Download Extending the stressgradient hypothesis is competition among

Oikos 000: 001–008, 2012
doi: 10.1111/j.1600-0706.2012.00355.x
© 2012 The Authors. Oikos © 2012 Nordic Society Oikos
Subject Editor: Dustin Marshall. Accepted 13 November 2012
Extending the stress-gradient hypothesis – is competition among
animals less common in harsh environments?
I. C. Barrio, D. S. Hik, C. G. Bueno and J. F. Cahill­
I. C. Barrio ([email protected]), D. S. Hik, C. G. Bueno and J. F. Cahill, Dept of Biological Sciences, Univ. of Alberta, Edmonton, AB,
T6G 2E9 Canada. ICB also at: Inst. de Investigación en Recursos Cinegéticos (CSIC-UCLM-JCCM), Ronda de Toledo s/n, Ciudad Real,
ES-13071 Spain.­
The role of positive interactions has become widely accepted as a mechanism shaping community dynamics. Most empirical
evidence comes from plant communities and sessile marine organisms. However, evidence for the relative role of positive
interactions in organizing terrestrial animal communities is more limited, and a general framework that includes positive interactions among animals is lacking. The ‘stress gradient hypothesis’ (SGH) developed by plant ecologists predicts
that the balance between positive and negative interactions will vary along gradients of biotic and abiotic stress, with
positive interactions being more important in stressful environments. Paralleling the SGH, stress gradients for terrestrial
herbivores could be equated to inverse primary productivity gradients, so we would expect positive interactions to prevail
in more stressful, low productivity environments. However, this contradicts the typical view of terrestrial animal ecology
that low primary productivity systems will foster intense competition for resources among consumers. Here we use alpine
herbivores as a case study to test one of the predictions of the SGH in animal communities, namely the prevalence of
positive interactions in low productivity environments. We identify potential mechanisms of facilitation and review the
limited number of examples of interspecific interactions among alpine herbivores to assess the role of positive and negative interactions in structuring their communities. A meta-analysis showed no clear trend in the strength and direction
of interactions among alpine herbivores. Although studies were biased towards reporting significant negative inter­
actions, we found no evidence of competition dominating in harsh environments. Thus, our results only partially support
the SGH, but directly challenge the dominant view among animal ecologists. Clearly, a sound theoretical framework is
needed to include competition, positive and neutral interactions as potential mechanisms determining the structure of
animal communities under differing environmental conditions, and the stress-gradient hypothesis can provide a solid
starting point.
Interactions among organisms can be a major force
structuring biotic communities. Species interactions are
often defined based on their outcome for each of the interacting species, in terms of growth, reproduction and/or
survival (Arthur and Mitchell 1989). In this sense, neutral
interactions are those that have no net adverse effect on the
fitness of the interacting species; negative interactions have
detrimental effects on one of the species, while positive
ones are those that benefit at least one of the species
involved without negatively affecting the other (Bertness and
Callaway 1994). Early ecologists recognized the importance
of both, positive and negative interactions as organizing
processes in communities (Clements 1916), but competition
was often considered the primary mechanism involved
in structuring both plant (Schoener 1983) and animal
communities (Menge and Sutherland 1987). The role of
positive interactions is now commonly recognized in some
fields of ecology, such as plant ecology, and it is mostly
accepted that the structure of biotic communities is the result
of the interplay between positive and negative interactions
(Holmgren et al. 1997, Bruno et al. 2003, Callaway 2007,
Gross 2008). However, although positive interactions
among animals have been described in some systems (Van
de Koppel and Prins 1998, Huisman and Olff 1998,
Arsenault and Owen-Smith 2002), animal ecologists have
not embraced the potential role of positive interactions in
structuring communities as readily as plant ecologists
(Gross 2008).
Most research on positive, facilitative interactions
has been developed for plants (reviewed by Brooker et al.
2008) and marine sessile organisms (Kawai and Tokeshi
2007). Evidence of facilitation among plants has come
mostly from harsh environments, such as deserts, salt
marshes, and arctic and alpine habitats (Brooker et al.
