynn Cross Research Professorship of the University of Oklahoma and the Sam Noble Museum support to LJV (http://samnoblemuseum.ou.edu/): Allowed the decision to publish.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Body temperature (Tb) in ectotherms influences all physiological and behavioral processes [1]. Consequently, maintenance of Tb within suitable limits is essential for ectotherms survival [2]. Thermoregulators actively maintain Tb within a restricted range of temperatures by heliothermy, i.e., by basking in the sun, or by thigmothermy, i.e., by contact with warm surfaces [3]. Thermoconformers do not actively thermoregulate, so their Tb parallels fluctuations in the environmental temperature [1, 4]. However, no lizard species has been shown to be a complete thermoconformer; all will move to avoid unfavorable extreme temperatures. This category is often used for species that select areas with relatively uniform temperatures, such as shaded forest, where active thermoregulation is not needed to maintain relatively stable body temperatures. Using a strictly thermoconforming strategy requires that species have broad thermal tolerances [1], and experience high variation in Tb throughout the day, season and geographic range.
In the field, lizards are usually active at a restricted range of Tb. It is commonly assumed that these temperatures represent their actual thermal preferences [5]. However, laboratory experiments show that the variance in Tb range observed in nature for tropical lizards exceeds both the preferred Tb and the voluntary Tb range observed when the animals are subjected to thermal gradients [6–7]. Consequently, tropical lizards may already be experiencing Tb’s at or above their physiological optima [8], putting them dangerously close to their upper thermal thresholds. These upper thermal limits are likely to be exceeded in the next few decades as a consequence of climate change [9]. An alternative interpretation is that preferred Tb and voluntary Tb reflect not only physiological limits, but are also tailored to specific activities, such as digestion, reproduction and foraging for different types of prey [1], and that laboratory studies do not fully reflect the range of motivational states. Field activity temperatures may vary seasonally, independent of variation in environmental temperatures (e.g., [10]).
The influence of ambient temperature on key physiological traits is described by thermal performance curves (TPC) [2]. A species’ thermal sensitivity can be visualized and quantified through TPCs, which reveal several important thermal properties of ectotherms. These include the optimal temperature (Topt), for maximal animal performance; the breadth of temperatures that results in a species performing at ≥ 80% of its optimal capacity (B80); and the thermal tolerance range, which is the difference between the critical thermal minimum and maximum temperatures (CTmin and CTmax), i.e., the extreme temperatures that an individual can maintain locomotor function [11]. Topt can vary within and among species and varies among physiological traits, according to the hypothesis of multiple physiological optima [1, 12]. Locomotor performance is one of the best-studied traits in thermal physiology, because it is related to Darwinian fitness and presumably reflects the ability to escape from predators, capture prey, and reproduce [13]. TPCs are also useful in assessing extinction risk of ectotherms. Because global warming may alter the spatial distribution of preferred microclimates [14], animals that rely on behavioral thermoregulation may experience a reduction in the time available for activity during periods when preferred microclimates become too rare to locate without overheating [15]. Restriction in activity time can result in extirpation or extinction if the remaining time is insufficient to perform all the necessary functions for successful breeding and recruitment [16].
Amazonia is a biogeographic region predicted to be strongly affected by climate change [17–18]. It covers about eight million square kilometers spread over nine South American countries [19]. Current estimates suggest that at least 210 species of lizards occur in the Amazon, although the actual diversity is poorly known [20–21]. Observed trends in the region’s climate include an overall reduction in precipitation and increased duration and intensity of droughts, especially in southern Amazonia [22], where climate change interactions with land-use change are stronger [23–24]. Recent studies indicate a long-term decreasing trend of carbon accumulation in Amazonia due to increased tree turnover and mortality rates [25]. Moreover, increased dryness may result in large-scale reductions in biomass, carbon uptake and net primary productivity [26]. Some models suggest that these changes may induce biome shifts in Amazonia, with the forest being replaced by drier vegetation associations, such as seasonal forests and savannas [27]. Therefore, recent and projected climate trends in Amazonia will likely result in a more open canopy and increased ambient temperature for forest-dwelling lizards. Despite the vastness and complexity of Amazonian habitats, thermal-physiology data for Amazonian lizards are limited, with most studies scattered among the major groups of Squamata. Most data are focused on reports of field-active Tb and there have been few controlled experiments on preferred or optimal Tb.
We aim to provide the first comprehensive summary of thermal physiology characteristics of Amazonian lizards, which is essential to enhance our understanding of the effects of global warming on current and future lizard diversity in this megadiverse region. We first characterize patterns of variation in Tbs of Amazonian lizards (including some species that occur peripherally, at the ecotone between the Amazonian rainforest and the savanna-like Cerrado, an ecophysiological tension interface). We also provide new data on the thermal biology of some of these species and summarize the information on lizards’ modes of thermoregulation. Moreover, we next analyze evolutionary trends among thermal and physiological traits by examining the consistency of trait variation with phylogeny (i.e., phylogenetic signal) and the correlations among traits after controlling for the influence of phylogeny.
Material and methods
Literature review
We carried out a literature survey for data on seven physiological traits of Amazonian lizards: field-active Tb, preferred temperature (Tpref), minimum and maximum voluntary temperatures (VTmin and VTmax), critical thermal minimum and maximum (CTmin, CTmax) and the optimal temperature for locomotor performance (Topt). Only data on Tb were available. Because some species have distributions extending beyond Amazonia into other biomes, our review extended beyond Amazonia, and included species from the Atlantic Rainforest, Caatinga, and Cerrado regions of Brazil, as well as the Lavrado, a savanna enclave in northeastern Roraima, Brazil. We also included data from lizards occurring in tropical forests of Central America that have similar ecophysiological traits. Species were classified as thermoregulators or thermoconformers based on whether the studies indicated they were heliotherms (thermoregulators) or non-heliotherms (defined here as thermoconformers). We also reviewed the literature to search for substrate (Tsub) and air-temperature (Tair) data associated with Tb, and obtained 45 studies from the last 50 years, with reported Tb’s from 62 species occurring in Amazonia.
Field data
We completed our dataset with data collected by the authors throughout the year. First, we included Tb data on eight species collected from 1993 to 1999 in seven localities in Amazonia: Estación Biológica de la Pontifícia Universidad Católica (Quito), within the Reserva de Producción Faunística Cuyabeno (Sucumbíos Province, Ecuador, 0°0’N, 76°10’W); Juruá River Basin, ca. 5km north of Porto Walter (Acre, Brazil, 8°15’S, 72°46’W); Ituxi River (Amazonas, Brazil, 8°20’S, 65°43’W); 30km NW of Caracaraí (Roraima, Brazil, 2°50’N, 60°40’W); Parque Estadual Guajará–Mirim, on the Formoso River (Rondônia, Brazil, 10°19’S, 72°47’W); SE of Manaus, on the margin of the Amazon River (Amazonas, Brazil, 3°20’S, 59°4’W); and Agropecuária Treviso, 101 km S and 18 km E of Santarém, close to Curuá-Una River (Pará, Brazil, 3° 9’ S, 54° 50’W) (Fig 1). For the species from these localities, cloacal temperatures were measured from adult individuals with Miller & Weber quick reading cloacal thermometers (resolution of 0.2°C). Literature and empirical data provide information on the general thermal characteristics of each species, and we do not address within-species variation due to factors such as reproduction, digestion and infection.
