Evidence map›Paper›PMID 41651946›Full record

ArticleScientific reports2026

Large ungulates will be present in most of Japan by 2050 owing to natural expansion and human population shrinkage.

Takahiro Morosawa, Hayato Iijima, Tomonori Kawamoto, Takahisa Kanno, Ryota Araki, Teruki Oka

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Takahiro MorosawaJapan Wildlife Research Center, 3-3-7, Kotobashi, Sumida-ku, Tokyo, 130-8606, Japan. tmorosawa@go.tuat.ac.jp.
Hayato IijimaForestry and Forest Products Research Institute, Matsunosato 1, Tsukuba, Ibaraki, 305-8687, Japan.
Tomonori KawamotoJapan Wildlife Research Center, 3-3-7, Kotobashi, Sumida-ku, Tokyo, 130-8606, Japan.
Takahisa KannoJapan Wildlife Research Center, 3-3-7, Kotobashi, Sumida-ku, Tokyo, 130-8606, Japan.
Ryota ArakiJapan Wildlife Research Center, 3-3-7, Kotobashi, Sumida-ku, Tokyo, 130-8606, Japan.
Teruki OkaForestry and Forest Products Research Institute, Matsunosato 1, Tsukuba, Ibaraki, 305-8687, Japan.

Funding

Minisitry of Agriculture, Forestry and Fisheries JPJ006258Ministry of Agriculture, Forestry and Fisheries JPJ006258
6 · The paper itself

Abstract

The aims of this study were to elucidate factors contributing to the expansion of the distributions of sika deer and wild boar in Japan and to predict the expansion of their distributions by 2025, 2050, and 2100. A predictive model was constructed using information on species distribution collected by the Ministry of the Environment in 1978, 2003 and 2014, days of snow cover, forested and road areas, elevation, human population, and distance to the nearest occupied cell as covariates to calculate the probability of distribution change. Factors contributing to distribution expansion were elucidated and distribution expansion was predicted. Distance to the nearest occupied cell had the strongest influence on distribution expansion, followed by the inherent ability of each species to expand its distribution. For sika deer, human population had a negative effect and forest area and number of days of snow cover have positively affected. For wild boar, forest area, snow days and population had high importance. Predictions of future distribution showed that both species will be distributed nationwidely by 2050.

Indexed as

DeerSus scrofaAnimalsEcosystemForestsHumansJapanPopulation DensityPopulation DynamicsClimateDispersal abilityDistribution expansionSika deerWild boar

Identifiers

PMID41651946
PMCPMC12932835

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.