vol. 173, no. 2
the american naturalist

february 2009
Lower Extinction Risk in Sleep-or-Hide Mammals
Lee Hsiang Liow,1,* Mikael Fortelius,2,3 Kari Lintulaakso,2 Heikki Mannila,4 and
Nils Chr. Stenseth1
1. Center for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, P.O. Box 1066, Blindern, N-0316 Oslo,
Norway; 2. Department of Geology, University of Helsinki, P.O. Box 64, FIN-00014 Helsinki, Finland; 3. Institute of Biotechnology,
University of Helsinki, P.O. Box 56, FIN-00014 Helsinki, Finland; 4. Helsinki Institute for Information Technology, Department of
Computer Science, University of Helsinki, P.O. Box 68, FIN-00014 Helsinki, Finland
Submitted July 15, 2008; Accepted September 11, 2008; Electronically published December 31, 2008
Online enhancements: appendix, tables.
abstract: An ever larger proportion of Earth’s biota is affected by
the current accelerating environmental change. The mismatches between organisms and their environments are now increasing in both
magnitude and frequency, resulting in lowered fitness and hence the
decline of populations. Under this scenario, species with behavioral
and/or physiological traits that provide them shelter from the environment are predicted to be less vulnerable to population declines
than species that are always exposed to the elements. Here, we coded
4,536 living mammal species for sleep-or-hide (SLOH) behavior,
including hibernation, torpor, and the use of burrows, among other
related traits. We demonstrate that species that exhibit SLOH behavior are underrepresented in high-risk International Union for
Conservation of Nature Red List categories. We found that SLOH
behavior contributes to lowering extinction risk even after we accounted for other factors that directly or indirectly buffer species
against extinction, such as larger geographic ranges and smaller body
sizes. This result is robust to analyses using phylogenetically independent contrasts. Sleep-or-hide behavior, made possible by a related
suite of physiological adaptations, allows mammals to function at
lower metabolic rates and/or buffer them from changing physical
elements. Mammals with SLOH behavior have a greater propensity
to survive in the current extinction crisis and probably also in past
crises because of reduced exposure to environmental stress.
Keywords: mammals, threat, hibernation, burrowing, physiology.
Introduction
The decline and eventual extinction of a species may be
viewed as a mismatch between its requirements and tolerances, and its environment. One solution to this mismatch is to move to a more suitable environment by migration or range shift (Araujo and Rahbek 2006;
Sekercioglu et al. 2008). However, populations may be
unable to respond quickly enough, and the rapid disap* Corresponding author; e-mail: l.h.liow@bio.uio.no.
Am. Nat. 2009. Vol. 173, pp. 264–272. 䉷 2008 by The University of Chicago.
0003-0147/2009/17302-50599$15.00. All rights reserved.
DOI: 10.1086/595756
pearance of natural habitats and high rate of climate
change today have increased the extinction risk of many
species (Sodhi et al. 2004; Fischer and Lindenmayer 2007;
Williams et al. 2007). All things being equal, species that
are able to better cope with a deteriorating environment
in situ have a greater advantage in surviving longer so as
to either (1) have a greater chance of encountering more
suitable environmental conditions in situ again and/or (2)
gain time in locating suitable environment by dispersal or
migration. The ability to hibernate or go into other low
metabolic modes and/or use shelters may increase population and species survival (Johnson 2002; Liow et al.
2008) by providing temporary refuge from periods of low
resource availability and/or unfavorable environmental
conditions. Sleep-or-hide (SLOH) behavior—defined in a
recent article (Liow et al. 2008) as including hibernation,
torpor, aestivation, dormancy, and the use of burrows,
chambers, tunnels, tree holes, and caves—is thought to
confer extra protection against natural enemies and seasonal and unexpected environmental changes (Reichman
and Smith 1990; Smith and Quin 1996; Drew et al. 2004;
Liow et al. 2008). This hypothesis, however, has not yet
been tested in a comprehensive manner.