Body-temperature data were collected between 1993 and 1999 and thermal preference and performance data were collected between 2014 and 2016. Lizards are illustrative of the eleven families found in the field. Numbers are representative of the localities, as follows: 1) 30km NW of Caracaraí (Roraima, Brazil, 2°50’N, 60°40’W); 2) Floresta Nacional do Amapá (Amapá, Brazil, 0°55’N, 51°36’W); 3) Estación Biológica de la Pontifícia Universidad Católica (Sucumbíos Province, Quito, Ecuador, 0°0’N, 76°10’W); 4) Floresta Nacional de Caxiuanã (Pará, Brazil, 1°44’S, 51°27’W); 5) Yasuni National Park (Ecuador, 1°5’S, 75°55’W); 6) Reserva Florestal Adolpho Ducke (Manaus, Brazil, 2°57’S, 59°55’W); 7) Agropecuária Treviso (Pará, Brazil, 3° 9’ S, 54° 50’W); 8) SE of Manaus (Amazonas, Brazil, 3°20’S, 59°4’W); 9) Juruá River Basin (Acre, Brazil, 8°15’S, 72°46’W); 10) Ituxi River (Amazonas, Brazil, 8°20’S, 65°43’W); 11) Centro de Pesquisas Canguçu (Pium, Tocantins, Brazil, 9°56’S, 49°47’W); 12) Parque Estadual Guajará–Mirim (Rondônia, Brazil, 10°19’S, 72°47’W); and 13) Los Amigos Biological Station (Peru, 12°34’S, 70°6’W).
Ecophysiological data were collected in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Comissão de Ética no Uso de Animais (CEUA)–INPA (Permit Number: 029/2014), CEUA–MPEG (Permit Number: 01/2015), and CEUA UnB (33716/2016). All collecting in Brazil was done under pertinent IBAMA (073/94-DIFAS) and SISBIO (13324–1, 49241, 50381, 44832–1) permits. All efforts were made to minimize discomfort to research animals.
We collected thermal-preference and performance data on 27 species between 2014 and 2016 in six localities, five in Amazonia—Floresta Nacional do Amapá (Amapá, Brazil, 0°55’N, 51°36’W), Floresta Nacional de Caxiuanã (Pará, Brazil, 1°44’S, 51°27’W), Reserva Florestal Adolpho Ducke (Manaus, Brazil, 2°57’S, 59°55’W); Los Amigos Biological Station (Peru, 12°34’S, 70°6’W), and Yasuni National Park (Ecuador, 1°5’S, 75°55’W), and one in the Amazonia-Cerrado ecotone—Centro de Pesquisas Canguçu (Pium, Tocantins, Brazil, 9°56’S, 49°47’W) (Fig 1). Specimens were captured by active search or with pitfall traps checked twice daily. Lizards were kept in captivity for a period of no more than three days, and were released at their site of capture after a recovery time of at least four hours after the last trial. While in captivity, animals were housed individually in plastic containers with air holes and a damp cloth for moisture, without access to food. We measured Tpref, CTmin, CTmax, and thermal performance curves on captive lizards. Table 1 shows the number of individuals used in each test by species. We measured snout-vent length (SVL) to 0.1mm with a Vernier caliper. A few voucher specimens of each species were euthanized with a lethal dose of Tiopental anesthetics, fixed in 10% formalin, and permanently stored in 70% ethanol. Voucher specimens were deposited in the Herpetological Collections of Museu Paraense Emílio Goeldi (MPEG), Pará, Brazil; Instituto Nacional de Pesquisas da Amazônia (INPA), Amazonas, Brazil; Universidade de Brasília, Distrito Federal (CHUNB), Brazil; Monte L. Bean Life Science Museum, Utah, USA; and Museo de Historia Natural de la Universidad Nacional Mayor de San Marcos, Lima, Peru.
We characterized the thermal biology of captured lizards with the following protocol. We measured the lizards’ Tb by using infrared thermometers, focusing the laser on the mid-portion of the animal’s ventral side, with approximately 15cm between animal and thermometer. We validated the use of body temperatures based on infrared thermometers with data on Zootoca vivipara, with high correlation between core and surface temperatures (0.85; n = 34, P<0.001). This species is of similar size to most of the lizards in our data set. Smaller species should present even higher correlations between core and surface temperatures, and we did not include any large species in the laboratory tests. Tpref and voluntary upper (VTmax) and lower (VTmin) temperatures were measured using a thermal gradient. Lizards were placed for 2 hours in plywood tracks 1m in length and 40cm wide, with a photothermal gradient of 15–40°C generated across each track using ice at one end and a heating lamp ~100W full spectrum at the other (sensu [28]). Tb was measured every 3–5 minutes, and Tpref was estimated as the mean of all Tb values recorded. The first measurement was made after five minutes of the animals’ positioning inside the track, to allow lizards to get acclimated to the track and reach their preference. VTmax and VTmin for each individual during this interval were estimated by the interquartile range of Tpref [29]. We measured Tpref for all individuals captured. Diurnal lizards were tested during the day, while nocturnal lizards were tested after sunset. Afterwards, lizards were arbitrarily chosen to undergo either the thermal tolerance or performance tests.
Critical temperatures were measured on 485 individuals of 26 species. An individual’s body temperature was decreased or increased in a chamber cooled by ice packs or heated by hot water until the animal lost its righting response. Each animal was tested for both CTmin and CTmax, and heated/cooled to their Tpref immediately after the tests. We always measured CTmin before CTmax, since the last may get the animals most impaired and thus needs that the animals have a longer recovery time. To calculate Topt, we measured locomotor-capacity experiments on 254 individuals. We stimulated each individual to run once at 2–7 randomly-assigned temperatures (15°, 20°, 25°, 30°, 35°, 40° and 43°C). Species that only occur in shady environments may suffer at extreme temperatures, and such species were run at 20°, 25°, 30° and 35°C. Lizards were allowed to recover at least four hours between trials. During the recovery period, we monitored their Tb and activity inside their containers at least once every hour, in order to assess their health and well-being after the stress tests. We only released the animals after we assessed that they had recovered their normal activity pattern. No animal died prior to the end of the experiments.
To measure performance, the experimenter manually stimulated lizards to run around a circular track [30]. A track with a 4m circumference was used for lizards with SVL≥50mm, and a track with 1m circumference was used for lizards SVL<50mm. Each trial ended when the lizard reached exhaustion and was unable to right itself when placed in a supine position. Animal performance was calculated as the voluntary distance traveled (number of times around the track x track circumference). Topt was the body temperature that yielded the highest value of locomotor performance. We determined Topt from the thermal performance curves.