2008), and led to the formulation of the ‘stress-gradient
hypothesis’ (SGH; Bertness and Callaway 1994). This
hypothesis predicts that the balance between positive and
negative interactions will vary along gradients of biotic
and abiotic stress and highlights the importance of positive
interactions in harsh environments, where amelioration of
EV-1
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Alpine
Net effect of interactions
stressful environmental conditions has greatest effect (Crain
and Bertness 2006). These predictions have stimulated
research in plant ecology for the last decade leading to recent
refinements of the hypothesis that incorporate different types
of stressors (resource, non-resource and biotic) and allow
for different shapes of the stress-facilitation relationship
(Maestre et al. 2009, Malkinson and Tielbörger 2010,
Holmgren and Scheffer 2010). Broadly, these modifications
keep the basic framework of the SGH but recognize that
facilitation, under some circumstances, may not be as important at the extreme ends of the stress gradient, allowing
for a hump-shaped relationship between the importance
of facilitation and stress (Malkinson and Tielbörger 2010,
Holmgren and Scheffer 2010). As proposed by the original
formulation of the SGH, the importance of facilitation
will increase in more severe environments through ameliorating stressors, particularly when stress is non-resource
driven (Maestre et al. 2009). However, facilitating species
are also competitors and, when stress gradients are determined by resources, competition for the shared resource can
overrule facilitation in very harsh environments (Holmgren
et al. 1997).
Some attempts have been made to extend the SGH to
other organisms, mainly aquatic invertebrates (Kawai and
Tokeshi 2007, Daleo and Iribarne 2009, Fugère et al.
2012), but a parallel body of evidence seems to be lacking for
other animals. Animal ecologists certainly recognize that
abiotic factors and/or the availability of resources can determine the strength and direction of interactions among
species within a trophic level (Dunson and Travis 1990,
Chesson and Huntly 1997, Pringle et al. 2007). However,
most research has focused on the importance of competition alone, without explicitly considering the potential
for positive interactions (but see Abrams and Nakajima
2007). Despite the long-standing debate that interactions
would be less important in determining community structure in harsher conditions, because harsher conditions
would lead to reduced population numbers and less chances
for interactions, Chesson and Huntly (1997) theoretically
demonstrated that harshness alone does not limit the role
of interactions (competition) in structuring communities.
More recently, empirical evidence showed that productivity can affect the strength of indirect interactions, with the
strongest interactions predicted to occur in low-productivity
environments (Pringle et al. 2007). Still, there is no general
framework in animal ecology relating the balance of positive
and negative interactions to gradients in environmental
conditions, and how the SGH may be applied to terrestrial
animals remains to be tested.
SGH studies usually define stress (sensu Grime 1977) as
those environmental conditions that limit producers’ ability
to convert energy into biomass (Callaway 2007, Maestre
et al. 2009). Equivalent gradients for animals would be
those of biotic or abiotic factors, such as primary producti­
vity (McNaughton et al. 1989), that affect consumers’
growth, i.e. the conversion of plant to animal biomass in
the case of primary consumers. In this sense, resource gradients can be equated to inverse stress gradients (Fugère
et al. 2012), with environments with lower net primary
productivity being more stressful to herbivores than highly
productive ones (Fig. 1). The SGH then, when applied to
+
a
SGH
0
Implied
understanding
Productivity
Stress
Figure 1. Predictions on the net effect of animal interactions
along a resource-driven stress gradient (productivity gradient). The
stress gradient hypothesis (SGH; solid line) developed by plant
ecologists, predicts that positive interactions (positive net effects)
would dominate in stressful conditions. Although explicit predictions about facilitation and productivity are lacking, the implied
knowledge of animal ecologists (dotted line) suggests that com­
petitive interactions (negative net effects) will dominate in low
productivity environments, while the occurrence of positive interactions among animals has been mostly recognized in high pro­
ductivity environments (e.g. tropical savannas, depicted by point
a). The box shows where alpine environments would be found,
and the contrasting expectations of each view.
herbivores, would predict that positive interactions will
prevail in less productive systems. Indeed, some examples
of positive interactions among herbivores have been
described in low productivity, severe ecosystems such as
salt-marshes (Van der Wal et al. 2000, Stahl et al. 2006)
or deserts (Edelman 2012). However, most references on
facilitation among herbivores come from productive ecosystems (Van de Koppel and Prins 1998, Arsenault and
Owen-Smith 2002), and the current view of animal eco­
logists that competition for resources will dominate in
less productive areas (e.g. desert grassland, Heske and
Campbell 1991; sagebrush steppe, Cheng and Ritchie
2006) or when conditions are harsher (Odadi et al. 2011b),
directly contradicts the predictions of the SGH (Fig. 1).
