ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
Schmaljohn C, Hjelle B, 1997. Hantaviruses: a global disease problem. Emerg Infect Dis 3: 95–104.
-
2.
Luo CW, Chen HX, 2003. Study on the factors influenced epidemic of hemorrhagic fever with renal syndrome. Chin J Vector Biol Control 14: 451–454.
-
3.
Engelthaler DM, Mosley DG, Cheek JE, Levy CE, Komatsu KK, Ettestad P, Davis T, Tanda DT, Miller L, Frampton JW, Porter R, Bryan RT, 1999. Climatic and environmental patterns associated with hantavirus pulmonary syndrome, Four Corners region, United States. Emerg Infect Dis 5: 87–94.
-
4.
Jonsson CB, Figueiredo LT, Vapalahti O, 2010. A global perspective on hantavirus ecology, epidemiology, and disease. Clin Microbiol Rev 23: 412–441.
-
5.
Langlois JP, Fahrig L, Merriam G, Artsob H, 2001. Landscape structure influences continental distribution of hantavirus in deer mice. Landscape Ecol 16: 255–266.
-
6.
Suzan G, Marce E, Giermakowski JT, Armien B, Pascale J, Mills J, Ceballos G, Gomez A, Aguirre AA, Salazar-Bravo J, Armien A, Parmenter R, Yates T, 2008. The effect of habitat fragmentation and species diversity loss on hantavirus prevalence in Panama. Ann N Y Acad Sci 1149: 80–83.
-
7.
Koch DE, Mohler RL, Goodin DG, 2007. Stratifying land use/land cover for spatial analysis of disease ecology and risk: an example using object-based classification techniques. Geospat Health 2: 15–28.
-
8.
Goodin DG, Paige R, Owen RD, Ghimire K, Koch DE, Chu YK, Jonsson CB, 2009. Microhabitat characteristics of Akodon montensis, a reservoir for hantavirus, and hantaviral seroprevalence in an Atlantic forest site in eastern Paraguay. J Vector Ecol 34: 104–113.
-
9.
Mills JN, 2005. Regulation of rodent-borne viruses in the natural host: implications for human disease. Arch Virol Suppl 19: 45–57.
-
10.
Gubler DJ, Reiter P, Ebi KL, Yap W, Nasci R, Patz JA, 2001. Climate variability and change in the United States: potential impacts on vector- and rodent-borne diseases. Environ Health Perspect 109 (Suppl 2): 223–233.
-
11.
Hjelle B, Glass GE, 2000. Outbreak of hantavirus infection in the Four Corners region of the United States in the wake of the 1997–1998 El Nino-southern oscillation. J Infect Dis 181: 1569–1573.
-
12.
Bi P, Tong S, Donald K, Parton K, Ni J, 2002. Climatic, reservoir and occupational variables and the transmission of haemorrhagic fever with renal syndrome in China. Int J Epidemiol 31: 189–193.
-
13.
Yan L, Fang LQ, Huang HG, Zhang LQ, Feng D, Zhao WJ, Zhang WY, Li XW, Cao WC, 2007. Landscape elements and hantaan virus-related hemorrhagic fever with renal syndrome, People's Republic of China. Emerg Infect Dis 13: 1301–1306.
-
14.
Zhang WY, Guo WD, Fang LQ, Li CP, Bi P, Glass GE, Jiang JF, Sun SH, Qian Q, Liu W, Yan L, Yang H, Tong SL, Cao WC, 2010. Climate variability and hemorrhagic fever with renal syndrome transmission in northeastern China. Environ Health Perspect 118: 915–920.
-
15.
Fang LQ, Wang XJ, Liang S, Li YL, Song SX, Zhang WY, Qian Q, Li YP, Wei L, Wang ZQ, Yang H, Cao WC, 2010. Spatiotemporal trends and climatic factors of hemorrhagic fever with renal syndrome epidemic in Shandong Province, China. PLoS Negl Trop Dis 4: e789.
-
16.
Jiang JF, Zuo SQ, Zhang WY, Wu XM, Tang F, De Vlas SJ, Zhao WJ, Zhang PH, Dun Z, Wang RM, Cao WC, 2008. Prevalence and genetic diversities of hantaviruses in rodents in Beijing, China. Am J Trop Med Hyg 78: 98–105.
-
17.
