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Abstract Over the past decade, there has been a rapid increase in the development of predictive models at the intersection of molecular ecology, genomics, and global change. The common goal of these ‘genomic forecasting’ models is to integrate genomic data with environmental and ecological data in a model to make quantitative predictions about the vulnerability of populations to climate change.Despite rapid methodological development and the growing number of systems in which genomic forecasts are made, the forecasts themselves are rarely evaluated in a rigorous manner with ground‐truth experiments. This study reviews the evaluation experiments that have been done, introduces important terminology regarding the evaluation of genomic forecasting models, and discusses important elements in the design and reporting of ground‐truth experiments.To date, experimental evaluations of genomic forecasts have found high variation in the accuracy of forecasts, but it is difficult to compare studies on a common ground due to different approaches and experimental designs. Additionally, some evaluations may be biased toward higher performance because training data and testing data are not independent. In addition to independence between training data and testing data, important elements in the design of an evaluation experiment include the construction and parameterization of the forecasting model, the choice of fitness proxies to measure for test data, the construction of the evaluation model, the choice of evaluation metric(s), the degree of extrapolation to novel environments or genotypes, and the sensitivity, uncertainty and reproducbility of forecasts.Although genomic forecasting methods are becoming more accessible, evaluating their limitations in a particular study system requires careful planning and experimentation. Meticulously designed evaluation experiments can clarify the robustness of the forecasts for application in management. Clear reporting of basic elements of experimental design will improve the rigour of evaluations, and in turn our understanding of why models work in some cases and not others. 

Abstract Hybridization offers insight into speciation and the forces that maintain barriers to reproduction, and hybrid zones provide excellent opportunities to test how environment shapes barriers to reproduction and hybrid fitness. A hybrid zone between the killifish, Fundulus heteroclitus and Fundulus grandis, had been identified in northeastern Florida, although the spatial structure and parameters that affect the distribution of the two species remain unknown. The present study aimed to determine the fine-scale spatial genetic patterns of the hybrid zone to test the hypothesis that species ranges are influenced by changes in dominant vegetation and to determine how differences in reproductive barriers between the two species influence the observed patterns. The area of overlap between the two species spanned ~37 km and showed a mosaic pattern of hybridization, suggesting the spatial structure of the hybrid zone is largely influenced by the environment. Environmental association analysis, however, suggested that while dominant vegetation had a significant influence on the spatial structure of the hybrid zone, a combination of environmental factors was driving the observed patterns. Hybridization tended to be rare at sites where F. heteroclitus was the more abundant species, suggesting that differences in preference for conspecifics can lead to differences in rates of introgression into parental taxa and likely result in a range-shift as opposed to adaptation in the face of climate change. 

Developing robust professional networks can help shape the trajectories of early career scientists. Yet, historical inequities in science, technology, engineering, and mathematics (STEM) fields make access to these networks highly variable across academic programmes, and senior academics often have little time for mentoring. Here, we illustrate the success of a virtual Laboratory Meeting Programme (LaMP). In this programme, we matched students (mentees) with a more experienced scientist (mentors) from a research group. The mentees then attended the mentors’ laboratory meetings during the academic year with two laboratory meetings specifically dedicated to the mentee’s professional development. Survey results indicate that mentees expanded their knowledge of the hidden curriculum as well as their professional network, while only requiring a few extra hours of their mentor’s time over eight months. In addition, host laboratories benefitted from mentees sharing new perspectives and knowledge in laboratory meetings. Diversity of the mentees was significantly higher than the mentors, suggesting that the programme increased the participation of traditionally under-represented groups. Finally, we found that providing a stipend was very important to many mentees. We conclude that virtual LaMPs can be an inclusive and cost-effective way to foster trainee development and increase diversity within STEM fields with little additional time commitment. 

Abstract As climate change causes the environment to shift away from the local optimum that populations have adapted to, fitness declines are predicted to occur. Recently, methods known as genomic offsets (GOs) have become a popular tool to predict population responses to climate change from landscape genomic data. Populations with a high GO have been interpreted to have a high “genomic vulnerability” to climate change. GOs are often implicitly interpreted as a fitness offset, or a change in fitness of an individual or population in a new environment compared to a reference. However, there are several different types of fitness offset that can be calculated, and the appropriate choice depends on the management goals. This study uses hypothetical and empirical data to explore situations in which different types of fitness offsets may or may not be correlated with each other or with a GO. The examples reveal that even when GOs predict fitness offsets in a common garden experiment, this does not necessarily validate their ability to predict fitness offsets to environmental change. Conceptual examples are also used to show how a large GO can arise under a positive fitness offset, and thus cannot be interpreted as a population vulnerability. These issues can be resolved with robust validation experiments that can evaluate which fitness offsets are correlated with GOs. 