Owing to the scarcity of examples in the animal literature
investigating animal interactions across stress gradients
explicitly (but see Lu et al. 2009), we focus here on one of
the specific predictions of the SGH, namely, that positive
interactions will prevail among herbivores in more stressful
(low productivity) environments, using as a case study
vertebrate herbivores in alpine areas. Alpine environments
are particularly suited to address this question because of
the highly seasonal conditions that determine a simpler
trophic structure associated with low net annual primary
productivity (Oksanen et al. 1981). Further, these are
the systems driving much of the plant-related research in
the stress-gradient hypothesis (Choler et al. 2001, Callaway
et al. 2002), and thus they are a logical system to consider
the predictions of the SGH for animals.
In the following sections we examine potential positive
interactions among alpine animals and review the literature for
examples of interspecific interactions among alpine vertebrate herbivores. We conducted a meta-analysis on the
available studies to assess the (relative) role of positive and
negative interactions in structuring alpine communities and
evaluate the applicability of the SGH to terrestrial vertebrates. We then discuss future directions for research that will
help in defining a framework to include positive, negative
and neutral interactions as potential drivers structuring animal communities under differing environmental conditions.
When, where and how can we expect facilitation
among alpine herbivores?
Herbivores may facilitate each other in a number of ways
(Table 1), probably through similar mechanisms as those
of plants (Callaway 2007). Broadly, facilitation among terrestrial herbivores can be mediated by the environment,
when one species directly benefits from the neighbour’s
effect on the environment, or by interactions with other
species, either predators or prey. Given the potential mechanisms by which alpine herbivores may facilitate each other
and based on the current understanding of the SGH,
we may be able to develop a predictive framework for the
occurrence of positive interactions among terrestrial animals.
However, detailed predictions are difficult without a mechanistic understanding of all the elements involved: the effects
of the facilitating species on the stressor, its nature and
magnitude, and the life histories and tolerance to stress of
both interacting species (Maestre et al. 2009).
In harsh abiotic environments the role of habitatmodifying species that ameliorate environmental stress can
be the main form of facilitation (Crain and Bertness 2006).
Indeed, among alpine plants, stress is mainly driven by
non-resource abiotic factors, such as temperature, and
studies in these areas generally support the predictions of
the SGH (Maestre et al. 2009). Similar to nurse plants
(Holmgren et al. 1997), many animals can ameliorate environmental conditions to others. The role played by such
habitat-modifying species has received special attention
through the development of the ‘ecosystem engineer’ concept (Jones et al. 1997). Indeed, many alpine animals have
Table 1. Examples of positive interactions among both plants and
herbivores in alpine areas. Shaded boxes reflect the most likely
mechanisms of facilitation for each group.
Positive interactions
among plants
Abiotic
amelioration of
conditions
temperature, wind
and soil instability at
high elevations1;
nurse plants2
Resources
increased soil
moisture5; N-fixation6
Predators
protection from
herbivory by
association with
an unpalatable
neighbour9
Positive interactions
among herbivores
use of other species’
burrows as temporary
or permanent shelter3,4
extended access to high
quality forage through
grazing7,8
eavesdropping on
heterospecifics’ antipredator behaviour10,11;
reduced predator
pressure though
co-occurring prey12
1
­ Callaway et al. 2002; 2Holmgren et al. 1997; 3Zeng and Lu
2008; 4Murdoch et al. 2009; 5Wied and Galen 1998; 6Thomas and
Bowman 1998; 7Mysterud et al. 2011; 8Odadi et al. 2011b;
9Callaway et al. 2000; 10Trefry and Hik 2009; 11Blumstein and
Armitage 1997; 12Bêty et al. 2002.
been described as ecosystem engineers (Aho et al. 1998,
Zhang et al. 2003), and one of the clearest examples is burrowing. Digging burrows is a widespread strategy among
alpine animals that allows them cope with environmental
extremes. Other species can benefit from these burrows
directly, using them as temporary or permanent shelter
(Zeng and Lu 2008, Murdoch et al. 2009), or indirectly
through the changes burrows induce in plant communities
(Wesche et al. 2007, Van Staalduinen and Werger 2007).
