An Integrated, Tentative Remote-Sensing Approach Based on NDVI Entropy to Model Canine Distemper Virus in Wildlife and to Prompt Science-Based Management Policies

SIMPLE SUMMARY: Canine distemper virus (CDV) is a pathogen that affects wildlife with particular regard to Canidae family such as red foxes, wolves, etc. In this study, we focus on CDV outbreaks in the Aosta Valley territory, an alpine region in the NW of Italy which was affected by important waves of this disease during the years 2015–2020 (hereinafter called τ). Ground data are collected on the entire territory at a municipality level. The detection of the canine distemper virus is performed by means of real-time PCR. By adopting satellite remote-sensing data, we notice that CDV trends are strongly related to anomalies in the NDVI entropy changes through (τ). A tentative local model is developed concerning on-the-ground data, helping veterinarians, foresters, and wildlife ecologists enforce management health policies in a One Health perspective. ABSTRACT: Changes in land use and land cover as well as feedback on the climate deeply affect the landscape worldwide. This phenomenon has also enlarged the human–wildlife interface and amplified the risk of potential new zoonoses. The expansion of the human settlement is supposed to affect the spread and distribution of wildlife diseases such as canine distemper virus (CDV), by shaping the distribution, density, and movements of wildlife. Nevertheless, there is very little evidence in the scientific literature on how remote sensing and GIS tools may help the veterinary sector to better monitor the spread of CDV in wildlife and to enforce ecological studies and new management policies in the near future. Thus, we perform a study in Northwestern Italy (Aosta Valley Autonomous Region), focusing on the relative epidemic waves of CDV that cause a virulent disease infecting different animal species with high host mortality. CDV has been detected in several mammalian from Canidae, Mustelidae, Procyonidae, Ursidae, and Viverridae families. In this study, the prevalence is determined at 60% in red fox (Vulpes vulpes, n = 296), 14% in wolf (Canis lupus, n = 157), 47% in badger (Meles meles, n = 103), and 51% in beech marten (Martes foina, n = 51). The detection of CDV is performed by means of real-time PCR. All the analyses are done using the TaqMan approach, targeting the chromosomal gene for phosphoprotein, gene P, that is involved in the transcription and replication of the virus. By adopting Earth Observation Data, we notice that CDV trends are strongly related to an altitude gradient and NDVI entropy changes through the years. A tentative model is developed concerning the ground data collected in the Aosta Valley region. According to our preliminary study, entropy computed from remote-sensing data can represent a valuable tool to monitor CDV spread as a proxy data predictor of the intensity of fragmentation of a given landscape and therefore also to monitor CDV. In conclusion, the evaluation from space of the landscape variations regarding the wildlife ecological corridors due to anthropic or natural disturbances may assist veterinarians and wildlife ecologists to enforce management health policies in a One Health perspective by pointing out the time and spatial conditions of interaction between wildlife. Surveillance and disease control actions are supposed to be carried out to strengthen the usage of geospatial analysis tools and techniques. These tools and techniques can deeply assist in better understanding and monitoring diseases affecting wildlife thanks to an integrated management approach.