Fine-Scale Microclimatic Variation Can Shape

Integrative and Comparative Biology
Integrative and Comparative Biology, volume 56, number 1, pp. 45–61
doi:10.1093/icb/icw016
Society for Integrative and Comparative Biology
SYMPOSIUM
Fine-Scale Microclimatic Variation Can Shape the Responses of
Organisms to Global Change in Both Natural and Urban
Environments
Sylvain Pincebourde,1,* Courtney C. Murdock,† Mathew Vickers‡ and Michael W. Sears§
*Institut de Recherche sur la Biologie de l’Insecte (IRBI, CNRS UMR 7261), Université François Rabelais, Faculté des
Sciences et Techniques, Tours, 37200, France; †Department of Infectious Diseases, College of Veterinary Medicine, Odum
School of Ecology, University of Georgia, Athens, GA 30602, USA; ‡Station d’Ecologie Théorique Expérimentale, UMR
5321, CNRS et Université Paul Sabatier, 2 route du CNRS, Moulis, 09200, France; §Department of Biological Sciences,
Clemson University, Clemson, South Carolina, 29634
From the symposium ‘‘Beyond the Mean: Biological Impacts of Changing Patterns of Temperature Variation’’ presented
at the annual meeting of the Society for Integrative and Comparative Biology, January 3–7, 2016 at Portland, Oregon.
1
E-mail: [email protected]
Synopsis When predicting the response of organisms to global change, models use measures of climate at a coarse
resolution from general circulation models or from downscaled regional models. Organisms, however, do not experience
climate at such large scales. The climate heterogeneity over a landscape and how much of that landscape an organism can
sample will determine ultimately the microclimates experienced by organisms. This past few decades has seen an important
increase in the number of studies reporting microclimatic patterns at small scales. This synthesis intends to unify studies
reporting microclimatic heterogeneity (mostly temperature) at various spatial scales, to infer any emerging trends, and to
discuss the causes and consequences of such heterogeneity for organismal performance and with respect to changing land
use patterns and climate. First, we identify the environmental drivers of heterogeneity across the various spatial scales that
are pertinent to ectotherms. The thermal heterogeneity at the local and micro-scales is mostly generated by the architecture
or the geometrical features of the microhabitat. Then, the thermal heterogeneity experienced by individuals is modulated
by behavior. Second, we survey the literature to quantify thermal heterogeneity from the micro-scale up to the scale of a
landscape in natural habitats. Despite difficulties in compiling studies that differ much in their design and aims, we found
that there is as much thermal heterogeneity across micro-, local and landscape scales, and that the temperature range is
large in general (49 8C on average, and up to 26 8C). Third, we examine the extent to which urban habitats can be used to
infer the microclimatic patterns of the future. Urban areas generate globally drier and warmer microclimatic patterns and
recent evidence suggest that thermal traits of ectotherms are adapted to them. Fourth, we explore the interplay between
microclimate heterogeneity and the behavioral thermoregulatory abilities of ectotherms in setting their overall performance. We used a random walk framework to show that the thermal heterogeneity allows a more precise behavioral
thermoregulation and a narrower temperature distribution of the ectotherm compared to less heterogeneous microhabitats.
Finally, we discuss the potential impacts of global change on the fine scale mosaics of microclimates. The amplitude of
change may differ between spatial scales. In heterogeneous microhabitats, the amplitude of change at micro-scale, caused
by atmospheric warming, can be substantial while it can be limited at the local and landscape scales. We suggest that the
warming signal will influence species performance and biotic interactions by modulating the mosaic of microclimates.
Introduction
Organisms are not all facing global change equally.
Climatic change can be represented as a signal emitted
by changes in the global atmosphere that are then
received by organisms. The analogy with communication networks can help define how the ‘warming signal’
is transmitted across the environment between global
atmosphere and organisms, and also to understand
Advanced Access publication April 23, 2016
ß The Author 2016. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved.
For permissions please email: [email protected].
46
how this signal, once perceived by the receiver, can be
modified. The signal differs across latitudes and altitudes since the amplitude of warming increases towards the poles (Dillon et al. 2010; IPCC 2014).
Then, the signal can be attenuated, magnified, or
remain unchanged as it is transmitted across different
habitats, depending on their physical properties
(Woods et al. 2015). Finally, the signal is filtered by
organisms (Kingsolver and Watt 1983), biophysically
(e.g., their body coloration, shape, and size; Gates
1980), behaviorally (e.g., thermoregulation; Huey and
Slatkin 1976) and physiologically (e.g., thermal tolerance; Angilletta 2009). Understanding how the warming signal is modified by the habitat and filtered by the
organism can, therefore, be as important as defining
the amplitude of the environmental change itself (Sears
et al. 2011; Sears and Angilletta 2015). In this context,
mechanistic frameworks should be favored over correlative approaches when forecasting the ecological impacts of environmental changes (Buckley et al. 2010;
Kearney et al. 2010; Potter et al. 2013) to truly integrate the physical and biological processes behind habitat and organismal filtering of the climate signal.
When predicting the response of organisms to
global change, models use measures of climate at a
coarse resolution from general circulation models or
from downscaled regional models (Potter et al. 2013;
Kearney et al. 2014). Sophisticated biophysical
models have been developed in the 1960s (Gates
1980) to mechanistically integrate climatic variables
and ecophysiological traits of ectotherms to predict
their body temperature patterns across space and
time, and they have been applied to a great variety
of ectotherms (e.g., Tracy 1976; Gates 1980;
Kingsolver and Moffat 1982; Stevenson 1985; Grant
and Porter 1992; Helmuth 1998; Pincebourde and
Casas 2006; Kearney et al. 2009; Sunday et al.
2014). Nevertheless, most global studies using biophysical models to predict body temperature patterns
also use global or regional climatic inputs (e.g.,
Sunday et al. 2014). Individual ectotherms, however,
do not experience climate at such large scales.
Quoting George Bartholomew (1966), ‘‘. . . climate
in the usual sense of the term is little more than a
crude index to the physical conditions in which most
terrestrial animals live,’’ because these organisms are
living in ‘‘. . . cracks and crevices, holes in logs, dense
underbrush, tunnels, and nests’’ (Bartholomew 1966;
see also Huey and Bennett 2008). The climate heterogeneity over a landscape and how much of that
landscape an organism can sample will determine
ultimately the microclimates that organisms do experience (Grant and Dunham 1988; Sears et al. 2011;
S. Pincebourde et al.
Woods et al. 2015). Further, these microclimates may
magnify or buffer the amplitude of the effects of
climate change on organisms (Woods et al. 2015).
Body temperature integrates all aspects of the microclimate that ectotherms experience at their relevant
scales of space and time (Gates 1980). This past few
decades has seen an important increase in the
number of studies reporting microclimatic patterns
at small scales, focusing mostly on temperature. This
synthesis intends to unify studies reporting microclimatic temperature heterogeneity at various spatial
scales, to infer any emerging trends, and to discuss
the causes and consequences of such thermal heterogeneity for organismal performance and with respect
to changing land use patterns and climate.
In this synthesis, first we identify the environmental
drivers of thermal heterogeneity across the various
spatial scales that are pertinent to ectotherms.
Second, we survey the literature to quantify thermal
heterogeneity from the microscale up to the scale of a
landscape in natural habitats. Third, we examine the
extent to which urban habitats show microscale thermal heterogeneity comparable to natural habitats to
understand how regional warming (i.e., the urban
heat island) causes shifts in spatial heterogeneity of
microclimatic temperatures. Fourth, we explore the
interplay between microclimate temperature heterogeneity and the behavioral thermoregulatory abilities of
ectotherms in setting their overall performance.
Finally, we discuss on the potential impacts of global
change on the fine scale mosaics of microclimatic temperatures. We mainly deal with temperature as it is
one of the most important abiotic factors for ectotherms in general, and also because it is probably
the easiest variable to measure in the field. Other factors including humidity (e.g., Muth 1980) and gas
composition in the microhabitat (e.g., Pincebourde
and Casas 2016) can be especially important. Our approach can be applied to any of these factors, but the
scarcity of data on microclimatic variables other than
temperature may prevent any deep analysis such as
ours. We discuss further this point in our concluding
remarks. In addition, we focus on the spatial scaling of
thermal environments but the temporal variability of
microclimatic temperatures can also influence the response of organisms to environmental changes (e.g.,
Vasseur et al. 2014). These temporal issues are well
covered elsewhere (Dillon et al., this issue).
The drivers of microclimatic temperature heterogeneity across spatial scales
Significant deviations between macro- and microclimates are quite common in various ecosystems
Thermal heterogeneity across space
(Andrewartha 1944; Cloudsley-Thompson 1962;
Helmuth, Kingsolver and Carrington 2005; Scherrer
and Körner 2010; Suggitt et al. 2011). For example,
microhabitat temperatures for intertidal ectotherms
are poorly predicted by macro-environmental conditions (Seabra et al. 2011). Body temperature of mussels and limpets, for example, instead relies on
complex interplay among macro-environment and
local factors such as tidal regime, wave splash, radiation level, and exposure (Helmuth et al. 2006;
Mislan et al. 2009; Denny et al. 2011). Likewise, significant deviations have also been observed between
temperatures at the plant leaf surface (Linacre 1967;
Chelle 2005; Bernard et al. 2013) or ground surface
(Geiger 1959) and in surrounding air. Thus, compiling the drivers of microclimatic heterogeneity requires first that we define the spatial scale(s)
relevant for a given organism.
