Propagate the good practices

(based on Rule 10 of the article doi:10.1111/2041-210X.14033)

Inform the community and the next generation of ecologists about the issues discussed here and in other resources (e.g., de Bello et al., 2021). As outlined in other parts of the best practices, there are several issues to be aware of when measuring, collecting, handling, analysing, and publishing trait data, i.e. the life cycle of trait data.

Some may be straightforward; others require more technical knowledge or extensive reading of existing resources. In many cases, good procedures are not applied simply because ecological or evolutionary scientists are unaware that they exist, e.g., in trait quality control or using a standard structure. Educating can be done from the small scale of an informal conversation with a colleague, to teaching a large undergraduate class, up to participating in the collective creation of open access materials in several languages, accessible from any part of the world. Integrating trait-data-specific sections into ecology textbooks and modules in ecology courses could become a standard practice, which will undoubtedly be made more accessible by disseminating open-access material by the trait scientific community. You are welcome to use these ten rules as a starter when teaching your students, colleagues, and friends. For diving deeper into every single topic, we encourage you also to check more comprehensive resources such as the Handbook of Trait-Based Ecology (de Bello et al., 2021) or the activities of the Open Traits Network ( (Gallagher et al., 2020).

Train students:

Courses specific to trait-based research are often lacking at undergraduate and graduate levels. Where courses or modules are taught, the focus may be limited to a subset of the trait data life cycle (e.g., Collection and Analysis; Fig. 1), leaving students lacking critical skills (Feng et al., 2020). Open Educational Resources, including those built using incubators (Ryder et al., 2020), are one promising method for implementing such courses and modules more easily. In particular, authentic teaching experiences provide several benefits over traditional lectures or “cook-book” experiments (Brownell et al., 2012). They seem well suited to trait-based ecology given that many traits can be collected quickly and inexpensively and that many tools are available (see, e.g., de Bello et al., 2021). One example of such authentic teaching experiences, the TraitTrain plant functional trait courses (, has provided training across the entire trait data life cycle to hundreds of participants and has created scientific (Henn et al., 2018), data (Vandvik et al., 2020), methodological (Maitner et al., 2021), and pedagogical (Geange et al., 2021) publications.

Train colleagues:

Making colleagues aware of important developments in trait-based research via either formal (e.g. publishing protocols, giving talks) or informal means (e.g. conversations, social media, email) is a critical way of helping to advance the field. Further, trait-based research is an integrative field. It provides many opportunities for collaboration and idea-sharing across branches of life science, so discussing traits with a wide variety of colleagues is useful.

Train the world:

There is an urgent need for more comprehensive trait data across the globe and the tree of life (Feng et al., 2022), thus, increasing global access to training. Open access publications, tools, data, and educational resources help lower the barriers to participation (Evans & Reimer, 2009). Further, due to the relative ease, low cost, and tangible nature of many functional traits, they are well-suited for inclusion in elementary education and citizen science (e.g., Isaac & Martin, 2019; Schiller et al., 2021).