Using machine learning to identify the geographical drivers of Ceratitis capitata trap catch in an agricultural landscape

Abstract

The spatial distribution of Ceratitis capitata Wiedemann (Diptera: Tephritidae) trap catch was classified and related to a set of geographic variables to identify its main geographical drivers. Trap catch data were sourced from an area-wide integrated pest management (AW-IPM)1The integrated use of a combination of available management methods against a target pest including biological, chemical, and cultural management methods on an area-wide scale.1 programme and classified into statistically significant hot- and cold spots (HCSs)2Statistically significant spatial clusters of high values (hot spots) and low values (cold spots).2. Trap data of four consecutive fruiting seasons were combined to identify monthly and seasonal long-term HCSs. The main geographic drivers of the HCSs were identified using variable importance lists produced by the random forest (RF) machine learning (ML) algorithm. Long-term climate, topography, landscape and fruit fly management variables were used as predictor variables in RF to classify HCSs. The resulting RF models produced classification accuracies of up to 80%. In most cases, the most important variable was long-term rainfall, suggesting that this was the most prominent driver of C. capitata HCSs in our study region. The result of this study highlights the value of long-term pest monitoring data and long-term environmental data in understanding the spatial distribution of C. capitata trap catch in complex agricultural systems. This study sets out a framework to spatially quantify C. capitata trap catch into HCSs using monitoring data from AW-IPM programmes, enabling the investigation of complex ecological relationships through the use of ML algorithms. The results of these analyses could improve area-wide integrated fruit fly management programmes through more precise spatial planning of management actions, such as the sterile insect technique (SIT)3Biological environmentally-friendly insect pest control method.3, leading to better programme performance and reduced costs.