DOI: 10.5061/dryad.n8pk0p387
title: Temporal variability and flooding influence the ecological niche of <em>Biomphalaria</em> intermediate hosts for <em>Schistosoma mansoni</em> in rural Uganda

description: Understanding the niches of intermediate hosts and vectors for
 environmentally transmitted pathogens is crucial for identifying endemic
 areas, assessing habitat suitability, and targeting interventions. This
 study focuses on intermediate hosts of intestinal schistosomes, with over
 700m people at risk of lifelong infection. We compared habitat suitability
 and species interactions across 674 sites in 52 villages in rural Uganda
 between 2022–2024, capturing a severe flooding event. Spatiotemporal
 models incorporating a polygon-based method to account for space with time
 as a fixed effect were developed to analyse snail abundance for
 Biomphalaria sudanica and B. stanleyi. B. sudanica was associated with
 marshy sites near lake shorelines and presence of hyacinths, while B.
 stanleyi was more likely found in deeper waters with Vallisneria plants.
 However, cohabitation was common for both species. Habitat suitability for
 each species fluctuated temporally, and more starkly with extreme
 flooding, resulting in switching of species dominance. Our study suggests
 that events consistent with climate change may influence habitat
 suitability without necessitating an expansion of environmental areas. Our
 models enable tracking of dynamic ecological niches that, if replicated
 elsewhere and for other intermediate hosts or vectors, can be used to
 better target environmental and community interventions as environmental
 conditions change.
description: # Temporal variability and flooding influence the ecological niche of
 *Biomphalaria* intermediate hosts for *Schistosoma mansoni* in rural
 Uganda This repository contains the code for running the modelling
 pipeline in the paper "Temporal variability and flooding influence
 the ecological niche of *Biomphalaria* intermediate hosts for *Schistosoma
 mansoni* in rural Uganda". Journal: *Proceedings of The Royal Society
 B* Article DOI: 10.1098/rspb.2025.2083. ### Study summary This study,
 conducted within the SchistoTrack cohort in rural Uganda, specifically the
 districts of Buliisa, Pakwach, and Mayuge, analysed the ecological niche
 and species interactions of *Biomphalaria sudanica* and *B. stanleyi*,
 intermediate hosts of *Schistosoma mansoni*, across 674 water sites from
 2022–2024. Data were collected during four malacology surveys spanning dry
 and rainy seasons, including a major flooding event in 2022.
 Spatiotemporal models using polygon-based methods were developed to assess
 snail abundance and ecological niche variability across sites and time.
 ### Packages and package versions R version 4.2.1 (2022-06-23)
 tidyverse_1.3.1 glmmTMB_1.1.10 lmtest_0.9-40 DHARMa_0.4.5 pROC_1.18.0
 broom.mixed_0.2.9.4 performance_0.13.0 zeallot_0.1.0 MuMIn_1.48.4
 caret_6.0-92 deldir_1.0-6 sp_1.5-0 maptools_1.1-4 sf_1.0-7
 geosphere_1.5-20 factoextra_1.0.7 cluster_2.1.3 spdep_1.3-10 ### Files
 Data to run the snail models are included in a .csv and .RData file. It is
 recommended to use the .RData file so that the structure of the dataframe
 remains intact (e.g. for variables that are factors, etc.). * `dict.xlsx`:
 Data dictionary including variable definitions and relevant units. *
 `snail_models.R`: Code to run variable selection and GLMM models to
 identify the ecological niche of each species. Code for model validation
 using stratified k-fold and leave-one-out cross-validation methods. Needs
 to import `sites_snails_models.RData.` * `polygons.R`: Code for the
 construction of village- and cluster-based polygons. Data not included as
 they include household GPS locations and identify study participants that
 need to remain anonymous. * `spatial_autocorrelation.R`: Code to compute
 Moran's *I* to identify potential spatial autocorrelation. Data
 includes GPS locations and are thus not included (as above). *
 `sites_snails_models.RData`: Dataframe used for the snail models
 (anonymised villages and does not include any GPS locations). Recommended
 to load this when running `snails_models.R`. * `data.csv`: Same as
 `sites_snails_models.RData` but in `.csv` form for easier viewing.