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Species ranges are shifting in response to climate change, but most predictions disregard food–web interactions and, in particular, if and how such interactions change through time. Predator–prey interactions could speed up species range shifts through enemy release or create lags through biotic resistance. Here, we developed a spatially explicit model of interacting species, each with a thermal niche and embedded in a size-structured food–web across a temperature gradient that was then exposed to warming. We also created counterfactual single species models to contrast and highlight the effect of trophic interactions on range shifts. We found that dynamic trophic interactions hampered species range shifts across 450 simulated food–webs with up to 200 species each over 200 years of warming. All species experiencing dynamic trophic interactions shifted more slowly than single-species models would predict. In addition, the trailing edges of larger bodied species ranges shifted especially slowly because of ecological subsidies from small shifting prey. Trophic interactions also reduced the numbers of locally novel species, novel interactions and productive species, thus maintaining historical community compositions for longer. Current forecasts ignoring dynamic food–web interactions and allometry may overestimate species' tendency to track climate change. 

Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics, d (1) , d (2) , defined on the set of agents, X , which measure agents’ nearness in variables of interest. We apply the method of diffusion maps to the systems ( X , d ( i ) ) to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics d (1) and d (2) . Additionally, the form of the macro-scale organization is encoded in the covariances among the two sets of diffusion map components. Using these covariances we discern between different modes of collective behavior in a data-driven, unsupervised manner. This method is demonstrated on a synthetic flocking model as well as empirical fish schooling data. We show that our state classification subdivides the known behaviors of the school in a meaningful manner, leading to a finer description of the system’s behavior. 

Climate shocks can reorganize the social–ecological linkages in food-producing communities, leading to a sudden loss of key products in food systems. The extent and persistence of this reorganization are difficult to observe and summarize, but are critical aspects of predicting and rapidly assessing community vulnerability to extreme events. We apply network analysis to evaluate the impact of a climate shock—an unprecedented marine heatwave—on patterns of resource use in California fishing communities, which were severely affected through closures of the Dungeness crab fishery. The climate shock significantly modified flows of users between fishery resources during the closures. These modifications were predicted by pre-shock patterns of resource use and were associated with three strategies used by fishing community member vessels to respond to the closures: temporary exit from the food system, spillover of effort from the Dungeness crab fishery into other fisheries, and spatial shifts in where crab were landed. Regional differences in resource use patterns and vessel-level responses highlighted the Dungeness crab fishery as a seasonal “gilded trap” for northern California fishing communities. We also detected disparities in climate shock response based on vessel size, with larger vessels more likely to display spatial mobility. Our study demonstrates the importance of highly connected and decentralized networks of resource use in reducing the vulnerability of human communities to climate shocks.

 

A critical tool in assessing ecosystem change is the analysis of long‐term data sets, yet such information is generally sparse and often unavailable for many habitats. Kelp forests are an example of rapidly changing ecosystems that are in most cases data poor. Because kelp forests are highly dynamic and have high intrinsic interannual variability, understanding how regional‐scale drivers are driving kelp populations—and particularly how kelp populations are responding to climate change—requires long‐term data sets. However, much of the work on kelp responses to climate change has focused on just a few, relatively long‐lived, perennial, canopy‐forming species. To understand how kelp populations with different life history traits are responding to climate‐related variability, we leverage 35 yr of Landsat satellite imagery to track the population size of an annual, ruderal kelp,Nereocystis luetkeana, across Oregon. We found high levels of interannual variability inNereocystiscanopy area and varying population trajectories over the last 35 yr. Surprisingly, OregonNereocystispopulation sizes were unresponsive to a 2014 marine heat wave accompanied by increases in urchin densities that decimated northern CaliforniaNereocystispopulations. Some OregonNereocystis populations have even increased in area relative to pre‐2014 levels. Analysis of environmental drivers found thatNereocystispopulation size was negatively correlated with estimated nitrate levels and positively correlated with winter wave height. This pattern is the inverse of the predicted relationship based on extensive prior work on the perennial kelpMacrocystis pyriferaand may be related to the annual life cycle ofNereocystis. This article demonstrates (1) the value of novel remote sensing tools to create long‐term data sets that may challenge our understanding of nearshore marine species and (2) the need to incorporate life history traits into our theory of how climate change will shape the ocean of the future.

 

In light of rapid environmental change, quantifying the contribution of regional‐ and local‐scale drivers of coral persistence is necessary to characterize fully the resilience of coral reef systems. To assess multiscale responses to thermal perturbation of corals in the Coral Triangle (CT), we developed a spatially explicit metacommunity model with coral–algal competition, including seasonal larval dispersal and external spatiotemporal forcing. We tested coral sensitivity in 2,083 reefs across the CT region and surrounding areas under potential future temperature regimes, with and without interannual climate variability, exploring a range of 0.5–2.0°C overall increase in temperature in the system by 2054. We found that among future projections, reef survival probability and mean percent coral cover over time were largely determined by the presence or absence of interannual sea surface temperature (SST) extremes as well as absolute temperature increase. Overall, reefs that experienced SST time series that were filtered to remove interannual variability had approximately double the chance of survival than reefs subjected to unfiltered SST. By the end of the forecast period, the inclusion of thermal anomalies was equivalent to an increase of at least 0.5°C in SST projections without anomalies. Change in percent coral cover varied widely across the region within temperature scenarios, with some reefs experiencing local extinction while others remaining relatively unchanged. Sink strength and current thermal stress threshold were found to be significant drivers of these patterns, highlighting the importance of processes that underlie larval connectivity and bleaching sensitivity in coral networks.

 

Coral reefs are increasingly exposed to elevated temperatures that can cause coral bleaching and high levels of mortality of corals and associated organisms. The temperature threshold for coral bleaching depends on the acclimation and adaptation of corals to the local maximum temperature regime. However, because of larval dispersal, coral populations can receive larvae from corals that are adapted to very different temperature regimes. We combine an offline particle tracking routine with output from a high‐resolution physical oceanographic model to investigate whether connectivity of coral larvae between reefs of different thermal regimes could alter the thermal stress threshold of corals. Our results suggest that larval transport between reefs of widely varying temperatures is likely in the Coral Triangle and that accounting for this connectivity may be important in bleaching predictions. This has important implications in conservation planning, because connectivity may allow some reefs to have an inherited heat tolerance that is higher or lower than predicted based on local conditions alone.