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IDEA AND
PERSPECTIVE A conceptual framework for understanding thermal constraints
on ectotherm activity with implications for predicting
responses to global change
Alex R. Gunderson
1,2
* and
Manuel Leal
3
Abstract
Activity budgets influence the expression of life history traits as well as population dynamics. For
ectotherms, a major constraint on activity is environmental temperature. Nonetheless, we cur-
rently lack a comprehensive conceptual framework for understanding thermal constraints on
activity, which hinders our ability to rigorously apply activity data to answer ecological and evo-
lutionary questions. Here, we integrate multiple aspects of temperature-dependent activity into a
single unified framework that has general applicability. We also provide examples of the imple-
mentation of this framework to address fundamental questions in ecology relating to climate
change vulnerability and species’ distributions using empirical data from a tropical lizard.
Keywords
Activity, Anolis, behaviour, ectotherm, global change, physiology, temperature.
Ecology Letters (2016) 19: 111–120
INTRODUCTION
Ecologists have long known that the amount of activity that
animals engage in greatly influences population dynamics
(reviewed in Dunham et al. 1989; Frid & Dill 2002), as activ-
ity bouts generally represent the primary opportunities to
acquire energy and find mates. Activity itself also represents
an important energetic expenditure (Bennett 1978). In addi-
tion, activity patterns impact higher order ecological processes
by mediating interactions among species, such as predator–
prey interactions and interspecific competition (Schmitz et al.
1997). Several features of the biotic and abiotic environment
can influence activity budgets, but one of the most important
drivers of activity, especially for ectotherms, is temperature.
Evidence of this can be seen by observing populations of a
given species along latitudinal or elevational thermal gradi-
ents, where temperature-driven changes in the time available
for activity are often associated with changes in life history
traits (e.g. Grant & Dunham 1990; Adolph & Porter 1993;
Angilletta 2009). For example, southern populations of the
eastern fence lizard (Sceloporus undulatus, Bosc & Daudin
1801) grow faster than northern populations, and this differ-
ence in growth rate is correlated with greater energy assimila-
tion efficiency and a larger temporal activity window (on daily
and seasonal timescales) for energy acquisition (Angilletta
2001).
The importance of thermal constraints on activity have lead
to their frequent incorporation into analyses of the biological
effects of global change (e.g. Kearney et al. 2009; Sears et al.
2011; Buckley & Kingsolver 2012; Huang et al. 2013; Corser
et al. 2015). For example, a model that included thermal con-
straints on activity as the mechanistic link between climatic
variability and population processes predicted features of pop-
ulation dynamics and range expansion in the butterfly Hespe-
ria comma (Linneaus 1758) over the past several decades
(Bennie et al. 2013). A recent global analysis of extinction vul-
nerability in lizards predicted that, depending on specific con-
ditions, up to 39% of populations could go extinct by the end
of the century due to reduced activity times (Sinervo et al.
2010). Conversely, predicted increases in activity time, and
thus metabolic expenditure, under warming were recently
associated with reductions in the body size of North American
salamanders (Caruso et al. 2014). These studies highlight the
fact that, for many organisms, the negative consequences of
warming will not result from increased exposure to lethal tem-
peratures. Instead, they will result from sub-lethal effects, such
as energetic imbalances, that may be exacerbated by reduced
activity (Dillon et al. 2010; Woodin et al. 2013; Deutsch et al.
2015).
Despite recognition that thermal constraints on activity are
a fundamental component of ectotherm biology, we argue
that current models of thermal constraint on activity are too
simplistic and do not capture many of the ecologically and
evolutionarily important features of the temperature–activity
interaction. For example, the simple threshold model of
temperature-dependent activity utilised in Sinervo et al.
(2010) is likely to underestimate activity budgets of lizards
(Kearney 2013; Gunderson & Leal 2015), and threshold
1
Department of Biology and Romberg Tiburon Center, San Francisco State
University, 3150 Paradise Dr., Tiburon, CA, 94920, USA
2
Department of Integrative Biology, University of California, Berkeley, CA,
94720-3140, USA
3
Division of Biological Sciences, University of Missouri, 105 Tucker Hall,
Columbia, MO, 65211, USA
*Correspondence: E-mail: alexrgunderson@gmail.com
©2015 John Wiley & Sons Ltd/CNRS
Ecology Letters, (2016) 19: 111–120 doi: 10.1111/ele.12552
models generally do not account for fine-scale temperature-
dependent variation in behaviour across the thermal activity
window (Gunderson & Leal 2015). Thus, in many cases sim-
plified approaches to temperature-dependent activity will
overlook important behaviourally mediated mechanistic links
between spatial and temporal thermal variability (including
anthropogenic change) and observed population and/or
higher order ecological patterns.
Here, we propose a synthetic conceptual framework of tem-
perature-dependent activity designed to address gaps in our
current approaches and to serve as a helpful foundation for
future studies. The framework has numerous potential appli-
cations, including integration into mechanistic species distribu-
tion models, analyses of species interactions and community
dynamics and investigations of climatic adaptation. We start
by identifying and describing what we view as the fundamen-
tal components of activity that temperature influences, and
briefly discuss the ecological implications of each. Next, we
integrate these components of temperature-dependent activity
into a single framework while identifying how temperature-
dependent activity fits within the broader context of ectotherm
thermal physiology. To demonstrate the utility of our
approach and to provide working examples of how it can be
applied, we use the framework with data from our own stud-
ies of a tropical lizard, Anolis cristatellus (Dum!
eril & Bibron
1837). Specifically, we investigate the consequences of climate
warming for A. cristatellus, as well as what drives the distribu-
tional patterns of this species with respect to an ecologically
similar congener.
THE FOUR FUNDAMENTAL COMPONENTS OF
TEMPERATURE-DEPENDENT ACTIVITY
Our framework is built upon what we propose are four funda-
mental components of temperature-dependent activity: thresh-
olds,probabilities,modes and vigour. Below, we outline each
of the components individually before describing how they
can be conceptually linked.
Thresholds
In the broadest sense, activity can be defined as anytime an
animal is out of a nest, burrow or some other refuge. Using
this definition, activity budgets are calculated as the amount
of time out of a refuge within a given time frame (e.g. annu-
ally, daily; Fig. 1a). In order to estimate these activity bud-
gets, one needs to know the lower and upper thermal
thresholds for activity: the body temperatures below and
above at which activity will not occur (throughout, we will
refer to the range of temperatures suitable for activity as the
‘thermal activity window’). This approach to characterising
activity is simple, intuitive and can tell us a great deal about
the ecology of a species. For example, Porter (Porter et al.
