DOI: 10.5061/dryad.h9w0vt4gt
title: The effect of sampling density and study area size on landscape genetic inferences for the Mississippi slimy salamander (Plethodon mississippi)

description: In the field of landscape genetics, it is largely unknown how choices
 regarding population sampling density and study area size impact
 inferences about which habitat features impede vs. facilitate gene flow.
 While it is commonly recommended that sampling locations be spaced no
 further apart than the average individual dispersal distance, for low
 mobility species, this could lead to a logistically challenging number of
 sampling locations, or a small and unrepresentative study area. We
 assessed the effects of sampling density and study area size on landscape
 genetics inferences for a dispersal-limited amphibian, the Mississippi
 slimy salamander (Plethodon mississippi), via comparative analysis of
 nested datasets. Microsatellite-based genetic distances among individuals
 were divided into three datasets representing either sparse sampling
 across a large study area, dense sampling across a small study area, or
 sparse sampling across the same small study area. These datasets
 were each used as a response variable in maximum likelihood population
 effects models that assessed the nature and strength of the relationship,
 if any, between each of five land use classes (i.e., potential predictor
 variables) and the response variable. Comparative analyses were based on
 the rank order of effect (i.e., strongest to weakest), sign of effect
 (i.e., gene flow resistance vs. facilitation), spatial scale of effect,
 and functional relationship with gene flow. Outcomes were interpreted
 within the context of five possible combinations of congruence among
 datasets. We found that each best-fit model associated with the three
 datasets had the same sign of effect for hardwood forests, manmade
 structures, and pine forests. However, different sampling densities led to
 a different inferred functional relationship between agricultural areas
 and gene flow. Furthermore, study area size appeared to influence the
 inferred scale of effect of manmade structures and sign of effect of pine
 forests. Taken together, our findings provided evidence for an influence
 of sampling density, study area size, and sampling effort upon inferences.
 Accordingly, in the absence of strong a priori information about
 spatial-genetic structure and species’ life history traits, we recommend
 iterative subsampling and reanalysis of empirical datasets, coupled with
 continued investigation into the sensitivities of landscape genetics
 analyses using simulations or other controlled experimental
 designs. 
description: Genotypes were generated using tail tissue sampled from 183
 <i>P. mississippi </i>individuals from 33 locations in Holly
 Springs National Forest, Mississippi, USA. Tissue was stored in 95%
 ethanol following collection. Genomic DNA was extracted using a Qiagen
 DNeasy Blood and Tissue Kit (Valencia CA, USA), following the
 manufacturer's recommendations. Eight microsatellite loci reported by
 Spatola et al. (2013) were used to genotype individuals (Burgess &
 Garrick 2020; also see Supplementary Material and Tables S4, S5, and S6
 for amplification conditions, allele-calling approaches, and calculation
 of genotyping error rates). Landscape resistance
 rasters (in .asc format) were developed using a land use map partitioned
 into six land use classes (i.e., agriculture, hardwood forest, pine
 forest, manmade structures, water bodies, and wetlands) through a
 supervised classification in ERDAS Imagine 2014 (Hexagon Geospatial,
 Norcross, GA, USA) of NASA Landsat 8 satellite imagery (see Supplementary
 Material for details, and Table S1). The classified land use raster was
 used to create a series of rasters for each land use class using a moving
 window analysis in FRAGSTATS v. 4.2 (McGarigal et al. 2012). Moving window
 sizes were designated using the length of a side, thus a 250 m square
 moving window encompasses 0.0625 km<sup>2</sup>. The value of
 each pixel in the raster was determined by the percent of the surrounding
 window that contained a given land use class using the PLAND function.
 Five rasters were created for each land use class, with moving window
 sizes of 100, 250, 500, 750, and 1000 m. Burgess SM,
 Garrick RC (2020) Regional replication of landscape genetics analyses of
 the Mississippi slimy salamander, <i>Plethodon
 mississippi</i>. Landsc Ecol 35:337–351. McGarigal K, Cushman SA, Ene E (2012)
 FRAGSTATS v4: spatial pattern analysis program for categorical and
 continuous maps. Computer software program produced by the authors at the
 University of Massachusetts, Amherst.
 http://www.umass.edu/landeco/research/fragstats/fragstats.html
 Spatola BN, Peterman WE, Stephens NT, Connette GM, Shepard DB,
 Kozak KH, Selmlitsch  RD, and Eggert LS (2013) Development of
 microsatellite loci for the western slimy salamander (<i>Plethodon
 albagula</i>) using 454 sequencing. Conserv Genet Resour
 5:267–270.