DISPERSE, a trait database to assess the dispersal potential of European aquatic macroinvertebrates

Sarremejane, R. ORCID: 0000-0002-4943-1173, Cid, N., Stubbington, R. ORCID: 0000-0001-8475-5109, Datry, T., Alp, M., Cañedo-Argüelles, M., Cordero-Rivera, A., Csabai, Z., Gutiérrez-Cánovas, C., Heino, J., Forcellini, M., Millán, A., Paillex, A., Pařil, P., Polášek, M., Tierno de Figueroa, J.M., Usseglio-Polatera, P., Zamora-Muñoz, C. and Bonada, N., 2020. DISPERSE, a trait database to assess the dispersal potential of European aquatic macroinvertebrates. Scientific Data, 7 (1): 386.

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Abstract

Dispersal is an essential process in population and community dynamics, but is difficult to measure in the field. In freshwater ecosystems, information on biological traits related to organisms’ morphology, life history and behaviour provides useful dispersal proxies, but information remains scattered or unpublished for many taxa. We compiled information on multiple dispersal-related biological traits of European aquatic macroinvertebrates in a unique resource, the DISPERSE database. DISPERSE includes nine dispersal-related traits subdivided into 39 trait categories for 480 taxa, including Annelida, Mollusca, Platyhelminthes, and Arthropoda such as Crustacea and Insecta, generally at the genus level. Information within DISPERSE can be used to address fundamental research questions in metapopulation ecology, metacommunity ecology, macroecology and evolutionary ecology. Information on dispersal proxies can be applied to improve predictions of ecological responses to global change, and to inform improvements to biomonitoring, conservation and management strategies. The diverse sources used in DISPERSE complement existing trait databases by providing new information on dispersal traits, most of which would not otherwise be accessible to the scientific community.

Item Type: Journal article
Publication Title: Scientific Data
Creators: Sarremejane, R., Cid, N., Stubbington, R., Datry, T., Alp, M., Cañedo-Argüelles, M., Cordero-Rivera, A., Csabai, Z., Gutiérrez-Cánovas, C., Heino, J., Forcellini, M., Millán, A., Paillex, A., Pařil, P., Polášek, M., Tierno de Figueroa, J.M., Usseglio-Polatera, P., Zamora-Muñoz, C. and Bonada, N.
Publisher: Springer Science and Business Media LLC
Date: 11 November 2020
Volume: 7
Number: 1
Identifiers:
NumberType
10.1038/s41597-020-00732-7DOI
1388387Other
Rights: © The Author(s) 2020. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 13 Nov 2020 08:42
Last Modified: 13 Nov 2020 08:42
URI: http://irep.ntu.ac.uk/id/eprint/41645

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