Evidence map›Paper›PMID 41164452›Full record

ArticleEcology and evolution2025

Importance of Flow Metrics on Modeling Macroinvertebrate Community in Dammed Rivers: An Approach With Optimized Gradient Boosting.

Kei Nukazawa, Ryo Tanaka, Haruki Mineda

Abstract read
In one paragraph

Article in Ecology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kei NukazawaDepartment of Civil and Environmental Engineering, Faculty of Engineering University of Miyazaki Miyazaki Japan.ORCID https://orcid.org/0000-0001-5356-2064
Ryo TanakaDepartment of Civil and Environmental Engineering, Faculty of Engineering University of Miyazaki Miyazaki Japan.
Haruki MinedaDepartment of Civil and Environmental Engineering, Faculty of Engineering University of Miyazaki Miyazaki Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Habitat models that can predict the habitat suitability for riverine organisms and their distribution along environmental gradients are helpful in watershed environmental management. However, the impacts of dams on riverine communities and their habitats have not yet been considered in such models, although dams have considerably altered important riverine habitats. In this study, we aim to develop catchment-scale habitat models of macroinvertebrate communities with predictor variables characterizing the impacts of dams. We studied the Omaru River catchment in southwest Japan, where the river flow has been altered considerably because of multiple hydropower dams. Multiple machine learning techniques, such as XGBoost and LightGBM were used to model the habitat distributions of 170 macroinvertebrate taxa observed throughout the river catchment. We used predictor variables of dam impacts derived based on geographical information system data (hereafter, dam metrics) and physically simulated flow data using a hydrological model. Among the modeling techniques, gradient boosting algorithms (XGBoost and LightGBM) with optimized tree number parameters exhibited the highest mean accuracy among the analyzed taxa, followed by the random forest algorithm. The accuracy of the habitat models for the macroinvertebrate community and habitat groups considerably improved with the integration of dam metrics and flow predictors. Of the habit groups considered, clingers showed a keen response to low-flow metrics, presumably owing to flow alteration caused by the studied dams, as the downstream sections of these dams received only residual flow. Our findings indicate that (1) variables of dam impacts greatly improve the predictive capability of macroinvertebrates and (2) gradient boosting machines with optimized parameters are favorable for habitat modeling of the biotic community. Our models are helpful when river practitioners implement conservation measures as they better understand the environmental consequences of their flood protection designs.

Indexed as

ecological niche modelinghydrological simulationindicators of hydrologic alterationLightGBMrandom forest algorithmXGBoost

Identifiers

PMID41164452
PMCPMC12559814

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.