Lee HW, Lee PW, Johnson KM, 1978. Isolation of the etiologic agent of Korean hemorrhagic fever. J Infect Dis 137: 298–308.
-
18.
Glass GE, Cheek JE, Patz JA, Shields TM, Doyle TJ, Thoroughman DA, Hunt DK, Enscore RE, Gage KL, Irland C, Peters CJ, Bryan R, 2000. Using remotely sensed data to identify areas at risk for hantavirus pulmonary syndrome. Emerg Infect Dis 6: 238–247.
-
19.
MartÃnez-FreirÃa F, Sillero N, Lizana M, Brito JC, 2008. GIS-based niche models identify environmental correlates sustaining a contact zone between three species of European vipers. Divers Distrib 14: 452–461.
-
20.
Zhang WY, Fang LQ, Jiang JF, Hui FM, Glass GE, Yan L, Xu YF, Zhao WJ, Yang H, Liu W, Cao WC, 2009. Predicting the risk of hantavirus infection in Beijing, People's Republic of China. Am J Trop Med Hyg 80: 678–683.
-
21.
Peterson AT, 2006. Ecologic niche modeling and spatial patterns of disease transmission. Emerg Infect Dis 12: 1822–1826.
-
22.
Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberon J, Williams S, Wisz MS, Zimmermann NE, 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129–151.
-
23.
Gibson L, Barrett B, Burbidge A, 2007. Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot. Divers Distrib 13: 704–713.
-
24.
Hernandez PA, Franke I, Herzog SK, Pacheco V, Paniagua L, Quintana HL, Soto A, Swenson JJ, Tovar C, Valqui TH, Vargas J, Young BE, 2008. Predicting species distributions in poorly-studied landscapes. Biodivers Conserv 17: 1353–1366.
-
25.
Phillips SJ, Anderson RP, Schapire RE, 2006. Maximum entropy modeling of species geographic distributions. Ecol Modell 190: 231–259.
-
26.
Phillips SJ, Dudik M, 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161–175.
-
27.
Thorn JS, Nijman V, Smith D, Nekaris KA, 2009. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus). Divers Distrib 15: 289–298.
-
28.
Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, Distribut NP, 2008. Effects of sample size on the performance of species distribution models. Divers Distrib 14: 763–773.
-
29.
Hernandez PA, Graham CH, Master LL, Albert DL, 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29: 773–785.
-
30.
Saatchi S, Buermann W, Ter Steege H, Mori S, Smith TB, 2008. Modeling distribution of Amazonian tree species and diversity using remote sensing measurements. Remote Sens Environ 112: 2000–2017.
-
31.
Tinoco BA, Astudillo PX, Latta SC, Graham CH, 2009. Distribution, ecology and conservation of an endangered Andean hummingbird: the violet-throated metaltail (Metallura baroni). Bird Conserv Int 19: 63–76.
-
32.
Phillips SJ, Dudik M, Schapire RE, 2004. A maximum entropy approach to species distribution modeling. Proceedings of the 21st International Conference on Machine Learning. Banff, Alberta, Canada: ACM.
-
33.
Gilberto P, Graziano R, Alessandro F, 2008. Toward improved species niche modelling: Arnica montana in the alps as a case study. J Appl Ecol 45: 1410–1418.
-
34.
Linard C, Tersago K, Leirs H, Lambin EF, 2007. Environmental conditions and Puumala virus transmission in Belgium. Int J Health Geogr 6: 55.
-
35.
Fielding AH, Bell JF, 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24: 38–49.
-
36.
Swets JA, 1988. Measuring the accuracy of diagnostic systems. Science 240: 1285–1293.
-
37.
Araújo MB, Guisan A, 2006. Six (or so) research priorities for species distribution modelling. J Biogeogr 33: 1677–1688.
-
38.
Phillips SJ, 2009. A brief tutorial on Maxent, version: 3.3.1. Available at: http://www.cs.princeton.edu/~schapire/maxent/. Accessed August 19, 2009.
-
39.
Cantor SB, Sun CC, Tortolero-Luna G, Richards-Kortum R, Follen M, 1999. A comparison of C/B ratios from studies using receiver operating characteristic curve analysis. J Clin Epidemiol 52: 885–892.
-
40.
Cramer JS, 2003. Logit Models: From Economics and Other Fields. Cambridge, MA: Cambridge Univ. Press, 66–67.