1973; Adolph & Porter 1993; Kearney et al. 2009; Huang
et al. 2013) and Dunham (Grant & Dunham 1988, 1990; Dun-
ham et al. 1989; Dunham 1993; Angilletta et al. 2004) and
colleagues have used reptile model systems to demonstrate
that variation in key life history traits can be at least partially
explained by thermal effects on the time available for activity.
In addition, differences in thermal thresholds for activity
among interacting species can dictate spatial and temporal
overlap between taxa and thus the strength of their interac-
tions (Van Berkum et al. 1986; Huey et al. 2009). Porter high-
lighted the importance of different thresholds for activity in
predator–prey systems in an early classic study of thermal
constraints on activity, in which he calculated the annual tem-
poral overlap between the (occasionally carnivorous) desert
iguana Dipsosaurus dorsalis (Baird & Girard 1852) and hypo-
thetical invertebrate prey with representative thermal activity
thresholds (Porter et al. 1973).
Probabilities
Although thermal thresholds for activity are an extremely
important piece of the temperature and activity puzzle, not all
individuals in a population become active when temperatures
within the thermal activity window are available. Instead, the
number of individuals active within a population tends to
change in a continuous manner as temperatures increase from
one end of the thermal activity window to the other (e.g.
Huey & Pianka 1977; Hertz 1981; Corbet et al. 1993; Riis &
Nachman 2006; Andrew et al. 2013). In other words, the
probability that an individual will become (or remain) active
changes with temperature. For example, among desert ants
the number of workers that leave the nest to forage often
increases with surface (and presumably body) temperature up
to a maximum point, and then decreases again as tempera-
tures continue to rise (Jayatilaka et al. 2011; Fig. 2). The
number of individuals active under given thermal conditions
is often represented as proportions (relative to the peak num-
ber of active individuals observed; Fig. 1b), and referred to as
the intensity of activity: the more individuals that are active,
the more intense the activity. Temperature-dependent proba-
bilities of activity influence the effective density of a popula-
tion at any given time, and can consequently affect those
ecological processes that are density dependent, including
intra- and interspecific competition (Svensson et al. 2001;
Bailey et al. 2013; Kaspersson et al. 2013), predator–prey
interactions (Seitz et al. 2001; Hixon & Jones 2005) and host–
parasite dynamics (Arneberg et al. 1998).
Temperature-dependent thresholds and probabilities for
activity can be united under a single framework, as thresholds
for activity can be regarded as the upper and lower tempera-
tures at which the probability of activity transitions from zero
to greater than zero.
Modes
Both thresholds and probabilities of activity address the same
basic question: When will individuals in a population come
out of their refuges to engage in activity? However, one of the
fundamental questions these metrics do not address is: once
an animal is active, what types of activity does it engage in?
Not all behaviours within a given species repertoire are neces-
sarily going to be conducted at all body temperatures within
the thermal activity window. For example, the classic studies
by Brett demonstrated that reproductive behaviours of salmon
occur over a narrower range of body temperatures than
©2015 John Wiley & Sons Ltd/CNRS
112 A. R. Gunderson and M. Leal Idea and Perspective
activity per se, with the thresholds contingent on acclimation
temperature (Fig. 1c). In other words, modes of activity can
have thermal thresholds that differ from thresholds for activ-
ity overall. In addition, different modes of activity are likely
to have their own temperature-dependent probability func-
tions. For example, the proportion of individuals engaging in
courtship and reproductive activity (i.e. copulation) changes
with temperature in the garter snake Thamnophis sirtalis (Lin-
naeus 1758; Hawley & Aleksiuk 1975) and in Drosophila (Sch-
nebel & Grossfield 1984).
Vigour
Modes of activity describe what an active animal could
potentially do, but not ‘how much’ they will do, which we
refer to as activity vigour. Vigour can be evaluated in
numerous ways. It could be measured as the amount of
time an ‘active’ (i.e. out of a refuge) animal spends engaging
in physical activity. For example, a study of the tempera-
ture-dependent activity of Australian Varanid lizards found
that the amount of time engaged in locomotion changed
with body temperature (Christian & Weavers 1996; see also
Gunderson & Leal 2015). Vigour could also be described as
the rate at which certain behaviours occur, such as tempera-
ture-dependent changes in the rate at which crickets produce
song pulses (Martin et al. 2000; Fig. 4). Regardless of the
specific metric used, a measurement of vigour must capture
the extent of physical exertion during activity. Vigour is
likely to be tightly linked to metabolic expenditure (Bennett
1978) and to influence other aspects of a species’ ecology
such as predation risk (McPeek 1990; Skelly 1994; Rall
18 23 28 33
40
60
80
100
Prop. of outbound foragers
Hour of the day
No. pulses/s
Month of the year
Thermal tolerance
Activity threshold
Reproduction threshold
0-5 10-15 20-25 30-35 40-45
0
0.5
1
(b)
(c) (d)
(a)
Figure 1 (a) Predicted temporal windows for activity in the desert lizard Dipsosaurus dorsalis based on thermal activity thresholds (Porter et al. 1973). Times
unsuitable for activity are indicated by dark stippling. Modified and reprinted with permission from Springer Publishing. (b) The relative proportion of
workers leaving the nest under different thermal conditions for the ant Myrmecia croslandi (Taylor 1991). Figure from Jayatilaka et al. 2011. Modified and
reprinted with permission from The Company of Biologists. (c) Tolerance and activity polygons for the salmon Oncorhynchus nerka (Walbaum 1792). Note
that reproductive activity occurs within a narrower range of temperatures than activity overall. Figure from Fry (1971), data from Brett (1952, 1958) and
Brett & Alderdice (1958). Modified and reprinted with permission from Elsevier Limited. (d) The number of song pulses performed by the cricket Gryllus
integer (Scudder 1901) with respect to temperature. Figure from Martin et al. (2000). Modified and reprinted with permission from NRC Research Press.
Probability of activity
Te mp e ra t ur e
T - Territory defense
F - Foraging
M - Mating
FT
FT
M
Figure 2 A conceptual framework for thermal constraints on ectotherm
activity. Shown are hypothetical examples of an ectotherm under three
different thermal conditions. Temperature refers to the body temperature
the animal would experience upon leaving its refuge. The probability of
activity is indicated with a black dot. The area of each circle represents
the activity vigour that can be achieved at that temperature. Letters
denote the modes of activity available at that temperature. See text for
full explanation and a modification of this framework for organisms that
do not utilise refuges.
©2015 John Wiley & Sons Ltd/CNRS
Idea and Perspective Temperature-dependent activity in ectotherms 113
et al. 2012) and mating success (Welch et al. 1998; Lailvaux
& Irschick 2006).
