지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.2
- 수록면
- 37 - 51 (15page)
- DOI
- 10.15531/ksccr.2020.11.1.37
이용수
초록· 키워드
오류제보하기
Species distribution models (SDMs) are widely used for biodiversity assessment, habitat management, and climate change impact assessment due to their ability to quantitatively evaluate species distribution. However, due to model uncertainty, the use of SDMs in public policy management has been limited. In order to overcome the limitations, many studies have been conducted mainly focusing on an ensemble approach, which compensates for the uncertainty of a single model. Even though ensemble methodology has been proven to improve accuracy compared to single models, this was based on inner validation. As inner validation has established flaws, with using the data in the form of ‘point’, the need to assess outer validation with independent data in a polygon formations has been raised. In this study, we evaluated the accuracy of a Committee Averaging (CV) ensemble methodology using outer validation. In order to minimize uncertainty beyond the methodology setting, we used Pinus thunbergii, which has spatial specificity. As the outer validation method showed more accurate evaluation results, we used outer validation indices-sensitivity, specificity and accuracy-for comparison analysis between ensemble and single model results. Single models tend to overestimate compared to ensemble models, with a high value of sensitivity and a low value of specificity, whereas ensemble models tended to decrease the spatial uncertainty of single models, with generally high values of sensitivity, specificity and accuracy. Accordingly, the ensemble model methodology proved to improve accuracy by reducing the uncertainty of single models. Furthermore, through comparison analysis between outer and inner validation results, we additionally interpreted differences and limitations among inner validation, and have finally confirmed the need for further consideration in interpreting the results of the inner validation for both methodologies. Hence, outer validation using independent data should also be used.
목차
ABSTRACT
1. 서론
2. 재료 및 방법
3. 결과 및 고찰
4. 결론
REFERENCES
참고문헌 (55)
참고문헌 신청
Allouche O / 2006 / Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS) / J Appl Ecol 43(6):1223~1232
Arau’jo MB / 2005 / Reducing uncertainty in projections of extinction risk from climate change / Global Ecol. Biogeogr 14:529~538
Araújo MB / 2007 / Ensemble forecasting of species distributions / Trends Ecol. Evol 22:42~47
Araújo MB / 2005 / Reducing uncertainty in projections of extinction risk from climate change / Global Ecology and Biogeography 14:529~538
Austin M / 2002 / Spatial prediction of species distribution:An interface between ecological theory and statistical modelling / Ecological Modelling 157(2):101~118
Arau’jo MB / 2005 / Reducing uncertainty in projections of extinction risk from climate change / Global Ecol. Biogeogr 14:529~538
Araújo MB / 2007 / Ensemble forecasting of species distributions / Trends Ecol. Evol 22:42~47
Araújo MB / 2005 / Reducing uncertainty in projections of extinction risk from climate change / Global Ecology and Biogeography 14:529~538
Austin M / 2002 / Spatial prediction of species distribution:An interface between ecological theory and statistical modelling / Ecological Modelling 157(2):101~118
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UCI(KEPA) : I410-ECN-0101-2020-453-000454278