Dataset Information

The effect of sampling density and study area size on landscape genetics inferences for the Mississippi slimy salamander (Plethodon mississippi).

ABSTRACT: In landscape genetics, it is largely unknown how choices regarding sampling density and study area size impact inferences upon which habitat features impede vs. facilitate gene flow. While it is recommended that sampling locations be spaced no further apart than the average individual's dispersal distance, for low-mobility species, this could lead to a challenging number of sampling locations, or an unrepresentative study area. We assessed the effects of sampling density and study area size on landscape genetic inferences for a dispersal-limited amphibian, Plethodon mississippi, via analysis of nested datasets. Microsatellite-based genetic distances among individuals were divided into three datasets representing 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 a proxy for gene flow (i.e., the response variable) in maximum-likelihood population effects models that assessed the nature and strength of their relationship with each of five land-use classes (i.e., potential predictor variables). Comparisons of outcomes were based on the rank order of effect, sign of effect (i.e., gene flow resistance vs. facilitation), spatial scale of effect, and functional relationship with gene flow. The best-fit model for each dataset had the same sign of effect for hardwood forests, manmade structures, and pine forests, indicating the impacts of these land-use classes on dispersal and gene flow in P. mississippi are robust to sampling scheme. Contrasting sampling densities led to a different inferred functional relationship between agricultural areas and gene flow. Study area size appeared to influence the scale of effect of manmade structures and the sign of effect of pine forests. Our findings provided evidence for an influence of sampling density, study area size, and sampling effort upon inferences. Accordingly, we recommend iterative subsampling of empirical datasets and continued investigation into the sensitivities of landscape genetic analyses using simulations.

SUBMITTER: Burgess SM

PROVIDER: S-EPMC8207395 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Species are a fundamental unit of biodiversity, yet can be challenging to delimit objectively. This is particularly true of species complexes characterized by high levels of population genetic structure, hybridization between genetic groups, isolation by distance, and limited phenotypic variation. Previous work on the Cumberland Plateau Salamander, Plethodon kentucki, suggested that it might constitute a species complex despite occupying a relatively small geographic range. To examine this hypothesis, we sampled 135 individuals from 43 populations, and used four mitochondrial loci and five nuclear loci (5693 base pairs) to quantify phylogeographic structure and probe for cryptic species diversity. Rates of evolution for each locus were inferred using the multidistribute package, and time calibrated gene trees and species trees were inferred using BEAST 2 and *BEAST 2, respectively. Because the parameter space relevant for species delimitation is large and complex, and all methods make simplifying assumptions that may lead them to fail, we conducted an array of analyses. Our assumption was that strongly supported species would be congruent across methods. Putative species were first delimited using a Bayesian implementation of the GMYC model (bGMYC), Geneland, and Brownie. We then validated these species using the genealogical sorting index and BPP. We found substantial phylogeographic diversity using mtDNA, including four divergent clades and an inferred common ancestor at 14.9 myr (95% HPD: 10.8-19.7 myr). By contrast, this diversity was not corroborated by nuclear sequence data, which exhibited low levels of variation and weak phylogeographic structure. Species trees estimated a far younger root than did the mtDNA data, closer to 1.0 myr old. Mutually exclusive putative species were identified by the different approaches. Possible causes of data set discordance, and the problem of species delimitation in complexes with high levels of population structure and introgressive hybridization, are discussed.

Project description:PurposeTo characterize the effect of sampling window size on maps of foveal cone density derived from adaptive optics scanning light ophthalmoscope (AOSLO) images of the cone mosaic.MethodsForty-four AOSLO-derived montages of the foveal cone mosaic (300 x 300µm) were used for this study (from 44 individuals with normal vision). Cone photoreceptor coordinates were semi-automatically identified by one experienced grader. From these coordinates, cone density matrices across each foveal montage were derived using 10 different sampling window sizes containing 5, 10, 15, 20, 40, 60, 80, 100, 150, or 200 cones. For all 440 density matrices, we extracted the location and value of peak cone density (PCD), the cone density centroid (CDC) location, and cone density at the CDC.ResultsAcross all window sizes, PCD values were larger than those extracted at the CDC location, though the difference between these density values decreased as the sampling window size increased (p<0.0001). Overall, both PCD (r=-0.8099, p=0.0045) and density at the CDC (r=-0.7596, p=0.0108) decreased with increasing sampling window size. This reduction was more pronounced for PCD, with a 27.8% lower PCD value on average when using the 200-cone versus the 5-cone window (compared to only a 3.5% reduction for density at the CDC between these same window sizes). While the PCD and CDC locations did not occur at the same location within a given montage, there was no significant relationship between this PCD-CDC offset and sampling window size (p=0.8919). The CDC location was less variable across sampling windows, with an average per-participant 95% confidence ellipse area across the 10 window sizes of 47.56µm² (compared to 844.10µm² for the PCD location, p<0.0001).ConclusionCDC metrics appear more stable across varying sampling window sizes than PCD metrics. Understanding how density values change according to the method used to sample the cone mosaic may facilitate comparing cone density data across different studies.

Dataset Information

The effect of sampling density and study area size on landscape genetics inferences for the Mississippi slimy salamander (Plethodon mississippi).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets