Evidence map›Paper›PMID 42343949›Full record

ArticleEcology and evolution2026

Impact of an Updated Climate Database on Model Performance and Niche Variability in Species Distribution Modeling.

Jae-Woo Song, Sunhee Yoon, Sunghoon Jung, Wang-Hee Lee

Abstract read
In one paragraph

Article in Ecology and evolution, 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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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

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5 · Who and what money

Authors and funding

4 authors.

Jae-Woo SongDepartment of Smart Agriculture Systems Machinery Engineering Chungnam National University Daejeon Korea.
Sunhee YoonDepartment of Smart Agriculture Systems Chungnam National University Daejeon Korea.
Sunghoon JungDepartment of Applied Biology Chungnam National University Daejeon Korea.
Wang-Hee LeeDepartment of Smart Agriculture Systems Machinery Engineering Chungnam National University Daejeon Korea.ORCID https://orcid.org/0000-0002-8834-1779

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the continuous accrual of occurrence data, the climate datasets used in species distribution modeling (SDM), particularly in mechanistic frameworks such as CLIMEX, often remain based on outdated climate baselines. Here, we present an updated global climate database (1992-2021) and corresponding bioclimatic variables, specifically structured for CLIMEX compatibility while also supporting application across multiple SDM frameworks. By providing a temporally updated climate baseline and enhancing cross-framework usability, this resource addresses a key barrier to the integration of contemporary climate data into applied ecological modeling. To evaluate its utility, we developed CLIMEX and MaxEnt models for three species and compared model performance, predicted distributions, and niche similarity between the traditional 1961-1990 baseline and the updated 1992-2021 climate data. While overall model performance and broad niche overlaps remained similar between the two periods, localized habitat suitability and occurrence probabilities exhibited noteworthy variability. These results highlight the importance of temporally synchronized climate and occurrence data for reducing predictive uncertainty and accurately assessing climate impacts on species distributions. We provide the updated climate database and associated processing workflows through an open-access repository. By facilitating the use of contemporary climate data across both mechanistic and correlative SDM approaches, this resource supports reliable pest risk analyses, targeted surveillance, and evidence-based biosecurity decision-making under current environmental conditions.

Indexed as

climateCLIMEXdatabase updateMaxEntpotential distribution

Identifiers

PMID42343949
PMCPMC13288864

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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.