Ten (mostly) simple rules to future‐proof trait data in ecological and evolutionary sciences

Keller, Alexander; Ankenbrand, Markus J.; Bruelheide, Helge; Dekeyzer, Stefanie; Enquist, Brian J.; Erfanian, Mohammad Bagher; Falster, Daniel S.; Gallagher, Rachael V.; Hammock, Jennifer; Kattge, Jens; Leonhardt, Sara D.; Madin, Joshua S.; Maitner, Brian; Neyret, Margot; Onstein, Renske E.; Pearse, William D.; Poelen, Jorrit H.; Salguero‐Gomez, Roberto; Schneider, Florian D.; Tóth, Anikó B. und Penone, Caterina (22. November 2022): Ten (mostly) simple rules to future‐proof trait data in ecological and evolutionary sciences. In: Methods in Ecology and Evolution, Bd. 14, Nr. 2: S. 444-458

Abstract

Traits have become a crucial part of ecological and evolutionary sciences, helping researchers understand the function of an organism's morphology, physiology, growth and life history, with effects on fitness, behaviour, interactions with the environment and ecosystem processes. However, measuring, compiling and analysing trait data comes with data-scientific challenges. We offer 10 (mostly) simple rules, with some detailed extensions, as a guide in making critical decisions that consider the entire life cycle of trait data. This article is particularly motivated by its last rule, that is, to propagate good practice. It has the intention of bringing awareness of how data on the traits of organisms can be collected and managed for reuse by the research community. Trait observations are relevant to a broad interdisciplinary community of field biologists, synthesis ecologists, evolutionary biologists, computer scientists and database managers. We hope these basic guidelines can be useful as a starter for active communication in disseminating such integrative knowledge and in how to make trait data future-proof. We invite the scientific community to participate in this effort at http://opentraits.org/best-practices.html.

• Dokumententyp: Zeitschriftenartikel
• Publikationsform: Publisher's Version
• Fakultät: Biologie > Department Biologie II
• Themengebiete: 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften; Biologie
• ISSN: 2041-210X
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892