Successful conservation of global waterbird populations depends on effective governance

Tatsuya Amano (), Tamás Székely, Brody Sandel, Szabolcs Nagy, Taej Mundkur, Tom Langendoen, Daniel Blanco, Candan U. Soykan and William J. Sutherland
Additional contact information
Tatsuya Amano: Conservation Science Group, University of Cambridge, The David Attenborough Building
Tamás Székely: Milner Centre for Evolution, University of Bath
Brody Sandel: Santa Clara University
Szabolcs Nagy: Wetlands International Head Office
Taej Mundkur: Wetlands International Head Office
Tom Langendoen: Wetlands International Head Office
Daniel Blanco: Wetlands International LAC Argentina Office
Candan U. Soykan: National Audubon Society, Conservation Science, 220 Montgomery St., Suite 1000, San Francisco
William J. Sutherland: Conservation Science Group, University of Cambridge, The David Attenborough Building

Nature, 2018, vol. 553, issue 7687, 199-202

Abstract: Statistical modelling of global survey datasets of waterbirds as an indicator taxon for biodiversity changes in wetland ecosystems demonstrates that effective governance is the strongest predictor of species abundance increases and conservation benefits.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.nature.com/articles/nature25139 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:553:y:2018:i:7687:d:10.1038_nature25139

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/nature25139

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().