-
41.
Liu CR, Berry PM, Dawson TP, Pearson RG, 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28: 385–393.
-
42.
Araújo MB, Whittaker RJ, Ladle RJ, Erhard M, 2005. Reducing uncertainty in projections of extinction risk from climate change. Glob Ecol Biogeogr 14: 529–538.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 801 | 721 | 264 |
| Full Text Views | 516 | 22 | 1 |
| PDF Downloads | 215 | 13 | 1 |
Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China
Lan Wei
Lan Wei
State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China; Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Disease Control and Prevention of Chinese People's Liberation Army
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Quan Qian
Quan Qian
State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China; Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Disease Control and Prevention of Chinese People's Liberation Army
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Zhi-Qiang Wang
Zhi-Qiang Wang
State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China; Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Disease Control and Prevention of Chinese People's Liberation Army
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Gregory E. Glass
Gregory E. Glass
State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China; Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Disease Control and Prevention of Chinese People's Liberation Army
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Shao-Xia Song
Shao-Xia Song
State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China; Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Disease Control and Prevention of Chinese People's Liberation Army
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Wen-Yi Zhang
Wen-Yi Zhang
State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China; Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Disease Control and Prevention of Chinese People's Liberation Army
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Xiu-Jun Li
Xiu-Jun Li
State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China; Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Disease Control and Prevention of Chinese People's Liberation Army
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Hong Yang
Hong Yang
State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China; Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Disease Control and Prevention of Chinese People's Liberation Army
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Xian-Jun Wang
Xian-Jun Wang
State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China; Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Disease Control and Prevention of Chinese People's Liberation Army
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Li-Qun Fang
Li-Qun Fang
State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China; Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Disease Control and Prevention of Chinese People's Liberation Army
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Wu-Chun Cao
Wu-Chun Cao
State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China; Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Institute of Disease Control and Prevention of Chinese People's Liberation Army
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Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in Shandong Province, China. In this study, we combined ecologic niche modeling with geographic information systems (GIS) and remote sensing techniques to identify the risk factors and affected areas of hantavirus infections in rodent hosts. Land cover and elevation were found to be closely associated with the presence of hantavirus-infected rodent hosts. The averaged area under the receiver operating characteristic curve was 0.864, implying good performance. The predicted risk maps based on the model were validated both by the hantavirus-infected rodents' distribution and HFRS human case localities with a good fit. These findings have the applications for targeting control and prevention efforts.
Author Notes
Financial support: The study was supported by the Chinese National Science Fund for Distinguished Young Scholars (no. 30725032), Special Program for Prevention and Control of Infectious Diseases in China (no. 2008ZX10004-012, no. 2009ZX10004-720), Natural Science Foundation of China (no. 30590374, no. 30972521).
Authors' addresses: Lan Wei, Quan Qian, Xiu-Jun Li, Hong Yang, Li-Qun Fang, and Wu-Chun Cao, Beijing Institute of Microbiology and Epidemiology, State Key Laboratory of Pathogen and Biosecurity, Feng-Tai District, Beijing, Peoples' Republic of China, E-mails: weilanisme@gmail.com, qianquanmail@yahoo.com.cn, xjli@sdu.edu.cn, anni_hong@163.com, fanglq@nic.bmi.ac.cn, and caowc@nic.bmi.ac.cn. Zhi-Qiang Wang, Shao-Xia Song, and Xian-Jun Wang, Shandong Center for Disease Control and Prevention, Jinan, People's Republic of China, E-mails: wzq3678@126.com, songsong7921@163.com, and xjwang62@163.com. Gregory E. Glass, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, E-mail: ggurrigl@jhsph.edu. Wen-Yi Zhang, Institute of Disease Control and Prevention of Chinese People's Liberation Army, Feng-Tai District, Beijing, Peoples' Republic of China, E-mail: zwy0419@126.com.
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
Schmaljohn C, Hjelle B, 1997. Hantaviruses: a global disease problem. Emerg Infect Dis 3: 95–104.
-
2.
Luo CW, Chen HX, 2003. Study on the factors influenced epidemic of hemorrhagic fever with renal syndrome. Chin J Vector Biol Control 14: 451–454.
-
3.
