DOI: 10.5061/dryad.5x69p8dfp
title: Genetic structure and diversity of the declining orchid Gymnadenia conopsea in Scandinavia: Implications for conservation and management

description: Understanding how historical versus contemporary processes shape
 population genetic structure and diversity is important to design
 effective management actions for threatened species. We genotyped 1,834
 SNPs in 1,120 individuals in 110 Scandinavian populations of the declining
 orchid Gymnadenia conopsea, in three different habitat types, to examine
 if genetic structure was related to wind speed, terrain ruggedness, forest
 cover, and seasonality at the landscape scale, and if genetic diversity
 increases with census population size, and is higher in core habitats (fen
 and meadow) than in marginal, coastal habitats. We identified three
 genetic clusters and pronounced isolation by distance, consistent with two
 independent colonization routes after last glacial maximum, followed by
 admixture. Effective population size was highest in the admixed cluster.
 Estimates of effective migration indicated reduced gene flow along the
 Atlantic coast, between coastal and inland populations, and among southern
 meadow populations. High landscape resistance to gene flow was associated
 with complex topography and pronounced seasonality. Genetic diversity
 increased with population size, but did not vary among habitat types.
 Genetic diversity peaked in core habitats, i.e., southern meadows and
 inland fens along the Scandes mountains. Lowest genetic diversity was
 found on the Atlantic coast and in a few scattered populations. Current
 genetic structure suggests a strong legacy of historical events, and the
 high genetic diversity documented in the main Scandinavian range indicates
 that current viability and future adaptation potential is high. To
 maintain genetic diversity and connectivity between genetic groups, it is
 particularly important to preserve southern meadow populations, which
 currently are in strong decline. Generally, results illustrate how a
 declining species can help us understand the impact of historical and
 current processes, how landscape genetic data can inform proactive
 conservation, and how a slow genetic response to fragmentation can allow
 time to maintain genetic diversity through habitat restoration and
 management.
description: # Genetic structure and diversity of the declining orchid Gymnadenia
 conopsea in Scandinavia: Implications for conservation and management
 [https://doi.org/10.5061/dryad.5x69p8dfp](https://doi.org/10.5061/dryad.5x69p8dfp) ## Description of the data and file structure The data contains genotypic and ecological data from 110 Scandinavian populations of Gymnadenia conopsea. In each population, we genotyped 1834 SNPs in 11 individuals. For each population, we collected data on population size (= number of flowering individuals) and habitat type (meadow, fen, coastal), as well as geographic coordinates. Genotypic data are found in "clean_raw_variants1he.txt". Each row shows the genotype for all 1834 SNPs (first SNP = G1R1LP02p002_0002945, last SNP = G3R2YP08p503_040553) for one sample (= individual), where the sample is identified in the first column (first sample = 2AR1, last sample = ZA74). Population data are found in "populations.txt". Each row corresponds to one individual. Column headers indicate: * Individual = individual of Gymnadenia conopsea sampled in the field, n = 11 per population (fewer if population size was &lt;11) * Population = population name * County = county in Sweden or Norway * X = East geographic coordinate (WGS84 dec) * Y = North geographic coordinate (WGS84 dec) * hab0 = detailed habitat type (not used in analyses) * hab1 = habitat type (M = fen, E = meadow, S = coastal) * Part = geographic region (South or North) ## Code/software To analyse spatial genetic structure, we used Admixture v 1.3.0 (Alexander et al., 2009) and conStruct v 1.0.4 (Bradburd, 2019). We used the following R packages: * *ade4* version 1.7.18 (Dray & Dufour, 2007) * *adegenet* version 2.1.5 (Jombart & Ahmed, 2011) * *adespatial* version 0.3.20 (Dray et al., 2006) * *PCAviz* version 0.3.37 (Novembre et al., 2019) * *ResistanceGA* version 4.2-10 (Peterman, 2018) * *raster* version 3.6-31 (Hijmans, 2023) * *strataG* version 1.0.6. (Archer et al. 2017) The flow of the separate analyses are described in the uploaded R markdown files: * conStruct_evaluation.Rmd - analyses of spatial genetic structure * Effective_population_size.Rmd - analyses of effective population size * Resistance.R - analyses of landscape resistance * PCAviz_KS_v2.Rmd - PCA and visualization ## Access information Data was derived from the following sources: * data on mean annual wind speed were downloaded from the Global Wind Atlas ([https://globalwindatlas.info/en](https://globalwindatlas.info/en); Davis et al., 2023) * elevation data were downloaded from SRTM 90-meter resolution data ([https://bigdata.cgiar.org/srtm-90m-digital-elevation-database/](https://bigdata.cgiar.org/srtm-90m-digital-elevation-database/)) * data on forest land cover were downloaded from the CORINE land cover 2018 database, 100-meter raster ([https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac](https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac); European Environment Agency 2019). * climatic data were downloaded from worldclim (bioclimatic variable “precipitation seasonality” , BIO15, 30 s resolution, version 2, [https://worldclim.org/](https://worldclim.org/), Fick & Hijmans, 2017)