DOI: 10.6078/d1b715
title: A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning

description: Introduction - Control of vector populations is an effective strategy for
 addressing vector-borne disease transmission. Effective vector control
 requires knowledge of habitat use and connectivity. Our goal was to
 improve this knowledge for the tsetse species Glossina pallidipes, a
 vector of animal African trypanosomiasis, which is a wasting disease in
 livestock and represents a serious socioeconomic burden across sub-Saharan
 Africa. Methods and Results - We used random forest regression to: (i)
 Build and integrate models of G. pallidipes habitat suitability and
 genetic connectivity across Kenya and northern Tanzania, and (ii) provide
 novel vector control recommendations. Inputs for the models included
 field-survey records from 349 trap locations, genetic data from 11
 microsatellite loci from 659 flies and 29 sampling sites, and remotely
 sensed environmental data. The suitability and connectivity models
 explained approximately 80% and 67% of the variance in the occurrence and
 genetic data, and exhibited high accuracy based on cross-validation. The
 bivariate map showed that suitability and connectivity vary independently
 across the landscape and inform vector control recommendations. Post-hoc
 analyses show spatial variation in the correlations between the most
 important environmental predictors from our models and each response
 variable (e.g. suitability and connectivity) as well as heterogeneity in
 expected future climatic change of these predictors. Discussion - The
 bivariate map suggests vector control is most likely to be successful in
 the Lake Victoria basin, and supports the previous recommendation that
 most of eastern Kenya should be managed as a single unit. We further
 recommend that future monitoring efforts should focus on tracking
 potential changes in vector presence and dispersal around the Serengeti
 and the Lake Victoria basin based on projected local climatic shifts. The
 strong performance of the spatial models suggests potential for our
 integrative methodology to be used to understand future impacts of climate
 change in this and other vector systems. 
description: A description of the methods used to collect and process this
 dataset is available in the corresponding paper (Bishop et al.,
 2021).
description: The Bishop2021_HabitatSuitability_Data.csv file contains the data
 used in the habitat suitability model (i.e. information about the trap
 locations). Abbreviations: TrapNo (Trap Number), Lat (Latitude), Long
 (Longitude), NumberDays (number of days between StartDate (date traps were
 set out) and EndDate (date flies were collected from traps)).<br>
 <br> The Bishop2021_GenConModel_AllData.csv file contains the data
 used in the genetic connectivity model. All columns starting with
 "BIO" are the median values of each bioclimatic variable along
 straight paths between sites. The "kernel" column contains the
 median values along straight paths between sites from the kernel density
 layer. The "pixvals" column contains the geographic distance
 between sites in units of pixels (1 km resolution). The
 "Distance" column contains the Cavalli-Sforza and Edwards’ chord
 (CSE) genetic distances between sites. See methods of the paper (Bishop et
 al., 2021) for more detail.<br> <br> The
 Gpd_KenTza_11loci_659indv_genepop.txt file contains the microsatellite
 genotypes for the 659 individuals used in this study in GenePop format
 (https://genepop.curtin.edu.au/) and the
 Gpd_KenTza_11loci_659indv_sample_info.csv file provides information about
 these individuals.