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.