five

Life-history characteristics of European birds

收藏
NIAID Data Ecosystem2026-03-10 收录
下载链接:
http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.n6k3n
下载链接
链接失效反馈
官方服务:
资源简介:
Motivation: Birds are an extremely diverse group in terms of adaptations to environmental conditions, which is reflected in their life histories and ecological traits. Recently, functional aspects of avian diversity have been used frequently in comparative analyses as well as in community ecology studies; thus, open access to complete datasets of traits will be valuable. We focused on European bird species and compiled information about crucial ecological traits. This dataset is thus useful for research investigating large-scale patterns in European avifauna and can be used in various analyses in evolutionary ecology, macroecology or conservation biology. Main types of variables contained: We chose several types of avian traits, such as morphological (e.g., weight, wing, bill or tarsus length), reproductive (e.g., clutch size, egg mass, incubation period or type of young) and behavioural traits (type of nest, mating system or type of parental care), dietary (e.g., folivore, granivore, insectivore or carnivore) and habitat preferences (e.g., deciduous/coniferous forest, reed or grassland). Spatial location and grain: Europe; all breeding bird species (n = 499). Major taxa and level of measurement: In total, we created a dataset for 499 bird species breeding in Europe and 34 key life-history traits represented in 85 variables. As a primary source of information we used the comprehensive handbook The birds of the Western Palearctic. The traits provide information about species-specific mean values. We did not record values for different geographical subspecies (i.e., the trait value always represents the average for the whole breeding area of a particular species). Software format: The data file is in ASCII text, tab delimited, not compressed.
创建时间:
2018-11-16
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作