The environment can be seen as a complex superposition of multiple layers of air across space with
distinct properties. Tiny organisms like small insects
should be influenced by the microclimatic conditions
within the smallest boundary layer of air in their
environment (Kaspari et al. 2015). This layer can
reflect thermal variation near the rocky surface in
the intertidal (Helmuth 1998; Denny and Harley
2006), or along the plant leaf surface (Woods 2010,
2013; Pincebourde and Woods 2012). In contrast,
relatively large organisms experience thermal conditions reflective of larger boundaries of air and at
more local scales. For example, the body temperatures of large caterpillars are shaped by the air temperature within plant canopies (Woods 2013), and
forest canopies produce microclimates that diverge
from atmosphere conditions (Finnigan 2000;
Kaspari et al. 2015). The body temperatures of lizards can mirror variation in air temperature within
the ground boundary layer (Kearney et al. 2014).
Finally, the largest boundary layer reflecting the climate situation over the landscape would matter only
for the largest organisms, such as isolated trees or the
largest ectotherms such as the komodo’s dragon.
These boundary layer considerations help explain
the non-intuitive results of the biophysical modeling
of a spherical lizard, in which a large lizard gets
higher body temperatures than a small size animal
(Fig. 1). Larger sizes of spheres have larger characteristic dimensions which cause the boundary conductance for heat to increase, increasing in turn the
operative temperature. These results are consistent
with data published in Bakken and Gates (1975).
The differences in operative temperature offset the
heating rates of differently sized animals to produce
the patterns in the bottom plot of Fig. 1. Small
47
ectotherms tend to converge toward the surface temperature when this surface is cooler than the potential body temperature when in full sun. This
biophysical modeling approach also highlights the
important role of shading. The body temperature
can potentially be 30 8C higher when a large lizard
is in full sun compared to in the shade under the
vegetation for example (Fig. 1). The shade is not
distributed equally in space and this distribution
can be quite dynamic in space and time (Gates
1980). This spatio-temporal dynamics of shade also
differs between microhabitats, such as on the ground
versus on tree trunks (Huey et al. 1977). Therefore,
the boundary layer thickness should be analyzed relative to the body size of the organism.
Body size can be compared to the boundary layer
thickness to infer the actual conditions that a species
experiences. In general, the thickness of a boundary
layer can be simply calculated using atmospheric
wind speed and the size (characteristic dimension)
of the structure that generates the boundary layer,
assuming laminar air flow over a surface (Oke
1989). Therefore, the smallest boundary layer can
be computed using the smallest structure in the
microchabitat; a rock, a leaf, a branch, a piece of
decaying wood, etc. Typically, the thickness of this
boundary layer ranges from 1 mm to few centimeters
at the most (Oke 1989). This contrasts with the
thickness of more local boundary layers (e.g., plant
canopies) which can be as thick as few meters
(Finnigan 2000), and landscape boundary layers
which can be as thick as tens meters (Maitani 1979).
The drivers of microclimatic heterogeneity and effects of climate change on organism performance will
likely not be the same across different boundary
layers. For example, at the smallest boundary layer,
the drivers of temperature variation for small ectotherms include the thermal properties of the habitat
surface (bare rock, soil, leaf, etc.) as well as its geometrical features. Further, the orientation and the
slope of the surface relative to the position of the
heat source (i.e., the sun) influence greatly the
amount of energy received by the surface (Oke
1989). We propose that the thermal heterogeneity
available to tiny ectotherms can be estimated by measuring the roughness of their microhabitat, that is, the
diversity of the orientation and inclination angles
(Box 1). Even a relatively flat surface is rugose to
some extent, generating potentially diverse thermal
microenvironment for small arthropods (Caillon et
al. 2014). In intertidal organisms, the orientation
and slope of the rocky surfaces generate high levels
of thermal heterogeneity at microscale (Denny et al.
2011; Lathlean et al. 2012). If the habitat surface is
48
S. Pincebourde et al.
Fig. 1 Influence of body size on the body temperature of ectotherms. For a given set of climatic variables, organisms experience the
same conditions differently given features of their morphology, coloration, and thermoregulatory preferences and abilities. For instance,
here, we simulated radiative and thermal conditions for a hypothetical summer day. We used air temperatures, ground temperatures,
and radiation absorbed (in shade or in sun) to simulate the body temperatures of animals of differing sizes (ranging from 1-g to 1000-g,
modeled as spheres). Size alone creates thermal heterogeneity among organisms. Differences of up to 10 8C can be experienced at
midday across a range of body sizes. Thus, caution must be used when applying climatic data from weather stations without transforming the data to organism specific measures such as operative temperatures or potential body temperatures.
associated with another living organism, such as a
leaf, physiology and behavior of both species come
into play. For example, transpiration rates on plant
leaves modulate surface temperatures experienced by
insects (Pincebourde and Woods 2012). At larger
local scales (e.g., forest canopy), the diversity of leaf
angles generates most of the thermal variability found
among leaves exposed to the sun within a tree canopy
(Sinoquet et al. 2001). At even larger scales, the landscape, topography has been shown to induce a high
thermal heterogeneity that can be exploited by large
thermoregulating ectotherms (Porter et al. 2002; Sears
et al. 2011).
Microclimatic temperature heterogeneity across spatial scales
We surveyed the thermal heterogeneity reported by
studies dealing with processes at the various spatial
scales defined above. Our approach did not consist
in analyzing the whole set of published studies available. Our aim was to detail the most important and
representative studies from various biomes to suggest
a general trend and to call for more specific studies.
In this analysis, we excluded extreme thermal environments, such as geyser, for which changes of about
30 8C can be observed within a few millimeters
Thermal heterogeneity across space
49
mountain slope. The probability density function of radiative
energy incident to the surface pixels showed an important heterogeneity of energy distribution over the landscape (B). Globally,
the horizontal landscape remained cooler than the irregular
landscape. We assumed in this example that the whole surface
was exposed to solar radiation. Irregular terrain, however, would
produce shaded areas. Shaded portions would bring more surfaces at low radiative heat flux, possibly generating bimodal surface temperature distributions. For surfaces with variable
microtopography and multiple elements (e.g., bare rock, bark, and
leaves), the spatial heterogeneity in radiative heat absorption can
be estimated from the statistical distribution of slope and absorbance values. The other components of the heat budget, namely
surface evapotranspiration and heat loss via convection, can be
estimated from surface wetness and ambient air temperature. In
general, our approach allows us to estimate microclimate heterogeneity at the scale of organisms even when no spatially explicit information on environmental architecture is known.
Box 1. The major physical drivers of surface temperature are
the amount of solar radiation incident to the substrate (incoming
energy, Sp) and the portion of this energy it stores (absorptivity).
Simple geometrical relationships can be used to compute the
amount of radiation incident to a surface from factors such as
zenith angle (Z), solar azimuth (), and substrate slope (p) and
azimuth (p) (e.g., the cosine law of illumination which is based
on the angle between direct solar beam and the normal to
slope surface) (see Equations (1) and (2) (Oke 1989)). Then, the
amount of radiative energy received by a surface can be calculated for each pixel surface characterized by its angles (p, p)
(A), calculated with the sun at the zenith on June 6 in Tours,
France). The spatial distribution of the angles (p, p) should be
known to estimate the landscape thermal heterogeneity for a
small insect. In the example given here (B), the spatial distribution
of the angles (p, p) was estimated for a flat land, with a level
of rugosity sufficient to create slope with any azimuth angle, by
attributing a normal distribution to angles. We also biased this
normal distribution toward angle values of (458, 1808) for (p,
p) to simulate a similar rugous land but on a south facing
(Dunckel et al. 2009). Overall, it was not possible to
extract explicit temperature ranges across space from
most studies linking thermal tolerance of ectotherms
and environmental temperatures because these studies either focused on extreme temperatures only, or
they did not report metrics of temperature range
across space because that was not the purpose of
their studies. In addition, most studies reported temperature ranges that integrated both space and time,
typically by averaging daily minimal and maximal
temperatures within a given area. This difficulty
raises the importance of agglomerating microclimatic
raw data into a single database for all biomes and
latitudes/altitudes. Finally, we extracted the largest
temperature ranges when different conditions were
studied to remain conservative. Other metrics could
also be used but the temperature range (i.e., maximum minus minimum temperature) is the easiest
metric to compare between studies as it is inherently
standardized for ambient air temperature. Below, we
briefly detail the levels of thermal heterogeneity, and
the factors that generate them, across spatial scales
from the micro-scale to local and landscape scales.
Then, we compiled and analyzed the thermal heterogeneity data we collected.
Thermal heterogeneity within the smallest boundary
layer (microscale)
The expansion of biophysical ecology in the 60s
(Gates 1980), and the continuous developments of
thermal ecology (Angilletta 2009), generated a high
number of studies looking at the thermal processes at
microscale, including the variation in microclimate
and body temperatures and their influence on the
performance of organisms. One of the most striking
examples is the plant leaf microclimate (Jones 1999;
50
Stokes et al. 2006; Pincebourde and Woods 2012;
Pincebourde and Casas 2015). The surface of leaves
is quite heterogeneous with temperature ranges from
4 to 7 8C over single leaf surfaces (Woods 2013;
Caillon et al. 2014; Pincebourde and Suppo, this
issue). The temperature gradient over a leaf surface
is generated by wind effect across the surface (RothNebelsick, 2001), by possible variation in the stomatal behavior (Mott and Buckley 2000), and by the
leaf microtopography (Saudreau and Pincebourde,
personal communication). Therefore, small arthropods can encounter a great variety of surface temperatures by moving over few centimeters (Woods
2013; Caillon et al. 2014).
The rocky intertidal ecosystem provides another
model with high variability at small scale, within a
meter. Rock surface temperature (Lathlean et al.
2012) and mussel body temperature (Denny et al.
