Tree traits and soil properties influence carbon stocks: A comparison of protected areas in three physiographic regions of Nepal
樹木特性と土壌特性が炭素貯蔵量に与える影響：ネパール3つの自然地帯における保護区の比較 (AI 翻訳)
Srijana Khanal, Madan Koirala, Nawal Shrestha, Prakash Gautam, Furbe Lama, Allan Degen, Srijana Joshi, Ben Sparrow, Zhanhuan Shang
🤖 gxceed AI 要約
日本語
本研究は、ネパールの3つの主要な地形区域（ヒマラヤ、中丘陵、タライ）の保護区における地上部炭素（AGC）と土壌有機炭素（SOC）の貯蔵量とその決定要因を機械学習を用いて比較した。中丘陵の保護区が最も高いAGCを貯蔵し、ヒマラヤの保護区が最も高いSOCを貯蔵していた。AGCは主に樹木の形質、SOCは土壌特性によって予測された。保護区は気候変動緩和に重要な役割を果たすことが示された。
English
This study compares aboveground carbon (AGC) and soil organic carbon (SOC) stocks across three physiographic regions of Nepal (Himalayas, mid-hills, Terai) using machine learning. Mid-hill protected areas stored the most AGC, while Himalayan areas stored the most SOC. AGC was primarily driven by tree traits, SOC by soil properties. The findings highlight the role of protected areas in climate mitigation and the need for region-specific strategies.
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
日本においても、森林炭素吸収源の活用や国立公園の保全がGX政策の一部となっている。本研究成果は、保護区ごとの炭素貯蔵特性を考慮した管理の重要性を示唆しており、日本の森林・保護区管理にも応用可能性がある。
In the global GX context
This study provides empirical evidence on carbon storage in protected areas across different ecosystems, contributing to global knowledge on nature-based climate solutions. The machine learning approach offers a methodology applicable to other regions for assessing carbon sequestration potential and informing conservation policies.
👥 読者別の含意
🔬研究者:This paper offers a comparative machine-learning approach to identify drivers of carbon storage in different ecosystems, which can be replicated in other regions.
🏢実務担当者:Protected area managers can use the findings to prioritize management actions that enhance carbon sequestration, such as focusing on tree traits or soil properties depending on the region.
🏛政策担当者:Policymakers can leverage the evidence that protected areas contribute significantly to carbon storage, supporting arguments for expanding and effectively managing protected areas as part of climate mitigation strategies.
📄 Abstract（原文）
Protected areas (PAs) within biodiversity hotspots play a crucial role in climate change mitigation, yet evidence-based insights into factors governing their carbon storage potential remains limited. This study bridges this gap by employing an integrated, machine-learning approach to quantify aboveground carbon (AGC) and soil organic carbon (SOC) stocks and their drivers among three major physiographic regions of Nepal, namely, the Dhorpatan Hunting Reserve (DHR) in the Himalayas, the Shivapuri Nagarjun National Park (SNNP) in the mid-hills, and the Chitwan National Park (CNP) in the Terai. A total of 288 soil samples were collected using stratified random sampling based on predefined environmental parameters. Despite comparable conservation practices, these PAs exhibited different patterns of carbon storage, with PA in the mid-hills containing the most AGC and the Himalayan PA retaining the most SOC stocks. Based on principal component analysis, AGC was governed largely by tree structural traits while SOC was influenced by soil factors. By employing a machine-learning approach, random forest modelling identified the relative importance and non-linear interactions between tree traits and soil properties. These findings provide one of the first comparative assessment across Nepal’s major physiographic gradients integrating vegetation, soil and carbon pools. The results emphasize that effective strategies for maximizing carbon sequestration and biodiversity conservation requires strategies tailoring to distinct ecological processes operating within each physiographic region. • Mid-hill protected areas had highest aboveground carbon stock • Primarily tree traits predicted aboveground carbon (AGC) stock • Mainly soil properties predicted soil carbon stock • PCA and random forest identified biotic/abiotic factors carbon storage • Protected areas play a critical role in climate mitigation.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.gecco.2026.e04225first seen 2026-05-05 19:41:02
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。