American Naturalist最新文献

AbstractGenomic offset metrics are increasingly used to predict population maladaptation under changing climates, based on the assumption of a negative statistical relationship between offset measures and local relative fitness. Recent theoretical advances have confirmed this relationship by relating genomic offset to phenotypic trait distances along selection gradients. However, these metrics typically rely on the assumption that stabilizing selection, which maintains local adaptive optima, operates on fitness-related traits through Gaussian-shaped selection gradients. In this study, we extend the theory to accommodate more diverse forms of selection gradients and introduce more general genomic offset measures that preserve the fitness-offset relationship. We validate this generalization through simulations and demonstrate the utility of these new measures in predicting relative fitness in common garden experiments involving three plant species: pearl millet, a vital staple cereal grown in arid soils, and two emblematic North American tree species, balsam poplar and red spruce. Our findings indicate that assuming a local Gaussian-shaped selection gradient for climate adaptation is a robust approximation for these species. These results have important implications for validating genomic offset predictions using fitness proxies and for studies that aim to predict fitness loss based on genomic offset metrics.

AbstractHumans drive species evolution in numerous ways, ranging from the deliberate interventions of domestication to the indirect but far-reaching impacts of climate change. Anticipating how species will adapt to these pressures assumes that evolution is, to some extent, predictable. Evidence of parallel evolution from time series studies can inform such forecasts. In this article we review time series genomic studies, which directly quantify evolution by sampling populations over time. First, we evaluate the extent to which selection drives parallel adaptation in time series studies. We give specific attention to evolution in response to anthropogenic drivers of change and within host-parasite interactions, which represent major themes in the literature. Then we analyze the patterns seen in retrospective genomic time series studies to identify how distinct drivers of change influence evolutionary processes such as population structure, gene flow, and genetic diversity. Finally, we draw from current advancements in population genomics to anticipate how time series data will be analyzed in the near future to provide recommendations for both researchers and methods developers.

AbstractAccelerating land use and climate change threaten to disrupt relationships between adaptive variation and environmental optima of many species. Consequently, management must increasingly identify nonlocal genetic sources for restoration programs. Genomic offset methods, such as gradientForests, have shown promise in identifying these sources using genomic data, potentially bypassing the need for traditional, time-consuming transplant experiments. However, previous studies primarily used population-level allele frequencies (AFs) for training and population mean fitness for evaluation, ignoring individual variation within populations. Here, we used simulation data to compare the accuracy of genotype- and AF-based models, factorially evaluated using both individual and population mean fitness. With more than 810,000 evaluations of such models, we found that the number of loci had little impact on model performance. As expected, population-level evaluation provided an optimistic view of predictive performance for both genomic inputs. While genotype- and AF-based models showed similar qualitative and quantitative aspects, genotype-based models improved predictions in landscapes that differed from strict environmental clines by incorporating additional loci beyond those used by AF-based models. This suggests that genotype-based models may enhance offset predictions in environments that are discontinuous and have multiple populations in geographically distant yet similar environments. We close with recommendations for future use and evaluation of these tools.

AbstractAssessments of adaptive genetic diversity across multiple species are necessary for improving the efficacy of regional management in the context of climate change. Rapid loss of sea ice and rising water temperatures in the Arctic Ocean threaten marine species survival. Beluga whales (Delphinapterus leucas), narwhals (Monodon monoceros), and bowhead whales (Balaena mysticetus) are endemic Arctic whales adapted to cold-water conditions that depend on sea ice for foraging and protection from predators. We forecasted the degree of genetic mismatch these species may experience under future climate change scenarios by the next century using Canadian Arctic genomic samples in genotype-environment association models. When examining local adaptation to different environmental variables, we found that ice thickness was the strongest environmental predictor for bowhead whales, while temperature and chlorophyll concentration, an indicator of primary productivity, carried greater weight for beluga whales and narwhals. Notably, a higher degree of genetic mismatch for all three species was observed in the Canadian High Arctic and Hudson Bay, suggesting that whales in these areas may exhibit the greatest maladaptive risk to climate change. This multispecies assessment of Arctic-adapted whales provides insight into the spatial congruence between three genomic datasets and context for designing ecosystem-wide evolutionarily enlightened conservation strategies.

