New Seed-Collection Zones for the Eastern United States: The Eastern Seed Zone Forum

Pike, Carolyn C.; Potter, Kevin M.; Berrang, P.; Crane, Barbara S.; Baggs, Joanne E.; Leites, Laura P.; Luther, T.

doi:10.1093/jofore/fvaa013

Cited by 31 publications

(21 citation statements)

References 16 publications

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“…These experiments provide insight into the climate variables that best predict phenotypic performance upon transfer to a new site, but are time and labour-intensive, and not practical for most study systems. A simpler approach is to delineate climatebased seed zones from which propagules should be selected for restoration under the hypothesis that maladaptation of seedlings is minimized (and productivity is maximized) when movement of seeds is restricted to other sites with similar climate (e.g., Bower et al, 2014;Pike et al, 2020). The distinction between the "tradi- Gradients strongly associated with genomic variation (and portions of these gradients where genetic patterns change most rapidly) will have greater contribution to genetic offsets than will unimportant variables (or portions of gradients where allele frequencies generally are constant; Capblancq et al, 2020;Fitzpatrick & Keller, 2015).…”

Section: Discussionmentioning

confidence: 99%

Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests

Fitzpatrick

Chhatre

Soolanayakanahally

et al. 2021

Molecular Ecology Resources

107

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Gradient Forests (GF) is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we (i) provide the first experimental evaluation of the ability of "genomic offsets" -a metric of climate maladaptation derived from Gradient Forests -to predict organismal responses to environmental change, and (ii) explore the use of GF for identifying candidate SNPs.We used high-throughput sequencing, genome scans, and several methods, including GF, to identify candidate loci associated with climate adaptation in balsam poplar (Populus balsamifera L.). Individuals collected throughout balsam poplar's range also were planted in two common garden experiments. We used GF to relate candidate loci to environmental gradients and predict the expected magnitude of the response (i.e., the genetic offset metric of maladaptation) of populations when transplanted from their "home" environment to the common gardens. We then compared the predicted genetic offsets from different sets of candidate and randomly selected SNPs to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance than did "naive" climate transfer distances. However, sets of randomly selected SNPs predicted performance slightly better than did candidate SNPs. Our study provides evidence that genetic offsets represent a first order estimate of the degree of expected maladaptation of populations exposed to rapid environmental change and suggests GF may have some promise as a method for identifying candidate SNPs.

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Section: Discussionmentioning

confidence: 99%

Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests

Fitzpatrick

Chhatre

Soolanayakanahally

et al. 2021

Molecular Ecology Resources

107

View full text Add to dashboard Cite

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“…In large countries that include several ecoregions (e.g., according to the Ecoregions 2017 (31) ), it would be important as well to select species that match species assemblages of the natural vegetation; knowledge of native floras (many available online as for example via the World Flora Online website (32) or information from vegetation atlases (e.g, http://www.vegetationmap4africa.org) can be of help here. Similarly, where seed zonation maps have been developed (e.g., (33) ), or habitat distribution maps are available for individual tree species (e.g., (34; 35; 36; 37) ) these would be directly relevant in selecting species that match the environmental conditions of planting sites (and ideally future climatic conditions).…”

Section: Discussionmentioning

confidence: 99%

GlobalUsefulNativeTrees, a database of 14,014 tree species and their uses, supports synergies between biodiversity recovery and local livelihoods in landscape restoration

Kindt

Graudal

Lillesø

et al. 2022

Preprint

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Tree planting has the potential to improve the livelihoods of millions of people as well as to support environmental services such as biodiversity preservation. Planting however needs to be executed wisely if benefits are to be achieved. We have developed the GlobalUsefulNativeTrees (GlobUNT) database to directly support the principles advocated by the 'golden rules for reforestation', including planting tree mixtures that maximize the benefits to local livelihoods and the diversity of native trees. Developed primarily by combining data from GlobalTreeSearch with the World Checklist of Useful Plant Species, GlobUNT includes 14,014 tree species that can be filtered for ten major use categories, across 242 countries and territories. In a subcontinental comparison GlobUNT revealed that Malesia had the highest useful tree species richness (3,349) and was also richest for materials (2,723), medicines (1,533), human food (958), fuel (734), environmental uses (632), social uses (614), animal food (443), poisons (322) and invertebrate food (266).

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“…These experiments provide excellent insight into the climate variables that best predict phenotypic performance upon transfer to a new site, but are time and labor-intensive, and not practical for most study systems. A simpler approach is to delineate climate-based seed zones from which seeds should be selected for restoration under the hypothesis that maladaptation of seedlings is minimized (and production is maximized) when movement of seeds is restricted to other sites with similar climate (e.g., Bower, St Clair, & Erickson, 2014;Pike et al, 2020). The distinction between the "traditional" climate transfer distances used for seed zone delineation and genetic offsets is simply that genetic offsets use re-scaled climate distances based on the modeled associations with (adaptive) genomic variation, whereas climate distances typically weigh the included variables equally despite potential variation in their adaptive importance.…”

Section: Discussionmentioning

confidence: 99%

Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests

Fitzpatrick

Chhatre

Soolanayakanahally

et al. 2020

Preprint

View full text Add to dashboard Cite

Gradient Forests is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we provide the first experimental evaluation of the ability of 'genomic offsets' -a metric of climate maladaptation derived from Gradient Forests -to predict organismal responses to environmental change. We used high-throughput sequencing, genome scans, and several methods (including Gradient Forests) to identify candidate loci associated with climate adaptation in balsam poplar (Populus balsamifera L.). Individuals collected throughout balsam poplar's range also were planted in two common garden experiments. We used Gradient Forests to relate candidate loci to environmental gradients and to predict the expected magnitude of response (i.e., the genetic offset) of populations when transplanted from their "home" environment to the new environments in the common gardens. We then compared the predicted genetic offsets to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance in the common gardens: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance in the common gardens than did 'naive' climate distances. Our results provide preliminary evidence that genomic offsets may provide a first order estimate of the degree of expected maladaptation of populations exposed to rapid environmental change.

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New Seed-Collection Zones for the Eastern United States: The Eastern Seed Zone Forum

Cited by 31 publications

References 16 publications

Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests

Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests

GlobalUsefulNativeTrees, a database of 14,014 tree species and their uses, supports synergies between biodiversity recovery and local livelihoods in landscape restoration

Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests

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