Engelthaler DM, Mosley DG, Cheek JE, Levy CE, Komatsu KK, Ettestad P, Davis T, Tanda DT, Miller L, Frampton JW, Porter R, Bryan RT, 1999. Climatic and environmental patterns associated with hantavirus pulmonary syndrome, Four Corners region, United States. Emerg Infect Dis 5: 87–94.
-
4.
Jonsson CB, Figueiredo LT, Vapalahti O, 2010. A global perspective on hantavirus ecology, epidemiology, and disease. Clin Microbiol Rev 23: 412–441.
-
5.
Langlois JP, Fahrig L, Merriam G, Artsob H, 2001. Landscape structure influences continental distribution of hantavirus in deer mice. Landscape Ecol 16: 255–266.
-
6.
Suzan G, Marce E, Giermakowski JT, Armien B, Pascale J, Mills J, Ceballos G, Gomez A, Aguirre AA, Salazar-Bravo J, Armien A, Parmenter R, Yates T, 2008. The effect of habitat fragmentation and species diversity loss on hantavirus prevalence in Panama. Ann N Y Acad Sci 1149: 80–83.
-
7.
Koch DE, Mohler RL, Goodin DG, 2007. Stratifying land use/land cover for spatial analysis of disease ecology and risk: an example using object-based classification techniques. Geospat Health 2: 15–28.
-
8.
Goodin DG, Paige R, Owen RD, Ghimire K, Koch DE, Chu YK, Jonsson CB, 2009. Microhabitat characteristics of Akodon montensis, a reservoir for hantavirus, and hantaviral seroprevalence in an Atlantic forest site in eastern Paraguay. J Vector Ecol 34: 104–113.
-
9.
Mills JN, 2005. Regulation of rodent-borne viruses in the natural host: implications for human disease. Arch Virol Suppl 19: 45–57.
-
10.
Gubler DJ, Reiter P, Ebi KL, Yap W, Nasci R, Patz JA, 2001. Climate variability and change in the United States: potential impacts on vector- and rodent-borne diseases. Environ Health Perspect 109 (Suppl 2): 223–233.
-
11.
Hjelle B, Glass GE, 2000. Outbreak of hantavirus infection in the Four Corners region of the United States in the wake of the 1997–1998 El Nino-southern oscillation. J Infect Dis 181: 1569–1573.
-
12.
Bi P, Tong S, Donald K, Parton K, Ni J, 2002. Climatic, reservoir and occupational variables and the transmission of haemorrhagic fever with renal syndrome in China. Int J Epidemiol 31: 189–193.
-
13.
Yan L, Fang LQ, Huang HG, Zhang LQ, Feng D, Zhao WJ, Zhang WY, Li XW, Cao WC, 2007. Landscape elements and hantaan virus-related hemorrhagic fever with renal syndrome, People's Republic of China. Emerg Infect Dis 13: 1301–1306.
-
14.
Zhang WY, Guo WD, Fang LQ, Li CP, Bi P, Glass GE, Jiang JF, Sun SH, Qian Q, Liu W, Yan L, Yang H, Tong SL, Cao WC, 2010. Climate variability and hemorrhagic fever with renal syndrome transmission in northeastern China. Environ Health Perspect 118: 915–920.
-
15.
Fang LQ, Wang XJ, Liang S, Li YL, Song SX, Zhang WY, Qian Q, Li YP, Wei L, Wang ZQ, Yang H, Cao WC, 2010. Spatiotemporal trends and climatic factors of hemorrhagic fever with renal syndrome epidemic in Shandong Province, China. PLoS Negl Trop Dis 4: e789.
-
16.
Jiang JF, Zuo SQ, Zhang WY, Wu XM, Tang F, De Vlas SJ, Zhao WJ, Zhang PH, Dun Z, Wang RM, Cao WC, 2008. Prevalence and genetic diversities of hantaviruses in rodents in Beijing, China. Am J Trop Med Hyg 78: 98–105.
-
17.
Lee HW, Lee PW, Johnson KM, 1978. Isolation of the etiologic agent of Korean hemorrhagic fever. J Infect Dis 137: 298–308.
-
18.
Glass GE, Cheek JE, Patz JA, Shields TM, Doyle TJ, Thoroughman DA, Hunt DK, Enscore RE, Gage KL, Irland C, Peters CJ, Bryan R, 2000. Using remotely sensed data to identify areas at risk for hantavirus pulmonary syndrome. Emerg Infect Dis 6: 238–247.