However, alpine herbivores seem to be more constrained
by resource availability (i.e. primary productivity; Oksanen
et al. 1981, McNaughton et al. 1989) than by abiotic factors
themselves. In this sense, species that regulate the availability
of feeding resources to others are expected to have a greater
impact as facilitators (Table 1). Herbivores can induce
changes in resource quality, when grazing by one species
enhances the nutritional quality of forage for another
species, or in resource availability, when foraging by one
species makes resources more accessible to another species
(Arsenault and Owen-Smith 2002, Odadi et al. 2011b).
Vertebrate herbivores in alpine systems can have local
effects on vegetation (Jefferies et al. 1994, McIntire and Hik
2005, Mysterud et al. 2011), with cascading effects on
other species. As well, foraging activities of alpine herbivores,
and thus their acquisition of resources, are known to be
limited by predation risk (Morrison et al. 2004). Therefore,
relying on other species’ antipredator behaviours can be
advantageous. For example, it has been shown that an
alpine-dwelling lagomorph, the collared pika Ochotona
collaris, responds to alarm calls of heterospecifics by increasing vigilance, which likely improves its success in antipre­
dator responses (Trefry and Hik 2009). A similar response
has been reported for other alpine herbivores (Blumstein
and Armitage 1997, Shriner 1998), suggesting this might
be a common phenomenon. However, population consequences of such mechanisms remain to be tested.
Differences between alpine plants and animals in the
nature of the main stressor may lead to slightly different predictions. If stress gradients for alpine herbivores are mainly
resource-driven (i.e. productivity gradients), according to
the SGH, we would expect positive interactions to be the
dominant interaction type at moderately stressful conditions,
but not at the extreme ends of the stress gradient (Maestre
et al. 2009, Holmgren and Scheffer 2010). Life-history
strategies of alpine herbivores to cope with (or even avoid)
harsh winter conditions, like hibernation, food hoarding or
seasonal migration, may imply that these species co-occur
only during the more productive season, when competition
for resources is less likely to be the dominant interaction
type (Van de Koppel and Prins 1998). Therefore, according
to the SGH, in alpine environments we would expect to find
facilitation among herbivores except under exceptionally
harsh conditions, when limited availability of resources
can lead to facilitation being overruled by increased competition, resulting in a net negative effect (Odadi et al. 2011b).
Is there evidence for facilitation among alpine
herbivores?
To evaluate how common positive interactions are in alpine
communities we conducted a meta-analysis of published
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experimental studies (see Supplementary material Appendix 1
for details). An initial search retrieved 1467 studies, from
which less than one hundred were relevant to our question;
a comparable raw search on plants (substituting all animalrelated search terms with plant*) retrieved nearly twice
as many studies (2711). Most of the relevant studies on animal interactions were observational (53 out of 74; 71.6%),
which is not surprising given the difficulties associated
with conducting manipulative experiments on wild animals
in the field. Observational studies can provide a valuable
first step to understanding the potential mechanisms underlying species interactions (Darmon et al. 2012), but experimental evidence is needed to infer particular mechanistic
models (Novak and Wootton 2010). We could only find
nine experimental studies to include in our meta-analysis
that fulfilled our criteria and from which data could
be extracted (Supplementary material Appendix 1), and thus
we suggest our results are to be used to promote inquiry,
rather than representing a strong conclusion. The studies
reported changes in abundance of one species when cooccurring with another species and when occurring alone;
therefore our effect size, the standardized mean difference,
indicates positive or negative effects of a certain species
on the abundance of the focal one. The experimental
design used in this type of studies, analogous to neighbour
removal experiments in plant ecology (Callaway 2007),
should equally detect positive and negative interactions.
The experiments we found do not specifically test the
balance between positive and negative interactions across
productivity gradients, and therefore lack specific controls
Reference
Vial et al. 2011
Steen et al. 2005
Vial et al. 2011
Forsyth and Hickling 1998
Bêty et al. 2001
Forsyth and Hickling 1998
Vial et al. 2011
Steen et al. 2005
Rüttiman et al. 2008
Loe et al. 2007
Austrheim et al. 2007
Heske and Steen 1993
Bêty et al. 2001
Galindo and Krebs 1985
Heske and Steen 1993
in benign environments; however, we can still evaluate the
prevalence of positive or negative interactions in stressful,
low productivity environments.
