High-resolution prediction models for Rhipicephalus microplus and Amblyomma cajennense s.l. ticks affecting cattle and their spatial distribution in continental Ecuador using bioclimatic factors

Author:

Pérez-Otáñez Ximena1,Rodríguez-Hidalgo Richar2,Enríquez Sandra2,Celi-Erazo Maritza2,Benítez Washington2,Saegerman Claude3,Vaca-Moyano Franklin2,Ron-Garrido Lenin2,Vanwambeke Sophie O.1

Affiliation:

1. UCLouvain

2. Universidad Central del Ecuador

3. University of Liege

Abstract

Abstract In Ecuador, the main tick species affecting cattle are Rhipicephalus microplus and Amblyomma cajennense sensu lato. Understanding their spatial distribution is crucial. To assess their distribution, data from 2895 farms visited between 2012 and 2017 were utilized. Ticks were collected during animal inspections, with each farm's location georeferenced. Bioclimatic variables and vapor pressure deficit data from CHELSA were overlaid to develop predictive maps for each species using Random Forest (RF) models. The cross-validation results for RF prediction models showed high accuracy for both R. microplus and A. cajennense s.l. presence with values of Accuracy = 0.97 and 0.98, Sensitivity = 0.96 and 0.99, and Specificity = 0.96 and 0.93, respectively. A carefully selected subset of bioclimatic variables was used to describe the presence of each tick species. Higher levels of precipitation had positive effect on the presence of R. microplus but a negative effect on A. cajennense s.l. In contrast, isothermality (BIO3) resulted as more important for the presence of A. cajennense s.l. compared to R. microplus. As a result, R. microplus had a broader distribution across the country, while A. cajennense s.l. was mainly found in coastal areas with evident seasonality. The coexistence of both species in certain regions could be attributed to transitional zones, whereas high altitudes limited tick presence. This information can aid in developing appropriate tick management plans, particularly considering A. cajennense s.l.'s broad host range species and R. microplus's specificity for cattle. Moreover, the predictive models can identify areas at risk of associated challenging hemoparasite, requiring special attention and mitigation measures.

Publisher

Research Square Platform LLC

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