DOI: 10.5061/dryad.pvmcvdntv
title: Data from: Assessing the effects of land‑use intensity on small mammal community composition and genetic variation in Myodesglareolus and Microtus arvalis across grassland and forest habitats

description: Context: Land-use intensification can alter small mammal community
 composition and induce loss of genetic variation in remaining populations.
 Fragmented landscapes favor generalist and synanthropic species, which are
 potential reservoirs of pathogens and pose risks to agriculture and
 forestry. Objectives: This study aimed to evaluate the effects of land-use
 intensity on small mammal diversity in grassland and forest habitats, as
 well as on genetic variation patterns driven by geographic (Isolation by
 Distance) and environmental distance (Isolation by Resistance) factors.
 Methods: We analysed differences in small mammal community diversity on
 grassland and forest plots with varying land-use intensities. Genetic
 analyses were conducted on Myodes glareolus and Microtus arvalis
 populations from each habitat, using microsatellites. Maximum Likelihood
 Population Effects models were employed to elucidate gene flow patterns
 and significant differences in genetic structure based on land-use
 intensity. Results: Small mammal communities in grasslands were
 significantly less diverse than in forests. Land-use intensity had a
 significant effect on diversity within grassland but not within forest
 habitats. M. glareolus showed three genetic groups, while M. arvalis
 displayed no discernible population structure or landscape-related
 pattern. Land-use intensity did not significantly influence the genetic
 structure of either species. Gene flow in M. glareolus is best described
 by the IBR model. Conclusion: Land-use intensity significantly affects
 small mammal community composition, particularly in grasslands. Neither
 species' genetics is directly impacted by land-use intensity but
 rather by landscape connectivity and distance. Risk assessments for
 rodent-borne zoonotic pathogens and crop damage should be framed within a
 habitat connectivity context.
description: <em>M. glareolus</em>, caught on forest plots, and
 <em>M. arvalis</em>, caught on grassland plots, were
 genetically examined. DNA extraction was performed on tissue samples using
 Phenol-Chloroform-Isopropanol extraction (Hogan et al., 1986). A set of 12
 microsatellite loci for <em>M. glareolus</em> CG13G2, CG5F6,
 CG16E2, CG17E9, CG7C9, CG15F7, CG12B9, CG13F9, CG5G56, CG12A7 (Rikalainen
 et al., 2008) and MSCg-15 (Gockel et al., 1997) and a set of 11
 microsatellite loci for <em>M. arvalis</em> Ma29, Ma36, Ma54,
 Ma75, Ma25, Ma30 (Gauffre et al., 2007), Mar012, Mar113, Mar076, Mar080
 and Mar016 (Walser & Heckel, 2008) was applied. PCR was performed
 in a total volume of 15 µl containing a maximum of 24 ng of genomic DNA.
 Primer concentration varied between 0.15 µM and 0.25 µM in the
 <em>M. glareolus</em> multiplex system and 0.15 µM and 0.30 µM
 in the <em>M. arvalis</em> multiplex system. The QIAGEN
 Multiplex PCR Kit (QIAGEN) was used to perform the Multiplex PCR.
 <em>The M. glareolus</em> multiplex protocol describes an
 initial denaturation at 95°C for 5 min, 35 cycles of 94°C for 30 sec, 55°C
 for 30 sec, 60°C for 90 sec, 72°C for 30 sec, and the final extension at
 68°C for 10 min. The <em>M. arvalis</em> multiplex protocol
 describes an initial denaturation at 95°C for 5 min, 35 cycles of 95°C for
 30 sec, 55°C for 90 sec, 72°C for 30 sec, and the final extension at 68°C
 for 10 min. Fragment sizes were determined by electrophoresis on 4.5%
 (w/v) denaturing 19:1 acrylamide: bisacrylamide gels on the ABI Prism™ 377
 sequencer, using the GeneScan 2.0 software and a ROX-labelled commercial
 size standard as an internal standard (Applied Biosystems).
description: # Assessing the effects of land‑use intensity on small mammal community
 composition and genetic variation in Myodes glareolus and Microtus arvalis
 across grassland and forest habitats
 [https://doi.org/10.5061/dryad.pvmcvdntv](https://doi.org/10.5061/dryad.pvmcvdntv) The dataset ("Bexis*Microsatellite*data*MicrotusArvalis.xlsx" and Bexis*Microsatellite*data*MyodesGlareolus.xlsx") contains microsatellite data of 426 *Myodes glareolu*s individuals genotyped on 12 microsatellite loci and of 403 *Microtus arvalis* individuals genotyped on 11 microsatellite loci. The metadata file contains sampling information for two small mammal species—Myodes glareolus and Microtus arvalis. The variables include Ida (a unique identifier for each sample), Sampling month (the month when sampling occurred), Plot (sampling location), Habitat (general habitat type), Landuseintensity (a qualitative description of land use pressure), and LUI (a numerical Land Use Intensity index) and SMI. The Species column records the taxonomic identity of each individual sampled. Together, these variables describe the ecological and geographic context of each specimen ## Code/Software The input files for the Microsatellite toolkit and FSTAT analysis analysis can be found in the attachted files (Excel files). Following analysis is based on the output of the Microsatellite toolkit and FSTAT analysis (also to be found in the attached fiel (ending ".dat"). The subsequent workflow can be found here in short and in detail in the publication. Genetic diversity of populations is described through indices calculated with the Excel Microsatellite Tool Kit 3.1.1 and FSTAT (ver. 2.9.3). MICRO-CHECKER v. 2.2.3 was used to check the data regarding genotyping errors and the presence of null alleles. The impact of null alleles on FST estimation was evaluated with FREENA using the excluding null alleles (ENA) method. R-package pegas (ver. 1.2) was used to calculate deviations from Hardy–Weinberg equilibrium. We used STRUCTURE 2.3.4 software to determine the number of genetic clusters (K), testing the number of clusters from one to fifteen with ten iterations for each K (20,000 burn-ins, 200,000 Markov chain Monte Carlo replicates in each run) using the ‘Admixture’ model and assuming correlated allele frequencies. R-package pophelper (ver. 2.3.1) was used to determine the final number of clusters from ΔK. The probability of each individual belonging to one of the K clusters got transformed into a three-dimensional vector by Principal Coordinate Analysis (PCoA) of the Euclidean distance of each cluster probability using the R-package adegenet (ver. 2.1.10). The PCoA vectors were transferred via RGB algorithm to a genetic color code.