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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. 

Genomic data are increasingly being integrated into macroecological forecasting, offering an evolutionary perspective that has been largely missing from global change biogeography. Genomic offset, which quantifies the disruption of genotype–environment associations under environmental change, allows for the incorporation of intraspecific climate‐associated genomic differentiation into forecasts of habitat suitability. Gradient Forest (GF) is a commonly used approach to estimate genomic offset; however, major hurdles in the application of GF‐derived genomic offsets are (1) an inability to interpret their absolute magnitude in an ecologically meaningful way and (2) uncertainty in how their implications compare with those of species‐level approaches like Ecological Niche Models (ENMs). Here, we assess the climate change vulnerability of red spruce (Picea rubens), a cool‐temperate tree species endemic to eastern North America, using both ENMs and GF modeling of genomic variation along climatic gradients. To gain better insights into climate change risks, we derive and apply two new threshold‐based genomic offset metrics—Donor and Recipient Importance—that quantify the transferability of propagules between donor populations and recipient localities while minimizing disruption of genotype–environment associations. We also propose and test a method for scaling genomic offsets relative to contemporary genomic variation across the landscape. In three common gardens, we found a significant negative relationship between (scaled) genomic offsets and red spruce growth and higher explanatory power for scaled offsets than climate transfer distances. However, the garden results also revealed the potential effects of spatial extrapolation and neutral genomic differentiation that can compromise the degree to which genomic offsets represent maladaptation and highlight the necessity of using common garden data to evaluate offset‐based predictions. ENMs and our novel genomic offset metrics forecasted drastic northward range shifts in suitable habitats. Combining inferences from our offset‐based metrics, we show that a northward shift mainly will be required for populations in the central and northern parts of red spruce's current range, whereas southern populations might persist in situ due to climate‐associated variation with less offset under future climate. These new genomic offset metrics thus yield refined, region‐specific prognoses for local persistence and show how management could be improved by considering assisted migration.

 

Shifting range limits are predicted for many species as the climate warms. However, the rapid pace of climate change will challenge the natural dispersal capacity of long-lived, sessile organisms such as forest trees. Adaptive responses of populations will, therefore, depend on levels of genetic variation and plasticity for climate-responsive traits, which likely vary across the range due to expansion history and current patterns of selection. Here, we study levels of genetic and plastic variation for phenology and growth traits in populations of red spruce ( Picea rubens ), from the range core to the highly fragmented trailing edge. We measured more than 5000 offspring sampled from three genetically distinct regions (core, margin and edge) grown in three common gardens replicated along a latitudinal gradient. Genetic variation in phenology and growth showed low to moderate heritability and differentiation among regions, suggesting some potential to respond to selection. Phenology traits were highly plastic, but this plasticity was generally neutral or maladaptive in the effect on growth, revealing a potential liability under warmer climates. These results suggest future climate adaptation will depend on the regional availability of genetic variation in red spruce and provide a resource for the design and management of assisted gene flow. This article is part of the theme issue ‘Species’ ranges in the face of changing environments (Part II)’. 

Local adaptation to climate is common in plant species and has been studied in a range of contexts, from improving crop yields to predicting population maladaptation to future conditions. The genomic era has brought new tools to study this process, which was historically explored through common garden experiments.

In this study, we combine genomic methods and common gardens to investigate local adaptation in red spruce and identify environmental gradients and loci involved in climate adaptation. We first use climate transfer functions to estimate the impact of climate change on seedling performance in three common gardens. We then explore the use of multivariate gene–environment association methods to identify genes underlying climate adaptation, with particular attention to the implications of conducting genome scans with and without correction for neutral population structure.

This integrative approach uncovered phenotypic evidence of local adaptation to climate and identified a set of putatively adaptive genes, some of which are involved in three main adaptive pathways found in other temperate and boreal coniferous species: drought tolerance, cold hardiness, and phenology. These putatively adaptive genes segregated into two ‘modules’ associated with different environmental gradients.

This study nicely exemplifies the multivariate dimension of adaptation to climate in trees.

 

Signals of local adaptation have been found in many plants and animals, highlighting the heterogeneity in the distribution of adaptive genetic variation throughout species ranges. In the coming decades, global climate change is expected to induce shifts in the selective pressures that shape this adaptive variation. These changes in selective pressures will likely result in varying degrees of local climate maladaptation and spatial reshuffling of the underlying distributions of adaptive alleles. There is a growing interest in using population genomic data to help predict future disruptions to locally adaptive gene-environment associations. One motivation behind such work is to better understand how the effects of changing climate on populations’ short-term fitness could vary spatially across species ranges. Here we review the current use of genomic data to predict the disruption of local adaptation across current and future climates. After assessing goals and motivations underlying the approach, we review the main steps and associated statistical methods currently in use and explore our current understanding of the limits and future potential of using genomics to predict climate change (mal)adaptation. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 51 is November 2, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

Generalized dissimilarity modelling (GDM) is a powerful and unique method for characterizing and predicting beta diversity, the change in biodiversity over space, time and environmental gradients. The number of studies applying GDM is expanding, with increasing recognition of its value in improving our understanding of the drivers of biodiversity patterns and in implementing a wide variety of spatial assessments relevant to biodiversity conservation. However, apart from the original presentation of the GDM technique, there has been little guidance available to users on applying GDM to different situations or on the key modelling decisions required.

We present an accessible working guide to GDM. We describe the context for the development of GDM, present a simple statistical explanation of how model fitting works, and step through key considerations involved in data preparation, model fitting, refinement and assessment. We then describe how several novel spatial biodiversity analyses can be implemented using GDM, with code to support broader implementation. We conclude by providing an overview of the range of GDM‐based analyses that have been undertaken to date and identify priority areas for future research and development.

Our vision is that this working guide will facilitate greater and more rigorous use of GDM as a powerful tool for undertaking biodiversity analyses and assessments.

 

Understanding the factors influencing the current distribution of genetic diversity across a species range is one of the main questions of evolutionary biology, especially given the increasing threat to biodiversity posed by climate change. Historical demographic processes such as population expansion or bottlenecks and decline are known to exert a predominant influence on past and current levels of genetic diversity, and revealing this demo‐genetic history can have immediate conservation implications. We used a whole‐exome capture sequencing approach to analyze polymorphism across the gene space of red spruce (Picea rubens Sarg.), an endemic and emblematic tree species of eastern North America high‐elevation forests that are facing the combined threat of global warming and increasing human activities. We sampled a total of 340 individuals, including populations from the current core of the range in northeastern USA and southeastern Canada and from the southern portions of its range along the Appalachian Mountains, where populations occur as highly fragmented mountaintop “sky islands.” Exome capture baits were designed from the closely relative white spruce (P. glauca Voss) transcriptome, and sequencing successfully captured most regions on or near our target genes, resulting in the generation of a new and expansive genomic resource for studying standing genetic variation in red spruce applicable to its conservation. Our results, based on over 2 million exome‐derived variants, indicate that red spruce is structured into three distinct ancestry groups that occupy different geographic regions of its highly fragmented range. Moreover, these groups show small Ne , with a temporal history of sustained population decline that has been ongoing for thousands (or even hundreds of thousands) of years. These results demonstrate the broad potential of genomic studies for revealing details of the demographic history that can inform management and conservation efforts of nonmodel species with active restoration programs, such as red spruce. 

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.