A CONCEPTUAL FRAMEWORK FOR TEMPERATURE-
DEPENDENT ACTIVITY
Here, we present a novel conceptual model that integrates the
four components described above into a single cohesive
framework that can be used to describe the aggregate direct
effects of temperature on activity. The model is graphically
represented in Fig. 2, with hypothetical examples representing
an animal under a range of thermal conditions.
We begin by considering an inactive animal within a
refuge. From this starting point, there is a certain probability
that the animal will leave the refuge given the body tempera-
ture that it would acquire within its habitat. That probability
is dictated by the thermal thresholds for activity as well as
the shape of the function describing how the probability of
activity changes with temperature within the thermal activity
window. In Fig. 2, the probability of activity is zero under
the coldest condition, indicating that the animal’s activity
body temperature would be below its activity threshold. The
probability of activity is highest under the intermediate ther-
mal condition, and lower but still above zero for the warm-
est condition. If the animal does leave the refuge to become
active, the modes of activity that it can engage in are con-
strained by the body temperature it experiences. In the situa-
tion represented in Fig. 2, the animal would have the largest
behavioural repertoire (i.e. most modes available) under the
intermediate thermal condition. How vigorous the animals’
activity is (i.e. ‘how much’ activity it can engage in) also
depends on temperature. We denote this by the area of the
circle within which the available activity modes are written
in Fig. 2. In our hypothetical example, the animal would
engage in the most vigorous activity under the intermediate
thermal condition. In our example, activity vigour is dis-
tributed equally among all modes of activity (i.e. they occupy
the same area of the vigour circle), but this need not be the
case.
Parameter values for the four components of temperature-
dependent activity can likely shift over time due to heritable
genetic changes and phenotypic plasticity. Evolutionary adap-
tation is a primary means by which organisms can buffer
themselves from climate change (Hoffmann & Sgr"
o 2011;
Huey et al. 2012), and these activity-related traits could be
targets of selection in a changing world along with physiologi-
cal traits, such as thermal tolerance, that are most often con-
sidered (Kelly et al. 2011; Leal & Gunderson 2012; Logan
et al. 2014). Plasticity can clearly also contribute to the
expression of activity phenotypes in a manner similar to plas-
tic changes in temperature-dependent physiological traits (e.g.
Gunderson & Stillman 2015; Seebacher et al. 2015). For
example, acclimation to cold or warm temperatures, respec-
tively, can lower or raise the thresholds for activity in the
aphid Myzus persicae (Sulzer 1776; Alford et al. 2012), and,
as demonstrated in Fig. 1c, thermal acclimation results in
shifts in the activity thresholds of the salmon Oncorhynchus
nerka. Parameter values for activity components are also
likely to change with life stage in many organisms, as occurs
for temperature-dependent physiological traits (e.g. Kingsolver
et al. 2011).
The framework as discussed above is based on the biology
of organisms that take refuge when conditions are not suitable
for activity, representing a large fraction of ectotherms such
as many reptiles, amphibians, fish and arthropod species.
Nonetheless, not all aspects of the framework as described
will necessarily be applicable to all life stages of a species or
even all species. For instance, there are organisms and life
stages of organisms that do not take shelter within physical
refuges or nests, such as many pelagic freshwater and marine
taxa that engage in more or less continuous activity (e.g.
swimming and feeding). For these organisms, defining thresh-
olds and probabilities of overall activity with respect to move-
ment from a refuge is not appropriate. Instead, the thresholds
and probabilities of overall activity for these organisms are
better defined with respect to critical thermal limits (CTs).
Critical thermal limits are defined as the temperatures at
which organisms lose the capacity for coordinated movement
and cannot escape life-threatening situations (Lutterschmidt &
Hutchison 1997b). In practice, CTs are generally measured as
temperatures at which organisms become incapacitated (Lut-
terschmidt & Hutchison 1997a). For continuously active
organisms, thresholds and probabilities of activity will be dic-
tated by these physiological limits on performance. This is not
likely to be the case for most animals that use refuges, as their
physiological tolerance range is typically much greater than
their thermal activity window (see, e.g. Fig. 1c and our
detailed data on a lizard system below). The application of
activity modes (e.g. thresholds for reproductive activity) and
vigour (e.g. swimming/feeding rates) will still be applicable to
continuously active animals. For example, aquatic crustaceans
in the genus Daphnia essentially swim constantly, and the vig-
our of their activity (measured by swimming rate) changes
continuously within their physiological tolerance limits (Zeis
et al. 2004).
Up to this point, we have generally described the tempera-
ture dependence of activity in terms of the thermal state of
the organism (i.e. body temperature). This is the simplest way
to discuss temperature-dependent activity, and may be suffi-
cient when describing activity patterns of organisms in habi-
tats in which thermal conditions are relatively homogenous at
a spatial scale relevant to the focal species (e.g. many aquatic
habitats and deeply shaded forests). However, activity is also
likely to be influenced by spatial patterns of thermal variabil-
ity where it occurs (Grant & Dunham 1988; Sears et al. 2011;
Sears & Angilletta 2015). In many habitats (e.g. the intertidal,
deserts) the body temperature that an active animal would
experience changes over small spatial scales due to variation
in features such as shade cover, topography and the physical
properties of substrates. The distribution of these potential
temperatures is known as the operative thermal environment
(Bakken 1992). In some localities, the risk of overheating may
be greater than in others (i.e. there are differences in the per-
centage of space in which operative temperatures exceed lethal
limits). An animal with a greater chance of encountering
extreme heat may be less likely to become active and/or may
engage in less vigorous activity than an animal with the same
body temperature in a less severe thermal environment. Thus,
©2015 John Wiley & Sons Ltd/CNRS
114 A. R. Gunderson and M. Leal Idea and Perspective
in many cases the activity of organisms will be conditional
not only on body temperature but on the thermal features of
the environment as well (Woods et al. 2014; Logan et al.
2015).
APPLICATION OF THE ACTIVITY FRAMEWORK
Here, we illustrate the implementation of our proposed frame-
work using data on the thermal biology of the lizard Anolis
cristatellus, a small arboreal insectivore that lives in both
mesic and xeric tropical forests at low to mid elevations (0 to
~300 m) on the greater Puerto Rican bank (Rand 1964).
Temperature-dependent activity in Anolis cristatellus
The lower and upper thresholds for overall activity were
taken as the minimum and maximum body temperatures
(20.2 and 36.1 °C respectively) that we measured for A.
cristatellus across seasons and habitats in Puerto Rico (see
Gunderson & Leal 2012, for sampling localities, times and
methodology). To incorporate modes of activity, we deter-
mined the thermal thresholds for reproductive activity, taken
as the upper and lower body temperatures at which we
observed copulation (26.0 and 32.6 °C respectively) during
focal observations of A. cristatellus in a separate study (Gun-
derson & Leal 2015).
