Evidence map›Paper›PMID 41681400›Full record

ArticleAnimals : an open access journal from MDPI2026

Ecological Drivers of Vertebrate Richness and Implications for Inland Wetland Survey in Korea.

Yein Lee, Minkyung Kim, Jae Geun Kim, Sangdon Lee

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2026. 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

4 authors.

Yein LeeDepartment of Environmental Science & Engineering, Ewha Womans University, Seoul 03760, Republic of Korea.ORCID 0009-0007-6245-7830
Minkyung KimDepartment of Environmental Science & Engineering, Ewha Womans University, Seoul 03760, Republic of Korea.
Jae Geun KimDepartment of Biology Education, Seoul National University, Seoul 08826, Republic of Korea.ORCID 0000-0002-9181-8830
Sangdon LeeDepartment of Environmental Science & Engineering, Ewha Womans University, Seoul 03760, Republic of Korea.ORCID 0000-0002-3282-873X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wetlands have been recognized as nature-based solutions to the climate crisis. This study evaluates the state of standardization in nationwide inland wetland survey datasets and analyzes terrestrial vertebrate patterns by integrating datasets with public environmental data. Species richness data for amphibians/reptiles (432 wetlands), birds (1183 wetlands), and mammals (72 wetlands) were compiled from 134 reports published between 2000 and 2021. Using generalized linear models (GLMs) and generalized additive models (GAMs), we assessed how 15 explanatory variables (climate, topography, wetland information, land use, and water quality) relate to species richness. Model families were chosen for each taxonomic group, and variables were selected using the Akaike information criterion (AIC) and ecological plausibility. Deviance explained was 55.5% for amphibians/reptiles, 60.1% for birds, and 52.4% for mammals. Wetland area and Normalized Difference Vegetation Index (NDVI) were positively associated with species richness across all groups. Despite the large volume of survey data, inconsistent reporting formats and limited metadata constrain longitudinal and time series analyses. Standardized protocols and metadata management are therefore needed to build a systematic national database that can support wetland ecological modeling and conservation policy.

Indexed as

amphibiansbirdsgeneralized additive model (GAM)generalized linear model (GLM)inland wetlandmammalsreptilesSouth Koreaspecies richnesswetland dataset

Identifiers

PMID41681400
PMCPMC12896779

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.