气候变化领域集成服务门户(CCPortal): Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest

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DOI	10.1111/eva.13354
	Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest
	Laruson, Aki Jarl; Fitzpatrick, Matthew C.; Keller, Stephen R.; Haller, Benjamin C.; Lotterhos, Katie E.
发表日期	2022
ISSN	1752-4571
起始页码	403
结束页码	416
卷号	15 期号:3
英文摘要	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.
英文关键词	biodiversity; climate change; gradient forest; landscape genetics; local adaptation; population genetics; quantitative genetics; simulation; SLiM
语种	英语
WOS研究方向	Evolutionary Biology
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000760896000001
来源期刊	EVOLUTIONARY APPLICATIONS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/280595
作者单位	Cornell University; University System of Maryland; University of Maryland Center for Environmental Science; University of Vermont; Cornell University; Northeastern University
推荐引用方式 GB/T 7714	Laruson, Aki Jarl,Fitzpatrick, Matthew C.,Keller, Stephen R.,et al. Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest[J],2022,15(3).
APA	Laruson, Aki Jarl,Fitzpatrick, Matthew C.,Keller, Stephen R.,Haller, Benjamin C.,&Lotterhos, Katie E..(2022).Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest.EVOLUTIONARY APPLICATIONS,15(3).
MLA	Laruson, Aki Jarl,et al."Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest".EVOLUTIONARY APPLICATIONS 15.3(2022).