Temperature-dependent probabilities of activity were esti-
mated using the body temperature data set from Gunderson
& Leal (2012). To do so, we first calculated the mean body
temperature of the lizards captured each hour of every day of
sampling. Next, we calculated the number of lizards captured
during each hour, under the assumption that the more lizards
captured, the more are out and active. In essence, we asked
how many lizards would be active if the operative thermal
environment allowed (or caused) them to attain a given mean
body temperature. We modelled the relationship between
body temperature and number of lizards captured by fitting a
second-order polynomial to the data since we expected the
relationship between lizards activity and temperature to be
nonlinear (Hertz 1981) (Fig. 3a). The polynomial function
was then transformed into a curve describing the probability
of activity by setting the peak activity predicted by the model
to one.
Temperature-dependent activity vigour was taken from
Gunderson & Leal (2015) (Fig. 3b). Vigour was measured as
the proportion of time engaged in physical activity (locomo-
tion or signal display) during 15 min focal observations
(N=299), and body temperatures were estimated for all focal
animals using copper lizard models (see Gunderson & Leal
2015, for details). The relationship between vigour and body
temperature was modelled using a restricted cubic spline
(Gunderson & Leal 2015). To simplify our activity data, we
combined the curves for the probability of activity and activ-
ity vigour into a single variable we call effective activity vigour
(Fig. 3c). To do this, we multiplied the probability and vigour
curves together. Effective activity vigour describes the
expected activity vigour at a given body temperature weighted
by the probability that an individual would be out of a refuge
at that temperature.
Considering all of the activity data for A. cristatellus
together (Fig. 4), several patterns emerge. First, not all modes
of activity will occur across the entire thermal activity win-
dow, as copulation activity occurs over a narrower range of
body temperatures (27–32.5 °C) than does activity overall
(20.2–36 °C; see also Fig. 1c). In addition, physical activity
per se does not occur across the entire activity window. Anolis
cristatellus becomes active at body temperatures as low as
20.2 °C, but, based on the effective activity vigour curve, they
do not engage in physical activity until they attain body tem-
peratures of c. 25 °C. The difference between when they will
26 28 30 32 34 36
0
5
10
15
# Lizards sampled
26 28 30 32 34 36
0.0
0.2
0.4
0.6
0.8
Activity vigor
26 28 30 32 34 36
0.00
0.02
0.04
0.06
0.08
0.10
0.12
Body temperature ( C)
Effective activity vigor
(a)
(b)
(c)
Figure 3 (a) Number of Anolis cristatellus sampled per hour vs. hourly
mean body temperature. The polynomial fit to the data was transformed
into a relative curve (maximum value =1) to describe the probability of
activity with respect to temperature. (b) Activity vigour of individual
lizards as a function of body temperature. Data from Gunderson & Leal
(2015). (c) Effective activity vigour curve, calculated by multiplying the
curves in (a) and (b). See text for explanation.
©2015 John Wiley & Sons Ltd/CNRS
Idea and Perspective Temperature-dependent activity in ectotherms 115
emerge and when they will begin physical activity is most
likely the range of temperatures at which they are basking if
the potential for thermoregulation exists. Finally, effective
activity vigour in this species is very sensitive to temperature
change, with a steep increase in effective vigour between c. 25
and 28 °C, and a steep decrease in effective vigour between c.
31 °C and the upper activity threshold of 36.1 °C.
A comprehensive view of the thermal ecology of A.
cristatellus can be achieved by integrating data on tempera-
ture-dependent activity and physiology (Fig. 4). The physio-
logical traits we include are lower and upper physiological
limits (CT
min
and CT
max
respectively; Leal & Gunderson
2012), temperature dependence of physiological rates (in this
case sprint speed; Gunderson & Leal 2012) and the preferred
temperature range T
p
, the range of temperatures they seek out
given the choice in a thermal gradient (Hertz et al. 1993). We
first note that the thermal activity window (20.2–36 °C) is
much narrower than the thermal tolerance range (the body
temperatures between CT
min
and CT
max
;~14–39 °C). The
difference is even more pronounced if one considers only the
temperatures at which physical activity will occur (~25–
36 °C). It would clearly be inaccurate to assume that organ-
isms will engage in activity over the full range of temperatures
that they can tolerate (Huey & Bennett 1990).
Second, activity rates are high when organisms are at pre-
ferred temperatures. The effective activity vigour curve for
A. cristatellus peaks and has a shoulder of relatively low
sensitivity to temperature change within the preferred
temperature range. Thus, even though activity is not limited
to the preferred temperature range, the ability to attain
body temperatures within the preferred range has positive
consequences for activity budgets (see also Gunderson &
Leal 2015).
Third, activity rates are more sensitive to temperature than
at least some physiological traits. The effective activity vigour
curve for A. cristatellus is considerably narrower than that for
locomotor performance (i.e. sprint speed) (see also Gunderson
& Leal 2015). High physiological performance capacity may
be necessary but not sufficient for high activity rates to occur.
In addition, the temperature of peak effective activity vigour
(31 °C) is lower than the temperature of peak locomotor per-
formance (33.3 °C).
APPLYING THE ACTIVITY FRAMEWORK TO
ECOLOGICAL QUESTIONS: CLIMATE CHANGE
VULNERABILITY AND SPECIES DISTRIBUTIONS
We now use the temperature-dependent activity data from A.
cristatellus to apply our activity framework to address two
fundamental ecological questions: (1) how vulnerable are
ectotherms to climate warming? and (2) how does temperature
impact the distribution of species?
Activity and vulnerability to climate warming
In a previous investigation of the effects of climate warming
on A. cristatellus, we used the thermal sensitivity of sprinting
(Fig. 4) to estimate physiological performance capacities
under current and predicted future thermal conditions in two
different habitat types on Puerto Rico, mesic and xeric forest
(Gunderson & Leal 2012). We assumed a 3 °C increase in air
temperature across Puerto Rico for future conditions (Huey
et al. 2009), which is within the range of likely temperature
increases predicted in Puerto Rico by the end of the 21st cen-
tury (IPCC 2014). That analysis demonstrated that warming
would have little effect on the physiological performance of
lizards in the mesic habitat, but that lizards in xeric habitat
would experience a decrease in performance of ~29%
(Fig. 5c).
