Details

Autor(en) / Beteiligte

• Rhodes, Charlotte G
• Loaiza, Jose R
• Romero, Luis Mario
• Gutiérrez Alvarado, José Manuel
• Delgado, Gabriela
• Rojas Salas, Obdulio
• Ramírez Rojas, Melissa
• Aguilar-Avendaño, Carlos
• Maynes, Ezequías
• Valerín Cordero, José A
• Soto Mora, Alonso
• Rigg, Chystrie A
• Zardkoohi, Aryana
• Prado, Monica
• Friberg, Mariel D
• Bergmann, Luke R
• Marín Rodríguez, Rodrigo
• Hamer, Gabriel L
• Chaves, Luis Fernando

Titel

Anopheles albimanus (Diptera: Culicidae) Ensemble Distribution Modeling: Applications for Malaria Elimination

Ist Teil von

• Insects (Basel, Switzerland), 2022-02, Vol.13 (3), p.221

Ort / Verlag

Switzerland: MDPI AG

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• In the absence of entomological information, tools for predicting spp. presence can help evaluate the entomological risk of malaria transmission. Here, we illustrate how species distribution models (SDM) could quantify potential dominant vector species presence in malaria elimination settings. We fitted a 250 m resolution ensemble SDM for Wiedemann. The ensemble SDM included predictions based on seven different algorithms, 110 occurrence records and 70 model projections. SDM covariates included nine environmental variables that were selected based on their importance from an original set of 28 layers that included remotely and spatially interpolated locally measured variables for the land surface of Costa Rica. Goodness of fit for the ensemble SDM was very high, with a minimum AUC of 0.79. We used the resulting ensemble SDM to evaluate differences in habitat suitability (HS) between commercial plantations and surrounding landscapes, finding a higher HS in pineapple and oil palm plantations, suggestive of presence, than in surrounding landscapes. The ensemble SDM suggested a low HS for at the presumed epicenter of malaria transmission during 2018-2019 in Costa Rica, yet this vector was likely present at the two main towns also affected by the epidemic. Our results illustrate how ensemble SDMs in malaria elimination settings can provide information that could help to improve vector surveillance and control.