Modelling and testing forest ecosystems condition account

森林生态学 生态系统 环境资源管理 生态系统服务 环境科学 森林经营 索引(排版) 地理 生态学 计算机科学 农林复合经营 生物 万维网
作者
Adrián G. Bruzón,Patricia Arrogante‐Funes,Fernando Santos-Martín
出处
期刊:Journal of Environmental Management [Elsevier BV]
卷期号:345: 118676-118676 被引量:7
标识
DOI:10.1016/j.jenvman.2023.118676
摘要

We developed an application model based on the System of Environmental Economic Accounting-Ecosystem Accounting (SEEA-EA) framework, endorsed by the United Nations Statistical Commission in 2021. This model enables mapping condition accounts for forest ecosystems using automated computation. We applied the model nationally in Spain between 2000 and 2015 to test its effectiveness. Our model follows five methodological steps to generate forest condition accounts: (i) definition and spatial delimitation of forest ecosystem types; (ii) selection of variables using the ecosystem condition typology encompassing physical, chemical, compositional, structural, functional, and landscape characteristics; (iii) establishment of reference levels, including lower (collapse) and upper (high ecosystem integrity) thresholds; (iv) aggregation of variables into condition index; and (v) calculation of a single condition index by rescaling the aggregated indicators between 0 and 1. The results obtained from the model provide valuable insights into the status and trends of individual condition indicators, as well as aggregated condition index values for forest ecosystems, in a spatially explicit manner. Overall, the condition of the forest ecosystems in Spain showed a slight increase, from 0.56 in 2000 to 0.58 in 2015. However, distinct trends were observed for each ecosystem type. For example, mixed Alpine and Macaronesia forests exhibited a significant improvement, while the continental Mediterranean coniferous forests did not show any change. This innovative approach to monitoring forest condition accounts has important potential applications in policy and decision-making processes. It can contribute to effective evidence-based nature conservation, ecosystem service management, and identifying restoration areas.
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