New research highlights the importance of trait variability within species in measuring biodiversity changes and how ecologists can incorporate that data into their assessments.
Around the world, ecologists are studying how species are responding to global changes in habitat, environment, and climate. Ecologists build essential biodiversity variables, or EBVs, from various sources of data and they serve as the underlying variables to assess biodiversity change through time.
Scientists can use EBVs to measure the achievement of policy targets and they play an important role in biodiversity-related policy decisions.
Building the framework
The new research, which appears in Nature Ecology and Evolution, began with an international gathering of scientists last year. A group of scientific experts convened in March 2017 at a workshop 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 behavior.
“Currently, there is no detailed framework for the empirical derivation of most EBVs,” says lead author W. Daniel Kissling, 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,” says Ramona Walls, an ecologist at the University of Arizona and a member of the university’s BIO5 Institute. Walls is also a senior science informatician for CyVerse, the National Science Foundation-funded computational infrastructure project that the BIO5 Institute houses.
“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,” Walls says.
Linking the ontologies between publications could then enable researchers to detect and report biodiversity change.
Open and sharable
“I was surprised that there is such a lack of species trait information in current policy assessments of biodiversity change,” Kissling says. “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 and genomic 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.
Walls next research project aims to address the challenge of promoting trait-based research.
The project, called Functional Trait Resource for Environmental Studies or FuTRES, will create 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 says. “The FuTRES project aims to do exactly that.”
Source: University of Arizona