In this article, we use all known species of extant mammals to investigate the following four points concerning
SLOH behavior. First, we check whether “sleeping” (hibernation, torpor, aestivation, dormancy) and “hiding”
(the use of burrows, chambers, tunnels, tree holes, and
caves) behaviors are disproportionately associated and
hence whether their co-analysis is justified. We supply further arguments in the discussion that these behaviors are
physiologically closely related and hence likely to have
common evolutionary origins in related taxa, thus further
justifying their co-analysis and interpretation. We then test
whether mammals with SLOH behaviors have a biased
representation in various International Union for Conservation of Nature (IUCN) Red List categories and thus
whether these mammals are at lower extinction risk. Third,
Sleep-or-Hide Mammals Survive Better 265
we consider additional factors known to contribute to lowering extinction risk, including larger geographic ranges
and smaller body sizes, in order to understand the relative
contribution of SLOH behavior to reducing vulnerability
to extinction, in context of other species traits. Because
these factors as well as SLOH behavior may be phylogenetically confounded, we tested whether our results remained robust after subjecting the data to phylogenetic
contrast analyses (Felsenstein 1985; Harvey and Pagel
1991).
Methods
SLOH and IUCN
Our data are from MammalBase, a compilation of species
attributes compiled by K. Lintulaakso on the basis of published and unpublished sources (Nowak 1991; Wilson and
Ruff 1999; Smith et al. 2003; Wilson and Reeder 2005;
Myers et al. 2007; NatureServe 2007). We summarize the
data from MammalBase used in this study. Work is in
progress to make MammalBase a public online resource,
but meanwhile, these data are available on request from
K. Lintulaakso.
The species attributes pertaining to SLOH behavior used
in this study were namely whether the species in question
(1) are hibernators, (2) go into torpor, (3) can be dormant,
(4) can go into aestivation mode, (5) use burrows, (6) use
tunnels, (7) use chambers, (8) use tree holes, or (9) are
cave dwellers. Note that these nine traits are not mutually
exclusive. A sleeper species is one that displays any of the
traits 1–4, and a hider species is one that displays one of
any of the traits 5–9. We treat the absence of mention of
these traits as true absences of the traits because of the
common practice of not mentioning absent traits in the
references used to compile MammalBase (Liow et al.
2008). We exclude marine species by removing the orders
Cetacea and Sirenia; the families Odobenidae, Phocidae,
and Otariidae; as well as those species coded as fully
aquatic in MammalBase in our analyses. Bats are also excluded from our analyses.
We downloaded the conservation status of Recent mammal species from the World Conservation Union (IUCN
2006). The categories we used are extinct (EX), extinct in
the wild (EW), endangered (EN), critically endangered
(CR), near threatened and the older category lower risk/
near threatened (NT p NT plus LR/nt), vulnerable (VU),
lower risk/conservation dependent (LR/cd), and least concern and the older category lower risk/least concern (LC
p LC plus LR/lc), listed here in decreasing order of extinction risk (table A1 in the appendix in the online edition
of the American Naturalist). Species assigned the category
data deficient (DD) are not used in the following analyses.
Figure 1: Proportions of mammals with combined sleep-or-hide (SLOH)
traits in ranked threat categories. Proportions of mammal species in
IUCN Red List categories plotted in decreasing rank of threat from left
to right for all SLOH behaviors combined. EX ⫹ EW, extinct and extinct
in the wild; CR, critically endangered; EN, endangered; NT, near threatened and the older category lower risk/near threatened (LR/nt); VU,
vulnerable; LR/cd, lower risk/conservation dependent; LC, least concern
and the older category lower risk/least concern (LR/lc). Circles are empirical proportions, and box plots are 10,000 bootstrapped mean proportions of species demonstrating the SLOH trait that would be found
in each category, given the actual number of species listed in the category.
Numbers above each box plot are the numbers of species in each given
IUCN category that have SLOH behavior.
There are 122 species assigned DD, and of these, only six
have at least one SLOH attribute.
