Future research directions
Here, we have focused primarily on ecological scenarios where organisms
are exposed to two primary stressors, with a third stressor (predation
risk) possibly arising as a consequence of increased energy acquisition
behaviour (trade-off 2). However, to develop a deeper understanding,
including means of accurately predicting the nature and strength of
interactive effects of multiple stressors, we must extend our focus to
scenarios involving more than two primary stressors, where higher-order
interactions can occur. While empiricists are incorporating increasingly
larger numbers of stressors into experiments (e.g. Beermann et
al. (2018), and fields outside of ecology have developed conceptual
models to define higher-order interactions among three or more stressors
(e.g. interactions among pharmaceutical drugs, Beppler et al.(2016)), our current framework suggests that explicitly considering
animal behavior in these efforts will generate novel insights relevant
to ecology and conservation. Furthermore, in light of recent advances in
simultaneously collecting large volumes of data on animal behavior and
multiple environmental variables in situ , such an integration is
both timely and likely to illuminate additional stressor-related
trade-offs and alternative behavioral responses used by organisms to
navigate such tradeoffs.
With regard to understanding variation in how organisms respond to
multiple environmental factors, for over a decade, there has been
mounting interest in the importance of consistent individual differences
in animal personalities or behavioural syndromes (e.g., in
aggressiveness, boldness or exploratory tendency; (Sih et al.2004; Reale et al. 2007; Sih et al. 2012) including
dispersal tendency (Cote et al. 2010), physiology (Biro & Stamps
2008) and life histories (Reale et al. 2010). Promising topics
that remain understudied include how individual differences in suites of
phenotypic traits relate to variation in how organisms balance the four
trade-offs discussed here.
Another fruitful direction for future studies would be to examine the
influence of a mix of genetic adaptation and transgenerational and
within-generation developmental plasticity (including learning) in
shaping an integrated response to multiple stressors. Examining
ecological and social factors that, in the past or present, shaped the
overall integrated response to multiple stressors could help identify
genetic or developmental constraints that affect the speed or trajectory
of adaption to multiple stressors (De Coninck et al. 2013;
Cambronero et al. 2018). In particular, understanding epigenetic
or developmental effects can reveal otherwise hidden mechanisms of
multiple stressor effects discussed in Box 4. With multi-generational
transgenerational plasticity, behavioural responses to stressors in one
generation can influence impacts of those stressors on others
generations into the future (Bell & Hellmann 2019).
Finally, while we have focused primarily on individual responses to
multiple stressors, in Box 5 and Figure 5 we expand our scope to
consider stressors, and their physiological and behavioral effects, in
the context of natural communities. Specifically, we outline how
stressors can affect species interactions and how the nature, strength,
and trophic position of these affected interactions can determine
qualitatively distinct outcomes for communities and ecosystems. Further
theoretical and empirical investigations of how our comprehensive
framework on stressor effects (Figure 1) could help explain the
structure and dynamics of natural communities offer a timely and
promising avenue for future study.
Acknowledgements
We thank Anne E. Todgham for her feedback on the initial conceptual
framework. This overall endeavor was supported by National Science
Foundation grant IOS 1456724 to AS. In addition, LKL was supported by an
Australia Awards Endeavour Fellowship, MAG was supported by a NSF
Postdoctoral Fellowship and University of Colorado Chancellor’s
Postdoctoral Fellowship. PCT was partly funded by the German Research
Foundation (DFG) as part of the SFB TRR 212 (NC³). IYL was supported by
National Science Foundation grant 1557836.