2011) vary by as much as 8 8C and 15 8C, respectively,
at a given time of the day and over short distances due to
the microtopography of the rock surface and to variation in orientation of mussel’s body toward the sun
(Dowd, personal communication). Lizards have also
been shown to exploit microscale thermal variation
(Muth 1977). Bakken (1989) showed that simple postural adjustments of a small lizard on an arboreal perch
could result in changes of up to 6.5 8C, and simply
moving from sun to shade could affect the operative
temperature by up to 16 8C. But the record for terrestrial systems probably is the microhabitat selected by a
flat-rock spider in Australia. The underside of rocks
selected for nesting can have a temperature range of
up to 20 8C over a few centimeters (Pike et al. 2012),
which results from the high heat load absorbed by the
rock in full sun and the much lower temperature of the
substrate it is resting on. A temperature range of similar
amplitude (about 17 8C) was also found around rocks
used by snakes as retreat-sites in northeastern
California (Huey et al. 1989). Other studies did
report similar temperature ranges for various types of
systems (Goller et al. 2014; Mota et al. 2015).These
studies demonstrate that ectotherms can access a thermal gradient over very small spatial scales (51 m) that is
as variable as thermal gradients found over larger, geographical scales (Kaspari et al. 2015; e.g.,41600 km in
the intertidal, Denny et al. 2011; 750 km of forest,
Caillon and Pincebourde, personal communication).
Thermal heterogeneity within intermediate boundary layers (local scale)
The local scale, as defined here, can represent spatial
scales similar to a portion of a crop field, a section of
forest with several large trees or the scale of a single
S. Pincebourde et al.
tree canopy. The size of this spatial scale largely surpasses body size, but organisms can move within the
local scale via short-range dispersion. The local scale
can also correspond to the home range of the individual (Briscoe et al. 2014). The recent technological
advances in unmanned aerial vehicles (UAVs)
equipped with infrared cameras has considerably
changed the way we probe the spatial heterogeneity
at local scale. For example, surface temperature
ranges of 20-30 8C were reported over 20 m in
crop fields in Andean agrosystems at the same time
of the day (Faye et al. 2015). Other approaches include thermography in mountains where one can
look at a slope frontally (Scherrer and Körner
2010) or infrared cameras fixed on crane towers
(Leuzinger and Körner 2007). For example, the surface temperature of temperate tree canopies is extremely variable with leaf surface temperatures
varying in a range of 15 8C (Leuzinger and
Körner 2007). This heterogeneity was species specific,
with each single canopy species showing a specific
temperature pattern (Leuzinger and Körner 2007).
In general, authors identified the plant structure as
the cause of this variability. Indeed, the underlying
process is again related to the architecture of the
plant canopies: the temperature distribution should
be related to the distributions in orientation and inclination angles of the leaves or other plant organs
(Sinoquet et al. 2009). The same process applied to
other types of surfaces (Cox and Smith 2011).
Thermal heterogeneity within the largest boundary
layers (landscape scale)
Thermal heterogeneity at the landscape scale integrates
local heterogeneity at the scale of the individual’s home
range to the area encompassing a population of interacting organisms. Largely, this level of heterogeneity is
driven by the attenuation of solar radiation over the
landscape surface as a result of differing slopes and aspects, vegetational cover (shaded vs. open), and underlying surface composition (soil type, rocky exposures,
litter composition, etc.; Sears et al. 2011; Suggitt et al.
2011; Ashcroft et al. 2012). Landscape heterogeneity is
likely to be very important given that many applications, such as species distribution models, assume climate envelopes over large swaths of habitat (30 m to
greater than 1 km pixels). For instance, using a simulation based approach, Sears et al. (2011) showed that
thermal heterogeneity of the substrate could be increased from negligible to greater than 20 8C across differently configured topographies (flat to hilly), despite
using the same climatic drivers. Likewise, using infrared
thermography, Scherrer and Körner (2010) showed an
Thermal heterogeneity across space
51
increase in the variation of surface temperatures from
minimal (up to 5 8C) at night to quite high (420 8C)
during the day in alpine grasslands. These findings carry
implications for predicted consequences of climate
change (but see below).
Comparative analysis
We collected temperature range data from various
ecosystems, including plant surfaces and intertidal
substrates (See Supplementary Table S1). We categorized the temperature range values according to
the spatial scale they represent: micro-scale, localscale and landscape scale. Because one has a priori
expectation of ordering between these three categories, we ran an ordered-heterogeneity test using the
complement of the P-value of a Kruskal–Wallis
analysis of variance (Pc) and the Spearman’s rank
coefficient (rc) (OH tests; Rice and Gaines 1994).
Globally, the mean temperature range at the
micro, local, and landscape scales did not differ
(Fig. 2; OH test: Pc x rc ¼ 0.36550.700 at a probability threshold ¼ 0.05, NS), although mean temperature range tended to increase with area.
Therefore, there is as much thermal heterogeneity
at all spatial scales, at least in terms of composition
(i.e., the ‘quality’ of temperature patches). The temperature range is not informative of the spatial
configuration of the various temperature patches.
Other metrics should be used but the lack of coherence between published studies and the lack of standard methodology impede further analyses on
configuration. Indeed, we lack studies that compared the empirical levels of thermal heterogeneity
across each relevant spatial scale on the same model
system and in a standard way. To our knowledge,
Pincebourde and Suppo (this issue) provided the
only attempt to do so across micro, local, and landscape scales. This study reported a significant increase in thermal variance and temperature range
when scaling up from leaf surface to landscape,
mostly because landscapes are more diverse structurally than single leaf surfaces (Pincebourde and
Suppo, this issue). By contrast, a modeling approach showed that the variance in mussel body
temperatures was larger when looking at local
scale compared to broader scales (Gilman et al.
2006). The tendency for increased heterogeneity
when scaling up is certainly more or less pronounced depending on the type of ecosystem.
Although data from the intertidal ecosystem were
included into the dataset, our analysis focused on
open-air temperature range values. The temperature
heterogeneity at a small spatial scale has been largely
Fig. 2 Mean temperature range (SD) at three different spatial
scales. Sample sizes (number of studies) are N ¼ 3, 11, and 12 at
the scales of landscape, local scale, and micro-scale, respectively
(see Table S1).
disregarded in most aquatic systems (streams, ponds,
etc.), except in the context of the vertical stratification
of deep lakes. The water temperature in lakes varies
substantially with depth, with water temperature differences of more than 10 8C at a depth410 m
(Mortimer 1952; Hondzo and Stefan 1993; Boehrer
and Schultze 2008) as a result of the heat flux balance
at the water surface (Dake and Harleman 1969). The
water temperature range within single ponds was
quantified in rare occasions. Moss (1969) reported
variation in water temperature of up to 5 8C with
depth in a 3 m-deep pond, but this vertical gradient
disappeared during the hottest period of the year,
offering few thermal refuges to ectotherms. Thermal
heterogeneity was also found in rivers and streams,
despite the water flow tends to homogenize water
temperature. The drivers of thermal heterogeneity
are mostly linked to the geo-morphology of the
river basin and to the structural complexity of the
channels (Hawkins et al. 1997; Caissie 2006). Spatial
variations of only a few degrees were found in general
between various biotopes (e.g., rifle vs. run vs. pool)
but even such a small micro-scale heterogeneity was
enough to generate significant deviations in degreedays for instance (Dallas and Rivers-Moore 2011). A
water temperature range of up to nearly 20 8C, however, was recorded in freshwater systems composed of
several types of channels (Wawrzyniak et al. 2013;
Brewitt and Danner 2014). Therefore, aquatic habitats
may show levels of thermal heterogeneity similar to
terrestrial habitats. To our knowledge, only one study
compared thermal metrics between aquatic and
nearby terrestrial habitats, reporting large temperature
52
changes across the aquatic-terrestrial interface
(Tonolla et al. 2010). But this study did not include
metrics such as temperature range. More field-based
research is needed to fully quantify the water temperature heterogeneity at small-scale.
Our analysis reveals that generally there is a high
level of surface temperature heterogeneity within the
micro- and local scales. This result implies that ectotherms can potentially find enough microclimate
diversity locally to thermoregulate properly and
escape lethal temperatures as long as the microclimatic temperature distribution remains mostly
below the temperature threshold of the species.
Indeed, this effect was also found in sessile organisms, with some individuals surviving to extreme
events when they live in the less exposed microhabitats (Dowd et al. 2015). This conclusion contradicts
a recent global analysis which indicated that ectotherms are barely able to buffer the amplitude of
warming by moving over a radius of 50 or 100 km
(Buckley et al. 2013). This study, however, integrated
climatology, topography and behavior at a coarse
grid size (10’ latitude), which may have confounded
the role of local thermal heterogeneity in substrate
and body temperatures (but see below).
Ultimately, the eco-physiology of ectotherms is
driven by their body temperature. Body temperature
can deviate from surface or habitat temperature
somewhat importantly due to the biophysical processes governing the energy budget of relatively
large organisms (Gates 1980). The realized body temperatures achieved by organisms in the field can be
compared to the surface or habitat temperature distributions to estimate the thermoregulatory abilities
or to what extent their biophysical properties magnify or reduce the environmental variance. In general, ectotherms remain at body temperatures close
to their surrounding habitat temperature when in the
shade but body temperature increases when exposed
to solar radiation, causing inevitably an increase in
the body temperature range at local scale compared
to habitat temperature range (Pincebourde et al.
2007; Sunday et al. 2014). Several important studies
showed recently that the body temperature patterns
of ectotherms put them at risk under a warming
scenario, in particular by restricting their activity
levels (Huey et al. 2012; Kearney 2013).
