도시생태현황지도 작성을 위한 육상동물 서식지 가치평가 방안 연구 = A Study on Fauna Habitat Valuation of Urban Ecological Maps

국문 초록 (Abstract)

도시생태현황지도는 자연환경보전법에 의해 시(市)단위 이상의 지방자치단체는 의무적으로 작성해야 하며 일반적으로 비오톱지도라고 한다. 그 동안 비오톱지도 관련 연구는 식생유형, 식생자연성, 토지이용, 경관생태학 배경으로 이루어 졌으며, 비오톱의 구성요소인 동물서식지 개념을 적용하지 못하는 한계성을 가지고 있었다. 이 연구는 이러한 한계성을 개선하기 위해 육상동물 잠재서식지 개념을 도입하여 비오톱 유형평가에 적용할 수 있는 방법론을 제안하기 위해 수행되었다. 육상동물의 잠재서식지는 머신러닝을 이용한 공간통계 방법을 이용하여 예측하였고 그 결과를 종합하여 종풍부도로 변환하였다. 비오톱 유형평가는 식생학적 가치, 동물서식지 가치로 구분하여 가치합산 하였다. 식생학적 가치는 식생의 자연성과 토지이용을 고려하여 5단계로 구분하였고, 동물 서식지 가치는 머신러닝으로 예측한 종풍부도를 5단계로 구분하여 비오톱 유형평가에 적용하였다. 이 연구는 그동안 비오톱 유형평가에 소극적으로 반영된 육상동물 현장조사 결과를 적극적으로 반영할 수 있는 방법론을 도출하였다는 것에 의미가 있으며 향후 도시생태현황지도 작성 매뉴얼 개정 시 고려될 필요가 있다.

다국어 초록 (Multilingual Abstract)

Abstract: URBAN ECOLOGICAL MAPS must be created by local governments by NATURAL ENVIRONMENT CONSERVATION ACT, and the maps are generally called biotope map. So far, biotope maps study was a tendency to focus on the type of vegetation, naturalness, land use, landscape ecology theories. However, biotope related studies have not reflected the concept of animal habitat, which is a component of biotope, and that is the limitation of biotope map research. This study suggest a methodology to predict potential habitats for fauna using machine learning to quantify habitat values. The potential habitats of fauna were predicted by spatial statistics using machine learning, and the results were converted into species richness. For biotope type assessments, we classified biotope values into vegetation value and habitat value and evaluated them using a matrix for value summation. The vegetation value was divided into 5 stages based on vegetation nature and land use, and the habitat value was classified into five stages by predicting the species richness predicted by machine learning. This is meaningful because our research can positively reflect the results of field surveys of fauna that were negatively reflected in the evaluation of biotope types in the past. Therefore, in the future, if the biotope map manual is revised, our methodology should be applied.

참고문헌 (Reference)

1 최일기, "한국과 독일의 비오톱지도화에 대한 비교" 한국환경생태학회 21 (21): 565-575, 2007

2 정성희, "포항시 비오톱의 유형 구분, 속성 분석 및 복원 방안" 한국하천호수학회 52 (52): 245-265, 2019

3 최일기, "전국적 적용을 위한 비오톱유형분류 제안" 한국환경생태학회 22 (22): 666-678, 2008

4 김지석, "시가화지역 식물군집 특성에 기초한 비오톱 유형분류" 한국환경생태학회 29 (29): 454-461, 2015

5 최일기, "비오톱평가를 위한 평가항목 및 평가체계 제안" 한국환경생태학회 23 (23): 594-602, 2009

6 최일기, "독일의 비오톱 유형분류에 대한 고찰" 한국조경학회 35 (35): 73-81, 2007

7 최낙훈, "도시생태현황지도를 활용한 침해조정 제도 국내 적용 사례 연구" 응용생태공학회 4 (4): 237-244, 2017

8 오정학, "경관생태학적 도시숲 관리를 위한 비오톱 평가지표 및유형 분류에 관한 연구" 한국지리정보학회 11 (11): 101-111, 2008

9 Naimi B, "sdm : a reproducible and extensible R platform for species distribution modelling" 39 : 368-375, 2016

10 d’Amen M, "Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models" 42 : 1255-1266, 2015

11 Vazquez DP, "Uniting pattern and process in plant-animal mutualistic networks : a review" 103 : 1445-1457, 2009

12 Franklin J, "The potential for conservation of Polynesian birds through habitat mapping and species translocation" 5 : 506-521, 1991

