Microclimate Data Improve Predictions of Insect Abundance Models Based on Calibrated Spatiotemporal Temperatures

International audienceSpecialty section: This article was submitted to Invertebrate Physiology, a section of the journal Frontiers in Physiology A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11 • C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies

Gestion intégrée des principaux ravageurs du cotonnier au Sénégal et en Afrique occidentale

Les pertes de récolte dues aux insectes ravageurs, en culture cotonnière, restent importantes au Sénégal et en Afrique occidentale. Les solutions proposées pour combattre ces insectes nuisibles sont axées sur la lutte chimique. Toutefois, compte tenu des effets néfastes de l’utilisation des pesticides (apparition de souches résistantes, pollution de l’environnement, intoxications) la recherche d’alternatives s’impose. Les principales espèces de ravageurs rencontrées, en particulier Helicoverpa armigera (Hübner), insecte redoutable sur cotonnier, peuvent faire l’objet d’une lutte à l’aide de méthodes alternatives utilisant des produits biologiques et la sélection d’espèces résistantes aux attaques du ou des ravageur(s). Les différentes méthodes de protection phytosanitaire du cotonnier pratiquées en alternative ou combinées avec les pesticides ainsi que divers autres aspects de la problématique de gestion des insectes ravageurs du cotonnier sont passés en revue.© 2015 International Formulae Group. All rights reserved.Mots clés: Cotonnier, ravageurs, lutte, pesticides, Sénégal, Afrique Occidental