Search for: All records

Abstract Rapid environmental change poses unprecedented challenges to species persistence. To understand the extent that continued change could have, genomic offset methods have been used to forecast maladaptation of natural populations to future environmental change. However, while their use has become increasingly common, little is known regarding their predictive performance across a wide array of realistic and challenging scenarios. Here, we evaluate the performance of currently available offset methods (gradientForest, the Risk‐Of‐Non‐Adaptedness, redundancy analysis with and without structure correction and LFMM2) using an extensive set of simulated data sets that vary demography, adaptive architecture and the number and spatial patterns of adaptive environments. For each data set, we train models using eitherall,adaptiveorneutralmarker sets and evaluate performance using in silico common gardens by correlating known fitness with projected offset. Using over 4,849,600 of such evaluations, we find that (1) method performance is largely due to the degree of local adaptation across the metapopulation (LA), (2)adaptivemarker sets provide minimal performance advantages, (3) performance within the species range is variable across gardens and declines when offset models are trained using additional non‐adaptive environments and (4) despite (1) performance declines more rapidly in globally novel climates (i.e. a climate without an analogue within the species range) for metapopulations with greaterLAthan lesserLA. We discuss the implications of these results for management, assisted gene flow and assisted migration. 

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 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. 

Abstract Gradient Forest (GF) is a machine learning algorithm designed to analyze spatial patterns of biodiversity as a function of environmental gradients. An offset measure between the GF‐predicted environmental association of adapted alleles and a new environment (GF Offset) is increasingly being used to predict the loss of environmentally adapted alleles under rapid environmental change, but remains mostly untested for this purpose. Here, we explore the robustness of GF Offset to assumption violations, and its relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation. We evaluate measures of GF Offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic “population genetic” model with a single environmentally adapted locus; and (3) a polygenic “quantitative genetic” model with two adaptive traits, each adapting to a different environment. We found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. However, neutral demography, genomic architecture, and the nature of the adaptive environment can all confound relationships between GF Offset and fitness. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of predicting maladaptation. 

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. 

Genome assembly can be challenging for species that are characterized by high amounts of polymorphism, heterozygosity, and large effective population sizes. High levels of heterozygosity can result in genome mis-assemblies and a larger than expected genome size due to the haplotig versions of a single locus being assembled as separate loci. Here, we describe the first chromosome-level genome for the eastern oyster, Crassostrea virginica. Publicly released and annotated in 2017, the assembly has a scaffold N50 of 54 mb and is over 97.3% complete based on BUSCO analysis. The genome assembly for the eastern oyster is a critical resource for foundational research into molluscan adaptation to a changing environment and for selective breeding for the aquaculture industry. Subsequent resequencing data suggested the presence of haplotigs in the original assembly, and we developed a post hoc method to break up chimeric contigs and mask haplotigs in published heterozygous genomes and evaluated improvements to the accuracy of downstream analysis. Masking haplotigs had a large impact on SNP discovery and estimates of nucleotide diversity and had more subtle and nuanced effects on estimates of heterozygosity, population structure analysis, and outlier detection. We show that haplotig masking can be a powerful tool for improving genomic inference, and we present an open, reproducible resource for the masking of haplotigs in any published genome. 

Background: 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 novel set of In silico 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. Zenodo archive of GitHub Repository of all code used to create the simulations. Every directory includes a README describing the code, and metadata files are included for the archived outputs. Modeling code details: Code was developed 2020-2022 Simulation code was developed in SLiM, recapitated in pyslim, filtered with vcftools, and analyzed with R. Code was developed by K. E. Lotterhos (PI) 

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.