Microclimatic heterogeneity in urban
environments
Human-modified habitats potentially show contrasting microclimatic patterns compared to natural habitats. Variation in socio-economic status and human
S. Pincebourde et al.
behavior shape the environments that organisms experience in urban ecosystems, potentially resulting in
changes in population growth rates, species interactions, disease transmission, and ultimately organism
fitness (Meineke et al. 2013). In general, urban centers tend to have very different temperature (Oke
1982; Arnfield 2003) and precipitation regimes
(Lacke et al. 2009; Shepherd et al. 2010; Niyogi et
al. 2011) relative to surrounding areas. Urban and
suburban landscapes have four main characteristics
that differ from rural areas (Larsen 2015): (1) the use
of dark, dense paving, and building materials, (2)
reduced amounts of vegetative cover, which decreases
natural shading and cooling by evapotranspiration,
(3) three-dimensional buildings that restrict air circulation and absorb solar radiation, and (4) the addition of ‘‘waste heat’’ due to increases in vehicular
traffic, air conditioning units, additional lighting,
and
industrial
equipments
or
processes.
Consequently, in the continental United States, for
example, cities have mean temperatures that are on
average 3 8C higher than non-urban areas, with the
exception of drier ecosystems. Night time temperatures also tend to be higher in urban centers resulting in smaller ranges in diurnal temperature
fluctuation (Kalnay and Cai 2003). These shifts in
temperature associated with urban heat islands can
also translate into changes in other weather variables,
such as precipitation regimes, wind speed, relative
humidity, etc. (USGCRP 2014), all of which will
impact the heat budget of organisms living in urbanized landscapes.
In addition to urban centers being on average
warmer, there is substantial variation in thermal
metrics within and across urban landscapes. The drivers of such variability are diverse and they operate
at different spatial scales. First, the material used for
urban infrastructures greatly impacts surface temperatures. Surface temperature deviations of up to 20 8C
can be observed between different materials under
the same environmental conditions (Doulos et al.
2004). Not only the nature of the material matters,
but also does the texture (roughness) and the surface
color. By contrast, the geometrical feature of the surface (size and thickness) does not generate significant
surface temperature deviations (Doulos et al. 2004).
Second, the urban architecture impacts the net solar
radiation and sensible heat flux, thereby generating
different surface temperatures depending on the orientation of the urban ‘‘canyon’’ (streets) (Todhunter
1990). Therefore, important microclimatic variation
may result from urban systems differing in their
symmetry and orientation. Third, the biophysical
composition of urban landscapes directly causes
Thermal heterogeneity across space
heterogeneity in surface temperatures. In particular,
the relative surface area corresponding to plant cover
and infrastructures impacts surface temperature in a
nonlinear fashion (Stabler et al. 2005; Guo et al.
2015). Interestingly, surface temperatures decrease
with distance from the core of the city on average,
but vegetation patches can modify this relationship
(Stabler et al. 2005). Finally, the amount of ‘‘waste
heat’’ generated by human activities introduces another source of thermal variation in urban areas
(Harlan et al. 2006; Jenerette et al. 2007).
Therefore, these drivers interact with each other to
generate the specific thermal signatures of urban
systems.
The composition of the urban landscape is important, but the spatial configuration of these drivers
matters as well (Li et al. 2013). For example, in
Baltimore (MD, United States), the relative surface
area of buildings increases surface temperatures while
plant cover mitigates the urban heat island (Zhou et
al. 2011). However, keeping the composition of the
urban landscape constant, changing the spatial configuration of land cover features (e.g., vegetation
edge density and complexity of vegetation) led to
different surface temperature patterns (Zhou et al.
2011). The physical connectedness between different
vegetation units (e.g., trees) can also contribute to
decrease the average surface temperature (Rhee et
al. 2014). Overall, edge effects seem to be important
in urban systems and likely contribute to the thermal
heterogeneity in cities.
Urban areas reach a level of complexity that differs
from natural habitats in that the geometrical features
of buildings are much more predictable and constant
than the architecture of vegetation which changes
dynamically throughout the season and from year
to year. The spatial configuration effects as caused
by the relative arrangement of the various infrastructures and vegetation areas would also be more predictable than the configuration effects in natural
systems, simply because the main drivers (architecture and material) are easily identifiable and well
known. Then, the thermal heterogeneity across
space in urban areas would be more predictable
than in natural habitats. The predictability of urban
landscapes makes them ideal systems for modeling
processes in a spatially explicit context (Zhang et
al. 2013).
Are ectotherms adapted to their urban mosaic of
microclimates? Answers to this question may help to
resolve how organisms will adapt to climate change.
Theory predicts that urban populations would exhibit higher and lower tolerance to extreme warm
and cold temperatures, respectively, relative to rural
53
populations because urban environments are characterized by warmer daily minimum, mean, and maximum temperatures (Oke 1982). Yet, there have been
relatively few studies to compare how urban populations differ in thermal tolerance and performance
relative to populations living in natural landscapes.
Angilletta et al. (2007) did find systematic differences
in the ability of urban leaf cutter ants to tolerate a
high temperature (42 8C) relative to rural leaf cutter
ants, suggesting that thermal tolerance of some
urban organisms can respond rapidly to changes in
climate. Whether this difference in tolerance across
urban and rural ant populations was due to phenotypic plasticity or genetic change was not determined. Further, there is some evidence to suggest
that the capacity for evolutionary shifts in upper
thermal limits is more constrained than lower thermal limits (Snyder and Weathers 1975; Araujo et al.
2013), and that a 2-4 8C warming scenario could be
problematic for many ectotherms, especially those
living in mid-latitude continental environments
(Hoffmann et al. 2013). Interestingly, the climatic
gradient from rural areas across urban landscapes is
expected to be larger under temperate compared to
tropical regions (Diamond et al. 2015), suggesting
more difficulties for ectotherms to adapt to urban
thermal environments when coming from surrounding rural habitats at mid-latitudes.
The idea that urban areas can reflect the climates
we will experience in the future is not new. Recently,
a study showed that cities simulate the global warming effect on scale insects (Youngsteadt et al. 2015).
Urban environments may, therefore, be a window to
future microclimatic mosaics for ectotherms. Urban
areas can be used in experiments to simulate the
effects of climate change on organisms (thermal tolerance, water balance, performance, nutrient regulation, etc.). Understanding how microclimate
variation in urban environments affects organism
performance could also provide information on
how we can mitigate the negative effects of climate
warming and extreme events on organisms.
The interplay between spatial heterogeneity and thermoregulatory abilities
The thermal environment is variable in space, and
how variable it is—or the degree of thermal heterogeneity in the environment—is most usefully defined
with reference to a target organism. Size and motility
of the organism are the factors determining the effective spatial heterogeneity of the thermal environment (Porter and Gates 1969; Vickers et al. 2011;
Sears and Angilletta 2015). As organisms become
54
larger (or faster), the grain over which they experience the environment becomes coarser due to their
increased thermal inertia (or to the relatively small
amount of time experiencing any one temperature
over its movement path). Smaller (or slower) organisms, on the other hand, experience the same environment at a much finer scale due to their quickly
changing body temperatures and relatively longer
durations at a particular microclimate. As such,
one could imagine interactions where small/slow organisms experience thermal environments at the
same grain as larger/faster organisms.
Despite the complexity of defining thermal heterogeneity, some properties are general. Spatial heterogeneity in the thermal environment can enable or
hinder behavioral thermoregulation depending on
whether the thermal environment has a temperature
distribution similar to- or different from- the preferred body temperature of the thermoregulator
(Fig. 3). To explore this in detail, consider a
random walk through the habitat that yields the perfect temperature in terms of both accuracy (location,
or mean body temperature), and precision (breadth,
spread, or variance of temperature). We assume our
real organism targets a single temperature value (i.e.,
its preferred body temperature) for optimal performance, and that the more random its walk is, the less
effort it requires. This might be because the organism
can devote energy toward other objectives (e.g., reproduction) while maintaining body temperature for
free. The final assumption is that on account of thermal inertia and the time to sample the habitat, our
random walker takes a certain amount of time to
realize its ultimate or equilibrium temperature
distribution.
If the habitat is as close to optimal as possible, the
random walker can achieve perfect temperature control and, therefore, a real organism in such an environment must invest no further effort in behavioral
thermoregulation (Huey and Slatkin 1976). Consider
that the total range of temperatures and the mean
temperature does not change in the habitat but that
the temperature patches are arranged differently—for
example, they are not aggregated in the heterogeneous habitat but are aggregated in the less heterogeneous. The random walker spends less time at any
one habitat temperature and therefore integrates over
time toward the mean more rapidly in a heterogeneous habitat. In this ergodic system, therefore, the
random walker in habitats of increasing spatial heterogeneity realizes its ultimate temperature distribution sooner, and has a narrower temperature
distribution than in less complex habitats. Thus,
the temperature distribution will have a narrower
S. Pincebourde et al.
temperature range (Fig. 3 top). Our random walker
achieves nearly optimal body temperature more
easily or more efficiently in this case. By corollary,
decreasing thermal heterogeneity increases the time
to realize the distribution and increases the final
temperature distribution breadth (a decrease in precision, Fig. 3 bottom). For our real organism to behaviorally thermoregulate, it must diverge further
from the random walk as thermal heterogeneity decreases: thermoregulation is more difficult. At the
limits of thermal heterogeneity (either perfectly
mixed, or zero heterogeneity), the thermal distribution becomes effectively constant and equal to the
mean, and behavioral thermoregulation becomes logically impossible. Sears and Angilletta (2015) showed
that the relative size, arrangement, and heterogeneity
of thermal patches affect the thermoregulatory performance of individuals similarly as described above.
As the relative abundance, dispersion of patches, and
within patch heterogeneity increased, so did thermoregulatory performance. Further, larger organisms
are less affected by all of these factors. In fact, in
these simulations, heterogeneity and arrangement
seemed to affect thermoregulatory ability as much
as the average temperature of the environment
itself. Taken together, these results demonstrated
that studies of behavioral thermoregulation should
continue to consider the spatial arrangement of temperature patches per se, instead of focusing exclusively on the mean temperature or temperature
range.
