Evidence map›Paper›PMID 42335112›Full record

ArticlePloS one2026

Bridging the biomass data gap: A literature-based Length-Weight Relationship framework for estimating representative dry weights of freshwater invertebrates in Korean rivers.

Jaehoon Yeom, Minji Kim, Sang Don Kim

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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Jaehoon YeomDepartment of Civil, Environmental, and Architectural Engineering, Korea University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-9040-699X
Minji KimSchool of Environment and Energy Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.ORCID https://orcid.org/0009-0003-7777-2498
Sang Don KimSchool of Environment and Energy Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.ORCID https://orcid.org/0009-0005-9512-0770

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Representative species weight is a critical ecological index for modeling, vulnerability assessment, and toxicity prediction, yet scientifically validated data for aquatic invertebrates remain limited. To address this gap, we present the first literature-based strategy to estimate and validate representative dry weights of freshwater invertebrate species in Korean rivers using Length-Weight Relationships (LWRs). Species length and dry weight records were compiled from domestic field guides, while dry weight records were compiled from both domestic and global literature. LWR coefficients (a and b) were then calculated at genus, family, and order levels and preprocessed under control conditions. Among the taxonomic levels tested, averaging genus-level coefficients yielded the highest concordance with field-measured dry weights (R2 = 0.6633, n = 240), outperforming broader taxonomic levels. Furthermore, a logarithmic correlation analysis confirmed that greater numbers of LWR sources improve predictive accuracy, particularly at the genus level. Based on this optimal strategy, representative dry weights were estimated for 563 taxa. This methodology fills a critical data gap by leveraging existing literature to generate reliable species-specific weight indices without additional field measurements. Our approach provides a quantitative foundation for biomass estimation in data-limited freshwater ecosystems and supports improved ecological modeling, conservation planning, and machine learning-based impact prediction.

Indexed as

BiomassInvertebratesRiversAnimalsEcosystemFresh WaterRepublic of Korea

Identifiers

PMID42335112
PMCPMC13289862

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.