We found no clear trend in the direction of the inter­
actions among alpine herbivores (p  0.790; Fig. 2). The
test for homogeneity indicated that studies were significantly heterogeneous (Q  39.8, df  14, p  0.001), but
we found a high proportion of the observed variance
reflecting real differences among studies (I2  80.9%). This
is surprising given the theoretical framework under which
these experiments were conducted, with most studies
assuming that competition among herbivores would be the
primary (if not the only) type of interaction in alpine
environments, due to their low primary productivity. To
detect this potential publication bias in our data set we
used the trim-and-fill method (Duval and Tweedie 2000),
that estimates the number of missing studies that might exist
in a meta-analysis based on the symmetry of the observed
studies in a funnel plot. Although the subset of studies
included in the meta-analysis were biased towards reporting
on significant competitive interactions (Supplementary
material Appendix 2), our results did not support the
prevalence of negative interactions among alpine herbivores.
Another potential source of bias are the life-histories
of the interacting species, which are known to affect the
outcome of their interactions (Maestre et al. 2009). Within
herbivore guilds, niche partitioning leading to co-existence
can be determined by body size and digestive ability (du Toit
2011). Since this would lead to the expectation of more
competitive interactions among herbivores of similar size or
Focal sp
Interacting sp
Blicks grass rat
bank vole
giant mole rat
tahr
greater snow goose large colony
chamois
brush−furred mouse
field vole
chamois
willow grouse
rodents
voles
greater snow goose small colony
singing vole
lemmings
livestock
sheep
livestock
chamois
lemmings
tahr
livestock
sheep
sheep
sheep
sheep
lemmings
lemmings
tundra vole
voles
Effect size [95% CI]
−1.13 [−2.85, 0.59]
−0.93 [−2.12, 0.26]
−0.66 [−2.30, 0.98]
−0.54 [−1.16, 0.07]
−0.43 [−0.70, −0.16]
−0.35 [−1.16, 0.46]
−0.14 [−1.74, 1.46]
−0.09 [−1.22, 1.04]
0.05 [−0.44, 0.53]
0.10 [−1.50, 1.70]
0.30 [0.02, 0.58]
0.40 [−1.00, 1.80]
0.40 [0.11, 0.70]
0.46 [−0.60, 1.52]
1.62 [0.04, 3.19]
RE Model
−0.04 [−0.31, 0.24]
−5
0
Effect size
5
Figure 2. Forest plot for the meta-analysis of experimental studies on herbivore interactions in alpine environments. Nine studies, reporting
14 independent comparisons, i.e. the focal species with and without the interacting species, are included. Sizes of points are proportional
to sample size of each study. Negative effect sizes (standardized mean differences) indicate competition, while positive ones indicate
facilitation. Studies are ordered by increasing effect size. The overall effect size estimated using a random effects meta-analysis (RE Model)
is shown.
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among those using the same feeding strategy, we tested for
the effect of differences in sizes between the focal and the
interacting species, and of taxonomic family. However,
we found no effects of these variables on our results (difference in size: Q  0.260, p  0.610; taxonomic family: Q  0.158, p  0.691).
A possibility that cannot be ruled out is that facilitative
interactions are occurring among alpine herbivores because
few studies have been designed to look for facilitation.
From the available studies, two found significant positive
interactions, whereas only one found competitive inter­
actions (Fig. 2). Although recent theoretical development
(Abrams and Nakajima 2007, Gross 2008) and some
empirical evidence (Austrheim et al. 2007, Loe et al. 2007)
suggest the role of positive interactions in organizing
animal communities, more research is clearly needed. An
alternative explanation is that, overall, the strength and
direction of herbivore interactions is actually zero, and
neutral interactions are more prevalent than previously
thought. Neutral interactions are those that do not have
measurable effects on the fitness of the interacting species,
and as such, have been regarded as a lack of interaction
(Arthur and Mitchell 1989). However, this ‘lack of inter­
action’, particularly the lack of strong competitive inter­
actions, represents a mechanism through which species can
coexist (Tokeshi 1999).
Future directions – how can we bridge the gap?
