Evidence map›Paper›PMID 40846874›Full record

ArticleScientific data2025

High-resolution global distribution projections of 10 rodent genera under diverse SSP-RCP scenarios, 2021-2100.

Yang Lan, Xiao Wu, Meng Xu, Keran Li, Yizhong Huan, Guangjin Zhou, Fei Lun, Wenlong Shang, Riqi Zhang, Yang Xie

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Ecological Niche Modeling of PathogenicPathogens (Basel, Switzerland) · 2026
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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

10 authors.

Yang LanSchool of Economics and Management, Beihang University, Beijing, 100191, China.ORCID 0000-0001-5383-7363
Xiao WuSchool of Economics and Management, Beihang University, Beijing, 100191, China.
Meng XuCollege of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China. xumeng1007@pku.edu.cn.
Keran LiState Key Laboratory of Mineral Deposit Research, School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, China.ORCID 0000-0002-7296-8729
Yizhong HuanCollege of Humanities and Development Studies, China Agricultural University, Beijing, 100083, China.
Guangjin ZhouSatellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094, China.
Fei LunCollege of Land Science and Technology, China Agricultural University, Beijing, 100193, China.
Wenlong ShangDepartment of Civil and Environmental Engieering, Imperial College London, SW7 2AZ, London, UK.
Riqi ZhangCentre for Environmental Policy, Imperial College London, London, SW7 1NE, UK.
Yang XieSchool of Economics and Management, Beihang University, Beijing, 100191, China. xieyangdaisy@buaa.edu.cn.ORCID 0000-0002-8864-960X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the potential impact of climate change on species distributions is crucial for biodiversity conservation and ecosystem management. Rodents, as one of the most diverse and widespread mammalian groups, play a critical role in ecological systems but also pose significant risks to agriculture systems and public health. Here, we present GridScopeRodents, a high-resolution global dataset projecting the distribution of 10 rodent genera from 2021 to 2100 under four CMIP6-based Shared Socioeconomic Pathway-Representative Concentration Pathway (SSP-RCP) scenario combinations. Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). The dataset encompasses four SSP-RCP scenarios (SSP126, SSP245, SSP370, SSP585) and 10 global climate models (GCMs), providing projections at 20-year intervals. GridScopeRodents serves as a valuable resource for research on biodiversity conservation, invasive species monitoring, agricultural sustainability, and disease ecology. The dataset is publicly available in GeoTIFF format and can be accessed via Figshare.

Indexed as

Animal DistributionBiodiversityClimate ChangeRodentiaAnimalsClimate ModelsConservation of Natural ResourcesEcosystem

Identifiers

PMID40846874
PMCPMC12373875

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.