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.