DOI: 10.5061/dryad.v6wwpzh4n
title: Parnassius smintheus SNP and associated weather and landscape variables

description: Weather is an important short-term, local driver of population size and
 dispersal, which in turn contribute to patterns of genetic diversity and
 differentiation within species. Climate change is leading to greater
 weather variability and more frequent extreme weather events. While the
 effects of long-term and broad-scale mean climate conditions on genetic
 variation are well studied, our understanding of the effects of weather
 variability and extreme conditions on genetic variation is less developed.
 We assessed the influence of temperature and snow depth on genetic
 diversity and differentiation of populations of the alpine butterfly,
 Parnassius smintheus. We examined the relationships between a suite of
 variables, including those representing extreme conditions, and
 population-level genetic diversity and differentiation across 1453 single
 nucleotide polymorphisms, using both linear and gravity models. We
 additionally examined effects of land cover variables known to influence
 dispersal and gene flow in this species. We found that extreme low
 temperature events and the lowest recorded mean snow depth were
 significant predictors of genetic diversity. Extreme low temperature
 events, mean snow depth, and land cover resistance were significant
 predictors of genetic differentiation. These results are congruent with
 known effects of early winter weather on population size and habitat
 connectivity on dispersal in P. smintheus. Our results demonstrate the
 potential for changes in the frequency or magnitude of extreme weather
 events to alter patterns of genetic diversity and differentiation.
description: We sampled 431 <em>Parnassius smintheus </em>whole
 body vouchers from 21 meadows in Alberta, Canada. DNA was extracted and
 genotyped using double digest restriction site associated DNA sequencing
 (ddRADseq). Reads were aligned to the congener <em>Parnassius apollo
 </em>using Bowtie2. After subsequent filtering (including maximum
 missing data per SNP of 85%, maximum missing data per individual 50%,
 minor allele frequency of 0.02), we retained 1453 SNPs for analysis.
 Expected heterozygosity and chord distance were estimated in R using the
 package <em>adegenet </em>(Jombart, 2008). 
 Temperature variables were obtained from from Natural Resources
 Canada’s interpolated spatial models (Hutchinson et al., 2009) and
 estimated over 1960-2014. Snow variables were obtained as 1 km gridded
 snow depth estimates from the National Operational Hydrologic Remote
 Sensing Center’s SNODAS model (National Operational Hydrologic Remote
 Sensing Center, 2004) for 2010-2018.
description: # Parnassius smintheus SNP and associated weather and landscape variables
 [https://doi.org/10.5061/dryad.v6wwpzh4n](https://doi.org/10.5061/dryad.v6wwpzh4n) ## Description of the data and file structure We sampled 431 *Parnassius smintheus* whole body vouchers from 21 meadows in Alberta, Canada. DNA was extracted and genotyped using double digest restriction site associated DNA sequencing (ddRADseq). Reads were aligned to the congener *Parnassius apollo* using Bowtie2. After subsequent filtering (including maximum missing data per SNP of 85%, maximum missing data per individual 50%, minor allele frequency of 0.02), we retained 1453 SNPs for analysis. Expected heterozygosity and chord distance were estimated in R using the package *adegenet* (Jombart, 2008).  Temperature variables were obtained from from Natural Resources Canada’s interpolated spatial models (Hutchinson et al., 2009) and estimated over 1960-2014. Snow variables were obtained as 1 km gridded snow depth estimates from the National Operational Hydrologic Remote Sensing Center’s SNODAS model (National Operational Hydrologic Remote Sensing Center, 2004) for 2010-2018. ### Files and variables #### File: Parnassius_smintheus_genetic_diversity_and_associated_weather_variables.csv **Description:** Genetic, weather, and landscape variables associated with 21 *Parnassius smintheus* populations in Alberta, Canada ##### Variables * pop: Population name (all caps) * pop_lower: Population name abbreviation (lower case) * region: One of three sampling geographic regions (B: Banff, EK: East Kananaskis, WK: West Kananaskis) * sample_size: Number of sequenced *Parnassius smintheus* * lat : latitude of population (centroid of sampled meadow) * lon: longitude of population (centroid of sampled meadow) * He: expected heterozygosity * mean_temp: mean November temperature * mean_meanmax: mean November daily high * mean_meanmin: mean November daily low * max_above6: number of temperature events above 6 degrees Celsius * min_below28: number of temperature events below -28 degrees Celsius * mean_snowNov: snow depth in November (mm), averaged across days in November and years 2010-2018 * min_meansnow: snow depth in November, averaged across days in November for the year with the lowest average snow depth for that population * PLAND2 : percent of the landscape coded as "open" (meadow or rock surfaces), within 2 km of the centroid of each sampled meadow * prop_meadow_2km: percent of the landscape coded as "meadow" (i.e., potential habitat), within 2 km of the centroid of each sampled meadow * cum_current: cumulative current calculated using Omniscape, averaged for each 25x25m raster cell within 200 m of the centroid of each sampled meadow #### File: Parnassius1453SNPs.vcf.gz **Description:** 1453 single nucleotide polymorphism genotypes for 431 Parnassius smintheus individuals, genotyped using double digest restriction site associated DNA sequencing. ## Code/software The csv file can be viewed in Microsoft Excel or free equivalents, and can be loaded into the free software R for analysis. We used the base R package to fit linear models of expected heterozygosity and the R package *GeNetIt* (Evans & Murphy, 2022) to fit gravity models of chord distance.  The R package *vcfR* can be used to view the VCF file. We used the *vcfR* package command vcfR2genind to convert the VCF to the commonly used genind format to calculate metrics of genetic diversity and differentiation using the R package *GeNetIt.* ## Access information Other publicly accessible locations of the data: * none Data was derived from the following sources: * Canadian Forest Service’s Earth Observation for Sustainable Development of Forests * Wulder, M. A., White, J. C., Cranny, M., Hall, R. J., Luther, J. E., Beaudoin, A., Goodenough, D. G., & Dechka, J. A. (2008). Monitoring Canada’s forests. Part 1: Completion of the EOSD land cover project. *Canadian Journal of Remote Sensing*, *34*(6), 549–562. [https://doi.org/10.5589/m08-066](https://doi.org/10.5589/m08-066) * National Operational Hydrologic Remote Sensing Center’s SNODAS (snow data assimilation system) model * National Operational Hydrologic Remote Sensing Center (2004). Snow data assimilation System (SNODAS) data products at NSIDC, version 1 [Data Set]. Boulder, Colorado USA. National Snow and Ice Data Center. [https://doi.org/10.7265/N5TB14TC](https://doi.org/10.7265/N5TB14TC). * Natural Resources Canada’s interpolated spatial models  * Hutchinson, M. F., McKenney, D. W., Lawrence, K., Pedlar, J. H., Hopkinson, R. F., Milewska, E., & Papadopol, P. (2009). Development and testing of Canada-wide interpolated spatial models of daily minimum–maximum temperature and precipitation for 1961–2003. *Journal of Applied Meteorology and Climatology*, *48*(4), 725–741. [https://doi.org/10.1175/2008JAMC1979.1](https://doi.org/10.1175/2008JAMC1979.1)