Bootstrapped Proportions
We calculated the proportion of all the species in each
IUCN category that have the SLOH traits in which we are
interested. To generate a null distribution of these proportions, we randomly sampled from the set of mammals
with that trait. The sampling was done without replacement, and the sample size is based on the number of
mammal species that are in the given IUCN category for
which we are generating the null expectation. We repeat
this sampling 10,000 times in each case and calculate the
proportion for each bootstrapped sample, whose distributions are plotted as box plots. The boxes in the box
plots are 25%–75% quantiles, and the whiskers are 1.5
times the box length (see figs. 1, 2).
of threat from left to right for each SLOH behavior. EX ⫹ EW, extinct and extinct in the wild; CR, critically endangered; EN, endangered; NT, near threatened and the older category lower risk/
near threatened (LR/nt); VU, vulnerable; LR/cd, lower risk/conservation dependent; LC, least concern and the older category lower risk/least concern (LR/lc). Circles are empirical proportions,
and box plots are 10,000 bootstrapped mean proportions of species demonstrating the SLOH trait that would be found in each category, given the actual number of species listed in the category
(numbers above each box plot).
Figure 2: Proportions of mammals with individual sleep-or-hide (SLOH) traits in ranked threat categories. Proportions of mammal species in IUCN Red List categories plotted in decreasing rank
Sleep-or-Hide Mammals Survive Better 267
Table 1: Contingency table of sleepers and hiders
Nonhiders
Hiders
Nonsleepers
Sleepers
3,314 (3,229.8)
959 (1,043.1)
30 (114.1)
121 (36.8)
Note: Numbers of living mammal species that are sleepers (hibernators; go into torpor, dormancy, or aestivation), nonsleepers,
hiders (users of burrows, tunnels, chambers, tree holes, or caves),
and nonhiders. Numbers in parentheses are expected numbers.
Regressions and Model Comparison
In order to further investigate the contribution of SLOH
behavior to extinction risk, we modeled threat as (1) a
binary response for each species using logistic regression
(Venables and Ripley 2002) and (2) an ordered multinomial response for each species using a proportional odds
model (Faraway 2006). In the binary response model, a
value of 0 means that the species is not threatened (IUCN
category LC and the older category LR/lc), and a value of
1 means that it is threatened (i.e., belonging to a known
IUCN category other than LC or LR/lc). Here, the binning
of IUCN categories into two broad categories serves the
purpose of distinguishing between those species that are
“thriving” and those that are in any perceivable danger of
population decline. This also increases sample sizes in the
threatened or “at risk” category in the binomial model. In
the ordered multinomial response model, we adhered to
the categories used in IUCN at the expense of having fewer
data points for each category. In both binomial and multinomial response models, we used a model selection approach (Burnham and Anderson 2002) to compare models
of extinction risk (see below). Only species with no missing
data or unknown states are used (N p 2,427). Combinations of the following continuous variables and factors
were used as predictors in our models: log-transformed
geographic distribution, log-transformed body mass,
trophic level (carnivore, herbivore, or omnivore), and
SLOH behavior (present or absent) for the reasons presented below.
Comparative research has shown that larger geographic
distributions (Purvis et al. 2000; Jones et al. 2003; Payne
and Finnegan 2007) contribute greatly to lowering extinction risk, hence we include it as an explanatory variable
in our models. Even though IUCN categories are in part
based on the reduction of geographic ranges, geographic
range and extinction risk remain highly correlated even
when considering only species listed as at risk based purely
on reduction in population extent (Purvis et al. 2000).
Geographic distributions of species in MammalBase are
extracted from Nowak (1991) and transformed into a coordinate system. The geographic distribution of each species is here measured as the count of 0.5 # 0.5 latitude/
longitude degree distribution cells. These counts give an
approximation of the average relative extent of species
distribution over the time we have had records of the
species.
Likewise, body size has repeatedly featured as an important explanatory factor in extinction selectivity studies
where smaller body sizes are associated with reduced extinction risk (Lyons et al. 2004; Cardillo et al. 2005; Liow
et al. 2008), hence the inclusion of body size as a variable
in our extinction risk models. The body masses of species
recorded in MammalBase are based on the study by Nowak
(1991) and supplemented using the following references:
Smith et al. (2003), Myers et al. (2007), and NatureServe
(2007).
