Open AccessValidation and inference of high‐resolution information (downscaling) of ENETwild abundance model for wild boar
Author(s) -
Acevedo Pelayo,
Croft Simon,
Smith Graham C,
BlancoAguiar Jose Antonio,
FernandezLopez Javier,
Scandura Massimo,
Apollonio Marco,
Ferroglio Ezio,
Keuling Oliver,
Sange Marie,
Zanet Stefania,
Brivio Francesca,
Podgórski Tomasz,
Petrović Karolina,
Soriguer Ramon,
Vicente Joaquín
Publication year - 2020
Publication title -
efsa supporting publications
Language(s) - English
Resource type - Journals
ISSN - 2397-8325
DOI - 10.2903/sp.efsa.2020.en-1787
Subject(s) - wild boar , downscaling , boar , abundance (ecology) , range (aeronautics) , environmental science , inference , grid cell , resolution (logic) , climate change , geography , ecology , computer science , biology , grid , botany , artificial intelligence , sperm , materials science , geodesy , composite material
The ENETWILD consortium provided in August 2019 a map at 10x10 km resolution for wild boar abundance based on hunting data. The availability of prediction maps at a spatial resolution comparable with the one of the home range of wild boar can be useful for further evaluation of risk of spread of African swine fever (ASF). Therefore, predictions of abundance on the basis of the wild boar home range are required. The downscaling procedure needs information on what resolution level is being used for predictions (hunting grounds, municipalities and NUTS3). This report presents the validation of previously produced hunting yield maps (10x10 km resolution) and new model projections downscaled at 2x2 km resolution. A new dataset based on hunting bag numbers was used as external data for validation. These data were arranged at two levels: at country level for the European scenario and at NUTS3 level for a scenario in Spain, where the data availability is higher than the rest of Europe in terms of quantity and quality. Very similar geographical patterns of wild boar abundance were obtained when the models were transferred to 2x2 km grid. The downscaled model predictions were aggregated at country and NUTS3 levels and compared against the external dataset. Our study confirmed that both 10x10 km and 2x2 km resolutions were able to detect spatial variation in wild boar hunting bags (high model performance) and to predict the numbers of wild boar hunted with relative precision (moderate model accuracy). Nevertheless, an overestimation of absolute number of hunted wild boar was observed using both resolutions. Reasons for this overestimation are discussed in this report. The linearity between predictions of hunting yield and external dataset was maintained, indicating that hunting yield predictions can be considered as a good proxy of wild boar abundance. Therefore, updated wild boar hunting yield data, collected at the finest spatial resolution as possible, is needed to correctly recalibrate our model at regional level, an in particular in eastern European countries.