Evidence map›Paper›PMID 42050334›Full record

ArticleScientific reports2026

Estimating the density of raccoon dogs (Nyctereutes procyonoides) and water deer (Hydropotes inermis) using thermal real-time drone surveys and distance sampling.

Yeon Woo Lee, Sumin Jeon, Jihye Son, Minyoung Kim, Hyeok Jae Lee, Soo Kyeong Hwang, Ryeoeun Youn, Choonghyun Kang, Jiwon Choi, Tae Ho Lee and 2 more

Abstract read
PubMed Full text at PMC Publisher
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. 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

12 authors.

Yeon Woo LeeDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
Sumin JeonDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
Jihye SonDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
Minyoung KimDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
Hyeok Jae LeeDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
Soo Kyeong HwangDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
Ryeoeun YounDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
Choonghyun KangDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
Jiwon ChoiDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
Tae Ho LeeDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
Hwanhee SeoDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
Jong Koo LeeDivision of Life Sciences, College of Sciences and Bioengineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea. jklee@inu.ac.kr.

Funding

Korea Forestry Promotion Institute RS-2024-00402265
6 · The paper itself

Abstract

Accurate estimation of population density and spatial distribution is essential for wildlife conservation and management, yet conventional survey methods face substantial limitations. Estimating the density of medium- to large-sized mammals remains particularly challenging due to difficulties in direct observation and methodological constraints. This study qualitatively evaluated the detection performance and species identification capability of real-time drone surveys and assessed the applicability of distance sampling for density estimation. Surveys were conducted in a reedbed in Yeonsu, Incheon, and a plantation forest in Chungju, South Korea. Real-time drone surveys combined with line-transect sampling were applied in Yeonsu, whereas point-transect sampling was used in Chungju. Across nine line-transect surveys, 270 raccoon dogs (Nyctereutes procyonoides) were recorded, yielding an estimated density of 31.8 ind/km

Indexed as

DeerRaccoon DogsAnimalsPopulation DensityRepublic of KoreaUnmanned Aerial DevicesDensity estimationDistance samplingRaccoon dogReal-time drone surveyWater deerWildlife drone survey

Identifiers

PMID42050334
PMCPMC13365454

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.