Select the right trait
(based on Rule 1 of the article doi:10.1111/2041-210X.14033)
Let your study question or hypothesis determine both the trait(s) to be used and how those traits are collected and analysed. Clear, upfront definitions of traits will avoid errors through e.g., confusion of scales and definitions, data gaps or inclusion of inadequate traits (Dawson et al., 2021; González-Suárez et al., 2012; Hulme et al., 2013; Messier et al., 2017).
Follow your hypothesis:
Increasingly, trait data describing organisms of interest are publicly available for reuse. However, primary trait collection is necessary for a large number of research questions, for instance those involving rare species, understudied regions or small spatial scales. Vast public availability extends the potential scope of what is possible with limited resources (e.g. Falster et al., 2021; Kattge et al., 2020). However, when reusing trait data, we relinquish control of what variables are collected, which species are sampled, and the methods used for collection (Koricheva et al., 2013). Undirected fishing expeditions for traits can yield large datasets. Still, these may not be appropriate to answer a given research question, for various reasons (e.g., coverage, geographical origin, distribution, meaningfulness, and resolution, Violle et al., 2015). Furthermore, the wealth of available trait data may distract from initial hypotheses, risking random exploration of the available traits and fishing for significant relationships without a clear focus. Thus, trait selection and collection should in most cases be primarily tethered to a concrete hypothesis, not defined by the availability of existing data. This rule does, however, not completely exclude extensive data exploration and data-driven discovery within a given range as relevant to the research question and subsequent streamlining (Violle et al., 2015).
Consider the scale:
Research questions define the appropriate hierarchical level for sampling: a continental-scale study of thousands of species may treat the intra-specific variation as statistical noise. In contrast, this variation may be the study focus on locally scaled projects. There is no “correct” scale, either in terms of spatial grain (e.g., km2, m2), temporal duration (e.g., seconds, years), or taxonomic coverage (e.g., clade, species, population or individual), but not every scale will be appropriate for every question. So, when defining the traits of interest, it is important to determine the scale at which these need to be collected or aggregated to match the research question (Messier et al., 2017).
Be aware of existing trait definitions and homologies:
Much effort has already gone into creating definitions and protocols for trait collection (Pérez-Harguindeguy et al., 2013). Yet, trait naming and corresponding descriptions may differ between studies and trait databases (Ankenbrand et al., 2018; Dawson et al., 2021; Kunz et al., 2022). For example, the activity cycle of animals is sometimes reported as a discrete value (e.g. Jones et al., 2009), or sometimes split into multiple binary traits such as “nocturnal”, “crepuscular”, “diurnal” (e.g. Wilman et al., 2014). Similarly, values may differ between resources (e.g. “therophyte” and “annual” are synonyms). Furthermore, when comparing traits and trait states across organisms, it is important to be aware of the ‘homology’ of the character. Homologous traits share similarity of structure, physiology, or development (often by common evolutionary ancestry), whereas non-homologous (or analogous) characters may perform a similar function, but differ in structure, physiology, or development.
Be pragmatic and transparent:
In a perfect trait research world, we could measure or retrieve the exact traits for the precise scale and organisms needed to answer our specific question. This vision is rarely applicable in practice. Instead, we often need to work with proxies for traits that are difficult to measure (e.g. hairiness of pollinators as a proxy for pollination effectiveness, Stavert et al., 2016), for inference of fitness (e.g. reproductive output as a performance trait to infer fitness, McGraw & Caswell, 1996, Violle et al., 2007), or for traits that are incomplete in a database (e.g. diet or behavioural traits are less complete than morphological traits, Oliveira et al., 2017). There is a common understanding of these technical or financial limitations in the scientific community; ultimately, we must be pragmatic to advance research questions. However, it is crucial to explain and justify the choice of traits, especially when these are used as proxies or “best available data” to allow fellow researchers to understand and evaluate whether such choices were valid for the specific research question at hand.
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