Data from: Genetic distinction between contiguous urban and rural multimammate mice in Tanzania despite gene flow

Special conditions are required for genetic differentiation to arise at a local geographical scale in the face of gene flow. The Natal multimammate mouse, Mastomys natalensis, is the most widely distributed and abundant rodent in sub-Saharan Africa. A notorious agricultural pest and a natural host for many zoonotic diseases, it can live in close proximity to man, and appears to compete with other rodents for the synanthropic niche. We surveyed its population genetic structure across a 180 km transect in central Tanzania along which the landscape varied between agricultural land in a rural setting and natural woody vegetation, rivers, roads and a city. We sampled M. natalensis across 10 localities, and genotyped 15 microsatellite loci from 515 individuals. Hierarchical STRUCTURE analyses show a K-invariant pattern distinguishing Morogoro suburbs (located in the centre of the transect) from nine surrounding rural localities. Landscape connectivity analyses in Circuitscape and comparison of rainfall patterns suggest that neither geographical isolation nor natural breeding asynchrony could explain the genetic differentiation of the urban population. Using the Isolation-with-Migration model implemented in IMa2 we inferred that a split between suburban and rural populations would have occurred recently (<150 years ago) with higher urban effective population density consistent with an urban-source to rural-sink of effective migration. The observed genetic differentiation of urban multimammate mice is striking given the uninterrupted distribution of the animal throughout the landscape and the high estimates of effective migration (2NeM = 3.0 and 29.7), suggesting a strong selection gradient across the urban boundary.Field data Gryseels et al. Rodent urban-rural genetic contrastExplanations for datafile "Field data Gryseels et al. Differentiation of M.natalensis in synanthropy.xls" This datafile contains trapping and location information of small mammals captured as described in the manuscript "Genetic distinction between contiguous urban and rural multimammate mice in Tanzania despite gene flow" ID: Unique ID code of each captured animal Project: the samples used in the manuscript originate from 2 different projects (see details in manuscript) Locality: Unique code of the locality (which consists of a set of 100 traps placed in a grid for 1 night) where the animal is captured Town: town, village or community where the animal is captured Date: the date the animal was captured Species: morphological identification of the animal (in case of Mastomys natalensis: confirmed by cytochrome b DNA sequence data) Latitude and longitude: the exact GPS location the animal was captured inside its trapParameter settings for STRUCTURE clustering analysesParameter settings for STRUCTURE clustering analyses used in the manuscriptMicrosatellite dataExplanations for datafile "Microsatellite data.xls" This datafile contains microsatellite genotype information of Mastomys natalensis. General comment: "-9" describes a missing datapoint Column headings: ID --> unique code of a captured and genotyped animal. For field information, see file "Field data Gryseels et al. Rodent urban-rural genetic contrast.txt" Info for STRUCTURE: Sample location information --> A number code depicting the location (at the village level) the genotyped animal was captured. This information is used in the clustering algorithm in the software STRUCTURE. Info for STRUCTURE: Putative population origin --> A number code depicting the location the genotyped animal was captured. This information is used in the clustering algorithm in the software STRUCTURE. Remaining columns --> microsatellite repeat numbers at 15 different loci (column headers = names of the loci)IMa2 input fileThe exact input file as used in IMa2 analyses in the manuscript.IMa2 commandsLandscape CostsExplanations for datafile "Landscape costs.xls" This datafile contains the classes of the landscape elements present in the "Transect" study area of the manuscript and their associated "cost values" for Mastomys natalensis. Landscape cover data was gathered from the Africover Spatially Aggregated Multipurpose Landcover database of Tanzania from the Food and Agriculture Organization of the UN (available at http://www.africover.org). Details on the land cover classification used can be found in Di Gregorio & Jansen (2000). These landscape elements were then translated into "landscape cost" values for Mastomys natalensis by 10 specialists, which indicates the ease of movement and the quality of the habitat for M. natelensis for that landscape element. This cost layer was then further used as a "habitat layer" in the software Circuitscape (see manuscript for details)