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Predicting rarity and decline in animals plants and mushrooms based on species attributes and indicator groups

机译:根据物种属性和指标组预测动物植物和蘑菇的稀有性和衰退

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摘要

In decisions on nature conservation measures, we depend largely on knowledge of the relationship between threats and environmental factors for a very limited number of species groups, with relevant environmental factors often being deduced from the relationship between threat and species traits. But can relationships between traits and levels of threats be identified across species from completely different taxonomic groups; and how accurately do well-known taxonomic groups indicate levels of threat in other species groups? To answer these questions, we first made a list of 152 species attributes of morphological and demographic traits and habitat requirements. Based on these attributes we then grew random forests of decision trees for 1183 species in the 18 different taxonomic groups for which we had Red Lists available in the Netherlands, using these to classify animals, plants, and mushrooms according to their rarity and decline. Finally, we grew random forests for four species groups often used as indicator groups to study how well the relationship between attribute and decline within these groups reflected that relationship within the larger taxonomic group to which these groups belong. Correct classification of rarity based on all attributes was as high as 88% in animals, 85% in plants, and 94% in mushrooms and correct classification of decline was 78% in animals, 69% in plants, and 70% in mushrooms. Vertebrates indicated decline in all animals well, as did birds for all vertebrates and vascular plants for all plants. However, butterflies poorly indicated decline in all insects. Random forests are a useful tool to relate rarity and decline to species attributes thereby making it possible to generalize rarity and decline to a wider set of species groups. Random forests can be used to estimate the level of threat to complete faunas and floras of countries or regions. In regions like the Netherlands, conservation policy based on attributes known to be relevant for the decline to birds, vertebrates or plants will probably also impact all aboveground terrestrial and freshwater macrofauna or macrophytes.
机译:在关于自然保护措施的决定中,我们很大程度上依赖于对非常少数物种组的威胁与环境因素之间关系的了解,而相关的环境因素通常是根据威胁与物种特征之间的关系推导出来的。但是,可以在完全不同的分类学组别的物种之间识别特征和威胁程度之间的关系吗?以及知名生物分类群如何准确地表明其他物种群的威胁程度?为了回答这些问题,我们首先列出了152种形态和人口统计学特征以及栖息地要求的属性。根据这些属性,我们在18个不同的生物分类组中为1183种树种种植了随机的决策树森林,我们在荷兰拥有这些植物的红色列表,并根据它们的稀有度和衰退程度将动物,植物和蘑菇分类。最后,我们为四个经常被用作指标组的物种组种植了随机森林,以研究这些组内属性与衰退之间的关系如何反映这些组所属的较大分类组内的关系。基于所有属性的稀有性正确分类在动物中高达88%,在植物中为85%,在蘑菇中为94%,对下降的正确分类在动物中为78%,在植物中为69%,在蘑菇中为70%。脊椎动物表明,所有动物的衰落都很好,所有脊椎动物的鸟类和所有植物的维管植物也都下降了。但是,蝴蝶不能很好地表明所有昆虫的数量都下降了。随机森林是将稀有性和衰退与物种属性相关联的有用工具,从而可以将稀有性和衰退归纳为更广泛的物种组。可以使用随机森林来估计对国家或地区完整动植物的威胁程度。在像荷兰这样的地区,基于与鸟类,脊椎动物或植物的衰落相关的已知属性的保护政策也可能会影响所有地上陆地和淡水大型动物或大型植物。

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