Scholarlink

<p>Knowledge of where wildlife species are and in what number is critical to support robust contingency planning for diseases affecting animal and human health. For common widespread species in particular, comprehensive surveillance is impractical and challenging to coordinate. This is where models can be useful. The ENETWILD project was commissioned by the European Food Safety Authority (EFSA) in 2017 to review available data and modelling methods to predict distributions for key species involved in diseases of concern. Here, we outline our latest methodology based on available occurrence data and apply it to predict distributions for twelve common European mammal species. We use the outputs of model evaluation to highlight areas where further data collection would be most helpful. Model performance was acceptable (Area-Under-Curve statistic above 0.7) within the limits of the available training data across eleven of the twelve species. However, beyond these limits the reliability of prediction was substantially reduced. Assessment of the available data suggested large areas of Eastern and Southern (particularly mountainous) parts of Europe have a distinct environmental signature not sufficiently captured by the existing sampling. Within Europe there remain environmental conditions which are not well represented by existing surveillance, predominantly in Eastern and Southern regions. To reliably apply a robust modelling approach to a Europe-wide context targeted data collection is required.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predicting the distribution of common wild mammal species across Europe - are there sufficient occurrence data?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月25日

查看全文
  • S. Croft,
  • DA Warren,
  • JA Blanco-Aguiar,
  • GC Smith

摘要

Knowledge of where wildlife species are and in what number is critical to support robust contingency planning for diseases affecting animal and human health. For common widespread species in particular, comprehensive surveillance is impractical and challenging to coordinate. This is where models can be useful. The ENETWILD project was commissioned by the European Food Safety Authority (EFSA) in 2017 to review available data and modelling methods to predict distributions for key species involved in diseases of concern. Here, we outline our latest methodology based on available occurrence data and apply it to predict distributions for twelve common European mammal species. We use the outputs of model evaluation to highlight areas where further data collection would be most helpful. Model performance was acceptable (Area-Under-Curve statistic above 0.7) within the limits of the available training data across eleven of the twelve species. However, beyond these limits the reliability of prediction was substantially reduced. Assessment of the available data suggested large areas of Eastern and Southern (particularly mountainous) parts of Europe have a distinct environmental signature not sufficiently captured by the existing sampling. Within Europe there remain environmental conditions which are not well represented by existing surveillance, predominantly in Eastern and Southern regions. To reliably apply a robust modelling approach to a Europe-wide context targeted data collection is required.