DOI: 10.5061/dryad.nvx0k6dqv
title: Pinus contorta landscape genetics and common garden data

description: Aim: Climate change poses significant challenges for tree species, which
 are slow to adapt and migrate. Insight into genetic and phenotypic
 variation under current landscape conditions can be used to gauge
 persistence potential to future conditions and determine conservation
 priorities, but landscape effects have been minimally tested in trees.
 Here, we use Pinus contorta, one of the most widely-distributed conifers
 in North America, to evaluate the influence of landscape heterogeneity on
 genetic structure as well as the magnitude of local adaptation versus
 phenotypic plasticity in a widespread tree species. Location: Western
 North America. Methods: We paired landscape genetics with fully reciprocal
 in situ common gardens to evaluate landscape influence on neutral and
 adaptive variation across all subspecies of P. contorta. Results:
 Landscape barriers alone play a minor role in limiting gene flow, creating
 marginal geographically-based structure. Local climate determines
 population performance, with survival highest at home but growth greatest
 in mild climates (e.g., warm, wet). Survival of two of the three
 populations tested was consistent with patterns of local adaptation
 documented for P. contorta, while growth was indicative of plasticity for
 populations grown under novel conditions and suggesting that some
 populations are not currently occupying their climatic optimum. Main
 Conclusions: Our findings provide insight into the role of the landscape
 in shaping population genetic structure in a widespread tree species as
 well as the potential response of local populations to novel conditions,
 knowledge critical to understanding how widely distributed species may
 respond to climate change. Geographically-based genetic structure and
 reduced survival under water-limited conditions may make some populations
 of widespread tree species more vulnerable to local maladaptation and
 extirpation. However, genetically diverse and phenotypically plastic
 populations of widespread trees, such as many of the P. contorta
 populations sampled and tested here, likely possess high persistence
 potential.
description: Methods are extensive, so please refer to manuscript for
 details. Twenty sampling locations were randomly
 selected from occurrences in regions one through four. In region four, we
 included two sampling locations representing proposed variety
 <i>yukonensis </i>(Strong, 2010), and we avoided sampling
 across much of central and southern British Columbia where commercial
 plantations of <i>latifolia</i> are common. Some region five
 samples of <i>latifolia</i> were provided by the FIA program
 (n = 13), with additional sampling locations selected to fill in gaps not
 sampled by FIA (n = 3). In region six, the two known populations were
 sampled. Ultimately, fifty-one locations were sampled. At each sampling
 location, one gram of current-year needles was collected from ten
 individuals (&gt;50m apart) and preserved using silica gel desiccant.
 Total genomic DNA was extracted using DNeasy plant kits (Qiagen, Valencia,
 CA) at the U.S. Department of Agriculture National Forest Genetics
 Laboratory (Placerville, CA). Of 15 highly polymorphic SSR markers
 initially tested (Lesser et al. 2012), nine amplified across all samples
 (Appendix Table A1). Loci were amplified in multiplex under identical
 conditions, with locus-specific primers 5’-tailed with universal primer
 sequences (as described by Missiaggia & Grattapaglia 2006, see
 Appendix 1 for details). PCR products were separated on a 3730xl Genetic
 Analyzer (Life Technologies, Carlsbad, CA), and peak sizes were determined
 using GeneMarker v2.2 (SoftGenetics LLC, State College, PA). Samples were
 scored three times to verify peaks and resolve conflicts.
 After screening and adjusting for null alleles, genotyping
 errors, and deviations from Hardy-Weinberg Equilibrium, we calculated
 pairwise F<sub>ST</sub> (i.e., the inbreeding coefficient or
 proportion of genetic variance contained within a subpopulation relative
 to total genetic variance) and the following parameters, averaged across
 loci, for each sampling location using GenAlEx
 (Peakall & Smouse, 2012): percent
 polymorphic loci (PPL), allelic richness (N<sub>A</sub>),
 number of effective alleles (N<sub>E</sub>), expected
 heterozygosity (H<sub>E</sub>), unbiased expected
 heterozygosity (uH<sub>E</sub>), and inbreeding levels
 (F<sub>IS</sub>). We used the ‘<i>pegas’</i>
 package in R (Paradis, 2010; R Core Team, 2019) to quantify population
 differentiation within and among sampling locations and subspecies using a
 hierarchical analysis of molecular variance (AMOVA).
 Pairwise genetic distances among sampling locations were
 calculated using conditional genetic distance (cGD), where genetic
 distances are based on genetic covariance and estimated from graph
 distances as the shortest path connecting pairs via population graph
 topology (Dyer et al., 2010). Pairwise cGD is more sensitive than
 traditional metrics (e.g., <i>F<sub>ST</sub></i>),
 accounting for both direct and indirect connectivity (Dyer et al., 2010).
 We estimated cGD using the ‘<i>GStudio</i>’ package in R (Dyer, 2016). We tested for range-wide genetic connectivity by comparing pairwise cGD to pairwise spatial and environmental distances, testing hypotheses of isolation by distance (IBD), barrier (IBB), resistance (IBR), and environment (IBE). For tests of IBD, we calculated pairwise Euclidean geographic distance (km) using Vincenty ellipsoid distance in the ‘<i style="text-indent:0.5in;">geosphere’</i> package in R (Hijmans et al., 2019). For IBB, we created a binary matrix representing hypothesized barriers to gene flow: Central Valley of California separating coastal and mountain populations, Juneau Icefield and Coast Mountain Range separating coastal Alaska and interior Yukon and British Columbia populations, and Great Basin-Intermountain West separating Sierra Nevada and Rocky Mountain populations. Tests of IBR were performed using a resistance map derived from habitat suitability modeling, representing landscape resistance to movement among populations. IBE was evaluated using among-population climate dissimilarities irrespective of spatial connectivity, calculated as pairwise Euclidean distances based on the first three principal components from an analysis of seven bioclimatic variables (‘<i style="text-indent:0.5in;">prcomp’</i> function in R). We used multiple approaches to evaluate which hypotheses (IBD, IBB, IBR, IBE) best describe observed patterns of genetic distance. First, we used Mantel and partial Mantel tests in the R package ‘<i style="text-indent:0.5in;">vegan</i>’ (Oksanen et al., 2018) under a reciprocal causal modeling framework (Cushman et al., 2013) to evaluate relative support as the difference between reciprocal partial Mantel tests for each hypothesis. Because Mantel and partial Mantel tests are criticized for their tendencies toward inflated type I error rates (Guillot & Rousset, 2013), we also implemented multiple matrix regression with randomization (MMRR, Wang, 2013) in the R package ‘<i style="text-indent:0.5in;">ecodist</i>’ (Goslee & Urban, 2007) to test for consistency of results, comparing all possible combinations of hypotheses to identify the models with the greatest support.
description: Note that Population 23 (contorta9 in manuscript) was only
 incldued in population genetics summary in GenAlEx. Samples from
 <i>contorta </i>9 (Jumbo Bog, Alaska) were difficult to score
 at numerous loci; we included this sampling location in population
 frequency statistics but excluded it from all population and landscape
 genetics analysis. All climate data was extracted from
 TerraClim.