Open Science principles for accelerating trait-based science across the Tree of Life

Abstract

raits, broadly speaking, are measurable attributes or characteristics of organisms. Traits related to function (for example, leaf size, body mass, tooth size or growth form) are often used to understand how organisms interact with their environment and other species via key vital rates such as survival, development and reproduction 1-5. Trait-based approaches have long been used in systematics and macroevolution to delineate taxa and reconstruct ancestral morphology and function 6-8 and to link candidate genes to phentoypes 9-11. The broad appeal of the trait concept is its ability to facilitate quantitative comparisons of biological form and function. Traits also allow us to mechanistically link organismal responses to abiotic and biotic factors with measurements that are, in principle, relatively easy to capture across large numbers of individuals. For example, appropriately chosen and defined traits can help identify lineages that share similar life-history strategies for a given environmental regime 12,13. Documenting and understanding the diversity and composition of traits in ecosystems directly contributes to our understanding of organismal and ecosystem processes, functionality, productivity and resilience in the face of environmental change 14-19. In light of the multiple applications of trait data to address challenges of global significance (Box 1), a central question remains: How can we most effectively advance the synthesis of trait data within and across disciplines? In recent decades, the collection, compilation and availability of trait data for a variety of organisms has accelerated rapidly. Substantial trait databases now exist for plants 20-23 , reptiles 24,25 , invertebrates 23,26-29 , fish 30,31 , corals 32 , birds 23,33,34 , amphibians 35 , mammals 23,36-38 and fungi 23,39 , and parallel efforts are no doubt underway for other taxa. Though considerable effort has been made to quantify traits for some groups (for example, Fig. 1), substantial work remains. To develop and test theory in biodiversity science, much greater effort is needed to fill in trait data across the Tree of Life by combining and integrating data and trait collection efforts.

Defining Relictual Biodiversity: Conservation Units in Speckled Dace (Cyprinidae:Rhinichthys osculus) of the Greater Death Valley Ecosystem

“…In the present situation, this would provide the platform from which results of individual studies could be compiled in a 444 consistent format, compared, and potentially categorized. A recent example is a platform that accommodates a trait-based approach to classification by delineating taxa from which ancestral 446 morphologies and their functions are reconstructed (Gallagher et al, 2020). A similar platform could be built so as to accommodate molecular studies of intraspecific diversity in an attempt to 448 seek consensus amongst results.…”

Global transpiration data from sap flow measurements: the SAPFLUXNET database

“…Since data sharing is only incipient in plant ecophysiology, sap flow datasets have not been traditionally available in open data repositories. Open data practices are now being implemented in databases, which fosters collaboration across monitoring networks in research areas relevant to plant functional ecology (Falster et al, 2015;Gallagher et al, 2020;Kattge et al, 2020) and ecosystem ecology (Bond-Lamberty and Thomson, 2010). The success of the data sharing and data re-use policies within the FLUXNET global network of ecosystem level fluxes has shown how these practices can contribute to scientific progress (Bond-Lamberty, 2018).…”

A Vegetation and Soil Survey Method for Surveillance Monitoring of Rangeland Environments

“…While the primary purpose of the program is to detect changes over large scales of space and over long periods of time, it also provides readily available resources to answer pressing current questions. For example, this method incorporates data collection (Tokmakoff et al, 2016) to address key long-standing questions for rangeland ecosystems (Morton et al, 2011), including understanding the role of soil and plant traits on productivity (Bastin et al, 2017a;Gallagher et al, 2020). The combination of vegetation data and samples can be used to extract trait data or genomic data to anticipate responses to environmental stressors across large gradients (Westoby et al, 2002;Wright et al, 2004;Guerin et al, 2012;Caddy-Retalic et al, 2017).…”