Inter- and intra-specific trait variation

The Inter/Intra project takes a quantitative approach to exploring how individual-level (and, especially, specimen-level) trait data inform our understanding of biological processes and progress the precision of comparative evolutionary and ecological analyses. Overarching aims of Inter/Intra are to a) explore existing theoretical framework(s) that underpin use of individual- level trait data in evolution and ecology; b) conduct focused trait-based analyses and quantify how individual-level trait data affect inferences; and c) identify areas where methodological advances by the community are necessary to accommodate such analyses in the future. The project builds on meaningful conversations at previous FuTRES workshops and will create a space for the team to fully explore these ideas in a data-driven way.

The Inter/Intra project is open to any researcher (PIs, postdocs, and graduate students) interested in the topics above, and so far, we have identified a small but diverse group of interested trait-based researchers committed to participating. Our group will spend the first FuTRES workshop identifying major uses of trait-based research in modern ecology and evolution; these discussions will guide the types of quantitative analyses we target for project goals. We envision targets will include, but not be limited to analysis of phylogenetic signal in single traits; inferring models of trait evolution on phylogenies or from time series (e.g. for paleo) data; and understanding how traits inform understanding of assembly in past or present mammal communities. In particular, we hope to build on the recent work of Balk et al. ("A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data", in press), to improve our understanding of process inferences from individual-level trait data sets.

The project will proceed by self-dividing our group into teams by analysis type. Within each team, we will strive to include researchers with data familiarity, functional (i.e., coding) expertise, and data visualization expertise. Our goal will be to utilize specimen-level data currently contained in the FuTRES data store; however, additional data that could be readily included in FuTRES, or data which is suitable for eventual inclusion in the store (e.g., pending extension of the trait ontology), will also be considered. For user-provided datasets suitable for FuTRES, we will work with researchers to achieve this prior to project publication. We anticipate that use cases will center primarily on morphological and life history traits of mammals, providing the opportunity to maximally explore ideas discussed at prior FuTRES workshops and currently available trait data.

Teams will be expected to meet routinely between FuTRES workshops to coordinate workload and conduct analyses (with McLean attending all meetings); our goal in subsequent FuTRES workshops will be reporting on analyses to other participants and FuTRES organizers for feedback. To ensure consistency and encourage repeatability across teams, all projects will be housed on a dedicated GitHub repository where code will be actively developed and stored, with links to the original datasets contained in FuTRES.

The life sciences are currently experiencing a major increase in trait-based research and development of different trait databases, with many of the latter existing at the species (or higher) levels and having variable taxonomic completeness. However, individual-level trait data provide a way to understand trait variation and link that variation to explicit ecological and environmental contexts. The Inter/Intra project seeks to demonstrate quantitatively how individual-level trait data enhance our insights into biological pattern and process. Progress towards this goal can be achieved within a relatively short timeline because our group will include researchers who routinely work with trait data; furthermore, many – but not all – are paleo- or neomammalogists and this speak a common language. Many participants currently hold cleaned trait data which will be immediately useful in the work, and they have expertise in ecological and evolutionary analyses that can be shared among teams and participants. Our vision is that this project will illuminate ways forward for the broader community in trait-based research.