[논문]동물 행동권 분석 방법론 고찰 - 괭이갈매기 사례 분석과 시사점 -

이성주; 이후승

doi:10.13047/kjee.2022.36.2.202

[국내논문] 동물 행동권 분석 방법론 고찰 - 괭이갈매기 사례 분석과 시사점 -
Animal Home Range Estimators - A Review and a Case Study - 원문보기

한국환경생태학회지 = Korean journal of environment and ecology, v.36 no.2, 2022년, pp.202 - 216

이성주 (한국환경연구원 환경평가본부 국토정책평가실) , 이후승 (한국환경연구원 환경평가본부 국토정책평가실)

초록
AI-Helper

동물은 그들의 필요와 외부 자극, 그리고 주변 환경에 반응하면서 특정한 행동과 이동 패턴을 보이며, 생활하면서 대부분의 시간을 보내는 일정한 영역인 행동권(home range)을 가진다. 행동권은 종의 존속과 보전에 매우 중요한 영역이라는 점에 기반하여 해외에서는 행동권 추정 방법론 개발 및 정책 반영이 활발하게 이루어지고 있다. 원격 추적 기술의 발전으로 인해 좌표간 시간 간격이 줄어들며 정밀해진 동물 추적 데이터는 기존 행동권 방법론에 한계점을 드러냈으며, 이를 보완하기 위하여 다양한 새로운 방법론이 개발되었다. 하지만, 국내 행동권 연구는 아직 더딘 편이며 새로이 개발된 방법론 도입도 전혀 이루어지지 않고 있는 상태이다. 본 연구는 동물 행동권의 더욱 정확한 추정을 목적으로 꾸준하게 개발되어 온 해외의 방법론들을 정리 및 소개하여 국내 도입을 촉진하는 것을 목적으로 한다. 먼저 크게 기하학적 그리고 통계적 추정 방법론으로 나눈 후 좌표들이 독립일 때와 자기상관성이 존재할 때의 경우로 나누어 총 7가지의 행동권 추정 방법론을 비교 및 고찰하였다. 실제 전남 신안군 불무기도에 번식하는 괭이갈매기(Larus Crassirostris)의 6월 한 달간 GPS 위치 추적 정보를 사용해 본 연구에서 소개한 방법론을 적용하여 행동권을 도출하였다. 행동권 결과를 비교분석 함으로써 각 방법론의 특징 및 한계점을 논의하였으며, 향후 동물 행동권을 분석하고자 하는 연구자가 본인이 가지고 있는 데이터 특성과 분석 목적에 알맞는 방법론을 선택할 수 있도록 행동권 분석 방법론 선택 의사결정 가이드라인을 제시하였다.

Abstract ▼ AI-Helper

Animals exhibit certain behaviors and movement patterns as they react to their internal needs, external stimuli, and surrounding environments. They have a bounded range in which they mostly spend their time, and it is referred to as a home range. Based on the fact that the home range is a critical area for the survival and preservation of species, there has been a growing body of research on developing more precise home range estimation methods to use the estimated ranges as a ground for establishing an effective conservation policy since the early 1940s. Recent rapid advancements in telemetry technology that resulted in the presence of autocorrelation between locations with short time intervals revealed the limitations of the existing estimators. Many novel estimators have been developed to compensate for it by incorporating autocorrelation in calculating home ranges. However, studies on the animal home range are still in their early stage in Korea, and newly developed methodologies have not yet been adopted. Therefore, this study aims to introduce the foreign home range estimation methods and foster domestic research activities on home ranges. Firstly, we compared and contemplated seven estimators by categorizing them into geometrical and statistical methodologies and then divided them into estimators that assume independent observations and those that consider autocorrelation in each category. After that, the home ranges of black-tailed gulls (Larus crassirostris) were calculated using GPS tracking data for the month of June and derived home range estimators by applying the methodology introduced in this study. We analyzed and compared the results to discuss the strengths and weaknesses of each method. Lastly, we proposed a guideline that can help researchers choose an appropriate estimator for home range calculation based on the animal location data characteristics and analysis purpose.

Keyword

표/그림 (8)

그림 Figure 1. Annual publication trends from 1948 to 2021 using "animal home range"as a keyword, retrieved from the Scopus database (https://www.scopus.com) on Jan 6th, 2022. Colored blocks other than navy illustrate the proportion of each estimator to the total home range research article. Estimators used are minimum convex polygon (MCP), kernel density estimation (KDE), local convex hull (LoCoH), Brownian bridge movement model (BBMM), and autocorrelated kernel density estimation (AKDE), and their name was included in the search keywords. Navy block illustrates the number of articles that were searched neither by including the name of five estimators in a keyword. The large proportion of such color blocks was due to the fact that the search was limited to the title, abstract, and keywords of publications. The black arrow indicates the year in which each estimator was first developed, and the value in the parenthesis beside each legend of the estimator means the total number of published research articles until 2021.
표 Table 1. Reviewed papers on animal home range estimation. GM and ST refer to geometric and statistical estimators, respectively. IID means independent and identically distributed and includes estimators that assume no autocorrelation among animal location points. Whereas, AC means autocorrelated and estimators in this category are specialized in dealing with autocorrelated data. Mimimum convex polygon (MCP), local convex hull (LoCoH), time local convex hull (T-LoCoH), kernel density estimation (KDE), Brownian bridge movement model (BBMM), dynamic Brownian bridge movement model (dBBMM), and autocorrelated kernel density estimation (AKDE) were reviewed in this paper
그림 Figure 2. A schematic diagram of the most frequently used method in geometric and statistical home range estimators, which are the most commonly used estimators in South Korea: minimum convex polygon (MCP, panel A) and kernel density estimation (KDE, panel B). Panel C is a one-dimensional kernel density estimate from the same location data used in panels A and B. Longitude value was taken as an example. Pink ticks along the x-axis indicate the location of each observation, and red curves are kernels placed over them. By summing the contribution of all kernels, the final kernel density plot (solid curve) is calculated. The value of bandwidth alters the degree to which kernels are spread.
표 Table 2. Demographic and tracking information of the surveyed individuals of black-tailed gulls in Bulmugi islet
표 Table 3. The area of home ranges computed using each estimator
그림 Figure 3. Home ranges estimates of black-tailed gulls breeding in Bulmugi islet using a biologging dataset for about a month. Panel A shows the telemetry locations of black-tailed gulls being tracked. Estimators include: (b) minimum convex polygon (MCP) (c) local convex hull (LoCoH), and (d) time local convex hull (T-LoCoH). GPS locations used for the analysis are illustrated in panel A as blue points.
그림 Figure 4. Home ranges estimates of black-tailed gulls breeding in Bulmugi islet using a biologging dataset for about a month. Estimators include: (a) kernel density estimation (KDE), (c) Brownian bridge motion model (BBMM), and (d) autocorrelated kernel density estimation (AKDE).
그림 Figure 5. Guideline for selecting home range (HR) estimators.

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저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

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