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7 results for “Genetic Offset”
Data from: Seeing the Forest for the trees: Assessing genetic offset predictions from Gradient Forest
<p><span>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.</span></p>
Population connectivity and genetic offset in the spawning coral Acropora digitifera in Western Australia
<p><span>Anthropogenic </span>climate change has caused widespread loss of species biodiversity and ecosystem productivity across the globe, particularly on tropical coral reefs. Predicting the future vulnerability of reef-building corals, the foundation species of coral reef ecosystems, is crucial for cost-effective conservation planning in the Anthropocene. In this study, we combine regional population genetic connectivity and seascape analyses to explore patterns of genetic offset (the mismatch of gene-environmental associations under future climate conditions) in <em>Acropora digitifera</em> across 12 degrees of latitude in Western Australia. Our data revealed a pattern of restricted gene flow and limited genetic connectivity among geographically distant reef systems. Environmental association analyses identified a suite of loci strongly associated with the regional temperature variation. These loci helped forecasting future genetic offset in random forest and generalised dissimilarity models. These analyses predicted pronounced differences in the response of different reef systems in Western Australia to rising temperatures. Under the most optimistic future warming predictions (RCP 2.6), we observed a general pattern of increasing genetic offset with latitude. Under the most extreme climate scenario (RCP 8.5 in 2090-2100), coral populations at the Ningaloo World Heritage Area were predicted to experience a higher mismatch in genetic composition, compared to populations in the inshore Kimberley region. The study suggest complex and spatially heterogeneous patterns of climate-change vulnerability in coral populations across Western Australia, reinforcing the notion that regionally tailored conservation efforts will be most effective at managing coral reef resilience into the future.</p>
Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure
<p>Predicting how tree populations will respond to climate change is an urgent societal concern. An increasingly popular way to make such predictions is the genomic offset (GO) approach, which aims to use genomic and climate data to identify populations that may experience climate maladaptation in the near future. More precisely, GO tries to represent the change in allele frequencies required to maintain the current gene-climate relationships under climate change. However, the GO approach has major limitations and, despite promising validation of its predictions using height data from common gardens, it still lacks broad empirical testing. In the present study, we evaluated the consistency and empirical validity of GO predictions in maritime pine (<em>Pinus pinaster</em> Ait.), a tree species from southwestern Europe and North Africa with a marked population genetic structure. First, gene-climate relationships were estimated using 9,817 SNPs genotyped in 454 trees from 34 populations; and candidate SNPs potentially involved in climate adaptation were identified. Second, GO was predicted using four methods, namely Gradient Forest (GF), Redundancy Analysis (RDA), latent factor mixed model (LFMM) and Generalised Dissimilarity Modeling (GDM), two sets of SNPs (candidate and control SNPs) and five climate general circulation models (GCMs) to account for uncertainty in future climate predictions. Last, the empirical validity of GO predictions was evaluated within a Bayesian framework by estimating the associations between GO predictions and two independent data sources: mortality data from National Forest Inventories (NFI), and mortality and height data from five common gardens in contrasting environments. We found high variability in GO predictions across methods, SNP sets and GCMs. Regarding validation, GO predictions with GDM and GF (and to a lesser extent RDA) based on the candidate SNPs showed the strongest and most consistent associations with mortality rates in common gardens and NFI plots. We found almost no association between GO predictions and tree height in common gardens, most likely due to the overwhelming effect of population genetic structure on tree height in this species. Our study demonstrates the imperative to validate GO predictions with a range of independent data sources before they can be used as informative and reliable metrics in conservation or management strategies.</p>
Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure
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Population connectivity and genetic offset in the spawning coral Acropora digitifera in Western Australia
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Data from: Seeing the Forest for the trees: Assessing genetic offset predictions from Gradient Forest
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Canadian Arctic whale genomic data and code to estimate genetic offsets
<div>This is a repository for code and data used in estimating genetic offsets in Arctic whales in the eastern Canadian Arctic. See README.md file for details.</div> <div><br>Associated publication:</div> <div>de Greef, E., Müller, C., Snead A., Rivkin, L. R., Ferguson, S. H., Watt, C. A., Marcoux, M., Petersen, S. D., and Garroway, C. J. (2025). Identifying areas of potential risk based on future genetic adaptability in three Arctic whale species. <em>The American Naturalist.</em></div> <div> </div>
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