Analysis
We used the statistical software environment R 3.3.3 [31] for all calculations. Dependence between thermal physiology parameters of a priori classification of thermoregulation modes, SVL, families and species were analyzed by simple stepwise regression and one-way analysis of variance. Shapiro and Levene’s tests were used, respectively, to test assumptions of normality and homogeneity of variance for parametric variables. We used the Pearson correlation coefficient to determine the correlation between Tb, Tsub and Tair. For comparative analyses, we used the chronogram for Squamata estimated by Zheng & Wiens [32], which included all of the species for which we were able to assemble thermophysiological traits. Phylogenetic signal was calculated based on Blomberg’s K [33], which is an evolutionary model-based metric of phylogenetic-signal strength. A K-value of one indicates that the distribution of trait values follows the expectation of Brownian motion model of evolution along the tree [33]. This indicates that trait variance among species accumulates in direct proportion to their divergence time, as measured by the branch lengths separating them in a phylogenetic tree [34–35]. Values of K<1 indicate that traits are less conserved than expected, an indication of adaptive evolution, whereas values of K>1 indicate that trait values are more conserved than expected by Brownian motion evolution. We used phytools [36] to calculate Blomberg’s K and to measure the phylogenetic pairwise correlations between all thermal traits.
TPC’s were generated for each species using the packages ggplot2 [37], grid [31], mgcv [38] and proc [39] to do a Generalized Additive Mixed Modeling (GAMM) [40]. These models use additive nonparametric functions to model covariate effects while accounting for overdispersion and correlation, by adding random effects to the additive predictor [41]. Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to select the best correlation structure prior to estimating the TPC. AIC measures the quality of fit of the model, penalized by model complexity, and BIC additionally considers the number of observations included in the model [42]. Lizard performance at different temperatures was the response and individual was included as a random effect. The extremes of the curve were fixed at the average CTmin and CTmax values for that species. We tested several correlation structures to select the best fit including: temporal correlation structures (autoregressive process [corAR1], continuous autoregressive process [corCAR1], and autoregressive moving average process [corARMA]) and spatial residual correlation structures (Gaussian spatial correlation [corGaus], exponential spatial correlation structure [corExp], rational quadratics spatial correlation [corRatio] and spherical spatial correlation [corSpher]). We chose the correlation structure that yielded the lowest AIC and BIC values [42].
Results
We obtained thermal data for 69 lizard species from eleven families (Table 1), including new data on field-active Tb, Tpref, thermal performance and tolerance from 27 species (Table 2). Among all species with physiological data, 64 are diurnal, one is cathemeral, and four nocturnal. Based on the literature, 38 species are classified as thermoconformers, while 31 are thermoregulators.
Lizards’ body temperature was positively correlated with environmental temperature (Tb and Tsub: r = 0.80, P<0.01; Tb and Tair: r = 0.67, P<0.05). Seven species generally classified as thermoconformers had Tsub/Tair higher than Tb, suggesting that these species do not gain additional heat from the environment, but may be thermoregulating by selecting lower temperatures or using evaporative cooling. A one-way ANOVA revealed significant differences in all physiological traits in relation to a priori classification of thermoregulation mode, SVL, family, and species. Fig 2 shows the range of temperatures for each evaluated trait for each thermoregulation mode. Species generally classified as thermoregulators had higher mean values for all thermal traits than those generally classified as thermoconformers, as well as larger variation in Tb, CTmax and Topt. Variation in Tpref, VTmin and CTmin was lower in species classified as thermoregulators and greater in species classified as thermoconformers, though mean values were relatively similar (Fig 3). In spite of these differences, our results show an overlap in most thermal traits between species classified as thermoregulators and those classified as thermoconformers, with some lizards considered thermoregulators having ranges of temperatures similar to others identified as thermoconformers. Thus, a dichotomous classification of thermoregulation mode may not be satisfactory.
Species were classified a priori as thermoregulators or thermoconformers. Values of temperature (x-axis) correspond to the mean value for each species.
Species were classified a priori as thermoregulator or thermoconformers. Lines indicate the maximum values.
We also found phylogenetic pairwise correlations between all thermal traits evaluated (Tb, Tpref, VTmin, VTmax, CTmin, CTmax, and Topt), except between (1) Topt and CTmax, and (2) CTmin, which only correlates with Topt (Table 3). Thus, selection on one thermal characteristic affects the evolution of all those considered here, except, possibly, in the two cases mentioned above. We detected significant departures from Brownian motion evolution for Tb (K = 0.64, P = 0.0001), Tpref (K = 0.49, P = 0.04), and VTmin (K = 0.5, P = 0.01), but not for the other thermal traits (VTmax, K = 0.39, P = 0.21; CTmin, K = 0.49, P = 0.12; CTmax, K = 0.50, P = 0.079; Topt, K = 0.74, P = 0.17). Lizards in the family Teiidae are comprised only of species classified as thermoregulators, and had the highest values for all three thermal traits. The lowest Tb was found in one species of Hoplocercidae (forest-dwelling lizards), and the lowest VTmin was observed in Gekkonidae, both families containing only species classified as thermoconformers. Gekkonidae and Dactyloidae presented the lowest Tpref. Although Dactyloidae is a mixed family, the only dactyloid species classified as thermoregulator in this study has no Tpref data available. Thus, all Tpref measurements for this family are from species classified as thermoconformers (six out of ten analyzed species), which may explain the low value found. Families with both thermoregulation modes tended to have intermediate mean values for all thermal parameters (Table 4). Tropiduridae had five species classified as thermoregulators with mean Tb 32.2°C. Of those, data on Tpref and VTmin value (29.1° and 28.8°C, respectively) were available only for Tropidurus hispidus. Among the three species of tropidurids classified as thermoconformers, the mean values of Tb, Tpref and VTmin were 28.5°, 27.5° and 26.3°C, respectively. Four gymnophthalmids were classified as thermoregulators and they had mean values of Tb, Tpref and VTmin of 29.4°, 27.5° and 25.8°C, respectively, while the other 13 species that are considered thermoconformers had mean values of Tb, Tpref and VTmin of 28.2°, 25.1° and 23.8°C, respectively. These results reflect the among-family pattern where families with species considered to be thermoregulatory had higher mean values of Tb, Tpref and VTmin than families which only have species classified as thermoconformers.
