Environmental context dependency in species interactions

Ecological interactions are not uniform across time and can vary with environmental conditions. Yet, interactions among species are often measured with short-term controlled experiments whose outcomes can depend greatly on the particular environmental conditions under which they are performed. As an alternative, we use empirical dynamic modeling to estimate species interactions across a wide range of environmental conditions directly from existing long-term monitoring data. In our case study from a southern California kelp forest, we test whether interactions between multiple kelp and sea urchin species can be reliably reconstructed from time-series data and whether those interactions vary predictably in strength and direction across observed fluctuations in temperature, disturbance, and low-frequency oceanographic regimes. We show that environmental context greatly alters the strength and direction of species interactions. In particular, the state of the North Pacific Gyre Oscillation seems to drive the competitive balance between kelp species, asserting bottom-up control on kelp ecosystem dynamics. We show the importance of specifically studying variation in interaction strength, rather than mean interaction outcomes, when trying to understand the dynamics of complex ecosystems. The significant context dependency in species interactions found in this study argues for a greater utilization of long-term data and empirical dynamic modeling in studies of the dynamics of other ecosystems.

Interactions between species drive patterns of diversity, stability, resilience, and productivity in nature (1–4). In any ecosystem, the collection of species interactions determines community dynamics. Yet, since environmental conditions can influence these species interactions and environmental conditions can vary greatly over space or time (5–9), shifting interspecies dynamics can drive complex ecosystem changes. For example, the Stress Gradient Hypothesis (10–12) posits that interactions among species within a trophic level can shift from competitive to facilitative across large gradients of stress (e.g., thermal, nutrient, or water stress), with important implications for community dynamics. Similar hypotheses have been posited about shifts in other key species-interaction types, like parasitism and mutualism (13, 14), and consumer–prey interactions (15).Although ecologists have long recognized that many important species interactions may vary greatly over time and space, this context dependency remains very difficult to effectively measure and describe. Field experiments that measure interactions can generally be performed at only a few places over a relatively short window of time. They are therefore inevitably subject to only a subset of potential environmental contexts that may not encompass the full range of conditions experienced by that ecosystem over longer time scales or broader geographies (16). The resulting constraints increase the chance that the profound influence of environmental context on the outcome of species interactions ranging from keystone predation (8) to competition (6, 17, 18) to protective symbioses (19–21) will remain underappreciated. Since expanding the temporal and spatial scales of such experiments to rectify these challenges is a daunting task, we need additional tools.Moreover, even when context dependency of species interactions has been examined explicitly, studies commonly focus on estimating mean interaction strengths, rather than more comprehensive examinations of interaction variance and dynamics (9). This averaging approach may be appropriate for answering certain questions, but if species interactions are highly variable in both magnitude and direction—and therefore “weak” when averaged—key species interactions that are important drivers of community change may be dismissed as insignificant observational noise (4).Meeting these significant challenges requires placing interspecific interactions into their appropriate full environmental contexts. Controlled experiments can sort out the relative and interactive effects of a few orthogonal environmental drivers at a time; for example, examination of the effects of ocean warming and acidification on algal competition (22). But as species-interaction webs and lists of important environmental variables grow in size, fully factorial experimental designs quickly become unwieldy, if not impossible, to implement. One potential solution may lie in coupling long-term ecological observations that span a large range of environmental contexts with analytical methods that can directly estimate context-dependent species interactions from those time-series observations of changing abundances. Since long-term records of species abundances exist for a wide range of ecosystems, such an approach could help to characterize environmental contingencies in species interactions far more rapidly and could explicitly examine interaction variability in both strength and direction in a broader array of contexts.Here, we explore this alternative approach through a case study, by examining the effects of environmental context on species interactions using nonlinear time-series analyses applied to long-term monitoring data from a southern California kelp forest (23). Kelp forests are diverse and temporally dynamic ecosystems, in which many important species interactions are well-documented through decades of experimental and comparative studies (24–26). The study of kelp forests has been foundational to ecological theory, especially regarding the relative influence of top-down and bottom-up structuring forces in ecosystems (27–31). Recently, however, findings from long-term kelp-forest research programs have begun to challenge many long-held beliefs about the drivers of kelp-forest ecosystem dynamics (32). In particular, a longer-term perspective has led to growing hints about the critical importance of environmental context—such as the level of physical disturbance or the current state of El Niño conditions—for understanding kelp-forest processes (33–37).To explore the insights that can be gleaned from time-series data to determine patterns of variation in species interactions and their relationships to environmental drivers, we use empirical dynamic modeling [EDM (38)]. EDM uses information from single or multiple time series to empirically model relationships between variables through the reconstruction of dynamic attractors . The general modeling framework for all EDM methods is readily adaptable to many different sorts of time-series variables, including environmental variables manifesting at different scales (39–41). Because the methods are specifically designed for nonlinear dynamic systems, EDM—in theory—should be able to illuminate context-dependent patterns across diverse types of species interactions. Recently developed EDM methods exist for uncovering dynamic species interactions from time-series data (38), but these methods have, to date, been applied only to simulated and planktonic communities. Their utility to the study of other ecological systems remains untested. We use EDM to explore how a kelp-forest species-interaction network varies over time and to establish environmental context dependency in interaction strength and direction.