Now consider shifting mean temperature with
varying spatial heterogeneity; a random walker in
habitat with suboptimal mean temperature and
high heterogeneity will have a narrow body temperature range that is far from the optimum. A thermoregulator here must invest high effort to correct its
body temperature (Fig. 3, top left). On the other
hand, suboptimal temperature and low heterogeneity
may result in off-center but variable body temperature that still overlaps the thermal optimum (Fig. 3,
bottom left). A thermoregulator might need to invest
relatively less effort here to maintain good control
over body temperature. This interaction of mean
temperature with its spatial heterogeneity can produce complex results.
This thought experiment involves habitats of different thermal heterogeneity, and holds provided the
habitats have similar mean and variance, that is, we
discuss only spatial heterogeneity. The situation will
become more complex as more factors are introduced: increasing the maxima, variance, or skewness
of the environmental temperature distribution will
have further effects on the performance of
Thermal heterogeneity across space
55
heterogeneity should be a priority as shifting thermal
heterogeneity may compound the effect of shifting
location and composition of the distributions.
Which way environments move on the two-way heterogeneity/location axis (Fig. 3) will determine the
severity of climate change to behavioral thermoregulators who, if not plastic enough in their response,
may find themselves suddenly in the wrong patch.
The effects of warming on the microclimate mosaic and ectotherms’
performance
Fig. 3 The interplay between thermal heterogeneity and mean
temperature on temperature experienced by a random walker.
The vertical axis represents some value of spatial heterogeneity
(e.g., agglomerating index, patchiness) while mean temperature
and temperature range remains the same. Therefore, habitat
heterogeneity refers to the spatial configuration of temperature
patches, and the spatial composition remains unchanged. The
horizontal axis represents a variation in mean temperature while
the spatial configuration and the temperature range remain
unchanged. In each quadrant is a realized body temperature
distribution, with optimal temperature indicated (Topt). In a
highly heterogeneous habitat (top), the body temperature experienced will be narrower than an equivalent habitat of lower
heterogeneity (bottom). As the mean temperature of the habitat
moves away from the optimum (left, only colder is shown, but
the same principal applies for hotter), the body temperature
distribution shifts toward ‘‘wrong’’ body temperatures. Then, the
walk in the more heterogeneous habitat suffers more than the
walk in the low heterogeneous environment as a byproduct of
the narrowness of the distribution.
thermoregulation (e.g., Sears and Angilletta 2015). In
particular, increased positive skewness will compound the effect of increased mean temperature
(Vasseur et al. 2014) and increased variance may
alter the variance of realized body temperature
(Huey and Slatkin 1976). Understanding and predicting precisely how global change impacts local
Theory in thermal ecology predicts that the physiological limits of ectotherms track shifts in environmental temperature. The mean tolerance of
ectotherms to elevated temperatures, however,
varies only weakly along latitude and altitude
(Addo-Bediako et al. 2000; Sunday et al. 2011;
Garcı́a-Robledo et al. 2016). These macroecological
studies also reveal a high level of variance between
species or population at a given latitude/altitude.
Physiological limits are certainly more correlated to
very local conditions rather than to global climatic
estimates (e.g., Stillman and Somero 1996; Kaspari et
al. 2015). Indeed, the temperature range we report at
the landscape scale (15 8C) is comparable in amplitude to the global mean temperature difference between the tropics and temperate regions, a metrics
that is often used in global analysis. In addition, the
inter-specific variability of CTmax at temperate latitudes (around 7 8C; Addo-Bediako et al. 2000;
Sunday et al. 2011) compares well to the levels of
thermal heterogeneity at the landscape, local and microscales. For example, the lethal temperature threshold of a temperate leaf mining caterpillar varies in
response to the changes in the microclimatic temperature across larval stages (Pincebourde and Casas
2015). This study shows that, at a given time in
the summer, the CTmax of larvae varies by about
6 8C when the microclimatic temperature range
encompasses about 7 8C within a single apple tree
canopy (Pincebourde et al. 2007). Therefore, we propose that the local microclimatic heterogeneity explains the lack of global coherence in the patterns
of thermal tolerance across space. This suggests that
the warming signal will influence species performance and biotic interactions by modulating the
mosaic of microclimates.
Nevertheless, our understanding of how global
change will modify the spatial distribution and arrangement of the various microclimates in a given
habitat is weak. Most of our knowledge is inferred
from laboratory experiments or simulation studies.
56
For example, warming ambient air reduced thermal
heterogeneity along leaf surfaces (Caillon et al. 2014).
The temperature range was substantially reduced at
the apple leaf surface, and the aggregation index was
increased, when air temperature increased (Caillon et
al. 2014). The thermal homogenization at microscale has consequences for the spider mites that
thermoregulate at the leaf surface: under elevated
temperatures, spider mites tried to remain in the
coldest parts of the leaf surface (Caillon et al.
2014). These organisms probably rely on physiological mechanisms to increase their resistance to high
temperatures. Further, a modeling study showed that
tree architecture influences significantly a leaf miner’s
larval development time and mortality, but that this
effect of manipulating tree structure was weak compared to the impact of climate change by 2100
(Saudreau et al. 2013). This suggests that at some
point the amplitude of warming can surpass the benefits of the thermal heterogeneity at micro and local
scales, especially for ectotherms that already select
microclimates that are warmer than ambient air
(e.g., leaf miners; Pincebourde et al. 2007) and for
species that are already challenged by their current
heterogeneous environment (e.g., lizards that delay
their activity to mornings; Grant and Dunham
1988). This ‘point of no return’ represents a period
at which no thermal refuge exists anymore in space,
causing the extinction of the population. This effect
might occur during an extreme event such as a heat
wave. The generality of this concept to any organism
remains to be assessed.
At the landscape scale, the topography can mitigate the negative impacts of warming on organisms.
For example, a shift in operative temperatures and
activity times for lizards were less extreme as topographic relief increased from flat to hilly (Sears et al.
2011). Similarly, a 2 8C warming in the alpine landscape caused the loss of only 3% of the coolest microhabitats, while 75% of current microhabitats were
reduced in abundance and 22% of microhabitats (the
warmest portion) increased in abundance (Scherrer
and Körner 2011). In this study, Scherrer and Körner
(2011) stated that ‘‘meter-scale thermal contrasts significantly exceed IPCC warming projections for the
next 100 years’’ in alpine ecosystems. While this
implies little risk of temperature-driven extinctions
of species, the competition for cool microclimates
may be exacerbated in a warmer world (Scherrer
and Körner 2011). These suggested that increased
thermal heterogeneity produced through processes
at the landscape scale might mediate the responses
to climate, and that predictions of the negative responses of species to increasing temperatures (e.g.,
S. Pincebourde et al.
multiple extinctions; Sinervo et al. 2010) might be
largely overestimated (Clusella-Trullas and Chown
2011). Yet, global studies largely ignore the influence
of local heterogeneity (but see exceptions; e.g.,
Helmuth et al. 2006).
Concluding remarks
Environmental heterogeneity promotes biodiversity
(Stein et al. 2014). There are multiple ways of quantifying heterogeneity. Our synthesis reveals some
methodological issues in how ecologists quantify
the spatial heterogeneity in surface or body temperatures. Our meta-analysis of spatial temperature
range was considerably limited by the fact that authors routinely report a temperature range that confound both space and time, simply because these
studies did not focus particularly on the spatial
issue alone. We encourage authors to provide the
temperature distributions in space as supplemental
materials, for example, or to upload their raw data
in data repositories. This approach requires that the
spatial resolution of the data is explicitly reported
along with the data. Indeed, the heterogeneity of microclimate and body temperatures at a fine spatial
scale is largely unknown for the vast majority of species. Such knowledge is crucial since the spatial resolution of the data can impact the inference we make
on the effects of climate changes (Potter et al. 2013).
The rise in the use of thermal imaging to explore
thermal landscapes (Faye 2015; Faye et al. 2015,
2016) will also certainly improve our global assessment of spatial heterogeneity in both natural habitats
and urban environments.
The use of environmental architecture or geometry
to assess the potential diversity of surface temperatures at micro and local scales is surprisingly rare.
Advanced technical tools exist, however, to measure
architectural traits in the micro-environment such as
LIDAR (light detection and ranging; Vierling et al.
2008), scanning lasers and electromagnetic digitizers.
Scanning lasers capture the 3D structure of surfaces
at a finer scale than LIDAR, and sometimes authors
coined the term of microtopography to describe the
level of roughness measured with these equipments.
For example, the use of a scanning laser to describe
the microtopography in the rocky intertidal showed
that the local distribution of intertidal ectotherms is
best predicted by the roughness of the rock surfaces
(Peglow 2013). Scanning lasers are robust and can
even be used underwater like in deep-sea ecosystems
(Du Preez and Tunnicliffe 2012). Electromagnetic
digitizing device allows to precisely measure the 3D
architecture of volumic structures such as vegetative
57
Thermal heterogeneity across space
elements within a tree or forest canopy (Sinoquet et
al. 2001). For example, this approach was applied to
predict the distribution of microclimates for insect
pests within apple tree canopies (Pincebourde et al.
2007; Saudreau et al. 2011, 2013). Therefore, the
technology already exists to capture the architectural
properties of any ecosystem, and this is the first step
toward the exhaustive quantification of the thermal
heterogeneity in space.
