Trade-off 1: Small-scale energetic and behavioural tradeoffs
Physiologists have rigorously investigated physiological trade-offs –
where the physiological mechanism of response to stressor A either
enhances (cross-tolerance) or interferes with (cross-susceptibility) the
physiological mechanism of response to stressor B (Todgham et al.2005; MacMillan et al. 2009; Sinclair et al. 2013; Hintzet al. 2019). Here, we focus on additional, less studied
behavioural and energetic mechanisms, whereby exposure to stressor A
changes the fitness costs of stressor B.
Many organisms respond to environmental stressors by adjusting their
space use (Clusella-Trullas & Chown 2014; Sears et al. 2016) or
temporal activity patterns (Gaynor et al. 2018; van der Vinneet al. 2019) to reduce exposure to stressors and, thus mitigate
physiological costs. These behavioural responses can occur over small
scales, which we refer to as ‘avoidance’, or large scales, which we
refer to as ‘escape’ (e.g., dispersal or dormancy, which we discuss
later: trade-off 4). The small-scale responses that we discuss here
differ from larger-scale escape responses in being relatively rapidly
reversible and typically requiring lower energy costs.
Behavioral responses can interact with physiological responses to
determine not only the net effect of a stressor on an organism, but also
whether multiple stressors interact antagonistically or synergistically.
Put simply, when two stressors require conflicting adaptive behavioural
responses, where the response to either increases exposure to the other,
the negative impact of the stressor pair can be enhanced. A core concept
from standard behavioural ecology trade-off theory (Houston & McNamara
1999) suggests that a key factor is the degree to which multiple
stressors are positively versus negatively correlated in space or time
(see Box 4 for further discussion of these correlations). If stressors
are positively correlated (e.g., if the same locations have high levels
of both stressors, while other locations have low levels of both), then
avoidance of one tends to also reduce exposure to the other; if the
stressors are negatively correlated (e.g., places with high levels of
one stressor have low levels of the other), then organisms face the
trade-off where avoidance of one could increase exposure to the other.
For example, salamander larvae avoid exposure to damaging ultraviolet
radiation by going to deeper water, but doing so exposes them to higher
predation risk from fish (Garcia et al. 2004).
Invoking parallel theory on avoidance of multiple predators (Matsuda and
Abrams 1996; Sih et al. 1998), we can predict how organisms should
respond behaviourally to multiple stressors. If avoidance of one
stressor increases exposure to the other, then organisms should more
heavily weigh avoidance of the more detrimental stressor(s). This
weighting could depend on both the level and inherent lethality of the
stressors, or on how earlier experience (or evolution) has shaped the
organism’s relative abilities to cope with the two stressors
physiologically. If both stressors can strongly reduce fitness, and if
behavioural avoidance itself incurs a high cost (e.g., restriction to
low-quality habitat), then organisms should not attempt small-scale
avoidance but should, instead rely only on coping with the stressors via
physiological responses - or on escape in space or time, if feasible
(e.g., energetically affordable; trade-off 4).
Further complexities arise depending, for example, on the spatial scale
of heterogeneity in stressor distributions relative to the organism’s
movement capacity (Schmitz et al. 2017; Fey et al. 2019).
Although numerous studies have examined behavioural avoidance of one
stressor, there is a need for a better understanding of factors that
explain when and why multiple stressors are negatively versus positively
correlated, and for more studies examining how organisms respond
behaviourally to conflicting (e.g., negatively correlated) stressors,
particularly in the broader context of additional layers of trade-offs.
An alternative mechanism that results in trade-offs arises when
increased energy devoted to coping physiologically with stressors
results in accelerating fitness costs. The mechanism could
involve increased stressor levels pushing organisms into homeostatic or
allostatic overload (McEwen & Wingfield 2003; Romero et al.2009), such that allocating energy to coping with any one stressor
strains the organism’s ability to cope with other stressors.
Additionally, and in a segue to the other major trade-offs in Figure 1,
the increased fitness costs could arise via increased mortality risk
associated with acquiring energy (trade-off 2) or via an energy
allocation trade-off that reduces growth or reproduction (trade-off 3).
Each of these mechanisms could result in ‘fitness cliffs’, or strong
nonlinearities that can cause even a small change (in this case, an
increase in the level of a stressor) to disproportionately reduce
fitness. For example, Delnat et al. (2019) reported a synergistic
increase in mosquito mortality when pesticides combined with high daily
temperature variation (14 °C), but no such interaction was observed
under lower (7 °C) or in the absence of daily temperature variation.