Analysis of occupancy and activity pattern of the water deer using remote camera traps

preprint OA: closed
Full text JSON View at publisher

Abstract

Water deer (Hydropotes inermis) are classified as “Vulnerable” on the IUCN Red List due to their declining population trend, indicating global concern. However, water deer are considered a harmful species in South Korea where the population is relatively abundant. The population status of water deer varies across their geographical range, but information on their distribution is limited due to insufficient research and reporting. This study, therefore, aimed to identify the environmental variables influencing the distribution of water deer and to provide insights into their daily behavior patterns. Camera traps were deployed at 108 points within 22 grid cells (each 5 km × 5 km), from March to September 2021, located in the central-east part of the Korean Peninsula. Water deer were detected 92 times across 17 grid cells. A single-season occupancy model revealed that occupancy decreases with the increase in normalized difference vegetation index (NDVI) (β_NDVI: -1.00±0.21), which is presumed to be related to the use of forbs or low woody plants as food resources. Detection was influenced by slope, with higher slopes likely limiting mobility and, thereby reducing detection (β_slope: -0.60±0.03). The diurnal behavior patterns were confirmed to be crepuscular, with activity primarily around sunrise and sunset. These findings are expected to enhance the understanding of this species and may be used for the management of water deer as a problematic species in South Korea, as well as for species conservation efforts internationally.
Full text 7,135 characters · extracted from preprint-html · click to expand
Analysis of occupancy and activity pattern of the water deer using remote camera traps | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecological Research This is a preprint and has not been peer reviewed. Data may be preliminary. 20 March 2025 V1 Latest version Share on Analysis of occupancy and activity pattern of the water deer using remote camera traps Authors : Naeyoung Kim , Jin Hong Lee , YongSu Park , Young Han You , and junsoo kim 0000-0003-0738-9648 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174244686.67567991/v1 Published Ecological Research Version of record Peer review timeline 342 views 161 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Water deer (Hydropotes inermis) are classified as “Vulnerable” on the IUCN Red List due to their declining population trend, indicating global concern. However, water deer are considered a harmful species in South Korea where the population is relatively abundant. The population status of water deer varies across their geographical range, but information on their distribution is limited due to insufficient research and reporting. This study, therefore, aimed to identify the environmental variables influencing the distribution of water deer and to provide insights into their daily behavior patterns. Camera traps were deployed at 108 points within 22 grid cells (each 5 km × 5 km), from March to September 2021, located in the central-east part of the Korean Peninsula. Water deer were detected 92 times across 17 grid cells. A single-season occupancy model revealed that occupancy decreases with the increase in normalized difference vegetation index (NDVI) (β_NDVI: -1.00±0.21), which is presumed to be related to the use of forbs or low woody plants as food resources. Detection was influenced by slope, with higher slopes likely limiting mobility and, thereby reducing detection (β_slope: -0.60±0.03). The diurnal behavior patterns were confirmed to be crepuscular, with activity primarily around sunrise and sunset. These findings are expected to enhance the understanding of this species and may be used for the management of water deer as a problematic species in South Korea, as well as for species conservation efforts internationally. Supplementary Material File (manuscript_20250320.docx) Download 62.84 KB Information & Authors Information Version history V1 Version 1 20 March 2025 Peer review timeline Published Ecological Research Version of Record 18 Feb 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Ecological Research Keywords 18: mammal 2: behavioral ecology activity pattern analysis of occupancy camera trap hydropotes inermis management Authors Affiliations Naeyoung Kim National Institute of Ecology View all articles by this author Jin Hong Lee Korea National Park Service View all articles by this author YongSu Park National Institute of Ecology View all articles by this author Young Han You Kongju National University View all articles by this author junsoo kim 0000-0003-0738-9648 [email protected] National Institute of Ecology View all articles by this author Metrics & Citations Metrics Article Usage 342 views 161 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Naeyoung Kim, Jin Hong Lee, YongSu Park, et al. Analysis of occupancy and activity pattern of the water deer using remote camera traps. Authorea . 20 March 2025. DOI: https://doi.org/10.22541/au.174244686.67567991/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.174244686.67567991/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a0030e9999823fe2',t:'MTc3OTUyODc4NA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: preprint-html

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00