We measured the thermal dependence of locomotor performance for ten species of Amazonian lizards (Fig 4). The best correlation structure and estimated TPC parameters for each of these ten species are presented in Table 5. The CTmax among all species classified as thermoregulators varied between 45–50°C. We observed small variation in the shape of the TPCs, with exception of Copeoglossum nigropunctautm and Cnemidophorus cryptus, which had almost linear curves and a high confidence interval. Gonatodes humeralis and Norops fuscoauratus are forest shade species that were classified a priori as thermoconformers whose TPC’s have broad plateaus, with CTmax reaching 40°C. In G. humeralis, Topt is closer to Tpref, while in N. fuscoauratus Topt is closer to VTmax. The TPC for Arthrosaura kockii had a plateau, which was slightly inclined at lower temperatures. Plica plica had a pattern similar to that of A. kockii, even though the former is considered a thermoconformer and the latter a thermoregulator. Plica plica had a Topt closer to VTmax, while in A. kockii Topt was closer to VTmin. For Ameiva ameiva there was a steep performance increase with higher body temperature, with a Topt greater than the mean values for VT and Tpref, and closer to Tb. In Cnemidophorus cryptus, Topt was greater than VT’s and Tpref, which is similar to A. ameiva, but below its Tb. Chatogekko amazonicus, Arthrosaura reticulata, and Leposoma percarinatum are found in the leaf litter and all three species have CTmax values near 35°C. Topt for Chatogekko amazonicus and A. reticulata’s was close to their VTmax, while for L. percarinatum’s Topt is nearer its Tb. Topt values for both species classified as thermoregulators and thermoconformers partially overlap, similar to values for the other thermal physiological parameters analyzed.
A) Chatogekko amazonicus; B) Gonatodes humeralis; C) Copeoglossum nigropunctatum; D) Norops fuscoauratus; E) Plica plica; F) Arthrosaura kockii; G) Arthrosaura reticulata; H) Leposoma percarinatum; I) Ameiva ameiva; and J) Cnemidophorus cryptus. Gray shaded region shows the 95% confidence interval. Black points represent the results of individual tests at different body temperatures: 15°, 20°, 25°, 30°, 35°, 40° and 43° for species classified as thermoregulators and 20°C, 25°C, 30°C and 35°C for shade-associated species classified as thermoconformers. Short vertical black lines indicate the number of trials at each temperature. Black lines at the curves’ extremes are the critical thermal minimum (CTmin) and critical thermal maximum (CTmax). Topt = optimal temperature; Tb = body temperature; Tpref = preferred temperature; VTmin = minimum voluntary temperature; VTmax = maximum voluntary temperature.
Discussion
We observed a non-significant phylogenetic correlation between Topt and CTmax, and considering that Topt is correlated with all other thermal traits, this is in agreement with the argument that tolerance limits have less relevance to thermoregulation than Topt [43–44]. On the other hand, this is incongruent with the results of Huey and Kingsolver [12] and Huey et al. [45], who found that Topt and CTmax are correlated and tend to coevolve. Possibly this is because these studies included many shade-loving species in which the highest obtainable temperatures are well below those likely to cause physiological stress. Conversely, the correlation between CTmin and Topt suggests that directional selection on CTmin will have a direct effect on locomotor performance, raising or lowering Topt and the mid-level performance temperature range. This is also in accordance with the hypothesis that species that restrict their activities to deeply-shaded areas may be more limited by low rather than high temperatures [46–47].
Blomberg et al. [33] found relatively low values of K indicative of low phylogenetic signal for physiological traits in Australian skinks (e.g., Tpref and Topt), as we report here for Tb, Tpref and VTmin. Such a result was expected considering the importance of environment on the thermal characters of ectotherms, which in turn influences nearly all their physiological processes. These traits are apparently influenced by factors other than phylogeny, such as phenotypic plasticity, which is the ability of an organism to express different phenotypes depending on its biotic and abiotic environments [48]. This factor influences not only the thermal physiology of lizards, but also their life histories [49–51]. The low signal could also be due to local adaptation, since it indicates that these traits have not been conserved evolutionarily [52].
For the thermal parameters VTmax, CTmin, CTmax and Topt, we found no departure from Brownian motion evolution. Nevertheless, some of these traits may be limited by physiological constraints common to all lizards, hindering the chances of evolutionary changes that would be reflected in the phylogeny. According to Huey & Kingslover [12], if the population lacks the underlying genetic variation to shift its thermal sensitivity adequately in response to selection, we might expect that this population will not be able to adapt and evolve rapidly enough to track changes in environmental temperatures, such as those caused by the climate warming. For example, broad variation in Tpref can be too low [53] or additive genetic variation can be overwhelmed by maternal effects [54] to allow rapid adaptation to climate warming measured on decadal time scales [16]. Alternatively, our sampling was insufficient to detect local adaptation since we did not designed our study for that. The ages of these species are probably all in the range of millions of years, indicating that all have passed through many climatic fluctuations that affected all parts of Amazonia to some extent (e.g., [55–58]), which may have led to genetic variation within and between populations.
Thermal conditions within lowland tropical forest are likely insufficient to permit Tbs below the level of thermoregulation found [6]. The ability to attain Tpref by basking in rainforest microenvironments may be limited due to lower heterogeneity in the thermal environment. All species with performance and Tb data available had Topt < Tb, although their Tb’s are within the broad plateau of their TPC. This is in disagreement with Bennett [59], who suggested that Topt is always higher than Tpref and Tb. Topt in all species is between 7.7–24.4°C below CTmax. By comparing Tb with CTmax in 19 species with available data, CTmax was between 6–12°C above the mean values of Tb, consistent with Ji et al.’s [60] study on Sphenomorphus indicus and Du et al.’s study [7] on Eumeces elegans males. When we consider the highest values of Tb measured, the difference drops to 4–11°C for most species, except Kentropyx calcarata and Tropidurus hispidus, where the difference is ~0.5°C and 3.5°C, respectively. Overall, our results partially support Hoffman and Sgró’s claim [9] that tropical lizards have their thermal traits close to their upper thermal thresholds, which are likely to be exceeded in the next few decades. Nevertheless, we measured Topt only for running speed. It may be that Topt for other activities is closer to that observed for Tb. Within the forest, it is less likely that availability of temperatures lower than Topt will limit any of the species in the near future, especially in the case of heliotherms, which are probably presently more limited by low than high temperatures.
Although we found statistical differences between the thermoregulatory modes generally attributed to the species for all physiological traits, most species appear to show substantial overlap in their thermal physiology regardless of their a priori classification into thermoregulatory modes. Thus, the tropical lizard species here studied do not form discrete categories, as suggested by Pough and Gans [3]. We tested the a priori categories for thermoregulatory mode because these continue to be used to describe lizard thermoregulation, especially in relation to the predicted effects of climate change. However, the relationship between body and environmental temperatures in lizards shows a cline rather than discrete categories [3, 61] and future studies would gain by abandoning them and using continuous variables when more detailed information is available [62].
Geographic variation in climate can lead to differences in thermal physiology among species [63], so the physiological data obtained for some species outside Amazonia must be seen as an approximation of the thermal traits for those species in this region. Among all species included in this study, smaller animals had the lowest temperatures for all traits, probably due to their relatively low thermal inertia [64–65]. Chatogekko amazonicus, A. ameiva and C. cryptus had Topt close to their Tb’s, so these species can achieve their highest locomotor performance under current environmental conditions. In contrast, P. plica and A. reticulata achieve their Topt closer to Tpref, and G. humeralis, C. nigropunctatum and A. kockii achieve Topt closer to VTmax; in both cases, Topt was lower than Tb obtained from field measurements. Norops fuscoauratus and Leposoma percarinatum had Topt considerably above Tb, Tpref, and VTmax, closer to CTmax. For these species, Topt might reflect the thermal optimum of other physiological processes, or an intermediate thermal optimum for different processes [64]. Among the species with Tb>Topt, the high Tb likely reflects the thermal optima for other physiological process, such as digestion [11, 66–68].
