LandRCBM：森林動態と炭素会計を橋渡しし森林炭素予測を前進させる
LandRCBM: Bridging forest dynamics and carbon accounting to advance forest carbon predictions (原題)
Dominique Caron, Camille Giuliano, Eliot J. B. McIntire, Susan Murray, Céline Boisvenue
🤖 gxceed AI 要約
日本語
森林景観モデルLandRと炭素収支モデルspadesCBMを統合したLandRCBMを提案。樹木成長・競争・更新・分散などの生態過程からIPCC森林炭素報告に必要な炭素プールとフラックスを追跡する。カナダ北方林5000万haで過去の火災と500年シミュレーションを実施し、種・撹乱動態が短期・長期の炭素軌道を左右することを示した。
English
LandRCBM integrates the forest landscape model LandR with the carbon budget model spadesCBM to predict IPCC-reportable forest carbon pools and fluxes from ecological processes like growth, competition, regeneration and dispersal. Simulations across 50 million hectares of Canadian boreal forest under historical and 500-year fire regimes show how species and disturbance dynamics shape short- and long-term carbon trajectories. The open, modular framework moves beyond yield-curve approaches toward spatially explicit, process-based forest carbon modelling.
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
日本の森林吸収源は温対法・NDC算定の重要要素であり、林野庁の森林吸収量報告やJ-クレジット森林管理プロジェクトの精緻化に関わる。プロセスベースの空間明示型炭素モデルは国内の吸収源インベントリ高度化の参考になる。
In the global GX context
Process-based, spatially explicit forest carbon models strengthen the land-use and forestry components of national GHG inventories under the Paris Agreement and IPCC guidance, complementing corporate nature/land-sector disclosure under TNFD and CSRD. It offers a template for moving forest carbon accounting from yield curves to ecological dynamics.
👥 読者別の含意
🔬研究者:生態過程ベースの森林炭素モデル統合の設計と不確実性（初期化・林齢推定）の論点を学べる。
🏢実務担当者:森林関連の炭素クレジットや自然資本開示における吸収量算定の精緻化に関わる示唆。
🏛政策担当者:国別温室効果ガスインベントリの森林吸収源算定手法の高度化を検討する際の参考になる。
📄 Abstract（原文）
Forests are central to the global carbon cycle, acting as the largest terrestrial carbon sinks. To estimate forest carbon stocks and predict their response to management interventions and environmental change, predictive models need to account for interacting processes such as tree growth, disturbances, succession, and dispersal, all of which are spatially constrained across landscapes. Understanding how these processes jointly influence carbon stocks therefore requires integrating forest landscape and carbon budget modelling approaches. Here, we introduce LandRCBM, a model integrating the forest landscape model LandR with the carbon budget model spadesCBM. LandRCBM tracks the carbon pools and fluxes required for IPCC forest carbon reporting by predicting carbon trajectories from ecological processes including tree growth, competition, regeneration, and dispersal. We demonstrate the model’s capabilities by simulating carbon dynamics under historical fires between 2000 and 2024 and under simulated fire regimes over 500 years in managed and unmanaged forests across a 50-million-hectare region of the northwestern Canadian boreal forest. Our simulations highlight the flexibility of the framework and demonstrate how species and disturbance dynamics shape short- and long-term carbon trajectories. We also identify key future research directions, including reducing uncertainty associated with model initialization and stand age estimates. Built following modular, open, and extensible principles, LandRCBM facilitates the integration of new data products and ecological processes. By moving beyond yield-curve–driven approaches and representing forest carbon as an emergent property of ecological dynamics, LandRCBM contributes to the ongoing transition toward spatially explicit forest carbon modelling based on ecological processes.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.ecolmodel.2026.111882first seen 2026-10-18 04:50:26
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