DOI: 10.5281/zenodo.15944869
title: Data and scripts from: Host competence–abundance relationships drive the dilution effect across multiple small mammal-borne pathogens

description: Data





Sites informations: 



a. Spatial information regarding the BioRodDis project sites in France. 

This CSV file contains the coordinates of each site (X_longitude, Y_Latitude) and altitude in meter. The sites are coded as follows: FR = France, F = forests, P = urban parks; followed by the locality FRFGLA (La Glacière) , FRFGRI (Griffe du Diable), FRFMIG (Mignovillard), FRFCOR (Cormaranche-en-Bugey), FRPDLL (Domaine Lacroix Laval), FRPLTO (Lyon Tête d’Or).

File name : CoordinatesSites _FranceBioRodDis2020-2022.csv

b. Trapping survey regarding the BioRodDis project sites in France. 

This CSV file contains trapping information, including:





Location: Locality and Code Locality (FR = France, F = forests, P = urban parks), followed by the specific localities: FRFGLA (La Glacière), FRFGRI (Griffe du Diable), FRFMIG (Mignovillard), FRFCOR (Cormaranche-en-Bugey), FRPDLL (Domaine Lacroix Laval), FRPLTO (Lyon Tête d’Or).




Date Information: Date_Installation (dd/mm/yyyy) and Period (fall = f; spring = s, followed by the year).




Survey Details: Number_Line, Date_Survey (dd/mm/yyyy), Number_Survey (survey frequency), Number_Trap (1-20).




Trap Location: Latitude_Trap, Longitude_Trap, Code_Trapping, and Trapping status (0 = no trapping; 1 = capture; 2 = FV = closed/empty; 4 = moved; X = other).




Identification: Code_Id (NCHA-IDnumber).




Methods: Method of detection (morphology or molecular: COI, Ap-PCR).




Species: Species trapped.




Trap Type: Habitat, INRA_Forest, BTS for rats, INRA_building.



File name : TrappingSurvey_FranceBioRodDis2020-2022.csv

For additional information on trapping, please refer to: https://bdj.pensoft.net/article/95214/

 

2.               Pathogens information 

a. Bacterial Species from Metabarcoding Analysis of Spleen Samples

i) Post-sequencing Processing

Information concerning the small mammal samples and the positive and negative controls multiplexed in the 16Sv4 and MiSeq sequencing run and MiSeq raw sequences of the 16Sv4 rRNA gene from spleen of small mammal samples 

The sequencing data and related information are available here: https://zenodo.org/records/12518286 

 ii) Post-filtering Processing

Table of abundance of ASVs for small mammals samples whose spleen have been sequenced

This biom file contains the abundance data, representing the number of reads obtained after data filtering, for each ASV from the MiSeq runs and for each small mammal spleen sample (N=1270). It also includes taxonomy information for each ASV (based on the Silva database). The results from the two PCR replicates per sample were summed and filtered using both negative and positive controls.

File name: Biom_Spleen_EuropeanASVs_SILVA138.1_Pintail100_2023-03-02.biom1

b. Viruses from Serological Analysis

Presence/absence table for the selected pathogens across individual hosts

The CSV file includes presence/absence data for the selected pathogens across individual hosts (N=1542), identified by their unique codes (NCHA-ID number). Orthopoxvirus (Poxv) is detected through IFA serological analyses. Pathogens are labeled as follows: Poxv = Orthopoxvirus.

File name: VirusIFA.csv

c. Leptospirosis from Lip32 Gene Amplicon qPCR Analysis

Presence/absence table for the selected pathogens across individual hosts

The CSV file includes presence/absence data for the selected pathogens across individual hosts (N=1549), identified by their unique codes (NCHA-ID number). Pathogenic leptospirosis is detected through the LIP32 gene. Pathogens are labeled as follows: Lept = Leptospirosis.

File name: Lepto_Lip32.csv

For more information on the pathogens, please see the article https://doi.org/10.1101/2025.06.23.661073 and the associated data:  https://10.5281/zenodo.15671364

 

3. Final preprocess

The final preprocessed CSV file contains all the essential information for performing statistical analyses on biodiversity and pathogen prevalence, with the following variables:





Code_Id: NCHA-IDnumber (N=1549)




Pathogens: Name of selected pathogens (N=7)




Presence: Presence/absence of pathogens (0/1)




Species: Species of small mammals (N=9)




Code_Species: Taxonomic abbreviation (first letter of the genus and the first three letters of the species)




Code_Locality: Code for the studied localities ": Sampling locations, with codes such as FRFGLA (La Glacière) , FRFGRI (Griffe du Diable), FRFMIG (Mignovillard), FRFCOR (Cormaranche-en-Bugey), FRPDLL (Domaine Lacroix Laval), FRPLTO (Lyon Tête d’Or).(N=6)




Periods: Seasons (s = spring, f = fall) followed by the year (N=5)




Period_Loc: Groupings of periods and sites (N=23)




PeriodLocSp: Association of periods, sites, and species




Sex: Sex (F for female, M for male)




AgeClass: Age class representing the functional group (0 = immature/juvenile mature, 1 = adult mature)




AnthroPCA1: First axis of the PCA representing the level of site anthropization




biogeo_PCA1: First axis of the PCA representing biogeographic differences between sites




adjTS_SP_relativ: Relative abundance of species by sites and periods




Richness_Sp: Species richness by sites and periods




shannonSp: Species diversity (Shannon index) by sites and periods




score1_TS_sp: First axis of species composition by sites and periods.



File name: FinalPreprocess_SpatialBiodivSamplingPath_FrBioRodDis2020-2022.csv

Scripts 

The zip file contains various scripts corresponding to each step of the analysis, available in .rmd (Rmarkdown format and html) for R scripts.

Preprocess Scripts:

- RMarkdown script (with detailed HTML output) on the preprocessing of anthropization indicators "Antro" and environmental indicators "Biogeo" for the spatial sites: Spatial.rmd

- RMarkdown script (with detailed HTML output) for preprocessing biodiversity indices from trapping success: SurveyBodiv0524.rmd

- RMarkdown script (with detailed HTML output) for preprocessing ID sampling information, including species identification, sex determination, and maturity calculation based on sexual characteristics: PreprocessSampling_ArticleFinal.rmd

- RMarkdown script (with detailed HTML output) on pathogen preprocessing (pathogen dataset assembly, selection of potential pathogenic bacteria, prevalence calculation).: Pathogens_scriptFinal_article.rmd

Analysis biodiversity-prevalence of pathogens scripts: 

- RMarkdown script (with detailed HTML output) on the analyses of the relationships between small mammal species biodiversity and pathogen prevalence, as well as the underlying mechanisms: PathPrev_Biodiv_Article.rmd

- RMarkdown script (with detailed HTML output) on additional analyses of the relationships between small mammal species biodiversity and pathogen prevalence, with a focus on different indicators such as species richness, species composition, and the use of various statistical methods like ANCOVA (GLMM) and SEM: Supp_PathPrev_Biodiv_Article.rmd