We found no evidence of competition being the main
interaction type among herbivores in low productivity
alpine environments; on the contrary, neutral or positive
interactions may prevail among terrestrial herbivores inhabiting harsh environments. Our results thus, only partially
support the SGH, but interestingly, directly challenge the
dominant view of animal ecologists of competition dominating in harsh environments (Fig. 2). However, how
irreconcilable are these views? Maybe part of the solution
comes from a closer scrutiny of the assumptions of the
studies that have supported those views, under the light
of the recent developments of the SGH. For example, the
recent work by Odadi et al. (2011b) studied the balance of
species interactions in two periods of contrasting ‘harshness’
(dry vs wet season) using cattle as a focal species. They found
that wild ungulates compete with cattle in the dry season,
while facilitation prevails in the wet, more productive
season, and this result led them to challenge the SGH.
However, other studies have reported positive interactions
in the same system during the dry season; zebras are facilitated by elephants in the presence of cattle (Young et al.
2005), and cattle and donkeys facilitate each other when
herded at low stocking densities (Odadi et al. 2011a). In
this case, the identity of the interacting species may play
an important role, even determining opposite outcomes in
highly stressful situations (Maestre et al. 2009). As well,
the relative densities of each herbivore type are known to
affect the net effect of interactions (Odadi et al. 2011a, du
Toit 2011), probably altering the perception of stress of
the interacting species; low resource supply coupled with
low population densities of consumers might not be as
stressful as if it were coupled to higher numbers of consumers (Chesson and Huntly 1997). Maybe this is the reason
why many studies working with livestock, that usually
occur at artificially high densities, might be biased towards
competitive effects, unless occurring in very productive
environments (Mishra et al. 2004, Bagchi et al. 2004,
Odadi et al. 2011b). A revision of results under the recent
developments of the SGH will help reconcile both views,
and guide future research of animal ecologists addressing
this topic (Table 2).
Table 2. Suggestions (research questions) for future research on the balance of positive and negative animal interactions along stress
gradients, and predictions based on the current knowledge of the SGH.
Topic
Stress gradient
Identity of interacting
species
Length of the stress
gradient
Measures of
performance
Biotic stress
Multiple stressors
Potential research questions
Predictions based on the SGH
How does each species respond to the stress
Animal species will respond differently to each interacting
gradient? Does this vary across and/or within
species; facilitation will be more frequent between
trophic guilds?
different trophic guilds.
Is the net effect of species interactions among
When stress is resource-driven (e.g. primary productivity),
animals non-linear along stress gradients?
responses will be non-linear, with facilitation being more
Does this hold for both resource and
important when stress is high but not extreme. For
non-resource driven stress gradients?
non-resource gradients (e.g. abiotic constraints), facilitation
will dominate at high stress levels.
Do different measures of performance reflect
Some measures (e.g. population size) will reflect longer-term
changes in the strength and direction of
effects of interactions while others (e.g. reproduction,
interactions similarly?
mortality) may better indicate shorter-term effects.
How does predation affect the balance
Other mechanisms of facilitation may arise when predation
between positive and negative interactions
is accounted for (e.g. apparent mutualism), leading to
between animals?
facilitation at low resource stress levels.
How do stressors interact? How does this affect When considering multiple stressors (e.g. predation and
the outcome of interactions among animals?
productivity), interactions among stressors alter the net
outcome of interactions expected based on a single
stressor.
Mechanisms of interaction
Indirect interactions
How prevalent are indirect interactions among
animals?
Extension to community How frequent are positive interactions among
animals along stress gradients?
Indirect positive interactions are the most common type of
facilitation among animals.
Positive interactions among animals will be more common
in species-rich communities.
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Overall, more basic empirical evidence is needed on the
balance between positive and negative interactions among
terrestrial animals along environmental gradients. One
of the key aspects that needs to be carefully addressed in
future studies is the nature of the stress gradient and how
it is perceived by the interacting species (Table 2). Here,
we have focused on productivity as the main stressor for
primary consumers, but other sources of stress, including
those driven by abiotic conditions, need to be considered
too. A detailed understanding of the mechanistic cause of
stress and the physiological responses of the interacting
species to a specific stressor may help in defining predictions and experimental designs (Maestre et al. 2009). For
example, a species may not respond in the same way to
interacting species of different size or feeding strategy.