Our approach developed for temperature can be
applied to any environmental variable. Indeed, the
microclimate of organisms cannot be summarized
by temperature alone. The environment of organisms
is multifactorial and we need to integrate all these
abiotic variables to quantify the net effect of global
change on species. Different factors such as temperature, humidity, rainfall, physical disturbance (e.g.,
wave splash) can vary in their level of variance in
both time and space (Benedetti-Cecchi et al. 2006;
Koussoroplis and Wacker 2016). The spatial configuration of each variable should be considered, although some of them covary strongly (e.g.,
humidity and temperature). An important issue is
that a biological response to temperature can be misinterpreted as a direct effect of temperature while it
results from the effect of a variable that covaries with
temperature. For example, dissolved oxygen varies
with temperature in freshwater systems to the point
that thermal tolerance can be limited by dissolved
oxygen deprivation, a phenomenon that could be
expanded to oxygen limitation in terrestrial invertebrates (Verberk and Calosi 2012; Boardman and
Terblanche 2015). Other factors of anthropogenic
origin such as pollutants can also interact and
lower the performance of ectotherms. For example,
elevated temperatures and cadmium exposure led to
oxygen limitation by impaired performance in
oxygen supply through ventilation and circulation
in oysters, suggesting that the thermal tolerance is
lower in marine ectotherms exposed to polluted
waters compared with pristine environments
(Lannig et al. 2008). In general however, the influence of the covariance structure of the multifactorial
environment on the performance of ectotherms remains to be determined (Koussoroplis and Wacker
2016). Biophysical models of ectotherms’ body temperature have the advantage of integrating environmental variables into a single output, the body
temperature. Therefore, these models seem to be
good candidates to tackle these questions (but see
Denny et al. 2009; Dowd et al. 2015).
The spatial heterogeneity of microclimatic temperatures as described in this review, and combined with
the temporal variability of temperatures (Dillon et al.,
this issue; Sheldon and Dillon, this issue), is ultimately filtered by organisms both behaviorally and
physiologically. The way organisms exploit this
spatio-temporal heterogeneity is influenced by biotic
interactions, including herbivory (Pincebourde and
Casas 2015), competition (e.g., Crespo-Pérez et al.
2015) and predation (e.g., Barton and Schmitz
2009). Climate change can impact species by strengthening these biotic interactions, putting prey, and the
weakest competitors under risk of extinction by modifying the activity of interacting species (Barton and
Schmitz 2009; Huey et al. 2012). We suggest that the
spatial scale at which we shall expect the strongest
shifts in thermal niches and species distributions can
be the scale at which climate change causes the largest
modification in the thermal heterogeneity. Analyzing
the levels of microclimate heterogeneity across spatial
scales promises net progresses in global change
biology.
Acknowledgments
We thank Arthur Woods and Michael Dillon for
organizing the symposium ‘‘Beyond the mean:
Biological impacts of changing patterns of temperature variation’’ at the annual meeting of the SICB in
Portland, OR (January, 2016). We also acknowledge
the other participants to the symposium for fruitful
discussions. We warmly thank Raymond Huey, Wes
Dowd and Olivier Dangles for their feedback on a
previous version of the manuscript. The symposium
was sponsored by the divisions DAB, DCE, DCPB,
DEE, and DIZ.
Funding
This work was partially supported by the SICB,
Agence Nationale pour la Recherche [MicroCliMite,
ANR-2010 BLAN-1706-02 to SP; PROTECTODO,
Région Centre 2016 to SP; LABEX TULIP, ANR10-LABX-41 to MV]; [NDHET to MV]; the
AnaEE-France project [ANR-11-INBS-0001 to MV];
and the ‘‘Investissements d’Avenir’’ program.
Supplementary data
Supplementary Data available at ICB online.
References
Addo-Bediako A, Chown SL, Gaston KJ. 2000. Thermal tolerance, climatic variability and latitude. Proc Roy Soc Lond
B 267:739–45.
Andrewartha HG. 1944. Air temperature records as a guide to
the date of hatching of the nymphs of Austroicetes cruciata
Sauss. (Orthoptera). Bull Entomol Res 35:31–41.
Angilletta MJ. 2009. Thermal adaptation—a theoretical and
empirical synthesis. Oxford, UK: Oxford University Press.
58
Angilletta MJ, Jr, Wilson RS, Niehaus AC, Sears MW, Navas
CA, Ribeiro PL. 2007. Urban physiology: city ants possess
high heat tolerance. PLoS One 2:e258.
Araujo MB, Ferri-Yanez F, Bozinovic F, Marquet PA,
Valladares F, Chown SL. 2013. Heat freezes niche evolution.
Ecol Lett 16:1206–19.
Arnfield AJ. 2003. Two decades of urban climate research: a
review of turbulence, exchanges of energy and water, and
the urban heat island. Inter J Clim 23:1–26.
Ashcroft M, Gollan J, Warton D, Ramp D. 2012. A novel
approach to quantify and locate potential microrefugia
using topoclimate, climate stability, and isolation from
the matrix. Glob Change Biol 18:1866–79.
Bakken GS. 1989. Arboreal perch properties and the operative
temperature experienced by small animals. Ecology 70:922–30.
Bakken GS, Gates DM. 1975. Heat-transfer analysis of animals: some implications for field ecology, physiology, and
evolution. In: Gates DM, Schmerl RB, editors. Perspectives
in biophysical ecology Part IV, vol. 12. Springer Berlin
Heidelberg.
Bartholomew GA. 1966. Interaction of physiology and behavior under natural conditions. In: Bowman RI, editor.
The galapagos.Berkeley, CA: University of California
Press.
Barton BT, Schmitz OJ. 2009. Experimental warming transforms multiple predator effects in a grassland food web.
Ecol Lett 12:1317–25.
Benedetti-Cecchi L, Bertocci I, Vaselli S, Maggi E. 2006.
Temporal variance reverses the impact of high mean intensity of stress in climate change experiments. Ecology
87:2489–99.
Bernard F, Sache I, Suffert F, Chelle M. 2013. The development of a foliar fungal pathogen does react to leaf temperature!. New Phytol 198:232–40.
Boardman L, Terblanche JS. 2015. Oxygen safety margins set
thermal limits in an insect model system. J Exp Biol
218:1677–85.
Boehrer B, Schultze M. 2008. Stratification of lakes. Rev
Geophys 46:RG2005.
Brewitt K, Danner E. 2014. Spatio-temporal temperature variation influences juvenile steelhead (Oncorhynchus mykiss)
use of thermal refuges. Ecosphere 5:92.
Briscoe N, Handasyde K, Griffiths S, Porter WP,
Krockenberger A, Kearney MR. 2014. Tree-hugging koalas
demonstrate a novel thermoregulatory mechanism for arboreal mammals. Biol Lett 10:20140235.
Buckley LB, Tewksbury JJ, Deutsch CA. 2013. Can terrestrial
ectotherms escape the heat of climate change by moving?
Proc Roy Soc Lond B 280:20131149.
Buckley LB, Urban MC, Angilletta MJ, Crozier LG, Rissler LJ,
Sears MW. 2010. Can mechanism inform species’ distribution models? Ecol Lett 13:1041–54.
Caillon R, Suppo C, Casas J, Woods HA, Pincebourde S.
2014. Warming decreases thermal heterogeneity of leaf surfaces: implications for behavioural thermoregulation by arthropods. Func Ecol 28:1449–58.
Caissie D. 2006. The thermal regime of rivers: a review. Fresh
Biol 51:1389–406.
Chelle M. 2005. Phylloclimate or the climate perceived by
individual plant organs: what is it? How to model it?
What for? New Phytol 166:781–90.
S. Pincebourde et al.
Cloudsley-Thompson JL. 1962. Microclimates and the distribution of terrestrial arthropods. Ann Rev Entomol 7:199–222.
Clusella-Trullas S, Chown SL. 2011. Comment on ‘‘Erosion of
lizard diversity by climate change and altered thermal
niches’’. Science 332:537.
Cox TE, Smith CM. 2011. Thermal ecology on an exposed
algal reef: infrared imagery a rapid tool to survey temperature at local spatial scales. Coral Reefs 30:1109–20.
Crespo-Pérez V, Régnière J, Chuine I, Rebaudo F, Dangles O.
2015. Changes in the distribution of multispecies pest assemblages affect levels of crop damage in warming tropical
Andes. Glob Change Biol 21:82–96.
Dake JMK, Harleman DRF. 1969. Thermal stratification in
lakes: analytical and laboratory studies. Water Resour Res
5:484–95.
Dallas H, Rivers-Moore N. 2011. Micro-scale heterogeneity in
water temperature. Water SA 37:505–12.
Denny MW, Harley CDG. 2006. Hot limpets: Predicting body
temperature in a conductance-mediated thermal system.
J Exp Biol 209:2409–19.
Denny MW, Dowd WW, Bilir L, Mach KJ. 2011. Spreading
the risk: Small-scale body temperature variation among intertidal organisms and its implications for species persistence. J Exp Mar Biol Ecol 400:175–90.
Denny MW, Hunt L, Miller L, Harley CDG. 2009. On the
prediction of extreme ecological events. Ecol Monogr
79:397–421.
Diamond S, Dunn RR, Frank SD, Haddad NM, Martin RA.
2015. Shared and unique responses of insects to the interaction of urbanization and background climate. Cur
Opinion Ins Sci 11:71–7.
Dillon ME, Wang G, Huey RB. 2010. Global metabolic impacts of recent climate warming. Nature 467:704–6.
Dillon ME, Woods HA, Wang G, Fey SB, Vasseur DA,
Telemeco RS, Marshall K, Pincebourde S. Life in the frequency domain: the biological impacts of changes in climate variability at multiple time scales. Integr Comp Biol
this issue.
Doulos L, Santamouris M, Livada I. 2004. Passive cooling of
outdoor urban spaces. The role of materials. Solar Energy
77:231–49.
Dowd W, King F, Denny M. 2015. Thermal variation, thermal
extremes and the physiological performance of individuals.
J Exp Biol 218:1956–67.
Du Preez C, Tunnicliffe V. 2012. A new video survey method
of microtopographic laser scanning (MiLS) to measure
small-scale seafloor bottom roughness. Limnol Oceanogr:
Methods 10:899–909.