We hypothesize that many tropical rainforest lizards may be affected by high environmental temperatures, considering that their locomotor performance is better at lower temperatures than they are already experiencing in field. Our inferences are in agreement with other studies with fewer species and at higher latitudes, which predict that many tropical lizards are at an imminent risk of extinction due to human induced global warming [8, 16, 69]. Additional factors are affecting large parts of Amazonia such as forest loss, degradation, and fragmentation [70]. These factors can cause rapid microclimate changes towards hotter and drier conditions that climate models are unable to predict with accuracy [71]. Most of the species in this study had some margin for an increase in average Tb with low cost to performance, since their Tbs are still within the broad plateau of their TPC. However, selection is weaker on species with broad TPCs than narrow TPCs, which have a lower capacity to evolve to track changes in climate [12].
Sinervo et al. [16] provided the first model of the potential effects of a warming climate on species distributions based on thermal physiology. Integrative models such as those that incorporate phenotypic plasticity and genetic variability will allow projections of adaptive radiation occurring under warmer environments [72–73]. Both phenotypic plasticity and genetic variability are directly dependent on environmental conditions that allow lizards to gain and lose heat [16, 60, 74]. We expect that, with more temperature data on a higher diversity of Amazonian lizards’ species, we can better understand the effects of climate change on these animals. Also, investigations taking into account the phylogeographic history of Amazonian lizard species, many of which are known to exhibit cryptic diversity and high population structure, will be important to refine and help detect geographic divergence of thermal traits and extinction risks.
Conclusions
This study represents the first effort to compile and provide novel thermal-biology data obtained across wide geographic ranges and taxonomic diversity of Amazonian lizards. We integrated field and literature data with phylogenetic inferences to better understand how updated ecophysiological traits can serve as a baseline to inform predictions of global warming effects on the future of rainforest lizards.
Although lizards generally classified as thermoregulators and thermoconformers show significant differences in their thermophysiological characters, our results indicate that these groups do not form discrete categories, since most species are intercalated in their thermal physiology regardless of their thermoregulation modes. Most species considered to be thermoconformers in Amazonia prefer warmer microhabitats to gain additional heat from the environment and thus cannot be considered thermoconformers in the strict sense, corroborating the idea that thermoconformity is one extreme of a continuum with thermoregulation in the other extreme [3].
Our results suggest that selection on CTmin will affect locomotor performance directly by amplifying or reducing Topt and the range of temperatures of mid-level performance. We found no phylogenetic signal for CTmax, CTmin, Topt and VTmax. In turn, Tb, Tpref and VTmin are less conserved than expected, so they appear to be influenced by factors other than phylogeny, such as strong selection or phenotypic plasticity.
Lizards are excellent models for investigating the biological effects of climate change. Although Amazonian lizards have an apparent margin for an increase in Tb with low cost to performance, suggesting they may show some resilience to warming, their broad TPC’s may not allow rapid evolutionary response to a quickly warming climate. More studies on the thermal physiology of Amazonian lizards are needed to obtain data representative of the high species diversity in the region so we can better understand the effects of climate change on their distribution and densities.
Supporting information
S1 File. Preferred, voluntary and critical temperatures from all individuals tested in ecophysiological experiments.
Tpref = preferred temperature; VTmin = Minimum voluntary temperature; VTmax = Maximum voluntary temperature; CTmin = Minimum critical temperature; CTmax = Maximum critical temperature. Available on doi: 10.6084/m9.figshare.5293756.
https://doi.org/10.1371/journal.pone.0192834.s001
(XLSX)
Acknowledgments
We are grateful to A. Costa, A. Missassi, A.P. Costa, J. Fenker, J. Cosenza, G. Lima, L. Chrisley and L. Franco for their help during fieldwork, and J. W. Sites, Jr. (BYU) for assistance. We also thank Museu Paraense Emílio Goeldi (MPEG), Universidade Federal do Pará (UFPA), Institutos Nacionais de Ciência e Tecnologia—Centro de Estudos Integrados da Biodiversidade Amazônica (INCT-CENBAM) and the Programa de Pesquisa em Biodiversidade (PPBio) for technical support. FPW thanks the L’Oreal-UNESCO For Women In Science Program for technical support. We acknowledge for financial support the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Sistema Nacional de Pesquisa em Biodiversidade (SISBIOTA)/Rede Diversidade de anfíbios e répteis e helmintos parasitas associados na Região Amazônica–CNPq/FAPESPA, Sam Noble Museum, University of Oklahoma Research Council, Conselho Nacional do Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa do Distrito Federal (FAPDF), the Partnerships for Enhanced Engagement in Research from the U.S. National Academy of Sciences and U.S. Agency of International Development (PEER NAS/USAID), Fundação de Amparo à Pesquisa do Amazonas (FAPEAM), National Science Foundation (NSF) and Pesquisador Visitante Especial (PVE) grant from CNPq.
References
- 1.
Huey RB. Temperature, physiology and the ecology of reptiles. In: Gans C, Pough FH, editors. Biology of the Reptilia, vol. 12. Physiological Ecology. New York: Academic Press; 1982. pp. 25–91.
- 2. Huey RB, Stevenson RD. Integrating thermal physiology and ecology of ectotherms. Am Zool. 1979;19: 357–366.
- 3.
Pough FH, Gans C. The vocabulary of reptilian thermoregulation. In: Gans C, Pough FH, editors. Biology of the Reptilia Vol. 12: Physiological Ecology New York: Academic Press; 1982. pp. 17–23.
- 4. Huey RB, Slatkin M. Costs and benefits of lizard thermoregulation. Q Rev Biol. 1976;51: 363–384. pmid:981504
- 5. Licht P, Dawson WR, Shoemaker VH, Main AR. Observations on the thermal relations of Western Australian lizards. Copeia. 1966;1966: 97–110.
- 6. DeWitt CB. Precision of thermoregulation and its relation to environmental factors in the desert iguana Dipsosaurus dorsalis. Physiol Zool. 1967;40: 49–66.
- 7. Du WG, Yan SJ, Ji X. Selected body temperature thermal tolerance and thermal dependence of food assimilation and locomotor performance in adult blue-tailed skinks Eumeces elegans. J Therm Biol. 2000;25: 197–202.
- 8. Huey RB, Deutsch CA, Tewksburry JJ, Vitt LJ, Hertz PE, Pérez HJA, et al. Why tropical forest lizards are vulnerable to climate warming. Proc Biol Sci. 2009;
- 9. Hoffmann AA, Sgrò CM. Climate change and evolutionary adaptation. Nature. 2011;470: 479–485. pmid:21350480
- 10. Magnusson WE. Body temperatures of field-active Amazonian Savanna Lizards. J Herpetol. 1993;27: 53–58.
