Measuring variation of body sizes in fish of the same species can give important insights into overexploitation and unsustainable harvesting of economically important fish populations. Picture: Jan Dierking.
Measuring Species Traits to Monitor Biodiversity
Management of global biodiversity requires up-to-date, reliable and comparable biodiversity data. Essential biodiversity variables such as species traits is one way to monitor the global state of biodiversity.
Around the world, ecologists are studying how species are responding to global changes in habitat, environment and climate. New research shows how trait variability within species can be incorporated in essential biodiversity variables to monitor of how organisms respond to global change.
Essential biodiversity variables, or EBVs, are constructed from various sources of data and are the underlying variables to assess biodiversity change through time. EBVs can be used to measure the achievement of policy targets and play an important role in biodiversity-related policy decisions.
University of Arizona ecologist Ramona Wells, a member of the UA BIO5 Institute, is co-author on a recent Nature Ecology and Evolution paper, "Measuring species traits for biodiversity policy goals," which began with an international gathering of scientists last year.
A group of scientific experts convened in March 2017 at a workshop hosted by the Horizon 2020 project Global Infrastructure for Supporting Biodiversity Research, or GLOBIS-B, funded by the European Commission, to discuss developing a standardization for species traits. Species traits are an important EBV category that can include measurements of phenology, morphology, reproduction, physiology and migration behaviour.
“Currently, there is no detailed framework for the empirical derivation of most EBVs,” said W. Daniel Kissling, lead author of the paper and a researcher at the University of Amsterdam Institute for Biodiversity and Ecosystem Dynamics. “We provide a conceptual framework with practical guidelines for building global, integrated and reusable EBV data products of species traits."
Developing community-supported ontologies for EBVs would allow ecologists to use standard terms for measurements.
“An ontology is a code that lets a computer identify the traits that you tell it to search for, such as flower color or femur length,” said Walls, a senior science informatician for CyVerse, the UA-led, National Science Foundation-funded computational infrastructure project housed in the UA’s BIO5 Institute. “If a researcher can tell the computer to search for data that matches the ontology term, then they don’t have to read a hundred or so papers to find the data.”
The ontologies could then be linked between publications to enable researchers to detect and report biodiversity change.
"I was surprised that there is such a lack of species trait information in current policy assessments of biodiversity change," Kissling said. "We outline the steps needed for data-intensive science and effective global coordination to advance the inclusion of species trait information into indicators of biodiversity change, and how collected trait data can be shared in an open and machine-readable way."
Walls works to address the need for open, machine-readable trait data across life sciences. Her efforts include the development of ontologies for integrating biodiversity, agricultural, andgenomic data, contributions to community data standards, and research into information management systems that allow researchers to share their data more easily while better tracking their use through time.
A recently funded NSF proposal for which Walls is a principal investigator aims to address the challenge of promoting trait-based research. Functional Trait Resource for Environmental Studies (FuTRES), is a cooperative grant to Walls and her colleagues: Edward Davis at the University of Oregon; Ray Bernorat at Howard University; and Robert Guralnick at the University of Florida. Of the $1.3 million grant, $420,135 has been awarded to the UA.
FuTRES is a workflow for assembling functional trait data measured at the specimen level and an ontology-based database to serve that data. A key aspect of FuTRES is the ability to collect, store, aggregate, and share data at the individual or specimen and higher levels without loss of information.
“As we describe in the Nature Ecology and Evolution paper, biodiversity ecologists need trait data that computers can read and interpret,” Walls said. “The FuTRES project aims to do exactly that.”