Evidence map›Paper›PMID 40632695›Full record

ArticlePloS one2025

Tecomella undulata under threat: The impact of climate change on the distribution of a valuable tree species using a machine learning model.

Ehsan Ghafouri, Gholamabbas Ghanbarian, Artemi Cerdà, Saeideh Ghafouri

Abstract read
In one paragraph

Article in PloS one, 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

4 authors.

Ehsan GhafouriDepartment of Natural Resources and Environmental Engineering, School of Agriculture, Shiraz University, Shiraz, Iran.ORCID https://orcid.org/0009-0000-3111-3591
Gholamabbas GhanbarianDepartment of Natural Resources and Environmental Engineering, School of Agriculture, Shiraz University, Shiraz, Iran.ORCID https://orcid.org/0000-0002-0428-9601
Artemi CerdàDepartment of Geography, Soil Erosion and Degradation Research Group, University of Valencia, Valencia, Spain.ORCID https://orcid.org/0000-0001-5326-4489
Saeideh GhafouriDepartment of Natural Resources and Environmental Engineering, School of Agriculture, Shiraz University, Shiraz, Iran.ORCID https://orcid.org/0009-0007-6001-389X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate change has emerged as a significant driver of biodiversity loss, with profound implications for species distribution. This study assessed the current and future distribution of Tecomella undulata (Desert teak), an economically and medicinally valuable species facing threats from climate change. MaxEnt model, built using 44 occurrence points and environmental data including bioclimatic factors and Digital Elevation Model (DEM), demonstrated an impressive Area Under the Curve (AUC) value of around 0.91 and a True Skill Statistic (TSS) value of 0.79, indicating excellent predictive performance. Temperature seasonality (Bio4) emerged as the most crucial variable, contributing 35.9% to the modeling, followed by the mean temperature of the wettest quarter (Bio8) and precipitation seasonality (Bio15). The habitat suitability maps showed a strong presence of T. undulata in the southern regions of Iran, with Fars and Bushehr provinces being particularly conducive to its growth. Future projections under Shared Socioeconomic Pathways (SSP) scenarios SSP245 and SSP585 for 2030, 2050, 2070, and 2090 suggested a decline in suitable habitats for T. undulata, with high-suitability areas projected to decrease by up to 98% and unsuitable habitats predicted to increase. The study underscores the urgency for tailored conservation measures to mitigate the impacts of climate change on this valuable species.

Indexed as

Climate ChangeMachine LearningTreesBiodiversityConservation of Natural ResourcesEcosystemIranSeasonsTemperature

Identifiers

PMID40632695
PMCPMC12240331

What OpenQuestion holds

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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.