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When plants establish outside their native range, their ability to adapt to the new environment is influenced by both demography and dispersal. However, the relative importance of these two factors is poorly understood. To quantify the influence of demography and dispersal on patterns of genetic diversity underlying adaptation, we used data from a globally distributed demographic research network comprising 35 native and 18 nonnative populations of Plantago lanceolata Species-specific simulation experiments showed that dispersal would dilute demographic influences on genetic diversity at local scales. Populations in the native European range had strong spatial genetic structure associated with geographic distance and precipitation seasonality. In contrast, nonnative populations had weaker spatial genetic structure that was not associated with environmental gradients but with higher within-population genetic diversity. Our findings show that dispersal caused by repeated, long-distance, human-mediated introductions has allowed invasive plant populations to overcome environmental constraints on genetic diversity, even without strong demographic changes. The impact of invasive plants may, therefore, increase with repeated introductions, highlighting the need to constrain future introductions of species even if they already exist in an area.

Given the negative fitness effects that pathogens impose on their hosts, the benefits of resistance should be universal. However, there is marked variation across plant species in the number of nucleotide-binding leucine-rich repeat receptors, which form a cornerstone of defense. The growth–defense trade-off hypothesis predicts costs associated with defense investment to generate variation in these traits. Our analysis comparing features of the intracellular immune-receptor repertoires with trait data of 187 species shows that in wild plants, the size of the molecular defense repertoire correlates negatively with growth. By contrast, we do not find evidence for a growth–defense trade-off in agricultural plants. Our cross-species approach highlights the central role of defense investment in shaping ecological trait variation and its sensitivity to domestication. [ABSTRACT FROM AUTHOR]

Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. Ecological selection ubiquitously shapes communities by selecting for individuals with the most suitable trait combinations. Detecting selection types on key traits across environmental gradients and over time has the potential to reveal the underlying abiotic and biotic drivers of community dynamics. Here, we present a model‐based predictive framework to quantify the multidimensional trait distributions of communities (community trait spaces), which we use to identify ecological selection types shaping communities along environmental gradients. We apply the framework to over 3600 boreal forest understory plant communities with results indicating that directional, stabilizing, and divergent selection all modify community trait distributions and that the selection type acting on individual traits may change over time. Our results provide novel and rare empirical evidence for divergent selection within a natural system. Our approach provides a framework for identifying key traits under selection and facilitates the detection of processes underlying community dynamics. [ABSTRACT FROM AUTHOR]

Predicting the emergence, spread and evolution of parasites within and among host populations requires insight to both the spatial and temporal scales of adaptation, including an understanding of within-host up through community-level dynamics. Although there are very few pathosystems for which such extensive data exist, there has been a recent push to integrate studies performed over multiple scales or to simultaneously test for dynamics occurring across scales. Drawing on examples from the literature, with primary emphasis on three diverse host-parasite case studies, we first examine current understanding of the spatial structure of host and parasite populations, including patterns of local adaptation and spatial variation in host resistance and parasite infectivity. We then explore the ways to measure temporal variation and dynamics in host-parasite interactions and discuss the need to examine change over both ecological and evolutionary timescales. Finally, we highlight new approaches and syntheses that allow for simultaneous analysis of dynamics across scales. We argue that there is great value in examining interplay among scales in studies of host-parasite interactions.

Aim: Joint species distribution models (JSDMs) are an important tool for predicting ecosystem diversity and function under global change. The growing complexity of modern JSDMs necessitates careful model selection tailored to the challenges of community prediction under novel conditions (i.e., transferable models). Common approaches to evaluate the performance of JSDMs for community‐level prediction are based on individual species predictions that do not account for the species correlation structures inherent in JSDMs. Here, we formalize a Bayesian model selection approach that accounts for species correlation structures and apply it to compare the community‐level predictive performance of alternative JSDMs across broad environmental gradients emulating transferable applications. Innovation: We connect the evaluation of JSDM predictions to Bayesian model selection theory under which the log score is the preferred performance measure for probabilistic prediction. We define the joint log score for community‐level prediction and distinguish it from more commonly applied JSDM evaluation metrics. We then apply the joint community log score to evaluate predictions of 1918 out‐of‐sample boreal forest understory communities spanning 39 species generated using a novel multinomial JSDM framework that supports alternative species correlation structures: independent, compositional dependence and residual dependence. Main conclusions: The best performing JSDM included all observed environmental variables and compositional dependence modelled using a multinomial likelihood. The addition of flexible residual species correlations improved model predictions only within JSDMs applying a reduced set of environmental variables highlighting potential confounding between unobserved environmental conditions and residual species dependence. The best performing JSDM was consistent across successional and bioclimatic gradients regardless of whether interest was in species‐ or community‐level prediction. Our study demonstrates the utility of the joint community log score to compare the predictive performance of JSDMs and highlights the importance of accounting for species dependence when interest is in community composition under novel conditions. [ABSTRACT FROM AUTHOR]