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Abstract As climate change intensifies, the genetic diversity and composition of natural populations will become critical for adaptation and survival. Standing genetic diversity within populations differs across a species’ range, due to past demographic and natural selection processes driving the accumulation of adaptive, neutral, and deleterious variation. While accumulating genomic knowledge could be used to evaluate population extinction risk from local mal-adaptive genetic makeups, testing such approaches in natural environments remains challenging. Leveraging the genomic resources ofArabidopsis thaliana, we created experimental synthetic populations of similar genetic diversity but differing genetic makeups by mixing 245 natural accessions with different levels of potentially-climate-adaptive alleles and/or genomic burden of deleterious mutations. We planted our populations in a climate change field experiment simulating a gradient of declining rainfall. By tracking survival and reproduction of 135 synthetic experimental populations over three years, we show substantial predictability of genetic makeup on survival and population growth rate. Further, the accumulation of deleterious mutations and locally (mal)adaptive alleles synergistically reduces fitness in increasingly stressful climates. Our findings underscore that for populations to have the greatest chance of surviving climate change, the optimal combination of genomic makeups is essential.
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Sampling effect in predicting the evolutionary response of populations to climate change
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.
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- Award ID(s):
- 1741627
- PAR ID:
- 10508269
- Publisher / Repository:
- wiley
- Date Published:
- Journal Name:
- Molecular Ecology Resources
- ISSN:
- 1755-098X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/1755-0998.13828
