Introduction
Physiologists and ecologists have long studied environmental stressors,
which we define broadly as abiotic (e.g., chemical toxin, low oxygen,
ocean acidification, pH, temperature) or biotic (e.g., disease,
predation risk, food scarcity) factors that negatively affect individual
fitness or the growth, abundance or persistence of a population or
community (Boone et al. 2007; Pincebourde et al. 2012;
Killen et al. 2013) (Box 1). But stressors rarely act in
isolation, stimulating a recent focus on multiple stressors (see Figure
1 and Box 2, Figure 2) and their potential for synergistic impacts
(Przeslawski et al. 2015; Cote et al. 2016; Cambroneroet al. 2018; Petitjean et al. 2019). Despite this effort,
Orr et al. (2020) concluded that “over the past 20
years….very few, if any, general patterns have emerged from
meta-analyses (Crain et al. 2008; Holmstrup et al. 2010;
Dieleman et al. 2012; Jackson et al. 2016; Yue et
al. 2017; Lange et al. 2018)” addressing the effects of
stressor combinations. Our thesis is that a clear theoretical
understanding of behavioural and life-history plasticity in response to
multiple stressors can help explain the observed context-dependent
variation in stressor effects.
Conceptual models of how organisms respond physiologically to
environmental variation (e.g, the Allostatic Load (McEwen & Wingfield
2003; Wingfield 2013) and Reactive Scope Models (Romero et al.2009) provide frameworks for understanding how stressors affect fitness
(or performance). These effects are expressed through physiological
mediators, both within a range that does not reduce fitness (the
reactive scope) and in scenarios that push organisms into an overload
that reduces survival. Behaviour plays a role in these models through
feeding, locomotion, aggression, anxiety, fear, fleeing, vigilance, and
migration, and mediates the physiological responses to stressors (see
Table 1 in Romero et al. 2009). But a full, trade-off based
integration of behavioural mechanisms and life-history implications is
essential to achieve a deeper, more predictive understanding of these
relationships. Organismal responses are triggered by cues (whether
single or multiple), which provide information on the nature and
intensity of stressors. Whether and how organisms respond, and the
effectiveness of their responses, can depend critically on the
reliability of cues, and how cues interact (see Box 3).
In this synthesis, we outline a conceptual framework that integrates
animal behaviour, bioenergetics and life history trade-offs to identify
ways that behaviour and life history plasticity shape the impacts of
stressor exposure on individual fitness (Figure 1). First, in addition
to physiological responses, small-scale behaviours (e.g., incremental
shifts in space use or activity schedules) that reduce exposure to one
stressor can simultaneously alter an organism’s vulnerability to the
effects of a second stressor, or alter the magnitude of these effects on
fitness (trade-off 1 ). Second, because responding
physiologically to stressors requires energy, foraging activity can
increase in response to stressors, thereby enhancing exposure to
additional risks, e.g., predation (trade-off 2 . If the organism
has obtained the energy it requires, it will then need to allocate
energy between behavioural or physiological stress responses and
fitness-enhancing life history demands (i.e. reproduction and growth;trade-off 3 ). Trade-offs 2 and 3 emphasize
how stressors can reduce fitness indirectly by limiting overall
energetic budgets, increasing foraging-related risks or drawing energy
away from alternative life history needs. Finally, at a larger spatial
or temporal scale, organisms can respond to stressors by actively
escaping exposure through space (e.g., via longer-distance dispersal) or
time (e.g., via dormancy or diapause). Escape in space or time typically
incurs other costs (trade-off 4) .
Our integrated framework highlights 1) the relative importance of direct
versus indirect costs of stressors, where, for example, the indirect
costs of an abiotic stressor might include increased predation risk or
reduced mating success; 2) the role of the scales of spatial and
temporal correlations between stressors, resources and other risks; and
3) the need to better understand mechanisms resulting in ‘fitness
cliffs’ – situations where a relatively small increase in stressor
levels results in a large decrease in fitness. In the following
sections, we provide detailed descriptions of each of the four
fundamental and interrelated trade-offs (Fig. 1) and the broad insights
they offer.