AbstractGenomic forecasting is an emerging area of predictive ecology and evolution that leverages high-throughput sequencing to incorporate information on genomic variation into quantitative predictions of biological responses to environmental change. A central and increasingly applied concept in this field is genomic offset, a measure of the mismatch between current genomic composition and that predicted in new environments. This special issue brings together six studies spanning theoretical and methodological development, empirical evaluation, time series analysis, and conservation applications aimed at advancing the potential of genomic offset and related forecasting approaches for predicting population responses to environmental change. Contributions explore how genomic offset relates to stabilizing selection, compare individual- versus population-level offset models, and evaluate predictions using common garden experiments, long-term forest inventories, genomic time series, and conservation-relevant taxa. Collectively, the articles underscore both the promise and the current limitations of genomic forecasting while emphasizing that model predictive performance is often context dependent and influenced by statistical method, loci choice, and fitness proxies. Future progress will require rigorous validation, theory and methods development, and broader taxonomic coverage to ensure that genomic forecasting can realize its potential for informing biodiversity management in a rapidly changing world.

AbstractGenomic offsets are increasingly being used to forecast maladaptation expected from the decoupling of gene-environment associations caused by an abrupt shift in climate. Such gene-environment mismatches can arise temporally from rapid climate change in situ and also spatially through the introduction of nonlocal propagules to a new site. Studies have begun to evaluate genomic offsets using ground-truth observations of fitness traits measured in common gardens. However, empirical common garden evaluations of genomic offset predictive performance using independent training and testing data remains rare, and to our knowledge no studies have conducted fully reciprocal comparisons derived from replicated genomic and common garden data from independent sample sets. Here, we report an evaluation experiment of genomic offsets using red spruce (Picea rubens) based on two independently generated exome-capture datasets from different range-wide sets of populations. For each dataset, we train a gradient forest model using climate predictors and generate spatial genomic offset predictions for (1) common gardens planted with the same populations (within-set evaluations) and (2) common gardens planted with populations not used in model training (between-set evaluations). By leveraging multiple gardens planted at different times and locations, we also explore how predictive performance varies across garden environments and fitness proxies. We find the expected negative correlation between genomic offset and fitness across most comparisons, with the strongest associations for juvenile growth followed by adult survival. Our approach presents an important step forward for common garden evaluations of genomic offset and their ability to predict maladaptation under environmental change.

AbstractHow biodiversity and ecosystem functions change with succession has proven to be difficult to predict. Generally, it is thought that species accumulation over time should increase function, yet other successional trajectories can have alternative effects on diversity and function. We hypothesize that community diversity and function may respond in opposite ways to successional drivers such as nutrient availability, species interactions, or abiotic stress. The microbial communities within Sarracenia purpurea leaves perform degradation functions, providing essential nutrients to the plant, but we know little about how succession within the leaf influences bacterial diversity and degradation. We collected pitcher plant fluid from leaves aged 2-24 weeks to use in microcosm experiments. We used amplicon sequencing and a degradation assay to quantify diversity and ecosystem function. Because bacterivore activity increases with leaf age, we hypothesize that bacterial diversity will decrease over time, enhancing function if functionally important species are tolerant to predation. We thus added a common bacterivore to half of the replicated microcosms. We found that succession had opposite effects on diversity and function in pitcher plant bacteria but was unrelated to predator activity. As the leaves aged, bacterial degradation increased while diversity declined, with no significant effects from predator addition. This negative relationship between biodiversity and function likely results from functional traits associated with low nutrient availability or poor competitive ability. By broadening the landscape of successional scenarios and identifying their underlying mechanisms, we can advance our ability to predict diversity and functional dynamics in natural communities.