Species with more specialized diets are typically less resilient or more extinction prone (Viranta 2003; Jernvall
and Fortelius 2004). We used trophic levels as a proxy for
specialization where we expect that omnivores, being generalists, should be less extinction prone, all other things
being equal. Tropic levels recorded in MammalBase were
divided into three categories: namely, species that consume
mainly animals (carnivore), mainly plants (herbivore), and
a mixture (omnivore; table A2). These assignments in
MammalBase were based on two references (Nowak 1991;
Jernvall 1995). All of our models include geographic distribution and body mass because they are thought to be
important explanatory variables in studies concerning
mammal extinction risk (Cardillo 2003; Cardillo et al.
2003, 2005).
Our four models are of the form
threat category(ies) ∼ D ⫹ M ⫹ TL ⫹ SLOH,
threat category(ies) ∼ D ⫹ M ⫹ SLOH,
threat category(ies) ∼ D ⫹ M ⫹ TL,
threat category(ies) ∼ D ⫹ M,
where D p log-transformed geographic distribution, M p
log-transformed body mass, TL p trophic level (carnivore,
herbivore, or omnivore), and SLOH p presence or absence of SLOH behavior. The Akaike Information Criteˆ
rion (AIC) was calculated as ⫺2 log (L(v)Fy)
⫹ 2K, where
the first term is two times the negative log likelihood of
the given model, i, with the estimated parameters represented by v given the data y, and the second term includes
K, the number of parameters estimated (Burnham and
Anderson 2002). Akaike weights for the ith model are
calculated as
exp (⫺1/2D i)
冘rp1 exp (⫺1/2D r) ,
4
where D i p AICi ⫺ AIC min.
268 The American Naturalist
Table 2: Contingency table of combined categories of
threat and sleep-or-hide (SLOH) traits
SLOH
Non-SLOH
Threat p 0
Threat p 1
331 (285)
1,232 (1,278)
112 (158)
752 (706)
Note: Numbers of living mammal species with (SLOH) and without (non-SLOH) sleep-or-hide behaviors in the low-threat IUCN
category (least concern [LC] and the older category lower risk/least
concern [LR/lc], where threat p 0) and high-threat IUCN categories
(all known IUCN categories except LC and LR/lc, where threat p
1). Numbers in parentheses are expected values.
Phylogenetically Independent Contrasts
We calculated phylogenetically independent contrasts
(PICs; Felsenstein 1985; Harvey and Pagel 1991) on the
basis of the recently published mammalian phylogenetic
tree (Bininda-Emonds et al. 2007). An updated version of
this supertree was obtained from the authors (S. A. Price,
personal communication) and used in all analyses involving PICs. Because of some inconsistencies in nomenclature, there were 57 names in the Bininda-Emonds et al.
(2007) supertree that had no definite match to those in
MammalBase. We excluded these uncertain matches in all
subsequent analyses. The PDAP module (Midford et al.
2008) of Mesquite (Maddison and Maddison 2008) was
used to generate PICs and to examine the conformation
of the transformed variables to statistical assumptions in
parametric statistics. The PICs used in subsequent linear
regressions reflect namely (1) differences in ordered IUCN
categories, where a greater positive contrast indicates decreased risk from the first to the second sister taxon (where
LC and LR/lc p 0; LR/cd and LR/nt p 1; VU p 2; NT
p 3; CR p 4, EN p 5, EW p 6, EX p 7); (2) contrasts
in trophic level, where a positive contrast indicates a diet
that is more general, that is, consisting of both animals
and plants (carnivores p 1, herbivores p 1, omnivores
p 0) from the first to the second sister taxon; (3) differences in SLOH, where a greater positive contrast indicates
decreasing SLOHness (or a particular SLOH trait) from
the first to the second sister taxon; (4) contrasts in logtransformed body mass; and (5) contrasts in log-transformed geographic ranges. The contrasts in ordered IUCN
categories are treated as the response variables, and they
are positivized (Garland et al. 1992). Best estimates of
the age of the nodes, as given in the published supertree
(Bininda-Emonds et al. 2007), are used in branch length
calculations for PICs. Because there were polytomies in
the mammalian tree (Bininda-Emonds et al. 2007), we
followed Garland’s recommendation (Purvis and Garland
1993; Garland and Diaz-Uriarte 1999) to reduce the degrees of freedom in subsequent regressions. We applied
linear regression analyses to the PICs through the origin
(Garland et al. 1992) and used a model selection approach
to compare the same four models as described in the
“Discussion.”
