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

Abstract

1. 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, compiling and analysing trait data comes with data-scientific challenges due to the complex nature of trait data.2. 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.3. This article is particularly motivated by its last rule, i.e. to propagate the good practices, and has the intention of bringing awareness of the different facets of a trait's life cycle to the community.4. When it comes to working with trait data, we gain particularly as an interdisciplinary community of field biologists, synthesis ecologists, 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 how to make trait data future-proof.

Socioeconomics and biogeography jointly drive geographic biases in our knowledge of plant traits: a global assessment of the Raunkiærian shortfall in plants

“…Given the current state of available data, large scale (e.g., country, biome, global) analyses of plant traits must be interpreted with caution and should attempt to quantify uncertainty caused by these massive data gaps. Moving forward, researchers can help by publishing their data and metadata openly, including the full set of raw and derived trait data (Keller et al ., 2022). At larger scales, efforts to mobilize and integrate existing datasets, particularly those focused on particular geographic regions (e.g., Tavşanoğlu & Pausas, 2018; Falster et al ., 2021) or types of traits (e.g.…”