AbstractCoevolution requires reciprocal genotype-by-genotype (G × G) interactions for fitness, which occur when the fitness of a genotype in one species depends on the genotype it interacts with in another species and vice versa. However, in mutualisms, when G × G interactions are mutually beneficial, simple models predict that the resulting positive feedbacks will erode genetic variation. Here, we explore how genotype-by-environment (G × E) interactions, which occur when the fitnesses of different genotypes respond differently to different environments, and G × G × E interactions, which occur when the environment changes the outcome of G × G interactions, maintain variation in mutualisms. We build a spatial population genetic model in which the fitnesses of two partners depend on mutually beneficial G × G, G × E, and G × G × E interactions. Our analysis shows that variation will always be maintained via migration-selection balance with stronger G × E than G × G interactions. However, strong G × G interactions can erode variation by allowing genotypically matched partners to fix, and, more surprisingly, weak G × G interactions can erode variation by allowing genotypically mismatched partners to fix at high dispersal rates, leading to apparent maladaptation between partners. We parameterize our model using data from three published reciprocal transplant experiments, infer the relative strengths of G × E and G × G, and discuss the implications for the maintenance of genetic variation.

AbstractHumans provide massive inputs of food to wildlife, with profound ecological and evolutionary consequences. By potentially altering wildlife host immunity, density, and behavior, provisioning can influence transmission of wildlife pathogens and thus may impose strong selection pressure on pathogens. But surprisingly we lack theory on the eco-evolutionary consequences of provisioning for host-pathogen dynamics. Here we develop a mathematical model of the eco-evolutionary dynamics of a wildlife pathogen under provisioning, motivated by Mycoplasma gallisepticum, a bacterial pathogen that emerged, spread, and evolved higher virulence in provisioned house finches. We model how provisioning influences the evolution of pathogen virulence, defined here as the mortality increase associated with infection in identical background hosts. In our model, house finches recover from infection and acquire incomplete immunity; this incomplete immunity is stronger if their initial infection was with a more virulent pathogen strain (as previously found empirically). We find that even when provisioning improves individual host fitness (via survival, fecundity, or immune defenses), it should still select for higher pathogen virulence and thus may actually lead to declines in host populations. These negative effects arise because provisioning magnifies the impact of incomplete immunity, selecting for higher virulence and driving host populations down. Our results highlight that food provisioning can select for more virulent pathogens, with potentially far-reaching implications for conservation.

AbstractUnder radical environmental change, populations may need to adapt quickly to avoid substantial declines in abundance and threats to their persistence. The outcome of this race between evolution and demography depends on the genetic architecture of adaptation, which determines how fast evolution can proceed. In particular, adaptation may require coordinated evolution at multiple loci (e.g., cooperating ion transporters for ion uptake), with single-locus changes being deleterious. Such selection on coadapted genes leads to a fitness landscape with a valley, which can in turn favor the evolution of structural variants that link beneficial alleles at different loci. Here, we investigate how epistasis and recombination jointly affect population dynamics under such a fitness valley. We assume that adaptation occurs from standing genetic variation and model the eco-evolutionary dynamics deterministically. We show that recombination has strong impacts on population decline and recovery in this context. Higher recombination rates cause evolutionary trajectories to be pulled toward unfit states, leading to prolonged evolutionary plateaus, during which the population can decline precipitously. In highly detrimental cases where coadapted mutations are located on different chromosomes, chromosomal fusions that are preexisting at low frequency can lead to faster population recovery by allowing the genetic system to escape the attraction to unfit intermediate states. Our results provide insights into eco-evolutionary dynamics in systems where chromosome number varies drastically among sibling species, such as the copepod Eurytemora affinis species complex, and offer new perspectives on the impacts of genome architecture on population dynamics in stressful environments.