skip to main content


This content will become publicly available on October 3, 2024

Title: Novel genomic offset metrics integrate local adaptation into habitat suitability forecasts and inform assisted migration
Abstract

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.

 
more » « less
NSF-PAR ID:
10489035
Author(s) / Creator(s):
 ;  ;  ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Ecological Monographs
Volume:
94
Issue:
1
ISSN:
0012-9615
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. 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.

     
    more » « less
  2. Abstract

    Genomic data and machine learning approaches have gained interest due to their potential to identify adaptive genetic variation across populations and to assess species vulnerability to climate change. By identifying gene–environment associations for putatively adaptive loci, these approaches project changes to adaptive genetic composition as a function of future climate change (genetic offsets), which are interpreted as measuring the future maladaptation of populations due to climate change. In principle, higher genetic offsets relate to increased population vulnerability and therefore can be used to set priorities for conservation and management. However, it is not clear how sensitive these metrics are to the intensity of population and individual sampling. Here, we use five genomic datasets with varying numbers of SNPs (NSNPs = 7006–1,398,773), sampled populations (Npop = 23–47) and individuals (Nind = 185–595) to evaluate the estimation sensitivity of genetic offsets to varying degrees of sampling intensity. We found that genetic offsets are sensitive to the number of populations being sampled, especially with less than 10 populations and when genetic structure is high. We also found that the number of individuals sampled per population had small effects on the estimation of genetic offsets, with more robust results when five or more individuals are sampled. Finally, uncertainty associated with the use of different future climate scenarios slightly increased estimation uncertainty in the genetic offsets. Our results suggest that sampling efforts should focus on increasing the number of populations, rather than the number of individuals per populations, and that multiple future climate scenarios should be evaluated to ascertain estimation sensitivity.

     
    more » « less
  3. 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.

     
    more » « less
  4. 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)’. 
    more » « less
  5. 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. 
    more » « less