Modelling and testing forest ecosystems condition account

Area of Science:

• Environmental accounting
• Ecosystem assessment
• Forestry science

Background:

• The United Nations Statistical Commission endorsed the System of Environmental Economic Accounting-Ecosystem Accounting (SEEA-EA) framework in 2021.
• Monitoring forest ecosystem condition is crucial for conservation and management.
• A standardized, spatially explicit method for assessing forest condition was lacking.

Purpose of the Study:

• To develop and apply an automated model for mapping forest ecosystem condition accounts.
• To test the model's effectiveness nationally in Spain from 2000 to 2015.
• To provide insights into forest condition status and trends for policy-making.

Main Methods:

• Developed an application model based on the SEEA-EA framework.
• Defined forest ecosystem types and selected condition variables (physical, chemical, structural, etc.).
• Established reference levels, aggregated variables into an index, and rescaled to a 0-1 range.

Main Results:

• The model successfully generated spatially explicit forest condition accounts for Spain (2000-2015).
• Overall forest ecosystem condition in Spain slightly increased from 0.56 to 0.58.
• Distinct trends were observed: mixed Alpine and Macaronesia forests improved, while Mediterranean coniferous forests remained unchanged.

Conclusions:

• The automated SEEA-EA based model effectively monitors forest condition accounts.
• Findings highlight the need for tailored conservation strategies based on ecosystem-specific trends.
• The approach supports evidence-based nature conservation, ecosystem service management, and restoration planning.