Guiding ecosystem conservation using airborne lasers

Shalini Saxena

NASA

Industrialization and urbanization have drastically changed the face of our planet, and the number of untouched natural habitats for wildlife is shrinking. Conservationists are trying to understand remaining biodiversity in order to create sanctuaries that preserve it. One of the challenges they face is how to make connections among information derived from different methods of evaluating the Earth's life.

One approach to getting data on biological diversity involves field inventories of species. Another evaluates ecosystem processes by dividing the Earth into categories based on vegetation (forests or grasslands, for example) and subsequently analyzing properties of that category's plant life. But critical information is often missed when only one method is employed.

But these two types of inventories are actually linked. This link goes by the name "functional diversity," which represents the features of organisms that influence both their individual fitness and their contribution to the function of ecosystems that contain them. In a recent investigation published in Science, a team of ecologists has used an advanced aerial imaging method to explore the functional diversity of plant communities.

A good grasp of functional diversity is critical to understanding this study. At its core, functional diversity is a type of biodiversity that describes the activities and processes that organisms engage in as they interact with their surrounding community and ecosystem. To give an example, one plant may produce fruit that feeds other species while extracting nitrogen from the soil.

Mapping plant traits

Plants are an integral part of any ecosystem, and their diversity is inextricably linked to the biological, chemical, and physical processes that occur within that ecosystem. Though our understanding of plants' roles in ecosystems has grown over the years, we don't know enough about how their traits vary over larger areas. This makes coming up with effective conservation plans challenging.

A strong understanding of the functional diversity of an ecosystem can take years of study. The ecologists behind the new work wondered whether it was possible to get a decent understanding in a shorter amount of time. So they attempted to track functional diversity through remote measurement of the forest canopy, using traits that are able to indicate the presence of different plant species and communities, as well as their health.

In order to identify these critical plant canopy traits, the team took a step back to consider the most critical processes in plant growth and health. After identifying these processes, the ecologists were able to identify measurable traits directly associated with these processes. The most obvious one is photosynthesis, the process by which all plants use energy from sunlight to produce sugar. Photosynthesis is highly dependent on nitrogen and water in the leaves, as well as the leaf mass per unit area, all of which can be sensed.

Next, the team expanded its consideration to things that depend on the local conditions of a plant's habitat, such as topographic and soil features. The presence of key chemicals in leaves, like phosphorous and calcium, is indicative of these processes. The presence of these chemicals is also closely related to changes in the species that are present in tropical forests, and so they can be used to track turnover of the canopy.

Finally, the scientists thought about long-term processes, like evolutionary changes and response to pathogens. These can be tracked through defense compounds found in leaves, such as polyphenols and lignin.

Focusing on seven canopy traits, the researchers used remote sensing to explore the functional diversity of plant communities.

Peruvian forests

The team focused on Peruvian tropical forests as a model system, since they are exposed to a range of tropical conditions, pressures from land-use, and attention of conservationists. Combining advanced aerial imaging with a form of artificial intelligence, the ecologists generated maps of a large portion of the tropical biosphere, detailing several aspects of functional diversity.

Analysis revealed that the seven forest canopy traits selected by the ecologists were largely uncorrelated, so they provide a breadth of information. Mapping these traits revealed functional variation in the forests, driven by things like geology, elevation, hydrology, and climate.

To better understand what their data told them, the ecologists used 301 well-studied forest inventory plots located in the Peruvian Andes and Amazon. They found that canopy functional composition, based on information from their individual trait maps, was related to the species present, which were identified through the field inventory data.

The team integrated the seven mapped canopy traits to identify common functional properties among coexisting species. Using this information, they identified 36 functional classes of forest, which clustered into six forest functional groups. The researchers suggest that their spatially explicit data may be used to bridge the gap between the distribution of plant species and the biological processes that go on in forests.

The ecologists were particularly interested in understanding how their data could be used to further conservation efforts. Each functional forest group was analyzed relative to areas that are threatened, protected, or remain conservation opportunities based on government land allocation data. The researchers found that in each forest, up to 53 percent of the mapped area could be an opportunity for new conservation action, based on government information of how the forest is currently allocated.

This information could be used to guide conservation initiatives to mitigated continued loss of forests from the Andes-to-Amazon. But the newly minted method is far more important, since it works with data that's relatively quick and easy to obtain. That makes evaluating other regions for understanding of conservational opportunities easier.

Science, 2017. DOI: 10.1126/science.aaj1987 (About DOIs).