Spatial Interpolation in Applied Insect Ecology: A Review, Including Guidelines and Datasets for Practical Use

Spatial interpolation represents a fundamental approach in applied insect ecology, offering insight into species distributions and supporting biodiversity analysis, pest management and disease vector mapping. Insects - including important pollinators - face escalating threats due to habitat loss, climate change and anthropogenic pressures. As data‐driven decisions become more critical in addressing these ecological challenges, spatial interpolation techniques such as kriging and regression‐based models have become essential for estimating insect abundance in unsampled areas. This paper offers an in‐depth review of both geostatistical and non‐geostatistical methods employed in insect ecology, including ordinary kriging, universal kriging and machine learning‐based methods such as random forests and maximum entropy. We present a structured overview of their applications in pest management, disease vector mapping and biodiversity monitoring, and we provide practical guidelines for selecting appropriate spatial interpolation methods. In addition, we present several datasets that can support case studies in spatial modelling for insect ecology. Our findings underscore the advantages of integrating geostatistical approaches with environmental variables to enhance the accuracy of species distribution models. This review serves as a resource for entomologists and researchers seeking to advance ecological monitoring and management through spatial interpolation techniques.