Evidence map›Paper›PMID 40122919›Full record

ArticleScientific reports2025

The species distribution model based on the random forest algorithm reveals the distribution patterns of Neophocaena asiaeorientalis.

Rao Rongcheng, Huang Yi, Min Jialing, Yang Ying, Liu Fanning, Wu Xiya, Shi Xinyuan, Li Caigang, Dai Yingen, Huang Qinghai and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

12 authors.

Rao RongchengAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China.
Huang YiAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China.
Min JialingAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China.
Yang YingAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China.
Liu FanningAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China.
Wu XiyaAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China.
Shi XinyuanAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China.
Li CaigangAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China.
Dai YingenAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China.
Huang QinghaiAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China.
Yu JinxiangAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China. 7835711@qq.com.
Que JianglongAquatic Conservation and Rescue Center of Jiangxi Province, Nanchang, 330096, China. que_jianglong@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Species Distribution Model (SDM) provides a crucial foundation for the conservation of the Yangtze finless porpoise (YFP), a critically endangered freshwater cetacean endemic to China. In this study, we conducted population and habitat surveys, and employed the Random Forest algorithm (RF) to construct SDMs. We found that the habitat preference of YFP shows complex seasonality. Cyanobacteria and total phosphates have been identified as the predominant factors influencing the YFP distributions by affecting prey resources. We emphasize that ascertaining the presence and pseudo-absence points of YFP, in conjunction with the selection of key factors, constitutes the foundational element in the construction of SDMs. We suggest that the incorporation of techniques such as environmental DNA could expand the range of environmental factors, particularly with regard to the distribution of prey resources at the genus or species level. This study provides guidance for the SDMs of YFP and demonstrates the potential of machine learning algorithms in constructing SDMs for the endangered aquatic species.

Indexed as

AlgorithmsPorpoisesAnimalsChinaConservation of Natural ResourcesEcosystemEndangered SpeciesRandom ForestConservation biologyEndangered aquatic speciesHabitat preferenceHydrographic environmentMachine learning algorithm

Identifiers

PMID40122919
PMCPMC11930967

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.