Delineating retention tree groups based on open spatial data and ecosystem service indicators
Year: 2025
Type: article
Abstract: Forest harvesting can result in biodiversity loss, removal of carbon stocks, and habitat fragmentation. Leaving retention trees during harvesting is a practical solution to mitigate these effects. However, no planning method is available for optimizing different ecological benefits of retention trees. We developed a geospatial retention tree tool (ReTreeT) to plan locations for retention tree groups based on ecosystem service values. ReTreeT integrates ecosystem service indicators with a spatial clustering method to delineate hotspots of ecosystem service indicators that favor habitat provision and retention of carbon stock, associated with retention tree groups. Indicators we evaluated included tree species diversity, tree height heterogeneity, dead wood potential, and depth-to-water index. We generated proposals for retention tree groups based on the mapped ecosystem service hotspot values. We compared ReTreeT derived ecosystem service indicators to indicators calculated from realized retention tree groups at forestry sites through field measurements in Finland. The ReTreeT-proposed retention tree groups exhibited higher ecosystem service values than realized retention tree groups. Ecosystem service values in realized retention tree groups tended to represent average conditions in the harvest area. We compared ecosystem service values derived from remote sensing data with field measured data that confirmed the reliability of dead wood potential estimates, while enhancing tree diversity metrics could further improve ReTreeT’s performance.
Source: Forest Ecology and Management
Institutions: University of Helsinki, University of Eastern Finland
Language: English
Topic: Forest Management and Policy
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: hybrid
Estimated APC paid: $3,560
Funder: Helsingin Yliopisto
OpenAlex ID: W4413295337
