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A standardised approach towards selecting forests for strict protection in Germany

ドイツにおける厳格保護のための森林選定に関する標準化アプローチ (AI 翻訳)

Patricia Borel, Jan-Geert Bliefernicht, Eva Flinkerbusch, Martin Gutsch, Anke Höltermann, Ulrich Matthes, Mats Nieberg, Tobias Nowakowski, Max Tölle, Christopher P. O. Reyer

Regional Environmental Change📚 査読済 / ジャーナル2026-06-29#生物多様性Origin: EU
DOI: 10.1007/s10113-026-02632-9
原典: https://doi.org/10.1007/s10113-026-02632-9
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🤖 gxceed AI 要約

日本語

ドイツの森林を厳格保護するための標準化された多基準アプローチを開発。国家森林資源調査データを用いて、生物多様性と社会政治的条件に基づくシナリオを作成し、5%、10%、30%の保護目標を評価。高目標達成には保護価値の低い林分の組み入れが必要だが、複数の達成経路が存在することを示した。

English

This study develops a standardized multi-criteria approach to select forests for strict protection in Germany. Using national forest inventory data, it creates scenarios based on biodiversity and sociopolitical criteria, testing 5%, 10%, and 30% protection targets. Results show that achieving higher targets requires including lower-conservation-value stands, but multiple pathways exist.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本でも森林保護と生物多様性保全は重要なGX課題であり、特にEUの30by30目標に類似した国内目標(例:30by30ロードマップ)が進行中。本論文の標準化手法は、日本の森林保護区選定にも応用可能な示唆を与える。

In the global GX context

This paper directly addresses the EU Biodiversity Strategy's target of strictly protecting 10% of land area. It provides a replicable framework for member states like Germany, with implications for global biodiversity and climate mitigation targets such as 30x30.

👥 読者別の含意

🔬研究者:Provides a standardized, data-driven methodology for forest protection planning that can be adapted to other regions.

🏢実務担当者:Offers forest managers and conservation planners a clear multi-criteria framework to balance biodiversity and sociopolitical factors.

🏛政策担当者:Demonstrates feasibility and trade-offs for achieving ambitious strict-protection targets under EU and national biodiversity strategies.

📄 Abstract(原文)

Abstract Forests serve as key ecosystems for biodiversity conservation and climate change mitigation. The achievement of ambitious biodiversity targets, such as the EU Biodiversity Strategy’s target of strictly protecting 10% of the EU’s land area, requires the systematic identification of key forest areas for protection. However, clear guidance on selecting such forests is lacking. In this study, we address this issue by developing forest protection scenarios based on different conservation criteria and data from the third German National Forest Inventory. We used state-of-the-art indicators capturing different aspects of biodiversity: forest structure, habitat conservation, legal protection status, and the presence of old forests, alongside sociopolitical considerations including public ownership and equal distribution across federal states. Using a standardised, multi-criteria approach, we systematically identified forest areas for strict protection that best complied with each scenario’s criteria. Each scenario was then tested against three targets: strictly protecting 5%, 10%, and 30% of the total forest area. Our analysis revealed that, despite the application of different selection criteria in each scenario, the characteristics of the forests ultimately selected showed only moderate differences. Achieving higher protection targets, such as 10% or 30%, required including stands with lower conservation value, as evidenced by lower criterion overlap within selected forest areas. Although existing forest habitat types with no or minimal management requirements (i.e. 15.24% of German forest area) could theoretically serve as the selection pool for the 5% and 10% targets, sociopolitical considerations, such as having an equal spread of protected areas across federal states, required the incorporation of other forest areas. Overall, our findings provide a foundation for implementing Germany’s and the EU’s strict nature protection targets and demonstrate that multiple pathways exist to achieve them. However, strategic compromises may be required as the goals become more ambitious.

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