We take the same approach as Gunderson & Leal (2012)
here, but instead of applying the sprint performance curve to
the body temperature data, we apply the effective activity vig-
our curve (Fig. 3c) while assuming effective vigour is 0 for an
individual with a body temperature above the 36.1 °C activity
threshold. Thus, this analysis explicitly incorporates activity
thresholds, probabilities and vigour. We also conduct an anal-
ysis using modes of activity by asking how warming will influ-
ence reproductive behaviour, specifically copulation activity.
We ask what percentage of future microhabitats available to
A. cristatellus will be within the observed copulation thresh-
olds (Fig. 4) using the distribution of current operative tem-
perature measurements as a baseline (for a description of
operative temperature sampling, see Gunderson & Leal 2012).
As with the previous study, we conduct separate analyses for
lizards occurring in mesic and xeric habitats respectively
(Gunderson & Leal 2012).
Under current thermal conditions, mean effective activity
vigour is predicted to be very similar for A. cristatellus occur-
ring in mesic (X =0.10) and xeric habitats (X =0.11), despite
significant differences in mean body temperature (Gunderson
10 15 20 25 30 35 40
0.0
0.2
0.4
0.6
0.8
1.0
Body temperature ( C)
Relative rate
LTT LAT LCT TpUCT UAT UTT
Sprint
speed
Effective
activity
vigor
Figure 4 Temperature dependence of behavioural and physiological traits
for Anolis cristatellus. LTT, UTT: lower and upper thermal tolerance (i.e.
critical thermal limits). LAT, UAT: lower and upper activity thresholds
(the lowest and highest body temperatures measured in the field). LCT,
UCT: lower and upper copulation thresholds (the lowest and highest
body temperatures at which copulation was observed in the field). T
p
: the
preferred temperature range. Black line: relative effective activity vigour
(maximum effective vigour set to one). Grey line: relative sprint
performance curve (maximum sprint speed set to one). Data from Hertz
et al. (1993), Gunderson & Leal (2012), Leal & Gunderson (2012) and
Gunderson & Leal (2015).
©2015 John Wiley & Sons Ltd/CNRS
116 A. R. Gunderson and M. Leal Idea and Perspective
& Leal 2012). However, mean effective activity vigour is pre-
dicted to diverge as temperatures increase. Under warmer
future conditions, the mean effective activity vigour of A.
cristatellus in the mesic habitat is predicted to increase by
10% to 0.11, while mean effective activity vigour in the xeric
habitat is predicted to decrease by 55% to 0.05 (Fig. 5a).
Warming is also predicted to have an effect on reproductive
activity, as the percentage of available microhabitats suitable
for copulation is expected to decrease in both habitats, though
the decrease will be far more severe in the xeric (!83%) than
the mesic (!26%) habitat (Fig. 5b).
The results of the activity analyses are broadly in agreement
with our previous analysis using physiological data, in that
they predict more detrimental effects of warming for lizards in
the xeric habitat (Fig. 5). However, the activity data predict
some negative effects of warming in the mesic habitat (i.e. a
decrease in microhabitats suitable for copulation), something
not seen in the physiological data. Furthermore, activity data
predict greater disparities in warming effects between lizards
in the two habitats. Several studies, including our previous
work, have estimated climate change impacts based on
changes in physiological performance, and especially in
ectothermic vertebrates, changes in locomotor performance
(e.g. Huey et al. 2009; Gunderson & Leal 2012; Logan et al.
2013). Our current analysis suggests that these studies may
underestimate the negative impacts of warming by not consid-
ering the sensitivity of fine-scale activity to thermal change.
Ultimately, of course, the most realistic predictions of climate
change effects are likely to incorporate multiple aspects of
performance, including both behavioural and physiological
traits.
Thermal constraints on activity and species distributions
Understanding the factors that limit the spatial extent of a
species is a fundamental problem in ecology. One of the pri-
mary factors expected to influence species distributions is
physiological sensitivity to abiotic conditions (Bozinovic et al.
2011; Sunday et al. 2012). On Puerto Rico, A. cristatellus
occurs in forests up to ~300 m, above which it is replaced by
another morphologically and ecologically similar species, A.
gundlachi (Peters 1877) (both species are insectivores cate-
gorised as ‘trunk-ground’ ecomorphs; Losos 2009). Anolis
cristatellus has greater heat tolerance (Fig. 6) and lower water
loss rates (Hillman & Gorman 1977) than A. gundlachi, traits
that likely allow A. cristatellus to occur in warm, dry habitats
such as lowland xeric forest where A. gundlachi is absent.
However, at lower temperatures similar to those common in
the heavily shaded upland habitats of A. gundlachi,A.
cristatellus and A. gundlachi have similar (and high) levels of
physiological performance (Fig. 6a). Yet, A. cristatellus does
not occur in these upland habitats, and transplant experiments
have shown that A. cristatellus cannot survive there, even in
the absence of competition between the two species (Gorman
& Hillman 1977). Why is A. cristatellus unable to live in heav-
ily shaded upland habitats for which there is no evidence of
physiological detriment in terms of thermal physiology or
water balance?
A potential insight into why A. cristatellus is unable to sur-
vive under heavily shaded upland conditions can be found by
considering the temperature-dependent activity of A. cristatel-
lus in the context of the body temperatures available within
the A. gundlachi habitat. Mean diurnal body temperatures of
A. gundlachi in the uplands are c. 25 °C (Hertz et al. 1993).
This is the same mean body temperature one would expect A.
cristatellus to have in this heavily shaded habitat with little
opportunity for behavioural thermoregulation (Hertz et al.
1993; Gunderson & Leal 2012). Although 25 °C is within the
thermal activity window of A. cristatellus (Fig. 4), it is at the
lower threshold for physical activity (i.e. effective activity vig-
our transitions to zero at c. 25 °C). In contrast, the preferred
temperature range of A. gundlachi straddles this mean temper-
ature, meaning one would expect (and does observe; M. Leal
and A. Gunderson, pers. obs.) substantial activity from A.
gundlachi in this habitat. Even if A. cristatellus could survive
within this habitat, the mean temperature they would experi-
ence would be below their thermal threshold for copulation
(Fig. 6). Thus, even though A. cristatellus and A. gundlachi
should have similar physiological performance within the
upland A. gundlachi habitat, A. cristatellus is likely excluded
from this habitat by low temperature-dependent behavioural
capacities.
Mesic Xeric
(a) effective activity vigor
Predicted change under
warming (%)
–100
–80
–60
–40
–20
0
20
Mesic Xeric
(b) microhab. suitable for
repro.