- 11. Angilletta MJ Jr, Niewiarowski PH, Navas CA. The evolution of thermal physiology in ectotherms. J Therm Biol. 2002;27: 249–268.
- 12. Huey RB, Kingsolver JG. Evolution of resistance to high temperature in ectotherms. Am Nat. 1993;142: S21–S46.
- 13. Irschick DJ, Garland T Jr. Integrating function and ecology in studies of adaptation: investigations of locomotor capacity as a model system. Annu Rev Ecol Evol Syst. 2001;32: 367–396.
- 14. Sears MW, Raskin E, Angilletta MJ Jr. The world is not flat: Defining relevant thermal landscapes in the context of climate change. Integr Comp Biol. 2011;51: 666–675. pmid:21937668
- 15. Sears MW, Angilletta MJ Jr, Schuler MS, Borchert J, Dilliplane KF, Stegman et al. Configuration of the thermal landscape determines thermoregulatory performance of ectotherms. Proc Natl Acad Sci U S A. 2016;113: 10595–10600. pmid:27601639
- 16. Sinervo B, Méndez-de-la-Cruz F, Miles DB, Heulin B, Bastiaans E, Villagrán-Santa Cruz M, et al. Erosion of lizard diversity by climate change and altered thermal niches. Science. 2010;328: 894–899. pmid:20466932
- 17. Killeen TJ, Solórzano LA. Conservation strategies to mitigate impacts from climate change in Amazonia. Philos Trans R Soc Lond B Biol Sci. 2008;363: 1881–1888. pmid:18267917
- 18. Nobre CA, Sampaio G, Borma LS, Castilla-Rubio JC, Silva JS, Cardoso M. Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proc Natl Acad Sci U S A. 2016;11339: 10759–10768
- 19.
Eva HD, Huber O. A proposal for defining the geographical boundaries of Amazonia. Luxemburg: Office for Official Publications of the European Communities; 2005.
- 20. Avila-Pires TCS. Lizards of Brazilian Amazonia. Zool Verh. 1995;299: 1–706.
- 21. Ribeiro-Júnior M, Amaral S. Diversity distribution and conservation of lizards (Reptilia: Squamata) in the Brazilian Amazonia. Neotrop Biodivers. 2016;21: 195–421.
- 22.
IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri RK and Meyer LA editors]. IPCC, Geneva Switzerland. 2014; 151.
- 23. Feeley KJ and Rehm EM Amazon’s vulnerability to climate change heightened by deforestation and man-made dispersal barriers. Glob Change Biol. 2012;18: 3606–3614.
- 24. Zelazowski P, Malhi Y, Huntingford C, Sitch S, and Fisher JB. Changes in the potential distribution of humid tropical forests on a warmer planet. Philos Trans R Soc Lond B Biol Sci. 2010;369: 137–160.
- 25. Brienen RJW, Phillips OL, Feldpausch TR, Gloor E, Baker TR, Lloyd J, et al. Long-term decline of the Amazon carbon sink. Nature. 2015;519: 344–348. pmid:25788097
- 26. Feldpausch TR, Phillips OL, Brienen RJW, Gloor E, Lloyd J, Lopez-Gonzalez G. et al. Amazon forest response to repeated droughts. Global Biogeochem Cycles. 2016;
- 27. Salazar LF, Nobre CA. Climate change and thresholds of biome shifts in Amazonia. Geophys Res Lett. 2010; 37:5.
- 28.
Paranjpe DA, Cooper RD, Patten A, Sinervo B. Measuring thermal profile of reptiles in laboratory and field. In: Proceedings of Measuring Behavior 2012, 8th International Conference on Methods and Techniques in Behavioral Research (Utrecht The Netherlands August 28–31). 2012.
- 29. Kubisch EL, Corbálan V, Ibargüengoytía NR, Sinervo B. Local extinction risk of three species of lizard from Patagonia as a result of global warming. Can J Zool. 2016;94: 49–59.
- 30. Bennet AF. The thermal dependence of lizard behavior. Anim Behav. 1980;28: 752–762.
- 31.
R Development Core Team. R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing Vienna Austria. 2016; www.R-project.org
- 32. Zheng Y, Wiens JJ. Combining phylogenomic and supermatrix approaches and a time-calibrated phylogeny for squamate reptiles (lizards and snakes) based on 52 genes and 4162 species. Mol Phylogenet Evol. 2016;94: 537–547. pmid:26475614
- 33. Blomberg SP, Garland T Jr, Ives AR. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution. 2003;57: 717–745. pmid:12778543
- 34. Felsenstein J. Phylogenies and the comparative method. Am Nat. 1985;25: 1–15.
- 35.
Felenstein J. Inferring phylogenies. Sunderland: Sinauer Associates; 2004.
- 36. Revell LJ. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol Evol. 2012;
- 37.
Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag; 2009.
- 38. Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Series B. 2011;73: 3–36
- 39. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011;
- 40. Zajitschek SRK, Zajitschek F, Miles DB, Clobert J. The effect of coloration and temperature on sprint performance in male and female wall lizards. Biol J Linn Soc Lond. 2012;107: 573–582.
- 41. Lin X. Inference in generalized additive mixed models by using smoothing splines. J R Stat Soc Series B. 1999;61: 381–400.
- 42.
Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM. Mixed effects models and extensions in ecology with R. New York: Springer; 2009.
- 43.
Bartholomew GA. The role of physiology in the distribution of terrestrial vertebrates. In: Hubbs CL, editor. Zoogeography. Washington: American Association for the Advancement of Science; 1958. pp. 81–95.
- 44.
Huey RB. Ecology of lizard thermoregulation. PhD Dissertation, Harvard University. 1975.
- 45. Huey RB, Kearney MR, Krockenberger A, Holtum JAM, Jess M, Williams SE. Predicting organismal vulnerability to climate warming: roles of behavior physiology and adaptation. Philos Trans R Soc London Ser. B. 2012;367: 1665–1679.
- 46. Gasnier TR, Magnusson WE. and Lima AP. Foraging activity and diet of four sympatric lizard species in a tropical forest. J Herpetol. 1994;28: 187–192.
- 47. Vitt LJ. Ecology and life history of the wide-foraging lizard Kentropyx calcarata (Teiidae) in Amazonian Brazil. Can J Zool. 1991;69: 2791–2799.
- 48. Agrawal AA. Phenotypic plasticity in the interactions and evolution of species. Science. 2001;2945541: 321–326
- 49. Stearns SC, Koella JC. The evolution of phenotypic plasticity in life-history traits: predictions of reaction norms for age and size at maturity. Evolution. 1986;40: 893–913. pmid:28556219
- 50. Beuchat CA, Ellner S. A quantitative test of life history theory: thermoregulation by a viviparous lizard. Ecol Monogr. 1987;57: 45–60.
