Seaweed form as a carbon capture proxy: surface area-to-volume ratio as a general predictor of marine macroalgae productivity
炭素捕捉プロキシとしての海藻形態：表面積対体積比が海藻生産性の汎用予測因子に (AI 翻訳)
João P. G. Machado, Vinícius P. de Oliveira
🤖 gxceed AI 要約
日本語
本研究は、11種の熱帯海藻における表面積対体積比(AV)が一次生産性と炭素捕捉を予測するかを試験。AVは純一次生産性(NPP)と総一次生産性(GPP)と強く相関し、モデルはNPPとGPPの57-65%の分散を説明した。この関係に基づき、種から群集へスケールする形質ベースのフレームワーク「C_eaweed」を開発。低コストで再現可能な海藻炭素捕捉推定ツールとして、養殖、生態系モニタリング、ブルーカーボン会計に貢献する。
English
This study tested whether surface area-to-volume ratio (AV) predicts primary productivity and carbon capture across 11 tropical macroalgal species. AV strongly correlated with net and gross primary productivity (NPP, GPP), explaining 57-65% of variance. Based on these relationships, the authors developed C_eaweed, a trait-based framework that scales from species to communities, providing a low-cost, reproducible tool for estimating seaweed carbon capture. It supports applications in aquaculture, ecosystem monitoring, and blue carbon accounting.
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
日本はブルーカーボン戦略を推進しており、海藻養殖や生態系回復が注目されている。本モデルは種や環境に依存しない低コスト評価手法として、日本のブルーカーボン量の推定やJブルークレジット制度への応用が期待される。
In the global GX context
This paper introduces a low-cost, trait-based model for estimating seaweed carbon capture using surface area-to-volume ratio. While tested on tropical species, the approach is generalizable and supports global blue carbon accounting, aquaculture optimization, and ecosystem monitoring, complementing existing carbon credit methodologies.
👥 読者別の含意
🔬研究者:Provides a predictive functional trait (AV) for macroalgal productivity and a scalable framework for blue carbon modeling.
🏢実務担当者:Offers a simple, low-cost tool for estimating seaweed carbon capture in aquaculture or restoration projects.
🏛政策担当者:Informs blue carbon accounting methodologies and supports the development of carbon credit standards.
📄 Abstract（原文）
Abstract Seaweeds are major contributors to coastal primary production and are increasingly targeted for aquaculture and blue carbon strategies. However, general and low-cost models to estimate their productivity and carbon capture remain lacking. We tested whether the surface area to volume ratio (AV) predicts primary productivity across 11 tropical macroalgal species from the Brazilian coast, using ex situ light–dark bottle oxygen incubations. Net primary productivity (NPP), gross primary productivity (GPP), and respiration (R) were quantified and related to species-specific AV values obtained from morphometric analysis. AV was strongly correlated with NPP (r = 0.72) and GPP (r = 0.68), but weakly and negatively with R (r = –0.22). Regression models with AV as the predictor explained 57–65% of the variance in NPP and GPP, enabling derivation of predictive equations for primary productivity and carbon capture. Based on these relationships, we developed C eaweed , a trait-based framework that scales from species to communities by combining AV distributions with species frequencies. This model provides a reproducible and low-cost tool for estimating seaweed carbon capture and supports applications in aquaculture, ecosystem monitoring, and blue carbon accounting. C eaweed represents the first cross-taxa quantitative model linking algal morphology to carbon capture and establishes AV as a predictive functional trait for macroalgal productivity.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s10811-026-03934-5first seen 2026-06-18 05:01:40
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