Using landscape genomics to delineate future adaptive potential for climate change in the Yosemite toad (Anaxyrus canorus)
Abstract An essential goal in conservation biology is delineating population units that maximize the probability of species persisting into the future and adapting to future environmental change. However, future‐facing conservation concerns are often addressed using retrospective patterns that could...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Evolutionary Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/eva.13511 |
| _version_ | 1797948074476175360 |
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| author | Paul A. Maier Amy G. Vandergast Andrew J. Bohonak |
| author_facet | Paul A. Maier Amy G. Vandergast Andrew J. Bohonak |
| author_sort | Paul A. Maier |
| collection | DOAJ |
| description | Abstract An essential goal in conservation biology is delineating population units that maximize the probability of species persisting into the future and adapting to future environmental change. However, future‐facing conservation concerns are often addressed using retrospective patterns that could be irrelevant. We recommend a novel landscape genomics framework for delineating future “Geminate Evolutionary Units” (GEUs) in a focal species: (1) identify loci under environmental selection, (2) model and map adaptive conservation units that may spawn future lineages, (3) forecast relative selection pressures on each future lineage, and (4) estimate their fitness and likelihood of persistence using geo‐genomic simulations. Using this process, we delineated conservation units for the Yosemite toad (Anaxyrus canorus), a U.S. federally threatened species that is highly vulnerable to climate change. We used a genome‐wide dataset, redundancy analysis, and Bayesian association methods to identify 24 candidate loci responding to climatic selection (R2 ranging from 0.09 to 0.52), after controlling for demographic structure. Candidate loci included genes such as MAP3K5, involved in cellular response to environmental change. We then forecasted future genomic response to climate change using the multivariate machine learning algorithm Gradient Forests. Based on all available evidence, we found three GEUs in Yosemite National Park, reflecting contrasting adaptive optima: YF‐North (high winter snowpack with moderate summer rainfall), YF‐East (low to moderate snowpack with high summer rainfall), and YF‐Low‐Elevation (low snowpack and rainfall). Simulations under the RCP 8.5 climate change scenario suggest that the species will decline by 29% over 90 years, but the highly diverse YF‐East lineage will be least impacted for two reasons: (1) geographically it will be sheltered from the largest climatic selection pressures, and (2) its standing genetic diversity will promote a faster adaptive response. Our approach provides a comprehensive strategy for protecting imperiled non‐model species with genomic data alone and has wide applicability to other declining species. |
| first_indexed | 2024-04-10T21:38:47Z |
| format | Article |
| id | doaj.art-0d52015541c14b2c999ddf202d59f6cd |
| institution | Directory Open Access Journal |
| issn | 1752-4571 |
| language | English |
| last_indexed | 2024-04-10T21:38:47Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Evolutionary Applications |
| spelling | doaj.art-0d52015541c14b2c999ddf202d59f6cd2023-01-19T07:39:04ZengWileyEvolutionary Applications1752-45712023-01-01161749710.1111/eva.13511Using landscape genomics to delineate future adaptive potential for climate change in the Yosemite toad (Anaxyrus canorus)Paul A. Maier0Amy G. Vandergast1Andrew J. Bohonak2Department of Biology San Diego State University San Diego California USAWestern Ecological Research Center U.S. Geological Survey San Diego California USADepartment of Biology San Diego State University San Diego California USAAbstract An essential goal in conservation biology is delineating population units that maximize the probability of species persisting into the future and adapting to future environmental change. However, future‐facing conservation concerns are often addressed using retrospective patterns that could be irrelevant. We recommend a novel landscape genomics framework for delineating future “Geminate Evolutionary Units” (GEUs) in a focal species: (1) identify loci under environmental selection, (2) model and map adaptive conservation units that may spawn future lineages, (3) forecast relative selection pressures on each future lineage, and (4) estimate their fitness and likelihood of persistence using geo‐genomic simulations. Using this process, we delineated conservation units for the Yosemite toad (Anaxyrus canorus), a U.S. federally threatened species that is highly vulnerable to climate change. We used a genome‐wide dataset, redundancy analysis, and Bayesian association methods to identify 24 candidate loci responding to climatic selection (R2 ranging from 0.09 to 0.52), after controlling for demographic structure. Candidate loci included genes such as MAP3K5, involved in cellular response to environmental change. We then forecasted future genomic response to climate change using the multivariate machine learning algorithm Gradient Forests. Based on all available evidence, we found three GEUs in Yosemite National Park, reflecting contrasting adaptive optima: YF‐North (high winter snowpack with moderate summer rainfall), YF‐East (low to moderate snowpack with high summer rainfall), and YF‐Low‐Elevation (low snowpack and rainfall). Simulations under the RCP 8.5 climate change scenario suggest that the species will decline by 29% over 90 years, but the highly diverse YF‐East lineage will be least impacted for two reasons: (1) geographically it will be sheltered from the largest climatic selection pressures, and (2) its standing genetic diversity will promote a faster adaptive response. Our approach provides a comprehensive strategy for protecting imperiled non‐model species with genomic data alone and has wide applicability to other declining species.https://doi.org/10.1111/eva.13511adaptive potentialadmixtureconservation unitESUlandscape genomicsnatural selection |
| spellingShingle | Paul A. Maier Amy G. Vandergast Andrew J. Bohonak Using landscape genomics to delineate future adaptive potential for climate change in the Yosemite toad (Anaxyrus canorus) Evolutionary Applications adaptive potential admixture conservation unit ESU landscape genomics natural selection |
| title | Using landscape genomics to delineate future adaptive potential for climate change in the Yosemite toad (Anaxyrus canorus) |
| title_full | Using landscape genomics to delineate future adaptive potential for climate change in the Yosemite toad (Anaxyrus canorus) |
| title_fullStr | Using landscape genomics to delineate future adaptive potential for climate change in the Yosemite toad (Anaxyrus canorus) |
| title_full_unstemmed | Using landscape genomics to delineate future adaptive potential for climate change in the Yosemite toad (Anaxyrus canorus) |
| title_short | Using landscape genomics to delineate future adaptive potential for climate change in the Yosemite toad (Anaxyrus canorus) |
| title_sort | using landscape genomics to delineate future adaptive potential for climate change in the yosemite toad anaxyrus canorus |
| topic | adaptive potential admixture conservation unit ESU landscape genomics natural selection |
| url | https://doi.org/10.1111/eva.13511 |
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