Climate change is motivating a reassessment of how seeds are selected for reforestation, as rapid environmental change can lead to local maladaptation in trees. Genetic association studies and past seed source climate both have the potential to help identify appropriate planting stock, but these techniques have not been compared and tested as part of an operational planting program. In this study, we combined an analysis of SNPs associated with environmental gradients in sugar pine (P. lambertiana) with an analysis of post-fire seedling survival and growth in a restoration experiment. Our genotype-environment association (GEA) tests revealed 829 SNPs (single nucleotide polymorphisms) with significant association with climate gradients – especially April snowpack – 323 of which either had annotations that suggested potential functional importance or were identified by two different methods. We then built Bayesian models of survival and growth for all seedlings to test the effects of source elevation, a common proxy for climate. For the subset of seedlings alive and genotyped in 2020 (1–3 years after planting), we tested the relative predictive ability of source elevation versus the proportion of seedling alleles expected to be locally advantageous. We found that source elevation was generally better at predicting seedling performance than genotype indices, perhaps because of the limited scope of the association analysis. Seed sources from 500–1500 feet lower in elevation and one seed zone further south generally performed as well or better than local seed sources. This is likely because the temperatures in the planting area closely resemble that of those source regions in the mid-20th century, allowing seedlings to survive and exhibit the higher growth potential often seen in warmer-climate populations. By contrast, seedlings from cooler, wetter sources tended to perform poorly. This result, and those of similar previous studies, suggest that “climate matching” using past climate information for existing seed sourcing units is a reasonable starting point for finding seedlings suited to already-altered planting site climate conditions. However, further tests with more extensive genomic and performance data may improve the utility of genotype information for seed selection.

Uncovering the genetic basis of local adaptation is a major goal of evolutionary biology and conservation science alike. In an era of climate change, an understanding of how environmental factors shape adaptive diversity is crucial to predicting species response and directing management. Here, we investigate patterns of genomic variation in giant sequoia, an iconic and ecologically important tree species, using 1364 bi-allelic single nucleotide polymorphisms (SNPs). We use an FST outlier test and two genotype-environment association methods, latent factor mixed models (LFMM) and redundancy analysis (RDA), to detect complex signatures of local adaptation. Results indicate 79 genomic regions of potential adaptive importance, with limited overlap between the detection methods. Of the 58 loci detected by LFMM, 51 showed strong correlations to a precipitation driven composite variable and seven to a temperature-related variable. RDA revealed 24 outlier loci with association to climate variables, all of which showed strongest relationship to summer precipitation. Nine candidate loci were indicated by two methods. After correcting for geographic distance, RDA models using climate predictors accounted for 49% of the explained variance and showed significant correlations between SNPs and climatic factors. Here, we present evidence of local adaptation in giant sequoia along gradients of precipitation and provide a first step towards identifying genomic regions of adaptive significance. The results of this study will provide information to guide management strategies that seek to maximize adaptive potential in the face of climate change.

Research Highlights: Patterns of dispersal shape the distribution and temporal development of genetic diversity both within and among populations. In an era of unprecedented environmental change, the maintenance of extant genetic diversity is crucial to population persistence.  Background and Objectives: We investigate patterns of pollen dispersal and spatial genetic structure within populations of giant sequoia (Sequoiadendron giganteum).  Materials and Methods: The leaf genotypes of established trees from twelve populations were used to estimate the extent of spatial genetic structure within populations, as measured by the Sp statistic. We utilized progeny arrays from five populations to estimate mating parameters, the diversity of the pollen pool, and characteristics of pollen dispersal.  Results: Our research indicates that giant sequoia is predominantly outcrossing, but exhibits moderate levels of bi-parental inbreeding (0.155). The diversity of the pollen pool is low, with an average of 7.5 pollen donors per mother tree. As revealed by the Sp-statistic, we find significant genetic structure in ten of twelve populations examined, which indicates the clustering of related individuals at fine spatial scales. Estimates of pollen and gene dispersal indicate predominantly local dispersal, with the majority of pollen dispersal <253 m, and with some populations showing fat-tailed dispersal curves, suggesting potential for long-distance dispersal.  Conclusions: The research presented here represent the first detailed examination of the reproductive ecology of giant sequoia, which will provide necessary background information for the conservation of genetic resources in this species. We suggest that restoration planting can mitigate potential diversity loss from many giant sequoia populations.

PREMISE: Patterns of genetic structure across a species’ range reflect the long-term interplay between genetic drift, gene flow, and selection. Given the importance of gene flow in preventing the loss of diversity through genetic drift among spatially isolated populations, understanding the dynamics of gene flow and the factors that influence connectivity across a species’ range is a major goal for conservation of genetic diversity. Here we present a detailed look at gene flow dynamics of Sequoiadendron giganteum, a paleoendemic tree species that will likely face numerous threats due to climate change. METHODS: We used microsatellite markers to examine nineteen populations of S. giganteum for patterns of genetic structure and to estimate admixture and rates of gene flow between eight population pairs. Also, we used Generalized Dissimilarity Models to elucidate landscape factors that shape genetic differentiation among populations.  RESULTS: We found minimal gene flow between adjacent groves in the northern disjunct range. In most of the southern portion of the range, groves showed a signal of connectivity which degrades to isolation in the extreme south. Geographic distance was the most important predictor of genetic dissimilarity across the range, with environmental conditions related to precipitation and temperature explaining a small, but significant, portion of the genetic variance. CONCLUSIONS: Due to their isolation and unique genetic composition, northern populations of S. giganteum should be considered a high conservation priority. In this region, we suggest germplasm conservation as well as restoration planting to enhance genetic diversity.