Evolution and connectivity influence the persistence and recovery of coral reefs under climate change in the Caribbean, Southwest Pacific, and Coral Triangle.

Corals are experiencing unprecedented decline from climate change-induced mass bleaching events. Dispersal not only contributes to coral reef persistence through demographic rescue but can also hinder or facilitate evolutionary adaptation. Locations of reefs that are likely to survive future warming therefore remain largely unknown, particularly within the context of both ecological and evolutionary processes across complex seascapes that differ in temperature range, strength of connectivity, network size, and other characteristics. Here, we used eco-evolutionary simulations to examine coral adaptation to warming across reef networks in the Caribbean, the Southwest Pacific, and the Coral Triangle. We assessed the factors associated with coral persistence in multiple reef systems to understand which results are general and which are sensitive to particular geographic contexts. We found that evolution can be critical in preventing extinction and facilitating the long-term recovery of coral communities in all regions. Furthermore, the strength of immigration to a reef (destination strength) and current sea surface temperature robustly predicted reef persistence across all reef networks and across temperature projections. However, we found higher initial coral cover, slower recovery, and more evolutionary lag in the Coral Triangle, which has a greater number of reefs and more larval settlement than the other regions. We also found the lowest projected future coral cover in the Caribbean. These findings suggest that coral reef persistence depends on ecology, evolution, and habitat network characteristics, and that, under an emissions stabilization scenario (RCP 4.5), recovery may be possible over multiple centuries.

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries.

OBJECTIVE: To assess whether the basic reproduction number (R0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors other than interventions characterize initial vulnerability to the virus. METHODS: We fit logistic growth curves to reported daily case numbers, up to the first epidemic peak, for 58 countries for which 16 explanatory covariates are available. This fitting has been shown to robustly estimate R0 from the specified period. We then use a generalized additive model (GAM) to discern both linear and nonlinear effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R0. FINDINGS: We found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors-population between 20-34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality-as having strong relationships with R0, across countries. An intermediate level of youth and GINI inequality are associated with high R0, (n-shape relationships), while high city population and high social media use are associated with high R0. Pollution, temperature, and humidity did not have strong relationships with R0 but were positive. CONCLUSION: Countries have different characteristics that predispose them to greater intrinsic vulnerability to COVID-19. Studies that aim to measure the effectiveness of interventions across locations should account for these baseline differences in social and demographic characteristics.

Evolution reverses the effect of network structure on metapopulation persistence.

Global environmental change is challenging species with novel conditions, such that demographic and evolutionary trajectories of populations are often shaped by the exchange of organisms and alleles across landscapes. Current ecological theory predicts that random networks with dispersal shortcuts connecting distant sites can promote persistence when there is no capacity for evolution. Here, we show with an eco-evolutionary model that dispersal shortcuts across environmental gradients instead hinder persistence for populations that can evolve because long-distance migrants bring extreme trait values that are often maladaptive, short-circuiting the adaptive response of populations to directional change. Our results demonstrate that incorporating evolution and environmental heterogeneity fundamentally alters theoretical predictions regarding persistence in ecological networks.

Path-dependent institutions drive alternative stable states in conservation.

Understanding why some renewable resources are overharvested while others are conserved remains an important challenge. Most explanations focus on institutional or ecological differences among resources. Here, we provide theoretical and empirical evidence that conservation and overharvest can be alternative stable states within the same exclusive-resource management system because of path-dependent processes, including slow institutional adaptation. Surprisingly, this theory predicts that the alternative states of strong conservation or overharvest are most likely for resources that were previously thought to be easily conserved under optimal management or even open access. Quantitative analyses of harvest rates from 217 intensely managed fisheries supports the predictions. Fisheries' harvest rates also showed transient dynamics characteristic of path dependence, as well as convergence to the alternative stable state after unexpected transitions. This statistical evidence for path dependence differs from previous empirical support that was based largely on case studies, experiments, and distributional analyses. Alternative stable states in conservation appear likely outcomes for many cooperatively managed renewable resources, which implies that achieving conservation outcomes hinges on harnessing existing policy tools to navigate transitions.