–100
–80
–60
–40
–20
0
20
Mesic Xeric
(c) sprint performance
capacity
–100
–80
–60
–40
–20
0
20
Habitat
Figure 5 Predicted change in (a) mean effective activity vigour, (b) the amount of microhabitat suitable for reproductive activity (i.e. copulation) and (c)
sprint performance capacity for Anolis cristatellus in mesic and xeric habitats on Puerto Rico under predicted future thermal conditions. Data on changes
in sprint performance from Gunderson & Leal (2012).
©2015 John Wiley & Sons Ltd/CNRS
Idea and Perspective Temperature-dependent activity in ectotherms 117
CONCLUSIONS
Thermal constraints on activity play a crucial role in mediat-
ing many important ecological and evolutionary processes.
These effects emerge from the multi-faceted influence of tem-
perature on activity. Nonetheless, few studies include more
than one component of thermal activity constraint. This
shortcoming may stem from the lack of a conceptual frame-
work that identifies and integrates the multiple effects of tem-
perature on activity. We have attempted to provide a simple
version of just such a conceptual framework, along with
examples of its application, in the hope that it will facilitate
the incorporation of ecologically relevant thermal activity con-
straints into future analyses. The data required to implement
this framework are relatively easy to collect. They are all
based on standard behavioural and ecological measurements
that have been collected in one form or another for decades
but have become less common in recent years. Given the con-
tinuing threat of climate change, continued refinement of our
understanding of all aspects of abiotic constraints on organ-
isms is required to effectively predict and mitigate the effects
of anthropogenic activities on the natural world.
ACKNOWLEDGEMENTS
We thank Mike Angilletta, Ray Huey, Donald Miles, Mike
Logan, Brian Powell, Dave Steinberg, the Stillman and Todg-
ham labs, the Associate Editor and three anonymous reviewers
for helpful feedback about this manuscript and the ideas we
have presented. This work was partially funded by a National
Science Foundation (NSF) Doctoral Dissertation Improvement
Grant to ARG, NSF grant IOS-1451450 to JH Stillman, and
the Berkeley Initiative for Global Change Biology.
AUTHORSHIP
ARG developed the first version of the conceptual framework
presented, wrote the first draft of the manuscript and con-
ducted all analyses. ML contributed to refinement of the
framework and the manuscript.
REFERENCES
Adolph, S.C. & Porter, W.P. (1993). Temperature, activity, and lizard life
histories. Am. Nat., 142, 273–295.
Alford, L., Blackburn, T. & Bale, J. (2012). Effects of acclimation and
latitude on the activity thresholds of the aphid Myzus persicae in
Europe. J. Appl. Entomol., 136, 332–346.
Andrew, N.R., Hart, R.A., Jung, M.-P., Hemmings, Z. & Terblanche,
J.S. (2013). Can temperate insects take the heat? A case study of the
physiological and behavioural responses in a common ant, Iridomyrmex
purpureus (Formicidae), with potential climate change. J. Insect
Physiol., 59, 870–880.
Angilletta, M.J. (2001). Thermal and physiological constraints on energy
assimilation in a widespread lizard (Sceloporus undulatus). Ecology, 82,
3044–3056.
Angilletta, M.J. (2009). Thermal Adaptation: A Theoretical and Empirical
Synthesis. Oxford University Press, Oxford, UK.
Angilletta, M.J., Niewiarowski, P.H., Dunham, A.E., Leach!
e, A.D. &
Porter, W.P. (2004). Bergmann’s clines in ectotherms: illustrating a
life-history perspective with sceloporine lizards. Am. Nat., 164, E168–
E183.
Arneberg, P., Skorping, A., Grenfell, B. & Read, A.F. (1998). Host
densities as determinants of abundance in parasite communities. Proc.
R. Soc. B Biol. Sci., 265, 1283–1289.
Bailey, S.F., Dettman, J.R., Rainey, P.B. & Kassen, R. (2013).
Competition both drives and impedes diversification in a model
adaptive radiation. Proc. Biol. Sci., 280, 20131253.
Bakken, G.S. (1992). Measurement and application of operative and
standard operative temperatures in ecology. Am. Zool., 32, 194–216.
Bennett, A.F. (1978). Activity metabolism of the lower vertebrates. Annu.
Rev. Physiol., 40, 447–469.
Bennie, J., Hodgson, J.A., Lawson, C.R., Holloway, C.T., Roy, D.B.,
Brereton, T. et al. (2013). Range expansion through fragmented
landscapes under a variable climate. Ecol. Lett., 16, 921–929.
Bozinovic, F., Calosi, P. & Spicer, J.I. (2011). Physiological correlates of
geographic range in animals. Annu. Rev. Ecol. Evol. Syst., 42, 155–179.
Brett, J.R. (1952). Temperature tolerance in young Pacific salmon, genus
Oncorhynchus.J. Fish. Board Can., 9, 265–323.
Brett, J. (1958). Implications and assessments of environmental stress. In
The Investigation of Fish-Power Problems (ed Larkin, P.). University of
15 20 25 30 35 40
0.0
0.2
0.4
0.6
0.8
1.0
Relative sprint speed
*
A. gundlachi
A. cristatellus
15 20 25 30 35 40
0.0
0.2
0.4
0.6
0.8
1.0
Body temperature ( C)
Relative effective activity vigor
(b)
(a)
Figure 6 (a) Relative thermal performance curves for sprinting for Anolis
cristatellus and A. gundlachi. Error bars represent one standard error of the
mean. *A significant difference in CT
max
(t-test, P>0.001). (b)
Temperature-dependent activity of A. cristatellus in relation to the thermal
ecology of A. gundlachi within it’s shaded upland habitat. Solid line: relative
effective activity vigour of A. cristatellus. Red box: the preferred
temperature range of A. cristatellus. Dotted lines: copulation thresholds of
A. cristatellus. Blue box: the preferred temperature range of A. gundlachi.
Dashed line: the mean body temperature of A. gundlachi within its shaded
upland habitat. Preferred and body temperature data from Hertz et al.
(1993). Sprint and CT
max
data for A. cristatellus from Gunderson & Leal
(2012) and Leal & Gunderson (2012) respectively. Sprint and CT
max
data
for A. gundlachi were collected following identical methods.
©2015 John Wiley & Sons Ltd/CNRS
118 A. R. Gunderson and M. Leal Idea and Perspective
British Columbia, University of British Columbia Press, Vancouver,
CA, pp. 69–83.
Brett, J. & Alderdice, D. (1958). The resistance of cultured young chum
and sockeye salmon to temperatures below 0 °C. J. Fish. Board Can.,
15, 805–813.
Buckley, L.B. & Kingsolver, J.G. (2012). The demographic impacts of
shifts in climate means and extremes on alpine butterflies. Funct. Ecol.,
26, 969–977.
