Biodiversity Unprotected is Biodiversity Lost

This is a critical time for conservation, as many ecosystems across the globe are at risk of being lost due to deforestation, construction, large-scale agriculture, human encroachment, climate change etc. There are multiple agencies and organizations working on both a regional and global level to fast-track conservation efforts.

Scientists bring together collected data from a variety of biological and ecological sources and execute a workflow to define areas that successfully meet the required criteria. These data sources can include:

• species populations,
• vegetative land cover,
• nesting sites,
• seasonal feeding patterns
• or even the location of historic cultural heritage sites.

What is Currently Being Done

In 2021, U.S. President Joe Biden launched the ‘America the Beautiful’ initiative, with the stated goal of conserving at least 30 percent of U.S. lands and waters by 2030. Globally, the International Union for Conservation of Nature (IUCN) has been working since 2004 on a standard for identifying Key Biodiversity Areas (KBAs), which are defined as areas essential to supporting the persistence of global biodiversity at either a species or ecosystem level. Once KBAs are established and the information is shared with stakeholders, that area becomes prioritized for conservation planning.

Understanding the Problem

The IUCN is aware their KBA standard will evolve over time, and actively seeks input from users who work with the current criteria and may have suggestions for improvement. Our project sponsor Pat Comer is a member of the IUCN Commission on Ecosystem Management, and is interested in whether the current thresholds adequately allow for KBAs to be defined for all ecosystems.

Currently, the standard is set so that a KBA can be identified at a site that comprises 10% of the global extent of an ecosystem defined as ‘Vulnerable’ by the IUCN Red List of Ecosystems (RLE), or at 5% when the ecosystem is at the higher risk ‘Critically Endangered’ or ‘Endangered’ level. A concern is that the KBA minimum threshold criteria may interact with ecosystem types that naturally occur with very different spatial pattern, such as those that occur only as highly disbursed small patches or as long linear features, such in coastal zones or along riverine corridors.

Area of Interest

Our team examined nine different terrestrial ecosystems across the continental United States. They are presented below as a shapefile generated from open source GIS software and include a diverse set of spatial characteristics. The individual ecosystems were extracted from a national raster dataset (Landfire, 2016).

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Using Computer Coding to Help Identify Possible KBAs

Our project creates a toolkit for spatial analysis to help identify KBAs, across the continental United States. Our goal is to utilize computer coding to produce an automatic workflow that can analyze spatial distributions of any terrestrial ecosystem type and determine where it could meet the KBA threshold criteria. Our toolkit will also help determine if these current thresholds may be set too high, especially for ecosystems with linear or fragmented spatial characteristics. In these instances, it can be difficult to locate independent areas large enough to qualify for conservation using the current standard, or that are in close enough proximity to other areas to allow them to be managed together as a single conservation unit.

We aim to determine whether lower thresholds would be more successful in identifying KBAs for ecosystems with such spatial characteristics. If so, that may be a useful revision for the IUCN to make to their standard, as ultimately the goal is to identify KBAs for all threatened ecosystems.

Results of the analysis are shared with key stakeholders and decision makers, enabling them to efficiently direct limited resources towards solutions with the lowest cost and greatest chance for long term ecological success.

Visualizing Key Biodiversity Areas

Our methodology for identifying and visualizing KBAs relies primarily on a software called Marxan. Simply put, Marxan is a spatial optimization tool to identify places where habitat representation goals may most efficiently be met. Using area of each ecosystem type occurring within each hexagon, Marxan iteratively selects groups of hexagons in clusters that could contain sufficient amounts to meet KBA threshold criteria.

To visualize this process, we generated a heat map of each ecosystem. Red cells on the map depict areas with little to no ecosystem present. As we move along the gradient, from yellow to green, more and more of that particular ecosystem is present within a single hex cell. This is based on the proportion of ecosystem within that unit relative to the entire ecosystem extent. Green on the map indicates areas where we are likely to meet the given KBA threshold. Thus, our analysis directs conservation policy makers to the most efficient areas for conservation in order to meet the KBA threshold requirements. While to overall total amount of land protected may be less, the conservation value of the land is greater due to a higher concentration of target ecosystem within that area.

Steps for the Future

Ultimately, we hope to share this workflow with scientists around the globe so they can identify and protect their own KBAs to include threatened ecosystems and species. Therefore, we hope to make the current KBA identification process sensitive to a greater variety of ecosystem types, allowing it to be scaled up globally. KBA Identification is one key step in protecting these threatened ecosystems. If these areas are not identified, they are at risk of being destroyed, along with the biodiversity they support.

While our heat-maps provide a good indication for what areas of each ecosystem meets the threshold to be considered a KBA, we would like to enhance our workflow to generate a clear and precise answer for which areas should be protected. We are currently waiting for an update on the functionality of the Marxan software to allow us to perform the final analytical step that will produce the discrete statistical data we are looking for.

Find Out More

To find out more about our project, please visit our project repository on GitHub by following the link below. https://github.com/csandberg303/kba-threshold-sensitivity-analysis Here you can find more information about the project, our python code workflows used to generate our findings, and other project resources.

Resources

1. LANDFIRE, 2016, Existing Vegetation Type Layer, LANDFIRE 2.0.0, U.S. Department of the Interior, Geological Survey, and U.S. Department of Agriculture. Accessed 28 October 2021 at http://www.landfire/viewer.
2. Nested Hexagon Framework (NHF), developed by Mike Houts at the University of Kansas, provided by Pat Comer.