GABB: A global dataset of alpine breeding birds and their ecological traits

Devin R. de Zwaan, Davide Scridel, Tomás A. Altamirano, Pranav Gokhale, R. Suresh Kumar, Steven Sevillano-Ríos, Arnaud G. Barras, Libertad Arredondo-Amezcua, Addisu Asefa, Ricardo A. Carrillo, Ken Green, Carlos A. Gutiérrez-Chávez, Aleksi Lehikoinen, Shaobin Li, Ruey Shing Lin, Christopher J. Norment, Krista N. Oswald, Alexey A. Romanov, Julio Salvador, Kerry A. WestonKathy Martin

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Alpine ecosystems represent varied climates and vegetation structures globally, with the potential to support rich and functionally diverse avian communities. High mountain habitats and species are under significant threat from climate change and other anthropogenic factors. Yet, no global database of alpine birds exists, with most mountain systems lacking basic information on species breeding in alpine habitats, their status and trends, or potential cryptic diversity (i.e., sub-species distributions). To address these critical knowledge gaps, we combined published literature, regional monitoring schemes, and expert knowledge from often inaccessible, data-deficient mountain ranges to develop a global list of alpine breeding bird species with their associated distributions and select ecological traits. This dataset compiles alpine breeding records for 1,310 birds, representing 12.0% of extant species and covering all major mountain regions across each continent, excluding Antarctica. The Global Alpine Breeding Bird dataset (GABB) is an essential resource for research on the ecological and evolutionary factors shaping alpine communities, as well as documenting the value of these high elevation, climate-sensitive habitats for conserving biodiversity.

Original languageEnglish
Article number627
JournalScientific data
Volume9
Issue number1
DOIs
StatePublished - 1 Dec 2022

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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