Spatial evolutionary dynamics produce a negative cooperation-population size relationship.

Natural selection can favour cooperation, but it is unclear when cooperative populations should be larger than less cooperative ones. While experiments have shown that cooperation can increase population size, cooperation and population size can become negatively correlated if spatial processes affect both variables in opposite directions. We use a simple mathematical model of spatial common-pool resource production to investigate how space affects the cooperation-population size relationship. We find that only cooperation that is sufficiently beneficial to neighbours increases population size. However, spatial clustering variations can create a negative cooperation-population relationship between populations even when cooperation is highly beneficial, because clustering selects for cooperation but decreases population size. Individual-based simulations with variable individual movement rates produced variation in spatial clustering and the hypothesized negative cooperation-population relationships. These results suggest that variation in spatial clustering can limit the size of evolutionarily stable cooperating populations - an ecological dilemma of cooperation.

Defector clustering is linked to cooperation in a pathogenic bacterium.

Spatial clustering is thought to favour the evolution of cooperation because it puts cooperators in a position to help each other. However, clustering also increases competition. The fate of cooperation may depend on how much cooperators cluster relative to defectors, but these clustering differences have not been the focus of previous models and experiments. By competing siderophore-producing cooperator and defector strains of the opportunistic pathogen Pseudomonas aeruginosa in experimental microhabitats, we found that at the spatial scale of individual interactions, cooperator clustering lowers cooperation, but defector clustering favours cooperation. A theoretical model and individual-based simulations show these counterintuitive effects can arise when competition and cooperation occur at a single resource-determined scale, with population dynamics crucially allowing cooperators and defectors to cluster differently. The results suggest that cooperation relies on the regulation of sufficient defector clustering relative to cooperator clustering, which may be important in bacteria, social amoeba and cancer inhibition.

Patchiness in a microhabitat chip affects evolutionary dynamics of bacterial cooperation.

Localized interactions are predicted to favour the evolution of cooperation amongst individuals within a population. One important factor that can localize interactions is habitat patchiness. We hypothesize that habitats with greater patchiness (greater edge-to-area ratio) can facilitate the maintenance of cooperation. This outcome is believed to be particularly relevant in pathogenic microbes that can inhabit patchy habitats such as the human respiratory tract. To test this hypothesis in a simple but spatially controlled setting, we designed a transparent microhabitat chip (MHC) with multiple patchiness treatments at the 100 micron scale. The MHC is a closed system that sustains bacterial replication and survival for up to 18 hours, and allows spatial patterns and eco-evolutionary dynamics to be observed undisturbed. Using the opportunistic pathogen Pseudomonas aeruginosa, we tracked the growth of wild-type cooperators, which produce the public good pyoverdin, in competition with mutant defectors or cheaters that use, but do not produce, pyoverdin. We found that while defectors on average outperformed cooperators in all habitats, habitat patchiness significantly alleviated the ecological pressure against cooperation due to defection, leading to coexistence. Our results confirmed that habitat-level spatial heterogeneity can be important for cooperation. The MHC enables novel experiments, allows multiple parameters to be precisely varied and studied simultaneously, and will help uncover dynamical features of spatial ecology and the evolution of pathogens.

Local densities connect spatial ecology to game, multilevel selection and inclusive fitness theories of cooperation.

Cooperation plays a crucial role in many aspects of biology. We use the spatial ecological metrics of local densities to measure and model cooperative interactions. While local densities can be found as technical details in current theories, we aim to establish them as central to an approach that describes spatial effects in the evolution of cooperation. A resulting local interaction model neatly partitions various spatial and non-spatial selection mechanisms. Furthermore, local densities are shown to be fundamental for important metrics of game theory, multilevel selection theory and inclusive fitness theory. The corresponding metrics include structure coefficients, spatial variance, contextual covariance, relatedness, and inbreeding coefficient or F-statistics. Local densities serve as the basis of an emergent spatial theory that draws from and brings unity to multiple theories of cooperation.