Dunckel A, Cardenas B, Sawyer A, Bennett P. 2009. Highresolution in-situ thermal imaging of microbial mats at El
Tatio Geyser, Chile shows coupling between community
color and temperature. Geophys Res Lett 36:L23403.
Faye E. 2015. Thermal landscapes and pest dynamic in
Andean tropical agrosystems [PhD dissertation] Paris:
Université Pierre et Marie Curie.
Faye E, Dangles O, Pincebourde S. 2016. Distance makes the
difference in thermography for ecological studies. J Therm
Biol 56:1–9.
Faye E, Rebaudo F, Yanez-Cajo D, Cauvy-Fraunié S, Dangles
O. 2015. A toolbox for studying thermal heterogeneity
across spatial scales: from unmanned aerial vehicle imagery
Thermal heterogeneity across space
to landscape metrics. Meth Ecol Evol doi: 10.1111/2041210X.12488.
Finnigan J. 2000. Turbulence in plant canopies. Ann Rev
Fluid Mech 32:519–71.
Garcı́a-Robledo C, Kuprewicz EK, Staines CL, Erwin TL,
Kress WJ. 2016. Limited tolerance by insects to high temperatures across tropical elevational gradients and the implications of global warming for extinction. PNAS doi:
10.1073/pnas.1507681113.
Gates DM. 1980. Biophysical ecology. New York, USA:
Springer-Verlag.
Geiger R. 1959. The climate near the ground. Cambridge, UK:
Harvard University Press.
Gilman S, Wethey D, Helmuth B. 2006. Variation in the sensitivity of organismal body temperature to climate change
over local and geographic scales. PNAS 103:9560–5.
Goller M, Goller F, French S. 2014. A heterogeneous
thermal environment enables remarkable behavioral
thermoregulation in Uta stansburiana. Ecol Evol
4:3319–29.
Grant BW, Dunham AE. 1988. Thermally imposed time constraints on the activity of the desert lizard Sceloporus merriami. Ecology 69:167–76.
Grant BW, Porter WP. 1992. Modeling global macroclimatic
constraints on ectotherm energy budgets. Am Zool
32:154–78.
Guo G, Wu Z, Xiao R, Chen Y, Liu X, Zhang X. 2015.
Impacts of urban biophysical composition on land surface
temperature in urban heat island clusters. Land Urban Plan
135:1–10.
Harlan SL, Brazel AJ, Prashad L, Stefanov WL, Larsen L. 2006.
Neighborhood microclimates and vulnerability to heat
stress. Soc Sci Med 63:2847–63.
Hawkins CP, Hogue JN, Decker LM, Feminella JW. 1997.
Channel morphology, water temperature, and assemblage
structure of stream insects. J N Am Benthol Soc 16:728–49.
Helmuth B. 1998. Intertidal mussel microclimates: Predicting
the body temperature of a sessile invertebrate. Ecol Monogr
68:51–74.
Helmuth B, Broitman BR, Blanchette C, Gilman SE, Halpin P,
Harley CDG, O’Donnell MJ, Hofmann GE, Menge B,
Strickland D. 2006. Mosaic patterns of thermal stress in
the rocky intertidal zone: implications for climate change.
Ecol Monogr 76:461–79.
Helmuth B, Kingsolver JG, Carrington E. 2005. Biophysics,
physiological ecology, and climate change: Does mechanism
matter? Ann Rev Physiol 67:177–201.
Hoffmann AA, Chown SL, Clusella-Trullas S. 2013. Upper
thermal limits in terrestrial ectotherms: how constrained
they are? Func Ecol 27:934–49.
Hondzo M, Stefan HG. 1993. Regional water temperature
characteristics of lakes subjected to climate change. Clim
Change 24:187–211.
Huey RB, Bennett AF. 2008. Bart’s familiar quotations: The
enduring biological wisdom of George A. Bartholomew.
Physiol Biochem Zool 81:519–25.
Huey RB, Kearney MR, Krockenberger A, Holtum JAM, Jess
M, Williams SE. 2012. Predicting organismal vulnerability
to climate warming: roles of behaviour, physiology and
adaptation. Phil Trans Royal Soc B 367:1665–79.
59
Huey RB, Peterson CR, Arnold SJ, Porter WP. 1989. Hot
rocks and not-so-hot rocks: retreat-site selection by garter
snakes and its thermal consequences. Ecology 70:931–44.
Huey RB, Pianka ER, Hoffmann J. 1977. Seasonal variation in
thermoregulatory behavior and body temperatures of diurnal Kalahari lizards. Ecology 58:1066–75.
Huey RB, Slatkin M. 1976. Cost and benefits of lizard thermoregulation. Quart Rev Biol 51:363–84.
IPCC. 2014. Climate change 2014: impacts, adaptation, and
vulnerability. Cambridge, UK: Cambridge University Press.
Jenerette GD, Harlan SL, Brazel A, Jones N, Larsen L,
Stefanov WL. 2007. Regional relationships between surface
temperature, vegetation, and human settlement in a rapidly
urbanizing ecosystem. Landsc Ecol 22:353–65.
Jones HG. 1999. Use of thermography for quantitative studies
of spatial and temporal variation of stomatal conductance
over leaf surfaces. Plant Cell Env 22:1043–55.
Kalnay E, Cai M. 2003. Impact of urbanization and land-use
change on climate. Nature 423:528–31.
Kaspari M, Clay NA, Lucas J, Yanoviak SP, Kay A. 2015.
Thermal adaptation generates a diversity of thermal limits
in a rainforest ant community. Glob Change Biol
21:1092–102.
Kearney MR. 2013. Activity restriction and the mechanistic
basis for extinctions under climate warming. Eco Lett
16:1470–9.
Kearney MR, Isacc AP, Porter WP. 2014. Microclim: Global
estimates of hourly microclimate based on long-term
monthly climate averages. Sci Data 1:140006.
Kearney MR, Shamakhy A, Tingley R, Karoly DJ, Hoffmann
AA, Briggs PR, Porter WP. 2014. Microclimate modelling at
macro scales: a test of a general microclimate model integrated with gridded continental-scale soil and weather data.
Meth Ecol Evol 5:273–86.
Kearney MR, Shine R, Porter WP. 2009. The potential for
behavioral thermoregulation to buffer ‘‘cold-blooded’’ animals against climate warming. PNAS 106:3835–40.
Kearney MR, Simpson SJ, Raubenheimer D, Helmuth B. 2010.
Modelling the ecological niche from functional traits. Phil
Trans Royal Soc B 365:3469–83.
Kingsolver JG, Moffat RJ. 1982. Thermoregulation and the
determinants of heat transfer in Colias butterflies.
Oecologia 53:27–33.
Kingsolver JG, Watt WB. 1983. Thermoregulatory strategies of
Colias butterflies: thermal stress and the limits to adaptation in temporally varying environments. Am Nat
121:32–55.
Koussoroplis AM, Wacker A. 2016. Covariance modulates the
effect of joint temperature and food variance on ectotherm
life-history traits. Ecol Lett 19:143–52.
Lacke MC, Mote TL, Shepherd JM. 2009. Aerosols and associated precipitation patterns in Atlanta. Atmos Environ
43:4359–73.
Lannig G, Cherkasov AS, Pörtner HO, Bock C, Sokolova IM.
2008. Cadmium-dependent oxygen limitation affects temperature tolerance in eastern oysters (Crassostrea virginica
Gmelin). Am J Physiol 294:1338–46.
Larsen L. 2015. Urban climate and adaptation strategies.
Front Ecol Env 13:486–92.
Lathlean J, Ayre D, Minchinton T. 2012. Using infrared imagery to test for quadrat-level temperature variation and
60
effects on the early life history of a rocky-shore barnacle.
Limnol Oceano 57:1279–91.
Leuzinger S, Körner C. 2007. Tree species diversity affects
canopy leaf temperatures in a mature temperate forest.
Agri For Meteor 146:29–37.
Li X, Zhou W, Ouyang Z. 2013. Relationship between land
surface temperature and spatial pattern of greenspace: what
are the effects of spatial resolution? Land Urban Plan
114:1–8.
Linacre ET. 1967. Further notes on a feature of leaf and air
temperatures. Theor Appl Climatol 15:422–36.
Maitani T. 1979. A comparison of turbulence statistics in the
surface layer over plant canopies with those over several
other surfaces. Bound Layer Meteorol 17:213–22.
Meineke EK, Dunn RR, Sexton JO, Frank SD. 2013. Urban
warming drives insect pest abundance on street trees. PLoS
One 8:1–7.
Mislan KAS, Wethey DS, Helmuth B. 2009. When to worry
about the weather: role of tidal cycle in determining patterns of risk in intertidal ecosystems. Glob Change Biol
15:3056–65.
Mortimer CH. 1952. Water movements in lakes during
summer stratification; evidence from the distribution of
temperature in Windermere. Phil Trans Roy Soc Lond B
236:355–98.
Moss B. 1969. Vertical heterogeneity in the water column of
Abbot’s pond: I. The distribution of temperature and dissolved oxygen. J Ecol 57:381–96.
Mota C, Engelen A, Serrão E, Pearson G. 2015. Some
don’t like it hot: Microhabitat-dependent thermal and
water stresses in a trailing edge population. Func Ecol
29:640–49.
Mott KA, Buckley TN. 2000. Patchy stomatal conductance:
Emergent collective behavior of stomata. Trends Plant Sci
5:258–62.
Muth A. 1977. Body temperatures and associated postures of
the zebra-tailed lizard, Callisaurus draconoices. Copeia
1:122–5.
Muth A. 1980. Physiological ecology of desert iguana
(Dipsosaurus dorsalis) eggs: temperature and water relations. Ecology 61:1335–43.
Niyogi D, Pyle P, Lei M, Arya SP, Kishtawal CM, Shepherd
M, Chen F, Wolfe B. 2011. Urban modification of thunderstorms: An observational storm climatology and model
case study for the Indianapolis urban region. J App
Meteorol Climatol 50:1129–44.
