Liu OR and Gaines SD (2022), Environmental context dependency in species int...

ECB-ART-50932

Proc Natl Acad Sci U S A 2022 Sep 06;11936:e2118539119. doi: 10.1073/pnas.2118539119.

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Environmental context dependency in species interactions.

Liu OR , Gaines SD .

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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.

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	Fig. 1. Raw data for species and physical drivers included in the study. Points represent mean values across 10 spatial replicates, while vertical lines represent Â± 1 SD. All variables normalized to zero mean and unit variance, with linear time trends removed. Dashed lines distinguish herbivore (sea urchin) time series from time series of algae species.
	Fig. 2. Reconstructed interaction web using results of CCM. Each arrow represents a significant inferred causal signal and link width and opacity scale with the strength of causal forcing (see full results in SI Appendix). Species abbreviations: L. far, L. farlowii; M. fra, M. franciscanus; M. pyr, M. pyrifera; P. cal, P. californica; S. pur, S. purpuratus. Physical drivers: NPGO, MEI, PDO, SST, and SWH.
	Fig. 3. Smoothed kernel-density histograms of all estimated interactions by type. From top to bottom: Algal competition, urchin competition, herbivory, consumption (i.e., effects of algae on urchins), and intraspecies. Solid lines denote medians across all estimated interactions of that type.
	Fig. 4. Interaction of Macrocystis, Pterygophora, and Laminaria with Macrocystis recruits over time (solid lines). Normalized NPGO index is shown with dashed line. Boxes zoom in on the interactions in a year with elevated NPGO (2000) and a year with low NPGO (2005). Abbreviations are as in Fig. 2.
	Fig. 5. (A) Variation in the strength of competition between adult Macrocystis, Laminara, and Pterygophora, relative to the effect of the NPGO on those species. Shown are all significant inferred causal interactions between algae species. (B) Variation in the effect of adult Macrocystis, Laminara, and Pterygophora on Macrocystis recruitment, relative to the effect of the NPGO. Points indicate individual interaction strengths averaged among the 30 nearest state-space neighbors, as described in Materials and Methods. Solid lines are loess smooths.

References [+] :

Arkema, Direct and indirect effects of giant kelp determine benthic community structure and dynamics. 2009, Pubmed