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.
This content will become publicly available on October 3, 2024
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 (
- NSF-PAR ID:
- 10489035
- 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
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Summary 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.
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