-
19.
MartÃnez-FreirÃa F, Sillero N, Lizana M, Brito JC, 2008. GIS-based niche models identify environmental correlates sustaining a contact zone between three species of European vipers. Divers Distrib 14: 452–461.
-
20.
Zhang WY, Fang LQ, Jiang JF, Hui FM, Glass GE, Yan L, Xu YF, Zhao WJ, Yang H, Liu W, Cao WC, 2009. Predicting the risk of hantavirus infection in Beijing, People's Republic of China. Am J Trop Med Hyg 80: 678–683.
-
21.
Peterson AT, 2006. Ecologic niche modeling and spatial patterns of disease transmission. Emerg Infect Dis 12: 1822–1826.
-
22.
Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberon J, Williams S, Wisz MS, Zimmermann NE, 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129–151.
-
23.
Gibson L, Barrett B, Burbidge A, 2007. Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot. Divers Distrib 13: 704–713.
-
24.
Hernandez PA, Franke I, Herzog SK, Pacheco V, Paniagua L, Quintana HL, Soto A, Swenson JJ, Tovar C, Valqui TH, Vargas J, Young BE, 2008. Predicting species distributions in poorly-studied landscapes. Biodivers Conserv 17: 1353–1366.
-
25.
Phillips SJ, Anderson RP, Schapire RE, 2006. Maximum entropy modeling of species geographic distributions. Ecol Modell 190: 231–259.
-
26.
Phillips SJ, Dudik M, 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161–175.
-
27.
Thorn JS, Nijman V, Smith D, Nekaris KA, 2009. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus). Divers Distrib 15: 289–298.
-
28.
Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, Distribut NP, 2008. Effects of sample size on the performance of species distribution models. Divers Distrib 14: 763–773.
-
29.
Hernandez PA, Graham CH, Master LL, Albert DL, 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29: 773–785.
-
30.
Saatchi S, Buermann W, Ter Steege H, Mori S, Smith TB, 2008. Modeling distribution of Amazonian tree species and diversity using remote sensing measurements. Remote Sens Environ 112: 2000–2017.
-
31.
Tinoco BA, Astudillo PX, Latta SC, Graham CH, 2009. Distribution, ecology and conservation of an endangered Andean hummingbird: the violet-throated metaltail (Metallura baroni). Bird Conserv Int 19: 63–76.
-
32.
Phillips SJ, Dudik M, Schapire RE, 2004. A maximum entropy approach to species distribution modeling. Proceedings of the 21st International Conference on Machine Learning. Banff, Alberta, Canada: ACM.
-
33.
Gilberto P, Graziano R, Alessandro F, 2008. Toward improved species niche modelling: Arnica montana in the alps as a case study. J Appl Ecol 45: 1410–1418.
-
34.
Linard C, Tersago K, Leirs H, Lambin EF, 2007. Environmental conditions and Puumala virus transmission in Belgium. Int J Health Geogr 6: 55.
-
35.
Fielding AH, Bell JF, 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24: 38–49.
-
36.
Swets JA, 1988. Measuring the accuracy of diagnostic systems. Science 240: 1285–1293.
-
37.
Araújo MB, Guisan A, 2006. Six (or so) research priorities for species distribution modelling. J Biogeogr 33: 1677–1688.
-
38.
Phillips SJ, 2009. A brief tutorial on Maxent, version: 3.3.1. Available at: http://www.cs.princeton.edu/~schapire/maxent/. Accessed August 19, 2009.
-
39.
Cantor SB, Sun CC, Tortolero-Luna G, Richards-Kortum R, Follen M, 1999. A comparison of C/B ratios from studies using receiver operating characteristic curve analysis. J Clin Epidemiol 52: 885–892.
-
40.
Cramer JS, 2003. Logit Models: From Economics and Other Fields. Cambridge, MA: Cambridge Univ. Press, 66–67.
-
41.
Liu CR, Berry PM, Dawson TP, Pearson RG, 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28: 385–393.
-
42.
Araújo MB, Whittaker RJ, Ladle RJ, Erhard M, 2005. Reducing uncertainty in projections of extinction risk from climate change. Glob Ecol Biogeogr 14: 529–538.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 801 | 721 | 264 |
| Full Text Views | 516 | 22 | 1 |
| PDF Downloads | 215 | 13 | 1 |