Trade-off 4: Larger-scale spatial or temporal escape from
stressors
As an alternative to coping with the suite of stressors an organism
faces locally and immediately (the direct and indirect effects which are
discussed in the preceding sections on trade-offs 1-3), some organisms
can escape environmental stressors in space or time through
long-distance dispersal or some form of substantial, relatively
long-term reduction in metabolic demands. Escape in space (EIS) involves
an organism, temporarily or permanently, relocating to a new
environment. Seasonal migration, exhibited by various mammals, birds,
and insects, is a common, cyclical form of temporary EIS, often tracking
predictable large-scale variations in weather patterns and food
availability. EIS can also be triggered by anthropogenic environmental
stressors (Berg et al. 2010). For example, the onset of human
hunting, rather than the onset of severe weather (e.g., snowfall), was a
primary driver of autumn migration by red deer (Rivrud et al.2016). Similarly, individual mule deer that undertake long migrations
have been shown to dramatically reduce their risk of being harvested by
human hunters relative to short- and moderate-distance migrants in the
same population (Sawyer et al. 2016). Furthermore, non-migratory
butterfly species have moved over vast areas of habitat made unsuitable
by anthropogenic climate change to occupy new locations in Europe
(Parmesan et al. 1999). Escape in time (EIT) involves reducing
exposure and avoiding the costs of tolerance or dispersal by instead
entering into torpor (Humphries et al. 2003; Geiser 2004),
dormancy (including hibernation or estivation; (Danks 2000)), diapause
(a special case of dormancy based on suspended development; (Chapman
1998)), or resting stages (Smirnov 2014). EIT is commonly used by
animals to address extreme temperatures, drying conditions, and a
limited food supply (Thomas et al. 1990; Danks 2000; Gotoet al. 2001; Sarmaja-Korjonen 2003; Hairston Jr. & Fox 2009).
Theory on the evolution of adaptive dispersal and/or dormancy provides
insights regarding factors that influence when organisms should attempt
to escape in space or time (Levin et al. 1984; Snyder 2006; Bonte
& de la Pena 2009; Bonte & Dahirel 2017). Whether dispersal or
dormancy is adaptive depends on the expected net fitness payoff, which
depends on how organisms handle trade-offs 1-3 (Fig. 1) in both the
current environment, and in alternative environments, as well as costs
of escape in space or time (including mortality and the need for a
substantial front-end investment in energy stores that increases escape
success). Both dispersal and dormancy can involve substantial
uncertainty about expected fitness. For long-distance dispersal, there
is uncertainty about transit costs (that depend on both cost per unit of
distance or time, and distance relative to mobility) and often great
uncertainty over likely payoffs in prospective new environments. This
might be especially true now, following the unprecedented human-induced
rapid environmental change that is shaping natural habitats globally
(Crowley et al. 2019; Van de Waal & Litchman 2020).
Importantly, when organisms disperse to a new habitat, they might face a
different set of stressors that require a different set of behavioral
and physiological responses. Thus, the suitability of a new environment
could hinge on the organism’s plasticity in behaviour and physiology.
The degree of dissimilarity between the suite of dominant stressors in
an organism’s former environment compared to its new environment can
come with distinct costs; e.g., new stressors could require greater
energetic investment in establishing appropriate physiological or
behavioral responses. Furthermore, if, as is often the case, there are
behavioural or physiological carryovers (i.e., earlier experiences with
stressors influence later responses), then EIS can expand the scope of
multiple stressors to include stressors that do not co-occur in space or
time (see Box 4).
The benefit of escaping in time or space to a new environment is
proportional to not just the increase in quality in the new environment
but also to how long the new environment will remain of higher quality
(i.e., the degree of temporal stability). If stressor levels fluctuate
frequently or intensely over time, this can dilute the benefits of
escape. In short, the key for adaptive dispersal or dormancy is not the
spatiotemporal pattern of stressors per se; it is, instead, the
spatiotemporal pattern of fitness adjusted for costs of dispersal or
dormancy. In addition, because escaping to a new environment might also
result in greater competition or predation risk (e.g., if competitors or
predators make the same escape decision, or population demography yields
this result), there is a game-theoretic aspect to this dynamic that can
further complicate expectations.
Despite the various sources of complexity that can arise when
considering whether an organism should stay and cope with stressors or,
instead, attempt to escape them, simplified scenarios offer qualitative
insights. Generally, we expect that the probability that an organism
will attempt to escape stressors would scale with the potential for that
escape to be possible: if stressors are highly localized in space or
time, we would expect escape to be much more likely, relative to when
stressors are widespread over space or time (i.e., chronic), and, thus,
difficult or impossible to escape. If stressors are widespread in only
one-dimension (i.e., time or space), we expect a threshold to
exist for the other dimension, such that increasing the stressor’s (or
suite of stressors’) presence over this dimension causes the organism to
eventually shift from an optimal strategy of escape to one of tolerance
(Figure 3A). In other words, when stressors occur at large enough scales
in space and time, they become infeasible to avoid and tolerance becomes
the sole strategy.
If we expand to consider animal behavior in in the context of multiple
stressors, the horizon of possibilities quickly becomes much more
complex. For example, considering only two partially correlated
stressors, expressed in time and space relative to the scales of these
dimensions experienced by a focal organism, reveals that ten
qualitatively distinct sets of possible behavioural responses emerge
(Figure 3B). Generally, whenever one or more stressors is escapable in
space or time, formulating quantitative predictions about when organisms
will choose this strategy will hinge on the magnitude of effects of the
escapable stressor(s) and the cost-benefit ratio of choosing to escape
relative to choosing to tolerate the stressor(s).