000162936 001__ 162936 000162936 005__ 20251017144631.0 000162936 0247_ $$2doi$$a10.3390/insects16090904 000162936 0248_ $$2sideral$$a145365 000162936 037__ $$aART-2025-145365 000162936 041__ $$aeng 000162936 100__ $$aEritja, Roger 000162936 245__ $$aIntegrating Citizen Science and Field Sampling into Next-Generation Early-Warning Systems for Vector Surveillance: Twenty Years of Municipal Detections of Aedes Invasive Mosquito Species in Spain 000162936 260__ $$c2025 000162936 5060_ $$aAccess copy available to the general public$$fUnrestricted 000162936 5203_ $$aThe spread of the invasive mosquitoes Aedes albopictus, Aedes aegypti, and Aedes japonicus in Spain represents an increasing public health risk due to their capacity to transmit arboviruses such as dengue, Zika, and chikungunya, among others. Traditional field entomological surveillance remains essential for tracking their spread, but it faces limitations in terms of cost, scalability, and labor intensity. Since 2014, the Mosquito Alert citizen-science project has enabled public participation in surveillance through the submission of geolocated images via a mobile app, which are identified using AI in combination with expert validation. While field surveillance provides high accuracy, citizen science offers low-cost, large-scale, real-time data collection aligned with open data management principles. It is particularly useful for detecting long-distance dispersal events and has contributed up to one-third of the municipal detections of invasive mosquito species since 2014. This study assesses the value of integrating both surveillance systems to capitalize on their complementary strengths while compensating for their weaknesses in the areas of taxonomic accuracy, scalability, spatial detection patterns, data curation and validation systems, geographic precision, interoperability, and real-time output. We present the listing of municipal detections of these species from 2004 to 2024, integrating data from both sources. Spain’s integrated approach demonstrates a pioneering model for cost-effective, scalable vector surveillance tailored to the dynamics of invasive species and emerging epidemiological threats. 000162936 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es 000162936 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000162936 700__ $$aSanpera-Calbet, Isis 000162936 700__ $$0(orcid)0000-0001-7046-2997$$aDelacour-Estrella, Sarah$$uUniversidad de Zaragoza 000162936 700__ $$0(orcid)0000-0001-8198-8118$$aRuiz-Arrondo, Ignacio$$uUniversidad de Zaragoza 000162936 700__ $$aPuig, Maria Àngels 000162936 700__ $$aBengoa-Paulís, Mikel 000162936 700__ $$aAlarcón-Elbal, Pedro María 000162936 700__ $$aBarceló, Carlos 000162936 700__ $$aMariani, Simone 000162936 700__ $$aMartínez-Barciela, Yasmina 000162936 700__ $$aBravo-Barriga, Daniel 000162936 700__ $$aPolina, Alejandro 000162936 700__ $$aPereira-Martínez, José Manuel 000162936 700__ $$aGonzález, Mikel Alexander 000162936 700__ $$aEscartin, Santi 000162936 700__ $$aMelero-Alcíbar, Rosario 000162936 700__ $$aBlanco-Sierra, Laura 000162936 700__ $$aMagallanes, Sergio 000162936 700__ $$aCollantes, Francisco 000162936 700__ $$aFerraguti, Martina 000162936 700__ $$aGonzález-Pérez, María Isabel 000162936 700__ $$aGutiérrez-López, Rafael 000162936 700__ $$aSilva-Torres, María Isabel 000162936 700__ $$aSan Sebastián-Mendoza, Olatz 000162936 700__ $$aCalvo-Reyes, María Cruz 000162936 700__ $$aMendoza-García, Marian 000162936 700__ $$aMacías-Magro, David 000162936 700__ $$aCisneros, Pilar 000162936 700__ $$aCevidanes, Aitor 000162936 700__ $$aFrontera, Eva 000162936 700__ $$aMato, Inés 000162936 700__ $$aFúster-Lorán, Fernando 000162936 700__ $$aDomench-Guembe, Miguel 000162936 700__ $$aRodríguez-Regadera, María Elena 000162936 700__ $$aCasanovas-Urgell, Ricard 000162936 700__ $$aMontalvo, Tomás 000162936 700__ $$aMiranda, Miguel Ángel 000162936 700__ $$aFiguerola, Jordi 000162936 700__ $$0(orcid)0000-0003-0663-8411$$aLucientes-Curdi, Javier$$uUniversidad de Zaragoza 000162936 700__ $$aGarriga, Joan 000162936 700__ $$aPalmer, John Rossman Bertholf 000162936 700__ $$aBartumeus, Frederic 000162936 7102_ $$11009$$2773$$aUniversidad de Zaragoza$$bDpto. Patología Animal$$cÁrea Sanidad Animal 000162936 773__ $$g16, 9 (2025), 904 [27 pp.]$$pInsects$$tInsects$$x2075-4450 000162936 787__ $$tIntegrated surveillance records of invasive mosquito detections in Spanish municipalities, 2004–2024$$whttps://doi.org/10.5281/zenodo.15869762 000162936 8564_ $$s3738168$$uhttps://zaguan.unizar.es/record/162936/files/texto_completo.pdf$$yVersión publicada 000162936 8564_ $$s2946860$$uhttps://zaguan.unizar.es/record/162936/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000162936 909CO $$ooai:zaguan.unizar.es:162936$$particulos$$pdriver 000162936 951__ $$a2025-10-17-14:26:28 000162936 980__ $$aARTICLE