Results
There are 443 mammal species with at least one SLOH
trait known from MammalBase (see table A1). Among
these, a disproportionate number of sleepers are hiders
(table 1; x 2 p 259, P ! .00001). Among the 3,433 species
of mammals with a known IUCN Red List category (table
A1), those that are not threatened (LC or LR/lc) are more
likely to have SLOH traits (table 2; x 2 p 24.6, P !
.00001). Having at least one of the nine separate SLOH
traits increases the chances that a species falls into the
category of least risk according to the IUCN classification
(LC or LR/lc; fig. 1), compared with random expectations
from a bootstrap procedure. Having any of the nine separate traits gives in general the same pattern (fig. 2). The
actual proportions of mammal species that are least at risk
are always higher than the bootstrapped median proportion and often higher than the bootstrapped 75% quantile,
indicating that they are outliers in the bootstrapped distribution. Likewise, the higher risk the IUCN category a
species is in, the less likely it is to be a sleeper and/or a
hider in general (figs. 1, 2).
The comparison of four logistic regression models of
threat as a binary response indicates that the full model—
which includes geographic range, body size, trophic level,
as well as SLOH as factors—is the best by far (table A3).
As expected, smaller mammals are at a lower risk, as are
mammals with wider geographic distributions (table A4).
The odds of being threatened, given that a particular species has at least one SLOH trait, are 0.74. Similarly, the
comparison of the four equivalent models with IUCN responses as ordered multinomial categories gives analogous
results (tables A5, A6). We note here parenthetically that
interpreting the ranked categories on an interval scale and
Table 3: Comparisons of models of ranked threat categories
using phylogenetically independent contrasts (PICs)
Model
DPIC ⫹ MPIC ⫹ TLPIC ⫹ SLOHPIC
DPIC ⫹ MPIC ⫹ TLPIC
DPIC ⫹ MPIC ⫹ SLOHPIC
DPIC ⫹ MPIC
AIC
Akaike weights
3,316.9
3,323.2
3,445.7
3,452.5
.96
.04
0
0
Note: Akaike Information Criterion (AIC) and weights from linear regressions of contrasts of ranked IUCN Red List categories on contrasts of
the listed factors. The best model is in bold. DPIC, PICs for log-transformed
geographic distribution contrasts; MPIC, PICs for log-transformed body mass
contrasts; SLOHPIC, PIC for sleep-or-hide (SLOH)-ness; TLPIC, PIC for specialization in diet (where carnivores and herbivores are specialists and omnivores are generalists).
Sleep-or-Hide Mammals Survive Better 269
Table 4: Estimates from the best model
Coefficients
(best model)
SLOHPIC
TLPIC
MPIC
DPIC
Discussion
Estimate
SE
P
⫺.30
.04
.09
⫺.13
.10
.12
.14
.02
4.01E-03
7.55E-01
5.18E-01
1.30E-09
Note: Estimates of the coefficients from the best model (table 3).
Estimates significant at P p .05 are in bold. DPIC, phylogenetically
independent contrasts (PICs) for log-transformed geographic distribution contrasts; MPIC, PICs for log-transformed body mass contrasts; SLOHPIC, PIC for sleep-or-hide (SLOH)-ness; TLPIC, PIC for
specialization in diet (where carnivores and herbivores are specialists
and omnivores are generalists).
regressing the factors on this scale give qualitatively similar
results to using the multinomial response model. Hence,
even though the risk categories cannot be interpreted directly as if they reflect numerical degrees, the sign of the
differences between ranked IUCN categories of two species
indicates a comparable difference in risk. This last observation is important in our analyses of PICs where we
treated IUCN categories as if they lie on an interval scale.
