Dataset and Scripts for Article: "Leveraging non-traditional vector surveillance data for reliable species distribution modeling: insights from Common house mosquito Culex pipiens in Europe"
Zenodo (CERN European Organization for Nuclear Research)
Abstract
ABSTRACT Gaps in health surveillance systems expose the shortage of real-time data required for public health decision‑making. Non‑traditional data (NTD) from citizen science can help bridge these gaps, but such data are often affected by heterogeneous sampling effort and spatiotemporal biases. This study investigates how NTD from the Mosquito Alert citizen science platform can be used to accurately model the distribution of the common house mosquito, Culex pipiens, a competent vector for West Nile virus across Europe. We address the inherent sampling biases of opportunistic data by applying thermal niche filters and using sampling effort as a proxy to generate reliable inferred-absences. Our findings demonstrate that citizen observations outdoors yield the most ecologically consistent predictions when validated against independent trap data, whereas indoor observations reflect the host-seeking and shelter-seeking behavior of mosquitoes combined with more opportunistic sampling by citizens. By accounting for spatial and temporal heterogeneity, we provide a methodological framework for integrating citizen-contributed images into public health surveillance. Ultimately, the research proves that carefully filtered citizen science data can enhance the resolution of species distribution models for critical disease vectors. Authors and Affiliations Catuxa Cerecedo-Iglesias— Centre d'Estudis Avançats de Blanes (CEAB-CSIC), Blanes, Spain (catuxa.cerecedo@ceab.csic.es)Marta Pardo-Araujo—Centre d'Estudis Avançats de Blanes (CEAB-CSIC), Blanes, Spain (mpardo@ceab.csic.es)John R.B. Palmer—Universitat Pompeu Fabra (UPF), Barcelona, Spain (john.palmer@upf.edu)Fabrizio Montarsi—Istituto Zooprofilattico Sperimentale Delle Venezie, Legnaro, Italy (fmontarsi@izsvenezie.it)Luisa Barzon—Microbiology and Virology Unit, Padova University Hospital, Padova, Italy; Department of Molecular Medicine, University of Padova, Padova, Italy (luisa.barzon@unipd.it)Anna Papa—Department of Microbiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece (annap@auth.gr)Katerina Tsioka—Department of Microbiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece (aik.tsioka@gmail.com)Sandra Gewehr—Ecodevelopment SA, 57010, Filyro, Greece (gewehr@ecodev.gr)Reina Sikkema—Viroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands (r.sikkema@erasmusmc.nl)Marteen J.J. Schrama—Universiteit Leiden, Leiden, The Netherlands (m.j.j.schrama@cml.leidenuniv.nl)Marion P.G. Koopmans— Viroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands (m.koopmans@erasmusmc.nl)rederic Bartumeus—Centre d'Estudis Avançats de Blanes (CEAB-CSIC), Blanes, Spain; Centre de Recerca Ecològica i Aplicacions Forestals (CREAF), Cerdanyola del Vallès, Barcelona, Spain; Institut Català de Recerca i Estudis Avançats (ICREA), Barcelona, Spain (fbartu@ceab.csic.es). Contents This repository contains the scripts for the primary statistical analyses presented in the manuscript titled Leveraging non-traditional vector surveillance data for reliable species distribution modeling: insights from Common house mosquito Culex pipiens in Europe, submitted to Ecological Informatics. It also provides details on how the figures included in the manuscript were generated. The DATA directory contains cleaned datasets derived from both citizen science observations and trap-based information. It also includes additional datasets related to land use and other spatial variables required for georeferencing the analyses and figures. Script follows this structure: Weather_predictors folder contains the Python and R scripts used to download ERA5 data and derive the weather predictors included in the SDMs. The analysis scripts are organized sequentially (from 01 to09) to reflect the workflow followed throughout the study. The figures folder contains the PDF files of the main figures presented in the manuscript. The output folder contains the main outputs generated by the analyses. The Data folder contains the citizen science observations obtained through the Mosquito Alert platform and the trap-based surveillance data for Culex pipiens collected within the VEO project Prerequisites R version: 4.6.1. packages: tidyverse, sf, parallel, dplyr, data.table, terra, patchwork, ggplot2, cowplot, glmmTMB, DHARMa, MuMIn, caret, pROC, scales, lubridate, eurostat, ggpubr, units. Python (3.9.12) packages: cdsapi
Authors 12
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Centre d'Estudis Avançats de Blanes
Affiliation as printed
Centre d'Estudis Avançats de Blanes
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Centre d'Estudis Avançats de Blanes
Affiliation as printed
Centre d'Estudis Avançats de Blanes
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Affiliation as printed
Universitat Pompeu Fabra
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Istituto Zooprofilattico Sperimentale delle Venezie
Affiliation as printed
Istituto Zooprofilattico Sperimentale delle Venezie
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University of Padua · Azienda Ospedale - Università Padova
Affiliation as printed
University of Padua
Azienda Ospedale - Università Padova
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Aristotle University of Thessaloniki
Affiliation as printed
Aristotle University of Thessaloniki
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Aristotle University of Thessaloniki
Affiliation as printed
Aristotle University of Thessaloniki
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Affiliation as printed
Ecodevelopment SA
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Affiliation as printed
Erasmus MC
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Maarten J. J. Schrama Aachen
Affiliation as printed
Leiden University
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Affiliation as printed
Erasmus MC
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Institució Catalana de Recerca i Estudis Avançats · Consejo Superior de Investigaciones Científicas · Centre for Research on Ecology and Forestry Applications
Affiliation as printed
Centre for Research on Ecology and Forestry Applications
Institució Catalana de Recerca i Estudis Avançats (ICREA)
Consejo Superior de Investigaciones Científicas
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