- 51. Adolph SC, Porter WP. Temperature activity and lizard life histories. Am Nat. 1991;142: 273–295.
- 52. Blomberg SP, Garland T Jr. Tempo and mode in evolution: phylogenetic inertia adaptation and comparative methods. J Evol Biol. 2002;15: 899–910.
- 53. Sinervo B. The evolution of thermal physiology and growth rate between populations of the western fence lizard (Sceloporus occidentalis). Oecologia. 1990;83: 228–237. pmid:22160116
- 54. Paranjpe DA, Bastiaans E, Patten A, Cooper RD and Sinervo B. Evidence of maternal effects on temperature preference in side-blotched lizards: implications for evolutionary response to climate change. Evol Ecol. 2013;
- 55. Cheng H, Sinha A, Cruz FW, Wang X, Edwards RL, d’Horta FM. et al. Climate change patterns in Amazonia and biodiversity. Nat Commun. 2013; pmid:23361002
- 56. D’Apolito C, Absy ML. and Latrubesse EM. The hill of Six Lakes revisited: new data and re-evaluation of a key Pleistocene Amazon site. Quat Sci Rev. 2013;76: 140–155.
- 57. Hoorn C, Bogotá-A GR, Romero-Baez M, Lammertsma EI, Flantua SGA, Dantas EL, Dino R, do Carmo DA and Chemale F Jr. The Amazon at sea: Onset and stages of the Amazon River from a marine record, with special reference to Neogene plant turnover in the drainage basin. Glob Planet Change. 2017;153: 51–65.
- 58. Wang X., Edwards RL, Auler AS., Cheng H., Kong X., Wang Y., Cruz FW., Dorale JA. and Chiang H. Hydroclimate changes across the Amazon lowlands over the past 45,000 years. Nature. 2017;541: 204–209 pmid:28079075
- 59. Bennet AF. Thermal dependence of locomotor capacity. Am J Physiol. 1990;259: R253–R258. pmid:2201218
- 60. Ji X, Sun PY, Du WG. Selected body temperature thermal tolerance and food assimilation in a viviparous skink Sphenomorphus indicus. Neth J Zool. 1997;47: 103–110.
- 61.
Pianka E R. The lizard man speaks. University of Texas Press.1994; 26.
- 62. Magnusson WE, Paiva LJD, Rocha RMD, Franke CR, Kasper LA, Lima AP. The correlates of foraging mode in a community of Brazilian lizards. Herpetologica. 1985;41: 324–332.
- 63. Clusella-Trullas S, Blackburn TM, Chown SL. Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. Am Nat. 2011;177: 738–751. pmid:21597251
- 64.
Yannas S and Maldonado E. Thermal Inertia. In: Yannas S and Maldonado E, editors. Pascool handbook-Designing for summer comfort. A A Graduate School London. 1995.
- 65. Vitt LJ, Sartorius SS, Avila-Pires TCS, Zani PA, Esposito MC. Small in a big world: Ecology of leaf-litter geckos in New World tropical forests. Herpetological Monographs. 2005;19: 137–152.
- 66. Angilletta MJ Jr, Hill T, Robson MA. Is physiological performance optimized by thermoregulatory behavior?: a case study of the eastern fence lizard Sceloporus undulatus. J Therm Biol. 2002; 27: 199–204.
- 67. Van Damme R, Bauwens D, Verheyen RF. The thermal dependence of feeding behaviour food consumption and gut–passage time in the lizard Lacerta vivipara Jacquin. Funct Ecol. 1991;5: 507–517.
- 68. Ji X, Du W, Sun P. Body temperature thermal tolerance and influence of temperature on sprint speed and food assimilation in adult grass lizards Takydromus septentrionalis. J Therm Biol. 1996;21: 155–161.
- 69. Tewksbury JJ, Huey RB, Deutsch CA. Putting the heat on tropical animals. Science. 2008;320: 1296. pmid:18535231
- 70. Fearnside PM. A vulnerabilidade da floresta amazônica perante as mudanças climáticas. Oecol Bras. 2009;13: 609–618.
- 71. Pearson RG, Dawson TP. Predicting the impacts of climate change on the distribution of species: are bioclimatic envelope models useful? Glob Ecol Biogeogr. 2003;12: 361–371.
- 72. Chevin LM, Lande R, Mace GM. Adaptation plasticity and extinction in a changing environment: towards a predictive theory. PLoS Biol. 2010;8: e10003
- 73. Sgrò C, Lowe AJ, Hoffmann AA. Building evolutionary resilience for conserving biodiversity under climate change. Evol Appl. 2011;4: 326–337. pmid:25567976
- 74. Kearney M, Shine R, Porter WP. The potential for behavioral thermoregulation to buffer “cold blooded” animals against climate warming. Proc Acad Nat Sci. 2009;106: 3835–3840.
- 75. Vitt LJ, Zani PA. Ecology of the nocturnal lizard Thecadactylus rapicauda (Sauria: Gekkonidae) in the Amazon Region. Herpetologica. 1997;53: 165–179.
- 76. Vitt LJ, Carvalho CM. Niche partitioning in a tropical wet season: lizards in the Lavrado area of northern Brazil. Copeia. 1995;1995: 305–329.
- 77. Vitt LJ, Sartorius SS, Avila-Pires TCS, Esposito MC. Life at the river’s edge: ecology of Kentropyx altamazonica in Brazilian Amazonia. Can J Zool. 2001;79: 1855–1865.
- 78. Colli GR, Mesquita DO, Rodrigues PVV, Kitayama K. Ecology of the gecko Gymnodactylus geckoides amarali in a Neotropical Savanna. J Herpetol. 2003;37: 694–706.
- 79. Rocha CFD, Van Sluys M, Vrcibradic D, Kiefer MC, Menezes VA, Siqueira CC. Comportamento de termorregulação em lagartos brasileiros. Oecol Bras. 2009;13: 115–131.
- 80. Recoder R, Teixeira M, Camacho A, Rodrigues MT. Natural history of the tropical gecko Phyllopezus pollicaris (Squamata Phyllodactylidae) from a sandstone outcrop in Central Brazil. Herpetol Notes. 2012;5: 49–58.
- 81.
Vitt LJ. The ecology of tropical lizards in the Caatinga of northeast Brazil. Occasional papers of the Oklahoma Museum of Natural History. 1995;1: 1–29.
- 82.
Henderson RW, Powell R. Natural History of West Indian Reptiles and Amphibians. Gainesville: University Press of Florida; 2009.
- 83. Vitt LJ, Zani PA. Organization of a taxonomically diverse lizard assemblage in Amazonian Ecuador. Can J Zool. 1996;74: 1313–1335.
- 84. Vitt LJ, Souza RA, Sartorius SS, Avila-Pires TCS, Espósito MC. Comparative ecology of sympatric Gonatodes (Squamata: Gekkonidae) in the western Amazon of Brazil. Copeia. 2000;2000: 3–95.