The linear regression models of IUCN contrasts on contrasts in diet, SLOH, log-transformed body mass, and logtransformed geographic distributions agree with those
done with raw values where the full model, including
SLOH contrasts, has the largest Akaike weight by far (0.96;
table 3). When all mammals are included in the analyses,
the effect of SLOH is strong, while contrasts in body mass
and trophic level do not contribute significantly to IUCN
contrasts (table 4). Because more rodents have SLOH
traits, threat to rodents is less strongly affected by SLOH
traits than threat to nonrodents in general (fig. 3; app. A;
tables A7, A8).
Given the possibility that some of the SLOH traits may
not be homologous, we looked at each of the nine traits
individually using phylogenetic contrasts. There are naturally far fewer informative contrasts to be calculated
(since most mammals do not have any one of the nine
traits). But the PICs show that the contrasts for SLOH
traits are always negatively correlated with contrasts in
extinction risk in a phylogenetic context, with the exception of those mammals that use tunnels and aestivate (table
5). In these two last cases, the relationships are not statistically significant, but half of the other cases where there
is a negative correlation between IUCN contrasts and the
given SLOH traits (burrowers, tree hole and cave users)
are statistically significant (table 5). We show also that the
estimated correlations are more negative than a random
sampling of PICs between contrasts in IUCN ranks and
SLOH traits (last two columns in table 5).
Species’ perceptions and responses to environmental
changes differ because of differences in their biological
traits, which differentially influence their vulnerability to
extinction (Cardillo et al. 2005). For instance, the size of
geographic distributions has been repeatedly shown to be
a very important factor in extinction risk both in geologic
time and during this current extinction crisis (Cardillo et
al. 2003; Payne and Finnegan 2007). Species with larger
distributions are often less vulnerable because they spread
the risk among populations residing at different spatial
locations. Even though larger mammals often have larger
geographic distributions, they have been a target of both
natural and human-induced extinctions (Cardillo 2003;
Lyons et al. 2004; Cardillo et al. 2005; Liow et al. 2008)
for a varied suite of reasons, including slower generation
times (Brook and Bowman 2005) and the need for larger
home ranges (Cardillo 2003). Other factors that put species
at greater threat of extinction are specialist diets, attractiveness to people as pets, aggression toward livestock species, and diurnal habits (Johnson 2002; Missios 2004; Sodhi et al. 2004; Cardillo et al. 2005; Andren et al. 2006).
Among the frequently discussed traits, however, behavioral
and physiological characters of species have in general been
neglected.
Here, we have shown that behavioral and physiological
traits also contribute predictably to extinction risk; the
related suite of SLOH traits that allow species to cope with
Figure 3: Phylogenetic contrasts in ranked threat categories and sleepor-hide (SLOH)-ness. Phylogenetic contrasts in SLOHness are plotted
versus positivized contrasts in ranked IUCN Red List categories for all
mammals for which values can be computed. The solid regression line
through the origin shows that for an increase in risk, there is a drop in
SLOHness. Equivalent regression lines for phylogenetically independent
contrasts for rodents and nonrodents are also shown. Numerical values
for the slopes should be interpreted only in a relative sense, where the
effect of SLOH on reducing extinction risk is stronger for nonrodent
mammals than for rodents.
270 The American Naturalist
Table 5: Slopes of contrasts between sleep-or-hide (SLOH) traits and ranked threat
categories
Hibernators
Torpor
Dormancy
Aestivation
Burrowers
Chambers
Tunnels
Tree holes
Cave
Estimate
SE
P
N
Average estimate
(random)
Average P
(random)
⫺.209
⫺.263
⫺.687
.103
⫺.379
⫺1.094
.006
⫺.800
⫺1.739
.196
.187
.362
.362
.115
.174
.156
.162
.260
.287
.160
.060
.778
.001
.000
.968
.000
.000
198
368
146
140
898
382
642
401
219
.235
.207
.223
.211
.271
.240
.254
.230
.251
.152
.140
.277
.286
.090
.150
.118
.128
.227
Note: Estimates of linear regression slopes through the origin with IUCN contrasts as the dependent
variable and contrasts in separate SLOH traits as independent variables, their SEs, P values, and sample
sizes (N). Because the phylogenetically independent contrast (PIC) values are not normally distributed
even after applying various branch length transformations, we recalculated the slope of the relationship
between the IUCN PICs and randomized samples of the SLOH trait PICs to demonstrate that the
observed values are different from a random expectation. The last two columns hence show the average
estimated slopes and their average P values of the same linear regression, given 1,000 randomized
samples of the independent variables.
both expected and unexpected environment changes in
situ is at lower threat to extinction. The SLOH traits lower
conservation threat or extinction risk, even after accounting for body size, geographic distribution, and phylogeny,
three factors that do explain a large part of the variation.