Caruso, N.M., Sears, M.W., Adams, D.C. & Lips, K.R. (2014).
Widespread rapid reductions in body size of adult salamanders in
response to climate change. Glob. Change Biol., 20, 1751–1759.
Christian, K.A. & Weavers, B.W. (1996). Thermoregulation of monitor
lizards in Australia: an evaluation of methods in thermal biology. Ecol.
Monogr., 66, 139–157.
Corbet, S.A., Fussell, M., Ake, R., Fraser, A., Gunson, C., Savage, A.
et al. (1993). Temperature and the pollinating activity of social bees.
Ecol. Entomol., 18, 17–30.
Corser, J.D., White, E.L. & Schlesinger, M.D. (2015). Adult activity and
temperature preference drives region-wide damselfly (Zygoptera)
distributions under a warming climate. Biol. Lett., 11, 20150001.
Deutsch, C., Ferrel, A., Seibel, B., P€
ortner, H.-O. & Huey, R.B. (2015).
Climate change tightens a metabolic constraint on marine habitats.
Science, 348, 1132–1135.
Dillon, M.E., Wang, G. & Huey, R.B. (2010). Global metabolic impacts
of recent climate warming. Nature, 467, 704–706.
Dunham, A. (1993). Population responses to environmental change:
operative environments, physiologically structured models, and
population dynamics. In Biotic Interactions and Global Change (eds
Kareiva, P.M., Kingsolver, J.G. & Huey, R.B.). Sinauer Associates,
Sunderland, MA, pp. 95–119.
Dunham, A.E., Grant, B.W. & Overall, K.L. (1989). Interfaces between
biophysical and physiological ecology and the population ecology of
terrestrial vertebrate ectotherms. Physiol. Zool., 62, 335–355.
Frid, A. & Dill, L.M. (2002). Human-caused disturbance stimuli as a
form of predation risk. Cons. Ecol., 6, 11.
Fry, F. (1971). The effect of environmental factors on the physiology of
fish. Fish Physiol., 6, 1–98.
Gorman, G.C. & Hillman, S. (1977). Physiological basis for climatic niche
partitioning in two species of Puerto Rican Anolis (Reptilia, Lacertilia,
Iguanidae). J. Herpetol., 11, 337–340.
Grant, B.W. & Dunham, A.E. (1988). Thermally imposed time
constraints on the activity of the desert lizard Sceloporus merriami.
Ecology, 69, 167–176.
Grant, B.W. & Dunham, A.E. (1990). Elevational covariation in
environmental constraints and life histories of the desert lizard
Sceloporus merriami.Ecology, 71, 1765–1776.
Gunderson, A.R. & Leal, M. (2012). Geographic variation in
vulnerability to climate warming in a tropical Caribbean lizard. Funct.
Ecol., 26, 783–793.
Gunderson, A.R. & Leal, M. (2015). Patterns of thermal constraint on
ectotherm activity. Am. Nat., 185, 653–664.
Gunderson, A.R. & Stillman, J.H. (2015). Plasticity in thermal tolerance
has limited potential to buffer ectotherms from global warming. Proc.
Biol. Sci., 282, 20150401.
Hawley, A.W. & Aleksiuk, M. (1975). Thermal regulation of spring
mating behavior in the red-sided garter snake (Thamnophis sirtalis
parietalis). Can. J. Zool., 53, 768–776.
Hertz, P.E. (1981). Adaptation to altitude in two West Indian anoles
(Reptilia: Iguanidae): field thermal biology and physiological ecology.
J. Zool., 195, 25–37.
Hertz, P.E., Huey, R.B. & Stevenson, R. (1993). Evaluating temperature
regulation by field-active ectotherms: the fallacy of the inappropriate
question. Am. Nat., 142, 796–818.
Hillman, S. & Gorman, G. (1977). Water loss, desiccation tolerance, and
survival under desiccating conditions in 11 species of Caribbean Anolis.
Oecologia, 29, 105–116.
Hixon, M.A. & Jones, G.P. (2005). Competition, predation, and density-
dependent mortality in demersal marine fishes. Ecology, 86, 2847–
2859.
Hoffmann, A.A. & Sgr"
o, C.M. (2011). Climate change and evolutionary
adaptation. Nature, 470, 479–485.
Huang, S.P., Chiou, C.R., Lin, T.E., Tu, M.C., Lin, C.C. & Porter, W.P.
(2013). Future advantages in energetics, activity time, and habitats
predicted in a high-altitude pit viper with climate warming. Funct.
Ecol., 27, 446–458.
Huey, R.B. & Bennett, A.F. (1990). Physiological adjustments to
fluctuating thermal environments: an ecological and evolutionary
perspective. In Stress Proteins in Biology and Medicine (eds Morimoto,
R., Tissieres, A. & Georgopoulos, C.). Cold Springs Harbor
Laboratory Press, Cold Springs Harbor, NY, pp. 37–59.
Huey, R.B. & Pianka, E.R. (1977). Seasonal variation in
thermoregulatory behavior and body temperature of diurnal Kalahari
lizards. Ecology, 58, 1066–1075.
Huey, R.B., Deutsch, C.A., Tewksbury, J.J., Vitt, L.J., Hertz, P.E., P!
erez,
H.J.
!
A. et al. (2009). Why tropical forest lizards are vulnerable to
climate warming. Proc. Biol. Sci., 276, 1939–1948.
Huey, R.B., Kearney, M.R., Krockenberger, A., Holtum, J.A., Jess, M. &
Williams, S.E. (2012). Predicting organismal vulnerability to climate
warming: roles of behaviour, physiology and adaptation. Philos. Trans.
R. Soc. B Biol. Sci., 367, 1665–1679.
IPCC (2014). Climate Change 2013: The Physical Science Basis: Working
Group I Contribution to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change. Cambridge University
Press, Cambridge, UK.
Jayatilaka, P., Narendra, A., Reid, S.F., Cooper, P. & Zeil, J. (2011).
Different effects of temperature on foraging activity schedules in
sympatric Myrmecia ants. J. Exp. Biol., 214, 2730–2738.
Kaspersson, R., Sundstr€
om, F., Bohlin, T. & Johnsson, J.I. (2013). Modes of
competition: adding and removing brown trout in the wild to understand
the mechanisms of density-dependence. PLoS ONE, 8, e62517.
Kearney, M.R. (2013). Activity restriction and the mechanistic basis for
extinctions under climate warming. Ecol. Lett., 16, 1470–1479.
Kearney, M., Shine, R. & Porter, W.P. (2009). The potential for
behavioral thermoregulation to buffer ‘cold-blooded’ animals against
climate warming. Proc. Natl Acad. Sci., 106, 3835–3840.