As well, experiments need to be conducted across well
defined stress gradients. Even if in the first assessments
a rough distinction between high and low stress levels
might still be valid, empirical studies should incorporate
the fullest possible range because non-linear responses are
likely to occur along stress gradients (Maestre et al. 2009,
Malkinson and Tielbörger 2010). In addition, experiments
should ideally account for different measures of species’ performance and of interaction strength (Chase et al. 2002),
because these are likely to have an impact on the results.
Another source of stress that has received less attention
by plant ecologists when developing the SGH, is the biotic
stress incurred by consumers (Smit et al. 2009). Predation
can affect competitive outcomes between species (Chase
et al. 2002) and although theoretical development on
the interaction between predation and competition in animal communities has been extensive (Chase et al. 2002,
Chesson and Kuang 2008), its relationship with positive
interactions still needs to be addressed. Our approach
has explicitly excluded consumer pressure as a source of
stress, but future studies need to address this point. In
this case, gradients would be defined by varying predation
rates (or other suitable surrogates for predation pressure),
and interactions among prey species would be expected
to vary along these gradients. Some specific mechanisms
may lead to positive interactions when predation risk is the
main stressor. Similar to associational defence in plants
(Atsatt and O’Dowd 1976), when two animal species share a
common predator, increases in the abundance of one species
can lead to a reduction of predation rates on the other
less preferred prey, at least in the short-term, leading to an
apparent mutualism (Abrams and Matsuda 1996). As well,
it is likely that biotic and abiotic stress factors interact,
which ultimately affects the outcome of species interactions
(Smit et al. 2009). Therefore, consideration of multiple
simultaneous stress gradients can alter the balance between
interactions (Kawai and Tokeshi 2007).
The specific mechanisms of interaction have to be
accounted for as well in future studies (Table 2). While
plants and animals might be similar in some respects,
the differences between them may determine specific particularities in the ways they interact with each other. For
instance, plants might be more directly affected by their
neighbours, whereas mechanisms of facilitation among
animals might be predominantly indirect. In addition,
positive interactions among animals can be deferred in space
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or time, and thus might be more difficult to detect if
studies are not conducted at the appropriate scale (Karban
et al. 2012). Scaling-up studies on pair-wise interactions
to community levels may reveal more such indirect inter­
actions. For example, indirect facilitative effects in plant
communities have been hypothesized to be more frequent
in species-rich communities or in communities with several
co-occurring limiting factors (Brooker et al. 2008). Recent
work on animal communities demonstrates that the strongest indirect effects (interaction cascades) are likely to occur
in low-productivity systems (Pringle et al. 2007). Assessing
how prevalent indirect interactions are will greatly improve
our understanding of the organization and dynamics of
animal communities.
Conclusions
Despite recent claims from other disciplines on the need to
include positive interactions into ecological theory (Bruno
et al. 2003), the animal ecology literature seems far from
reflecting this trend. Given the lack of a well defined
theoretical framework, borrowing (and testing) hypotheses
from other disciplines might be a good starting point. In
this sense, the stress-gradient hypothesis developed by
plant ecologists can provide a solid framework that might
be applicable to other organisms, and its specific predictions need to be tested for terrestrial vertebrates using
manipulative experiments in the field. This is likely to be a
promising avenue for research in animal ecology, since
the few attempts to explicitly test the SGH on other model
systems, like detritivorous invertebrates (Fugère et al.
2012) or herbivorous crabs (Daleo and Iribarne 2009),
have been successful. Furthermore, animal ecologists can
build on plant ecologists’ experience and avoid some mistakes committed in the past, to design their experiments in a
more effective way. Although there is a long way to go, we
believe this is a promising field of research towards the
integration of different ecological disciplines.
­Acknowledgements – A. Mysterud kindly provided data for the
meta-analysis. We are very grateful to F. Maestre for constructive
criticism of previous versions of this work. Funding was provided
by NSERC Discovery Grants to DSH and JFC, awarded by
the Natural Sciences and Engineering Research Council (Canada).
ICB was supported by a postdoctoral fellowship provided by the
Consejería de Educación, Ciencia y Cultura (JCCM, Spain) and
the European Social Fund; CGB is beneficiary of a postdoctoral
grant from the AXA Research Fund and a Killam fellowship from
the Univ. of Alberta.
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