Ticks occupy diverse habitats, increasing the risk of human exposure. Assessing the public health threat posed by ticks requires rigorous monitoring of their distribution and of the prevalence of tick-borne pathogens. In France since 2017, the citizen science program CiTIQUE monitors human tick bites through multiple complementary approaches. Citizens can report bites and submit biting ticks to a national tick bank for research and surveillance. This study aimed to investigate human exposure to tick-borne microorganisms including pathogens across France, using ticks submitted through the CiTIQUE program. In total, 2009 ticks were selected from the CiTIQUE tick bank, identified, and screened for microorganisms using a real-time microfluidic PCR method. Most bites involved Ixodes ricinus nymphs except in Mediterranean regions where Dermacentor and Rhipicephalus ticks were more common. Twenty-six microorganisms were detected, eighteen of which are potentially pathogenic to humans. These pathogens were widely distributed across the country. Borrelia spp. were the most frequently detected pathogens with spatial variation among regions. Anaplasma phagocytophilum infection rates varied from region to region. Neoehrlichia mikurensis was found in seven out of twelve French regions. Rickettsia species diversity was highest in the southeast, associated with a greater diversity of vectors. Five percent of ticks were infected with more than one pathogen. Although spatial heterogeneity was observed, no region was free of infected ticks. This study demonstrates the power of citizen science for nationwide surveillance of tick-borne pathogens, providing a large-scale overview of pathogen diversity and distribution across France from crowdsourced tick data.
We evaluated five stacking-based meta-models – Multiple Linear Regression, Random Forest, XGBoost, and also Random Forest and XGBoost with environmental covariates (RF+ and XGB+, respectively) – against the multi-model median (MMM) and best individual process-based models for gross primary production (GPP), ecosystem respiration (RECO) and net ecosystem exchange (NEE) at two cropland and two grassland sites. We tested two validation strategies for GPP and RECO (70%/30% training/validation approach and the time-aware leave-one-year-out (LOYO) method), and three strategies for NEE (70%/30%, complete LOYO and independent validation using the meta-model based RECO-GPP). All meta-models were associated with improved RMSE, bias and correlation. Based on the LOYO validation strategy, average correlation increase was ∼ 2 % for GPP (0.2 %–4.4 %), 9 % for RECO (5 %–13.6 %) and 8 % for NEE (0.5 %–12.5 %). In the case of the independent validation strategy correlation increase was ∼ 40 % for NEE (24.5 %–64 %). Bias was nearly eliminated except at one cropland site. Wilcoxon signed-rank tests confirmed that improvements were statistically significant for 72 % of model-site-variable combinations, with particularly robust results for grassland sites where all meta-models significantly outperformed MMM, while one cropland site with limited data showed no significant improvements. SHapley Additive exPlanations (SHAP) analysis of XGB+ showed that diverse individual models, not always the top performers, contributed most, and that temperature – especially for RECO in croplands and NEE in grasslands – was the dominant environmental driver, while precipitation had minor effects. These findings highlight the predictive and diagnostic advantages of meta-modeling approaches over MMM, with potential applications across agroecosystem, Earth system and environmental model ensembles.
Abstract Measurement uncertainty can affect the classifications of individual as positive or negative, and thus, the diagnostic performances of a test. Existing methods to assess measurement uncertainty and its impact on diagnostic performance are difficult to apply in the absence of a gold standard. We proposed a method applicable to any quantitative diagnostic test and in the absence of a gold standard, and applied it to ELISA tests for Q fever serology in ruminants. We assessed measurement uncertainty using a mixed-effects model on data from an inter-laboratory proficiency testing. Then, we estimated the sensitivities and specificities accounting for measurement uncertainty. To do so, we combined, for each individual of a sample representative of the population, the probability of being truly seropositive and the probability of being positive when retested in another laboratory. We also estimated the sensitivity and specificity of each laboratory and of each batch, taking into account their bias. While the sensitivities and specificities of the ELISA tests were slightly affected by the measurement uncertainty, they varied between laboratories and between batches. This highlights the importance of harmonising analytical practices across laboratories and calibrating batches. The extent to which measurement uncertainty impacts diagnostic performance depends not only on the values of the inter- and intra-laboratory standard deviations, but also on the position of the cut-off in the population’s measurand distribution. Therefore, in addition to the analytical performance of a test, the position of the cut-off relative to the distribution of test value is an essential parameter to consider.
Les pullulations du campagnol terrestre provoquent d’importantes pertes fourragères et favorisent l’émergence de risques sanitaires dans les systèmes d’élevage herbagers de moyenne montagne. Le rôle de la composition botanique des prairies sur la dynamique spatiale de l’espèce est encore mal connu. Cette étude vise à évaluer dans quelle mesure la typologie multifonctionnelle des prairies du Massif central (typologie AEOLE), classant les prairies selon leur flore, permet de prédire le risque d’infestation par le campagnol terrestre dans les parcelles. L’étude a été conduite sur le domaine de l’Herbipôle INRAE de Laqueuille (Massif du Sancy, Puy-de-Dôme) au cours des phases de croissance (2024) et de pic (2025) du cycle de pullulation. 84 faciès de prairies rattachés à 10 types AEOLE ont été échantillonnés, dans lesquels les densités de campagnols ont été estimées par la méthode des diagonales indiciaires. Des modèles statistiques ont permis d’évaluer le pouvoir explicatif de la typologie AEOLE. Les résultats montrent des contrastes de densités de campagnols, principalement expliqués par le type de prairie. Les parcelles de fauche à fertilité moyenne ou élevée présentent les densités les plus élevées et constituent les principaux foyers de pullulation. Les pâtures, les prairies d’altitude, les landes et les prairies de fauche maigres sont, elles, faiblement colonisées. La typologie AEOLE constitue un outil opérationnel pour identifier les parcelles à haut risque de pullulation.
