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Data from: Climate change could fuel urinary schistosomiasis transmission in Africa and Europe

Cite this dataset

van der Deure, Tiem; Maes, Tim; Huyse, Tine; Stensgaard, Anna-Sofie (2024). Data from: Climate change could fuel urinary schistosomiasis transmission in Africa and Europe [Dataset]. Dryad.


This dataset contains primary, intermediate, and output data for "Climate change could fuel urinary schistosomiasis transmission in Africa and Europe". In this paper, we use mechanistic and correlative modelling to predict the distribution of schistosomiasis intermediate host snail Bulinus truncatus. Model projections suggest the suitable habitat for B. truncatus will increase by 17%, with new suitable habitat in Southern Europe and Central Africa, and a reduction in suitable habitat in the Sahel region.

README: Data from: Climate change could fuel urinary schistosomiasis transmission in Africa and Europe

This repo contains primary, intermediate, and output data for correlative and mechanistic models for Bulinus truncatus, a schistosomiasis intermedaite host snail. The mechanistic model used is a temperature-dependent intrinsic growth model, calibrated using life history data taken from the literature. The correlative model is an ensemble species distribution model, calibrated using occurrence data from natural history museums and GBIF and bioclimatic variables.

See also the associated publication: van der Deure, T., Maes, T., Huyse, T. & Stensgaard, A-S (2024). Climate change could fuel urinary schistosomiasis transmission in Africa and Europe. Global Change Biology.

Description of the data and file structure

The data is structured in 5 .zip files, which each contain a folder that can be used to reproduce a specific part of our analysis.

  • contains primary data for the mechanistic model, which are the life history data from several experiments in the literature. In all files, temperature is given in degrees C, proportions as a number between 0 and 1, and other traits have units indicated in the files. Individual-level snail data is from It contains the following files:
    • clutch_size.csv contains observations of the number of eggs per clutch, linked to individual snails
    • number_of_clutches.csv contains observations of the number of clutches per snail, linked to individual snails
    • survival_data.csv contains observations of the survival length for individual snails, and from groups of snails from various papers from the literature (for references: see the source column and the supplemantary materials of the publication).
    • traits_data.csv contains observations of hatching time, hatching success, and maturation time from the literature. For references, see the sourcepaper column and the supplemantary materials of the publication.
    • data_points_plot.csv contains summary data used in Figure 2 of the associated paper, but not in the analysis.
  • contains samples drawn from the posterior distribution of the life history traits, as well as summaries of the posteriors. These are derived from the life history data using hierarchicical Bayesian models, and are used to calculate the temperature-dependent intrinsic growth rate of Bulinus truncatus. We used a growing degree model to estimate hatching time and maturation time (with two variables T0 and GDD), a quadratic model to estimate egg-laying rate and lifespan (with three variables q, T0, and Tm), and a constant to estimate hatching success (with a single variable q). It has the following files:
    • draws_from_posterior.csv contains 5000 draws from each posterior distribution, labelled [trait]_[variable], e.g. hatchingtime_T0.
    • 5 further .csv files (corresponding to each trait) contain the mean, standard deviation, and 2.5% and 97.5% quantile estimate for each variable associated with that trait.
    • param_estimates.csv contains the same information as the 5 files named after traits collated into a single file.
  • contains three .csv files with all presences, the thinned presences, and background points, named all_presences.csv, thinned_presences.csv, and background_points.csv, respectively. These are used to calibrate the correlative model, and to assess the performance of the mechanistic model. The background points and thinned presences only contain coordinates, while the file will all presences also contains additional information such as the year of publication and identification method.
  • contains all outputs of the mechanistic model. It contains 5 sub-folders, one for the current climate (named current) and the others for each combination of socioeconomic pathways (SSP126 and SSP370) and time (middle of the century and end of the century) (named [ssp]_[time] where ssp is either ssp126 or ssp370 and time either 2041-2060 or 2081-2100. Each folder contains the following files:
    • mean.tif, which is a raster file with the mean intrinsic growth rate estimate.
    • median.tif, which is a raster file with the median intrinsic growth rate estimate.
    • prob_present.tif, which is the posterior probability that the intrinsic growth rate is above 0 for any cell.
    • std.tif, which is a raster file with the standard deviation between posterior samples. The four sub-folders for future climates have four corresponding netCDF (.nc) files, which have the same data, but separately for each global circulation model (GCM), where the Band dimension of the files contains each GCM.
  • contains all outputs from the correlative model. It has the following files and sub-folders:
    • current_set1.tif and current_set2.tif, the mean and median estimates of habitat suitability under current climatic conditions for 2 sets of predictor variables.
    • four files with the pattern average_[YY][SSP].tif, with average suitability predictions for each combination of SSP scenario and time.
    • four files with the pattern stdev_[YY][SSP].tif with the standard deviation between GCM scenarios.
    • A subfolder by_gcm, which contains result seperately for each GCM, organised in folders named in the same [YY][SSP] pattern, which each contain a file with the suitability prediction for each GCM.
    • A sub-folder mop with results from the mobility-oriented parity (mop) analysis. It contains 5 seperate .tif files for the mop results under current climate (mop_basic_surface_current.tif) and for each combination of time and ssp named mop_basic_surface_[YY][SSP].tif. In all the above filenames, [YY] is either 50 (for predictions for 2041-2060) or 90 (for predictions for 2081-2100), and [SSP] is either 126 for SSP126 or 370 for SSP370.


The code to reproduce the analysis is available at


European Commission, Award: 101000365, Horizon 2020

Knud Højgaards Foundation, Award: 16-11-1898

Knud Højgaards Foundation, Award: 20-11-0483

Research Foundation - Flanders, Award: 1S86319N