13 Jarvis PJ, "The mapping of urban habitat and its evaluation" University of Wolverhampton 2005

14 Distler T, "Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change" 42 : 976-988, 2015

15 Liu C, "Selecting thresholds for the prediction of species occurrence with presence-only data" 40 : 778-789, 2013

16 Andersen MC, "Regression-tree modeling of desert tortoise habitat in the central Mojave Desert" 10 : 890-900, 2000

17 R Core Team, "R: A language and environment for statistical computing" R Foundation for Statistical Computing

18 QGIS org, "QGIS geographic information system. Open Source Geospatial Foundation Project"

19 Mykra H, "Predicting the stream macroinvertebrate fauna across regional scales : influence of geographical extent on model performance" 27 : 705-716, 2008

20 Coops NC, "Predicting the complexity of habitat in forests from airborne videography for wildlife management" 18 : 2677-2682, 1997

21 Miyamoto A, "Predicting habitat distribution of the alien Formosan squirrel using logistic regression model"

22 Bascompte J, "Plant-animal mutualistic networks: the architecture of biodiversity" 38 : 567-593, 2007

23 Zimmermann NE, "New trends in species distribution modelling" 33 : 985-989, 2010

24 McComb WC, "Models for mapping potential habitat at landscape scales : an example using northern spotted owls" 48 : 203-216, 2002

25 Bleyhl B, "Mapping seasonal European bison habitat in the Caucasus Mountains to identify potential reintroduction sites" 191 : 83-92, 2015

26 Breininger DR, "Mapping Florida scrub jay habitat for purposes of land-use management"

27 Jordano P, "Invariant properties in coevolutionary networks of plant-animal interactions" 6 : 69-81, 2003

28 Druon JN, "Habitat mapping of the Atlantic bluefin tuna derived from satellite data : Its potential as a tool for the sustainable management of pelagic fisheries" 34 : 293-297, 2010

29 Fortuna MA, "Habitat loss and the structure of plant-animal mutualistic networks" 9 : 281-286, 2006

30 Graham CH, "Habitat history improves prediction of biodiversity in rainforest fauna" 103 : 632-636, 2006

31 Kim HS, "Gyeonggi-do’s Biotope Map Issues and Alternatives"

32 National law information center, "Guidelines on urban ecological maps"

33 Haslett JR, "Geographic information systems. A new approach to habitat definition and the study of distributions" 5 : 214-218, 1990

34 Pereira J, "GIS-based habitat modeling using logistic multiple regression-A study of the Mt. Graham red squirrel" 57 : 1475-1486, 1991

35 Wintle BA, "Fauna habitat modelling and mapping : a review and case study in the Lower Hunter Central Coast region of NSW" 30 : 719-738, 2005

36 Zhang C, "Ensemble machine learning: methods and applications" Springer Science & Business Media 2012

37 Araújo MB, "Ensemble forecasting of species distributions" 22 : 42-47, 2007

38 Andren H, "Elevated predation rates as an edge effect in habitat islands : experimental evidence" 69 : 544-547, 1988

39 Attrill MJ, "Ecotone or ecocline : ecological boundaries in estuaries" 55 (55): 929-936, 2002

40 Wright JF, "Development and use of a system for predicting the macroinvertebrate fauna in flowing waters" 20 : 181-197, 1995

41 Chetkiewicz C-LB, "Corridors for Conservation: Integrating Pattern and Process" 37 : 317-342, 2006

42 Kim HS, "Classification Biotope Type and Evaluation Value of Individual Biotope:Landscape Ecological Approaches" Dongguk University 2012

43 Yahner RH, "Changes in wildlife communities near edges" 2 : 333-339, 1988

44 Magura T, "Carabids and forest edge : spatial pattern and edge effect" 157 : 23-37, 2002

45 Buckley LB, "Can mechanism inform species’ distribution models?" 13 : 1041-1054, 2010

46 Raes N, "Botanical richness and endemicity patterns of Borneo derived from species distribution models" 32 : 180-192, 2009

47 Sukopp H, "Biotope mapping and nature conservation strategies in urban areas of the Federal Republic of Germany" 15 : 39-58, 1988

48 Pineda E, "Assessing the accuracy of species distribution models to predict amphibian species richness patterns" 78 : 182-190, 2009

49 Allouche O, "Assessing the accuracy of species distribution models : prevalence, kappa and the true skill statistic(TSS)" 43 : 1223-1232, 2006

50 Randin CF, "Are nichebased species distribution models transferable in space?" 33 : 1689-1703, 2006

51 Ki KS, "Application of Green Recreation Opportunity Based on Biotope Evaluation of Rural Region in the Seoul Metropolitan Area, Korea: A Case of Yangpeong-Gun" University of Seoul 2011

52 Hao T, "A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD" 25 : 839-852, 2019