Multivariate climate change presents an urgent need to understand how species adapt to complex environments. Population genetic theory predicts that loci under selection will form monotonic allele frequency clines with their selective environment, which has led to the wide use of genotype–environment associations (GEAs). This study used a set of simulations to elucidate the conditions under which allele frequency clines are more or less likely to evolve as multiple quantitative traits adapt to multivariate environments. Phenotypic clines evolved with nonmonotonic (i.e., nonclinal) patterns in allele frequencies under conditions that promoted unique combinations of mutations to achieve the multivariate optimum in different parts of the landscape. Such conditions resulted from interactions among landscape, demography, pleiotropy, and genetic architecture. GEA methods failed to accurately infer the genetic basis of adaptation under a range of scenarios due to first principles (clinal patterns did not evolve) or statistical issues (clinal patterns evolved but were not detected due to overcorrection for structure). Despite the limitations of GEAs, this study shows that a back-transformation of multivariate ordination can accurately predict individual multivariate traits from genotype and environmental data regardless of whether inference from GEAs was accurate. In addition, frameworks are introduced that can be used by empiricists to quantify the importance of clinal alleles in adaptation. This research highlights that multivariate trait prediction from genotype and environmental data can lead to accurate inference regardless of whether the underlying loci display clinal or nonmonotonic patterns. 

Complex statistical methods are continuously developed across the fields of ecology, evolution, and systematics (EES). These fields, however, lack standardized principles for evaluating methods, which has led to high variability in the rigor with which methods are tested, a lack of clarity regarding their limitations, and the potential for misapplication. In this review, we illustrate the common pitfalls of method evaluations in EES, the advantages of testing methods with simulated data, and best practices for method evaluations. We highlight the difference between method evaluation and validation and review how simulations, when appropriately designed, can refine the domain in which a method can be reliably applied. We also discuss the strengths and limitations of different evaluation metrics. The potential for misapplication of methods would be greatly reduced if funding agencies, reviewers, and journals required principled method evaluation. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 53 is November 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

Across many species where inversions have been implicated in local adaptation, genomes often evolve to contain multiple, large inversions that arise early in divergence. Why this occurs has yet to be resolved. To address this gap, we built forward-time simulations in which inversions have flexible characteristics and can invade a metapopulation undergoing spatially divergent selection for a highly polygenic trait. In our simulations, inversions typically arose early in divergence, captured standing genetic variation upon mutation, and then accumulated many small-effect loci over time. Under special conditions, inversions could also arise late in adaptation and capture locally adapted alleles. Polygenic inversions behaved similarly to a single supergene of large effect and were detectable by genome scans. Our results show that characteristics of adaptive inversions found in empirical studies (e.g. multiple large, old inversions that are F ST outliers, sometimes overlapping with other inversions) are consistent with a highly polygenic architecture, and inversions do not need to contain any large-effect genes to play an important role in local adaptation. By combining a population and quantitative genetic framework, our results give a deeper understanding of the specific conditions needed for inversions to be involved in adaptation when the genetic architecture is polygenic. This article is part of the theme issue ‘Genomic architecture of supergenes: causes and evolutionary consequences’. 

Supergenes are tightly linked sets of loci that are inherited together and control complex phenotypes. While classical supergenes—governing traits such as wing patterns in Heliconius butterflies or heterostyly in Primula —have been studied since the Modern Synthesis, we still understand very little about how they evolve and persist in nature. The genetic architecture of supergenes is a critical factor affecting their evolutionary fate, as it can change key parameters such as recombination rate and effective population size, potentially redirecting molecular evolution of the supergene in addition to the surrounding genomic region. To understand supergene evolution, we must link genomic architecture with evolutionary patterns and processes. This is now becoming possible with recent advances in sequencing technology and powerful forward computer simulations. The present theme issue brings together theoretical and empirical papers, as well as opinion and synthesis papers, which showcase the architectural diversity of supergenes and connect this to critical processes in supergene evolution, such as polymorphism maintenance and mutation accumulation. Here, we summarize those insights to highlight new ideas and methods that illuminate the path forward for the study of supergenes in nature. This article is part of the theme issue ‘Genomic architecture of supergenes: causes and evolutionary consequences’.