Kelly, M.W., Sanford, E. & Grosberg, R.K. (2011). Limited potential for
adaptation to climate change in a broadly distributed marine
crustacean. Proc. Biol. Sci., 279, 349–356.
Kingsolver, J.G., Woods, H.A., Buckley, L.B., Potter, K.A., MacLean,
H.J. & Higgins, J.K. (2011). Complex life cycles and the responses of
insects to climate change. Integr. Comp. Biol., 51, 719–732.
Lailvaux, S.P. & Irschick, D.J. (2006). A functional perspective on sexual
selection: insights and future prospects. Anim. Behav., 72, 263–273.
Leal, M. & Gunderson, A.R. (2012). Rapid change in the thermal
tolerance of a tropical lizard. Am. Nat., 180, 815–822.
Logan, M.L., Huynh, R.K., Precious, R.A. & Calsbeek, R.G. (2013). The
impact of climate change measured at relevant spatial scales: new hope
for tropical lizards. Glob. Change Biol., 19, 3093–3102.
Logan, M.L., Cox, R.M. & Calsbeek, R. (2014). Natural selection on
thermal performance in a novel thermal environment. Proc. Natl Acad.
Sci., 111, 14165–14169.
Logan, M.L., Fernandez, S.G. & Calsbeek, R. (2015). Abiotic constraints
on the activity of tropical lizards. Funct. Ecol., 29, 694–700.
Losos, J.B. (2009). Lizards in an Evolutionary Tree: Ecology and Adaptive
radiation of Anoles. University of California Press, Berkeley, USA.
Lutterschmidt, W.I. & Hutchison, V.H. (1997a). The critical thermal
maximum: data to support the onset of spasms as the definitive end
point. Can. J. Zool., 75, 1553–1560.
Lutterschmidt, W.I. & Hutchison, V.H. (1997b). The critical thermal
maximum: history and critique. Can. J. Zool., 75, 1561–1574.
Martin, S.D., Gray, D.A. & Cade, W.H. (2000). Fine-scale temperature
effects on cricket calling song. Can. J. Zool., 78, 706–712.
©2015 John Wiley & Sons Ltd/CNRS
Idea and Perspective Temperature-dependent activity in ectotherms 119
McPeek, M.A. (1990). Behavioral differences between Enallagma species
(Odonata) influencing differential vulnerability to predators. Ecology,
71, 1714–1726.
Porter, W., Mitchell, J., Beckman, W. & DeWitt, C. (1973). Behavioral
implications of mechanistic ecology. Oecologia, 13, 1–54.
Rall, B.C., Brose, U., Hartvig, M., Kalinkat, G., Schwarzm€
uller, F.,
Vucic-Pestic, O. et al. (2012). Universal temperature and body-mass
scaling of feeding rates. Philos. Trans. R. Soc. Lond. B Biol. Sci., 367,
2923–2934.
Rand, A.S. (1964). Ecological distribution in anoline lizards of Puerto
Rico. Ecology, 45, 745–752.
Riis, L. & Nachman, G. (2006). Migration, trapping and local dynamics of
whiteflies (Homoptera: Aleyrodidae). Agric. For. Entomol., 8, 233–241.
Schmitz, O.J., Beckerman, A.P. & O’Brien, K.M. (1997). Behaviorally
mediated trophic cascades: effects of predation risk on food web
interactions. Ecology, 78, 1388–1399.
Schnebel, E.M. & Grossfield, J. (1984). Mating-temperature range in
Drosophila.Evolution, 38, 1296–1307.
Sears, M.W. & Angilletta, M.J. (2015). Costs and benefits of
thermoregulation revisited: both the heterogeneity and spatial structure
of temperature drive energetic costs. Am. Nat., 185, E94–E102.
Sears, M.W., Raskin, E. & Angilletta, M.J. (2011). The world is not flat:
defining relevant thermal landscapes in the context of climate change.
Integr. Comp. Biol., 51, 666–675.
Seebacher, F., White, C.R. & Franklin, C.E. (2015). Physiological
plasticity increases resilience of ectothermic animals to climate change.
Nat. Clim. Chang., 5, 61–66.
Seitz, R.D., Lipcius, R.N., Hines, A.H. & Eggleston, D.B. (2001).
Density-dependent predation, habitat variation, and the persistence of
marine bivalve prey. Ecology, 82, 2435–2451.
Sinervo, B., Mendez-De-La-Cruz, F., Miles, D.B., Heulin, B.,
Bastiaans, E., Villagr!
an-Santa Cruz, M. et al. (2010). Erosion of
lizard diversity by climate change and altered thermal niches.
Science, 328, 894–899.
Skelly, D.K. (1994). Activity level and the susceptibility of anuran larvae
to predation. Anim. Behav., 47, 465–468.
Sunday, J.M., Bates, A.E. & Dulvy, N.K. (2012). Thermal tolerance
and the global redistribution of animals. Nat. Clim. Chang., 2, 686–
690.
Svensson, E., Sinervo, B. & Comendant, T. (2001). Density-dependent
competition and selection on immune function in genetic lizard morphs.
Proc. Natl Acad. Sci., 98, 12561–12565.
Van Berkum, F.H., Huey, R.B. & Adams, B.A. (1986). Physiological
consequences of thermoregulation in a tropical lizard (Ameiva festiva).
Physiol. Zool., 59, 464–472.
Welch, A.M., Semlitsch, R.D. & Gerhardt, H.C. (1998). Call duration as
an indicator of genetic quality in male gray tree frogs. Science, 280,
1928–1930.
Woodin, S.A., Hilbish, T.J., Helmuth, B., Jones, S.J. & Wethey, D.S.
(2013). Climate change, species distribution models, and physiological
performance metrics: predicting when biogeographic models are likely
to fail. Ecol. Evol., 3, 3334–3346.
Woods, H.A., Dillon, M.E. & Pincebourde, S. (2014). The roles of
microclimatic diversity and of behavior in mediating the responses of
ectotherms to climate change. J. Therm. Biol.,doi:10.1016/
j.jtherbio.2014.10.002.
Zeis, B., Maurer, J., Pinkhaus, O., Bongartz, E. & Paul, R.J. (2004). A
swimming activity assay shows that the thermal tolerance of Daphnia
magna is influenced by temperature acclimation. Can. J. Zool., 82,
1605–1613.
Editor, Lauren Buckley
Manuscript received 4 August 2015
First decision made 3 September 2015
Second decision made 11 October 2015
Manuscript accepted 30 October 2015
©2015 John Wiley & Sons Ltd/CNRS
120 A. R. Gunderson and M. Leal Idea and Perspective
