Combining genotype, phenotype, and environmental data to delineate site‐adjusted provenance strategies for ecological restoration

Abstract

Despite the importance of climate‐adjusted provenancing to mitigate the effects of environmental change, climatic considerations alone are insufficient when restoring highly degraded sites. Here we propose a comprehensive landscape genomic approach to assist the restoration of moderately disturbed and highly degraded sites. To illustrate it we employ genomic data sets comprising thousands of single nucleotide polymorphisms from two plant species suitable for the restoration of iron‐rich Amazonian Savannas. We first use a subset of neutral loci to assess genetic structure and determine the genetic neighbourhood size. We then identify genotype‐phenotype‐environment associations, map adaptive genetic variation, and predict adaptive genotypes for restoration sites. Whereas local provenances were found optimal to restore a moderately disturbed site, a mixture of genotypes seemed the most promising strategy to recover a highly degraded mining site. We discuss how our results can help define site‐adjusted provenancing strategies, and argue that our methods can be more broadly applied to assist other restoration initiatives.

A comprehensive landscape genomics approach for seed sourcing strategies in landscapes under varying degrees of habitat disturbance

“…The assumption is that if observed and predicted scores group together it is an indication that observed genotypes are locally adapted, while the opposite will indicate no local adaptation. In line with the conceptual premise by Lesica and Allendorf (1999), Carvalho et al (2020) found that observed and predicted genotypes for the highly degraded restoration site did not cluster together in either species, while the opposite was observed for the moderately disturbed site. In the praxis, this result suggests that local provenances are optimal to restore the moderately disturbed site, whereas mixed seed could be the best strategy to revegetate the highly degraded mining site, as local plants are probably no longer adapted to their climate and soil conditions.…”

“…In this context, in this issue of Molecular Ecology Resources, Carvalho et al (2020) advance the use of landscape genomic tools by developing a comprehensive approach to guide prerestoration provenance decisions across sites with varying environmental and disturbance regimes by considering both neutral and adaptive genetic variation via single nucleotide polymorphisms (SNPs) for two native plants (Dioclea apurensis and Mimosa acutistipula var. ferrea) of the Canga ecosystem in Brazil (Figure 1).…”

“…taken by Carvalho et al (2020) is the assessment of genotype-environmental associations (GEAs) and genotype-phenotype asso- (Rellstab et al, 2015). Using the combined putative adaptive loci detected with both statistical approaches in subsequent GEA and GPA-RDA analyses to identify population patterns, the authors determined that climatic variables appear to be more relevant than soil variables in both species; by contrast, for the phenotypic traits, species-specific population patterns were detected, suggesting different physiological responses among sites.…”

“…The framework proposed byCarvalho et al (2020) offers a comprehensive perspective to guide provenance delimitation and the optimal seed sourcing strategy upon the degree of habitat disturbance of restoration sites by taking advantage of the information that can be gathered for both neutral and adaptive variation. As the field of restoration genomics grows, there remain several methodological aspects that need to be explored, such as the power and performance of the available spatial modelling methods (GDM, sPCA, gradient forest) depending on the type of genomic and spatial data (linear versus.…”

Spotting genome-wide pigmentation variation in a brown trout admixture context

“…Jombart, Pontier and Dufour (2009) already mentioned that RDA was somewhat neglected in association studies despite now recognized desirable properties to limit false positives (Capblanc, Luu, Blum, & Bazin, 2018; Forester et al, 2018) and robustness to recombination rate variation (Lotterhos, 2019). Effectively, the use of RDA to investigate genotype-phenotype associations remains seldom (Talbot et al, 2017; Vangestel, Eckert, Wegrzyn, St. Clair, & Neale, 2018; Carvalho et al, 2020), while it became a standard in genotype-environment association studies (Forester, Lasky, Wagner, & Urban, 2018), including trout (Bekkevold et al, 2019). Basically, RDA allowed for distinguishing the part of variation collectively explained by RAD-loci and independent pigmentation variables, and hereby estimated to ∼35% of observed trait variation.…”