Living mammals able to shield themselves using “hideouts” or turn to low metabolic modes have a biased representation in IUCN Red List categories of lesser or no
threat. This pattern is clearer among nonrodent mammals
than among rodents (table A9; app. A), although the tendency for SLOH mammals to be at lower extinction risk
applies generally. We have suggested, using fossil mammal
data, that SLOH taxa, having the ability to shield themselves from environmental fluctuations over geologic timescales, are less prone to extinction and are under less selective pressure to evolve to cope with changes in the
environment (Liow et al. 2008). In the extinction crisis of
today, environmental pressures include climate change,
habitat destruction, selective hunting, and pollution. Although these environmental stresses may be both qualitatively and quantitatively different from past stresses, plastic responses of SLOH traits (Lehmer et al. 2006) to these
changes should have broadly similar effects on survival.
Because both the longer-term usage of subterranean and
other kinds of shelters and hibernation require physiological adaptations such as tolerance to hypoxia and thermoregulation (Reichman and Smith 1990; Ramirez et al.
2007), and because species exhibiting a torpor-hibernation
mode during their existence require some protection from
the physical environment, these seemingly disparate behaviors are disproportionately associated (table 1). Hiders
that use tree holes, caves, or burrows can not only hide
from enemies (Vermeij 1987; Smith and Quin 1996;
McKenzie et al. 2007) but also enjoy more constant physical environments in their hideouts (Boggs et al. 1984;
Reichman and Smith 1990). Among hiders, many are burrowers that at the same time also tend to be generalist
feeders (Nevo 1979) or omnivores (table A10). Additionally, many hider species also use their burrows or other
holes to cache food (Reichman and Smith 1990). On the
other hand, sleeper adaptations are usually turned on when
the environment gives signals of impendent unfavorable
conditions, whether these are seasonal or aseasonal. The
built-in plasticity in going to (and coming out of) lowmetabolism modes (Kawamichi 1996; Humphries et al.
2003; Lehmer et al. 2006) allows individuals to conserve
energy to better survive stressful conditions. Our results
show that the effect of being a sleeper is weaker than that
of being a hider in terms of conferring better survival,
although why this difference exists is to be speculated (table 5).
Recent studies have emphasized that when species are
compared across the same time plane, they are inevitably
compared at varying stages of their natural rise or decline
(Foote et al. 2007; Liow and Stenseth 2007). This heterogeneity also applies to this study. However, this heterogeneity should not systematically bias our results because
it is expected to apply equally to SLOH and non-SLOH
species.
Our results demonstrate that behavioral and physiological attributes can have far-reaching and predictable effects
on the fates of species, visible at multiple temporal scales,
from the ecological (this study) to the geological (Liow et
al. 2008). This is especially notable considering the global
Sleep-or-Hide Mammals Survive Better 271
nature of this study, since few biological factors have explanatory power in global data sets (Cardillo et al. 2008).
Species exhibiting SLOH behavior, made possible by a related suite of physiological adaptations, such that they are
capable of functioning at lower metabolic rates and/or
sheltering themselves from changing physical elements and
biotic adversaries, are at an added advantage in the current
extinction crisis and probably also in past crises.
Acknowledgments
We thank S. A. Price for sending us the updated mammal
supertree, the Mesquite community and J. O. Vik for advice, J. Damuth for sharing unpublished data, A. van der
Meulen and P. Pelaez-Campomanes for seminal discussions years ago, and two anonymous reviewers for valuable
suggestions and comments. This research is funded in part
by an Academy of Finland grant (to M.F.) and core funding
to the Center for Ecological and Evolutionary Synthesis.
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