Oke TR. 1982. The energetic basis of the urban heat island.
Quart J Royal Meteorol Soc 108:1–24.
Oke TR. 1989. Boundary layer climates. London: Routledge.
Peglow JR. 2013. Investigating the relationships between community structure and rugosity in the rocky intertidal using
terrestrial laser scanning [Master Thesis]. Faculty of the
Division of Science and Environmental Policy, California
State University, Monterey Bay CA: Monterey Bay.
Pike DA, Webb JK, Shine R. 2012. Hot mothers, cool eggs:
nest-site selection by egg-guarding spiders accommodates
conflicting thermal optima. Funct Eco l26:469–75.
Pincebourde S, Casas J. 2006. Multitrophic biophysical budgets: thermal ecology of an intimate herbivore insect–plant
interaction. Ecol Monogr 76:175–94.
S. Pincebourde et al.
Pincebourde S, Casas J. 2015. Warming tolerance across insect
ontogeny: influence of joint shifts in microclimates and
thermal limits. Ecology 96:986–97.
Pincebourde S, Casas J. 2016. Hypoxia and hypercarbia in
endophagous insects: Larval position in the plant gas exchange network is key. J Insect Physiol 84:137–53.
Pincebourde S, Suppo C. The vulnerability of tropical ectotherms to warming is modulated by the microclimatic heterogeneity. Integr Comp Biol this issue.
Pincebourde S, Sinoquet H, Combes D, Casas J. 2007.
Regional climate modulates the canopy mosaic of favourable and risky microclimates for insects. J Anim Ecol
76:424–38.
Pincebourde S, Woods HA. 2012. Climate uncertainty on leaf
surfaces: the biophysics of leaf microclimates and their consequences for leaf-dwelling organisms. Func Ecol
26:844–53.
Porter WP, Gates DM. 1969. Thermodynamic equilibria of
animals with environment. Ecol Monogr 39:227–44.
Porter WP, Sabo JL, Tracy CR, Reichman OJ, Ramankutty N.
2002. Physiology on a landscape scale: Plant-animal interactions. Integr Comp Biol 43:431–453.
Potter KA, Woods HA, Pincebourde S. 2013. Microclimatic
challenges in global change biology. Glob Chang Biol
19:2932–9.
Rhee J, Park S, Lu Z. 2014. Relationship between land cover
patterns and surface temperature in urban areas. GIScience
Rem Sens 51:521–36.
Rice WR, Gaines SD. 1994. The ordered-heterogeneity family
of tests. Biometrics 50:746–52.
Roth-Nebelsick A. 2001. Computer-based analysis of steady
state and transient heat transfer of small-sized leaves by
free and mixed convection. Plant Cell Env 24:631–40.
Saudreau M, Marquier A, Adam B, Sinoquet H. 2011.
Modelling fruit-temperature dynamics within apple tree
crowns using virtual plants. Ann Bot 108:1111–20.
Saudreau M, Pincebourde S, Dassot M, Adam B, Loxdale HD,
Biron DG. 2013. On the canopy structure manipulation to
buffer climate change effects on insect herbivore development. Trees 27:239–48.
Scherrer D, Körner C. 2010. Infra-red thermometry of alpine
landscapes challenges climatic warming projections. Glob
Change Biol 16:2602–13.
Scherrer D, Körner C. 2011. Topographically controlled thermal-habitat differentiation buffers alpine plant diversity
against climate warming. J Biogeo 38:406–16.
Seabra R, Wethey DS, Santos AM, Lima FP. 2011. Side matters: microhabitat influence on intertidal heat stress over a
large geographical scale. J Exp Mar Biol Ecol 400:200–8.
Sears MW, Angilletta MJ. Jr. 2015. Costs and benefits of thermoregulation revisited: both the heterogeneity and spatial
structure of temperature drive energetic costs. Am Nat
185:E94–E102.
Sears MW, Raskin E, Angilletta MJ. Jr. 2011. The world is not
flat: defining relevant thermal landscapes in the context of
climate change. Integr Comp Biol 51:666–75.
Sheldon KS, Dillon ME. Beyond the mean: biological impacts
of cryptic temperature change. Integr Comp Biol this issue.
Shepherd JM, Carter M, Manyin M, Messen D, Burian S.
2010. The impact of urbanization on current and future
Thermal heterogeneity across space
coastal precipitation: a case study for Houston. Env Plan BPlan Design 37:284–304.
Sinervo B, et al.. 2010. Erosion of lizard diversity by climate
change and altered thermal niches. Science 328:894–99.
Sinoquet H, Le Roux X, Adam B, Amaglio T, Daudet FA.
2001. RATP: a model for simulating the spatial distribution
of radiation absorption, transpiration and photosynthesis
within canopies: application to an isolated tree crown.
Plant Cell Env 24:395–406.
Sinoquet H, Pincebourde S, Adam B, Donès N,
Phattaralerphong J, Combes D, Ploquin S, Sangsing K,
Kasemsap P, Thanisawanyangkura S, et al.. 2009. 3-D
maps of tree canopy geometries at leaf scale. Ecology 90:283.
Snyder GK, Weathers WW. 1975. Temperature adaptations in
amphibians. Am. Nat 109:93–101.
Stabler LB, Martin CA, Brazel AJ. 2005. Microclimates in a
desert city were related to land use and vegetation index.
Urban For Urban Green 3:137–47.
Stein A, Gerstner K, Kreft H. 2014. Environmental heterogeneity as a universal driver of species richness across taxa,
biomes and spatial scales. Ecol Lett 17:866–80.
Stevenson RD. 1985. The relative importance of behavioral
and physiological adjustments controlling body temperature in terrestrial ectotherms. Am Nat 126:362–86.
Stillman J, Somero G. 1996. Adaptation to temperature stress
and aerial exposure in congeneric species of intertidal porcelain crabs (genus Petrolisthes): correlation of physiology,
biochemistry and morphology with vertical distribution.
J Exp Biol 199:1845–55.
Stokes VJ, Morecroft MD, Morison JIL. 2006. Boundary layer
conductance for contrasting leaf shapes in a deciduous
broadleaf forest canopy. Agr For Meteor 139:40–54.
Suggitt A, Gillingham P, Hill J, Huntley B, Kunin W, Roy D,
Thomas C. 2011. Habitat microclimates drive fine-scale
variation in extreme temperatures. Oikos 120:1–8.
Sunday J, Bates AE, Dulvy NK. 2011. Global analysis of thermal tolerance and latitude in ectotherms. Proc Royal Soc
Lond B 278:1823–30.
Sunday JM, Bates AE, Kearney MR, Colwell RK, Dulvy NK,
Longino JT, Huey RB. 2014. Thermal safety margins and
the necessity of thermoregulatory behavior across latitude
and elevation. PNAS 111:5610–5.
Todhunter PE. 1990. Microclimates variations attributable to
urban-canyon asymmetry and orientation. Phys Geogr
11:131–41.
Tonolla D, Acuna V, Uehlinger U, Frank T, Tockner K. 2010.
Thermal heterogeneity in floodplains. Ecosystems 13:727–40.
61
Tracy CR. 1976. A model of the dynamic exchanges of water
and energy between a terrestrial amphibian and its environment. Ecol Monogr 46:293–326.
USGCRP (US Global Change Research Program). 2014.
National climate assessment. Washington, DC: USGCRP.
Vasseur DA, DeLong JP, Gilbert B, Greig HS, Harley CDG,
McCann KS, Savage V, Tunney TD, O’Connor MI. 2014.
Increased temperature variation poses a greater risk to
species than climate warming. Proc Roy Soc B Lond
281:20132612.
Verberk WCEP, Calosi P. 2012. Oxygen limits heat tolerance
and drives heat hardening in the aquatic nymphs of the gill
breathing damselfly Calopteryx virgo (Linnaeus, 1758).
J Therm Biol 37:224–29.
Vickers M, Manicom C, Schwarzkopf L. 2011. Extending the
cost-benefit model of thermoregulation: high-temperature
environments. Am Nat 177:452–61.
Vierling KT, Vierling LA, Gould WA, Martinuzzi S,
Clawges RM. 2008. Lidar: shedding new light on habitat characterization and modeling. Front Ecol Environ
6:90–98.
Wawrzyniak V, Piégay H, Allemand P, Vaudor L, Grandjean
P. 2013. Prediction of water temperature heterogeneity of
braider rivers using very high resolution thermal infrared
(TIR) images. Intern J Remote Sens 34:4812–31.
Woods HA. 2010. Water loss and gas exchange by eggs of
Manduca sexta: trading off costs and benefits. J Insect
Physiol 56:480–7.
Woods HA. 2013. Ontogenetic changes in the body temperature of an insect herbivore. Func Ecol 27:1322–31.
Woods HA, Dillon EM, Pincebourde S. 2015. The roles of
microclimatic diversity and of behaviour in mediating the
responses of ectotherms to climate change. J Therm Biol
54:86–97.
Youngsteadt E, Dale AG, Terando AJ, Dunn RR, Frank SD.
2015. Do cities simulate climate change? A comparison of
herbivore response to urban and global warming. Glob
Change Biol 21:97–105.
Zhang C, Wu J, Grimm NB, McHale M, Buyantuyev A. 2013.
A hierarchical patch mosaic ecosystem model for urban
landscapes: model development and evaluation. Ecol Mod
250:81–100.
Zhou W, Huang G, Cadenasso ML. 2011. Does spatial configuration matters? Understanding the effects of land cover
pattern on land surface temperature in urban landscapes.
Land Urban Plan 102:54–63.

Download Report

Fine-Scale Microclimatic Variation Can Shape

Paperzz.com

Your Paperzz