IGSNRR OpenIR
Modelling species habitat suitability from presence-only data using kernel density estimation
Zhang, Guiming1,2; Zhu, A-Xing2,3,4,5,6; Windels, Steve K.7; Qin, Cheng-Zhi3,6
2018-10-01
Source PublicationECOLOGICAL INDICATORS
ISSN1470-160X
Volume93Pages:387-396
Corresponding AuthorZhu, A-Xing(azhu@wisc.edu)
AbstractWe present a novel approach for modelling and mapping habitat suitability from species presence-only data that is useful for ecosystem and species monitoring. The approach models the relationship between species habitat suitability and environment conditions using probability distributions of species presence over environmental factors. Resource availability is an important issue for modelling habitat suitability from presence-only data, but it is in lack of consideration in many existing methods. Our approach accounts for resource availability by computing habitat suitability based on the ratio of species presence probability over environmental factors to background probability of environmental factors in the study area. A case study of modelling and mapping habitat suitability of the white-tailed deer (Odocoileus virginianus) using presence locations recorded in aerial surveys at Voyageurs National Park, Minnesota, USA was conducted to demonstrate the approach. Performance of the approach was evaluated through randomly splitting the presence locations into training data to build the model and test data to evaluate prediction accuracy of the model (repeated 100 times). Results show that the approach fit training data well (average training area under the curve AUC = 0.792, standard deviation SD = 0.029) and achieved better-than-random prediction accuracy (average test AUC = 0.664, SD = 0.025) that is comparable to the state-of-the-art MAXENT method (average training AUC = 0.784, SD = 0.021; average test AUC = 0.673, SD = 0.027). In addition, the suitability-environment responses modelled using our approach are more amenable to ecological interpretation compared to MAXENT. Compared to modelling habitat suitability purely based on species presence probability distribution (average training AUC = 0.743, SD = 0.030; average test AUC = 0.645, SD = 0.023), incorporating background distribution to account for resource availability effectively improved model performance. The proposed approach offers a flexible framework for modelling and mapping species habitat suitability from species presence-only data. The modelled species-environment responses and mapped species habitat suitability can be very useful for ecological monitoring at ecosystem or species level.
KeywordHabitat suitability modelling and mapping Presence-only data Resource availability Kernel density estimation Ecological monitoring
DOI10.1016/j.ecolind.2018.04.002
WOS KeywordWHITE-TAILED DEER ; SAMPLE SELECTION BIAS ; DISTRIBUTIONS ; CONSERVATION ; BIODIVERSITY ; PREDICTION ; REGRESSION ; PATTERNS ; MUSEUM ; SPACE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[41431177] ; National Basic Research Program of China[2015CB954102] ; Natural Science Research Program of Jiangsu[14KJA170001] ; PAPD ; National Key Technology Innovation Project for Water Pollution Control and Remediation[2013ZX07103006] ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; Department of Geography, University of Wisconsin-Madison ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison ; the One-Thousand Talents Program of China ; National Natural Science Foundation of China
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; National Basic Research Program of China ; Natural Science Research Program of Jiangsu ; PAPD ; National Key Technology Innovation Project for Water Pollution Control and Remediation ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; Department of Geography, University of Wisconsin-Madison ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison ; the One-Thousand Talents Program of China ; National Natural Science Foundation of China
WOS Research AreaBiodiversity & Conservation ; Environmental Sciences & Ecology
WOS SubjectBiodiversity Conservation ; Environmental Sciences
WOS IDWOS:000452692600040
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/51258
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhu, A-Xing
Affiliation1.Univ Denver, Dept Geog & Environm, Denver, CO USA
2.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China
4.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Nanjing, Jiangsu, Peoples R China
5.State Key Lab Cultivat Base Geog Environm Evolut, Nanjing, Jiangsu, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
7.Voyageurs Natl Pk, Natl Pk Serv, Int Falls, MN USA
Recommended Citation
GB/T 7714
Zhang, Guiming,Zhu, A-Xing,Windels, Steve K.,et al. Modelling species habitat suitability from presence-only data using kernel density estimation[J]. ECOLOGICAL INDICATORS,2018,93:387-396.
APA Zhang, Guiming,Zhu, A-Xing,Windels, Steve K.,&Qin, Cheng-Zhi.(2018).Modelling species habitat suitability from presence-only data using kernel density estimation.ECOLOGICAL INDICATORS,93,387-396.
MLA Zhang, Guiming,et al."Modelling species habitat suitability from presence-only data using kernel density estimation".ECOLOGICAL INDICATORS 93(2018):387-396.
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