- 85. Nunes VS. Ciclo de atividade e utilização do habitat por Gonatodes humeralis (Sauria, Gekkonidae) em Manaus, Amazonas. Pap Avulsos Zool. 1984;35: 147–152.
- 86. Miranda JP, Ricci-Lobão A, Rocha CFD. Influence of structural habitat use on the thermal ecology of Gonatodes humeralis (Squamata: Gekkonidae) from a transitional forest in Maranhão Brazil. Zoologia. 2010; 27:35–39.
- 87. Vitt LJ, Avila-Pires TCS, Esposito MC, Sartorius SS, Zani PA. Sharing Amazonian rain-forest trees: ecology of Anolis punctatus and Anolis transversalis (Squamata: Polychrotidae). J Herpetol. 2003;37: 276–285.
- 88. Vitt LJ, Shepard DB, Vieira GHC, Caldwell JP, Colli GR, Mesquita DO. Ecology of Anolis nitens brasiliensis in Cerrado woodlands of Cantão. Copeia. 2008;2008: 144–153.
- 89. Vitt LJ, Avila-Pires TCS, Zani PA, Sartorius SS, Esposito MC. Life above ground: ecology of Anolis fuscoauratus in the Amazon rain forest and comparisons with its nearest relatives. Can J Zool. 2003;81: 142–156.
- 90. Stamps JA, Crews D. Changes in reproduction and social behavior in the lizard Anolis aeneus. Copeia. 1976;1976: 467–476.
- 91. Vitt LJ, Avila-Pires TCS, Zani PA, Esposito MC, Sartorius SS. Life at the interface: ecology of Prionodactylus oshaughnessyi in the western Amazon and comparisons with P. argulus and P. eigenmanni. Can J Zool. 2003;81: 302–312.
- 92. Vitt LJ, Sartorius SS, Avila-Pires TCS, Esposito MC. Life on the leaf litter: The ecology of Anolis nitens tandai in the Brazilian Amazon. Copeia. 2001;2001: 401–412.
- 93. Vitt LJ, Avila-Pires TCS, Zani PA, Esposito MC. Life in shade: the ecology of Anolis trachyderma (Squamata: Polychrotidae) in Amazonian Ecuador and Brazil with comparisons to ecologically similar Anoles. Copeia. 2002;2002: 275–286.
- 94. Vitt LJ, Caldwell JP. Ecological observations on Cerrado lizards in Rondônia Brazil. J Herpetol. 1993;27: 46–52.
- 95. Brattstrom BH. Body temperatures of reptiles. Am. Midl. Nat. 1965;73: 376–422.
- 96.
Savage JM. The Amphibians and Reptiles of Costa Rica. Chicago: The University of Chicago Press; 2002.
- 97. Kohlsdorf T, Navas CA. Ecological constraints on the evolutionary association between field and preferred temperatures in Tropidurinae lizards. Evol Ecol, 2006;206: 549–564.
- 98. Vitt LJ. An introduction to the ecology of Cerrado lizards. J Herpetol. 1991;25: 79–90.
- 99. Vitt LJ, Zani PA, Caldwell JP. Behavioural ecology of Tropidurus hispidus on isolated rock outcrops in Amazonia. J Trop Ecol. 1996;12: 81–101.
- 100. Vitt LJ, Zani PA, Avila-Pires TCS. Ecology of the arboreal tropidurid lizard Tropidurus (= Plica) umbra in the Amazon region. Can J Zool. 1997;75: 1876–1882.
- 101. Vitt LJ. Ecology of isolated open formation Tropidurus (Reptilia: Tropiduridae) in Amazonian lowland rain forest. Can J Zool. 1993;71: 2370–2390.
- 102. Mesquita DO, Colli GR, Franca FGR, Vitt LJ. Ecology of a Cerrado lizard assemblage in the Jalapão region of Brazil. Copeia. 2006;2006: 460–471.
- 103. Vitt LJ, Zani PA, Ecology of the elusive tropical lizard Tropidurus (= Uracentron) flaviceps (Tropiduridae) in lowland rain forest of Ecuador. Herpetologica. 1996;52: 121–132.
- 104. Vrcibradic D, Rocha CFD. The ecology of the skink Mabuya frenata in an area of rock outcrops in southeastern Brazil. J Herpetol. 1998;32: 229–237.
- 105. Vitt LJ, Blackburn DG. Ecology and life history of the viviparous lizard Mabouya bistriata (Scincidae) in the Brazilian Amazon. Copeia. 1991;1991: 916–927.
- 106. Vitt LJ, Avila-Pires TCS, Esposito MC, Sartorius SS, Zani PA. Ecology of Alopoglossus angulatus and A atriventris (Squamata: Gymnophthalmidae) in western Amazonia. Phyllomedusa. 2007;6: 11–21.
- 107. Dixon JR and Soini P. The reptiles of the upper Amazon Basin, Iquitos region Peru. Part 1 Lizards and amphisbaenians. Contrib Biol Geol. 1975;4: 1–58.
- 108.
Dixon JR and Soini P. The reptiles of the upper Amazon Basin, Iquitos region, Peru, I-VII. Milwaukee: Milwaukee Public Museum; 1986.
- 109. Fitch HS. Temperature and behavior of some equatorial lizards. Herpetologica. 1968;24: 35–38.
- 110. Vitt LJ, Avila-Pires TCS. Ecology of two sympatric species of Neusticurus (Sauria: Gymnophthalmidae) in the western Amazon of Brazil. Copeia. 1998;1998: 570–582.
- 111. Simmons PM, Greene BT, Williamson KE, Powell R, Parmerlee JS Jr. Ecological interactions within a lizard community on Grenada. Herpetologica. 2005;61: 124–134.
- 112. Sartorius SS, Vitt LJ, Colli GR. Use of naturally and anthropogenically disturbed habitats in Amazonian rainforest by the teiid lizard Ameiva ameiva. Biol Conserv. 1999;90: 91–101.
- 113. Mesquita DO, Colli GR. Geographical variation in the ecology of populations of some Brazilian species of Cnemidophorus (Squamata Teiidae). Copeia. 2003;2003: 285–298.
- 114. Mesquita DO, Colli GR, Costa GC, Franca FGR, Garda AA, Peres AK. At the water’s edge: ecology of semiaquatic teiids in Brazilian Amazon. J Herpetol. 2006;40: 221–229.
- 115. Vitt LJ, Zani PA, Caldwell JP, Carillo EO. Ecology of the lizard Kentropyx pelviceps (Sauria: Teiidae) in lowland rain forest of Ecuador. Can J Zool. 1995;73: 691–703.
- 116. Vitt LJ, Carvalho CM. Life in the trees: the ecology and life history of Kentropyx striatus (Teiidae) in the Lavrado area of Roraima Brazil with comments on the life histories of tropical teiid lizards. Can J Zool. 1992;70: 1995–2006.
- 117. Vitt LJ, Zani PA. Ecological relationships among sympatric lizards in a transitional forest in the northern Amazon of Brazil. J Trop Ecol. 1998;14: 63–86.