ENETwild modelling of wild boar distribution and abundance: update of occurrence and hunting data‐based models

Abstract

In October 2018 the ENETWILD consortium created suitability maps based on available data on wild boar occurrence at 10 km square resolution and initial version of abundance models based on hunting statistics at NUTS3 and NUTS2 resolution, that were statistically downscaled for MSs to 10x10 km grid squares. This report presents updated suitability map for wild boar presence based on additional occurrence data and new algorithms, and new models based on highresolution hunting yield data for MSs and neighbouring countries. New environmental variables closely associated with wild boar abundance and distribution were also included. Our results showed no consensus for a single best occurrence model: out of those tested, both Maxent and random forest could be considered the best options depending on the choice of assessment metric. Predictions from these models notably disagreed in eastern Europe where data on wild boar occurrence are limited. Despite agreement among models, predictions in the south appeared over-predicted, most likely due to a lack of contrasting absence data. Whilst there remain some methodological adjustments which could be tested, substantial improvement in the prediction from occurrence models relies on further collection of wild boar occurrence data in the east and complimentary data on survey effort in the south. The predictive performance of the hunting yield model was high. Although the incorporation of new data at higher spatial resolution markedly improved predictions, such data is still needed in some regions, ideally coupled with hunting effort, which would allow such estimates to be transformed into reliable densities. Comparison between predictions from the occurrence and hunting yield models showed they were statistically 1 ENETWILD Consortium: www.enetwild.com www.efsa.europa.eu/efsajournal 1 EFSA Supporting publication 2019:EN-1674Modelling wild boar distribution associated, but the strength of that relationship was dependent on the type of occurrence model and the bioregion. These findings are compatible with previous interpretations of the occurrence model, and highlight the relevance of obtaining more accurate data, especially from northern and eastern bioregions in Europe.

Modelling Spatial and Temporal Patterns of African Swine Fever in an Isolated Wild Boar Population to Support Decision-Making

“…The model identified a rate of spread of ASF of ∼4 km/month within the core population at densities around 15 boar/km 2 , reducing to nearer 2 km/month toward the population edge where densities were closer to that observed in Europe (5 boar/km 2 ). This compares well with an empirical description of 1.5 ± 1.3 km/month for the same disease in boar in Poland (27), 1-2 km/month in a number of eastern European countries (24), and a broader range of estimates for the unassisted spread of ASF cited in (7) between 8 and 25 km/year, though most fall between 10 and 17 km/year (0.8-1.4 km/month). Whilst not a definitive validation of our model, the similarity of our result with that of others suggests that our representation and parameterization of our model system is of some value and is free of substantial consistent bias.…”

The hard numbers of tuberculosis epidemiology in wildlife: A meta‐regression and systematic review

“…In Europe, TB prevalence in wild boar varied between 31.68% in Spain and 9.49% in France, with Italy (23.84%), Hungary (18.12%) and Portugal (13.03%) also being remarkably affected. All studied European countries showed higher estimated TB prevalence values, with the South bioregion that is comprised by Portugal, Spain and Italy, and where the estimation of wild boar density is higher (Enetwild consortium et al, 2019), also showed higher estimates of animal TB prevalence. Similar to the European badger model, larger sample sizes (>250) were associated with lower prevalence numbers, probably related to a more accurate assessment of estimated disease prevalence.…”

Strength of correlation between wildlife collision data and hunting bags varies among ungulate species and with management scale

“…Today, in addition to direct observations, the majority of European countries (64 %) uses hunting bag statistics as a proxy for assessing relative changes in the distribution and abundance of ungulates at national scales (Apollonio et al 2010;Putman et al 2011). Following the need of crossborder management (Linnell et al 2020), recent initiatives develop abundance models using hunting statistics at the European scale (ENETWILD-consortium et al 2019). Hunting bags, however, may not be reliable if the hunting effort of a species is not related to its density and when this relationship changes over time (Imperio et al 2010;Willebrand et al 2011;Ferretti et al 2016;Eriksen et al 2018).…”