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. 

Genotypes were generated using tail tissue sampled from 183 P. mississippi 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 km². 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, Plethodon mississippi. 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 (Plethodon albagula) using 454 sequencing. Conserv Genet Resour 5:267–270.