Ensemble habitat mapping of invasive plant species

Risk Analysis

Volumn 30, Issue 2, 2010, Pages 224-235

  Stohlgren, Thomas J ^a   Ma, Peter ^b   Kumar, Sunil ^c   Rocca, Monique ^c   Morisette, Jeffrey T ^a   Jarnevich, Catherine S ^a   Benson, Nate ^d  

^a FORT COLLINS SCIENCE CENTER   (United States)

^b NASA GODDARD SPACE FLIGHT CENTER   (United States)

^c Veterinary Dentistry and Oral Surgery   (United States)

^d NATIONAL PARK SERVICE   (United States)

Author keywords

Boosted regression trees; Invasive species; Maxent; Multivariate adaptive regression splines; Random forest; Species distribution modeling

Indexed keywords

BOOSTED REGRESSION TREES; CALIFORNIA; ENSEMBLE MODELS; FIELD DATA; FIELD VALIDATION; HABITAT MAPPING; INDIVIDUAL MODELS; INVASIVE PLANTS; INVASIVE SPECIES; LOGISTIC REGRESSIONS; MATCHING MODELS; MAXIMUM ENTROPY MODELS; MULTIVARIATE ADAPTIVE REGRESSION SPLINES; NATIONAL PARKS; PLANT SPECIES; RANDOM FORESTS; SATELLITE DATA; SPECIES DISTRIBUTION MODELS; SPECIES-ENVIRONMENT RELATIONSHIPS; SUITABLE HABITAT; TEST DATA; TETON NATIONAL PARK; THREE MODELS; WYOMING; YELLOWSTONES;

PARKS; RISK ANALYSIS; RISK ASSESSMENT; SAFETY FACTOR; SPLINES;

REGRESSION ANALYSIS;

HABITAT AVAILABILITY; INVASIVE SPECIES; MULTIVARIATE ANALYSIS; NATIONAL PARK; PLANT; RISK ASSESSMENT; SPECIES OCCURRENCE;

ARTICLE; BIOLOGICAL MODEL; ECOSYSTEM; ENVIRONMENT; GROWTH, DEVELOPMENT AND AGING; PLANT; PLANT LEAF; STATISTICAL MODEL; TREE; UNITED STATES;

ALASKA; CALIFORNIA; ECOSYSTEM; ENVIRONMENT; LOGISTIC MODELS; MODELS, BIOLOGICAL; PLANT LEAVES; PLANTS; TREES; WYOMING;

ALASKA; CALIFORNIA; GRAND TETON NATIONAL PARK; KINGS CANYON NATIONAL PARK; SEQUOIA NATIONAL PARK; UNITED STATES; WYOMING; YELLOWSTONE NATIONAL PARK;

EID: 76649135539     PISSN: 02724332     EISSN: 15396924     Source Type: Journal    
DOI: 10.1111/j.1539-6924.2009.01343.x     Document Type: Article

References (53)

1. Thuiller W, Richardson DM, Pysek P, Midgley GF, Hughes, GO, Rouget M. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Global Change Biology, 2005 11 (12 2234 2250.
2. Allen JA, Brown CS, Stohlgren TJ. Non-native plant invasions of the United States National Parks. Biological Invasions, 2009 11 (10 2195 2207.
3. Guisan A, Thuiller W. Predicting species distribution: Offering more than simple habitat models. Ecology Letters, 2005 8 (9 993 1009.
4. Hirzel AH, Hausser J, Chessel D, Perrin N. Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data Ecology, 2002 83 (7 2027 2036.
5. Araújo MB, New M. Ensemble forecasting of species distributions. Trends in Ecology & Evolution, 2007 22 (1 42 47.
6. Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W. Evaluation of consensus methods in predictive species distribution modeling. Diversity and Distribution, 2009 15 (1 59 69.
7. Landis WG (ed). Regional Scale Ecological Risk Assessment Using the Relative Risk Model. Boca Raton, FL : CRC Press, 2005.
8. Stohlgren TJ, Schnase JL. Risk analysis for biological hazards: What we need to know about invasive species. Risk Analysis, 2006 26 (1 163 173.
9. Crossman ND, Bass DA. Application of common predictive habitat techniques for post-border weed risk management. Diversity and Distribution, 2008 14 (2 213 224.
10. Roura-Pascual N, Brotons L, Peterson AT, Thuiller W. Consensual predictions of potential distributional areas for invasive species: A case study of Argentine ants in the Iberian Peninsula. Biological Invasions, 2009 11 (4 1017 1031.
11. Turner W, Spector S, Gardiner N, Fladeland M, Sterling E, Steininger M. Remote sensing for biodiversity science and conservation. Trends in Ecology & Evolution, 2003 18 (6 306 314.
12. Jönsson P, Eklundh L. TIMESAT - A program for analyzing time-series of satellite sensor data. Computers Geoscie, 2004 30 (8 833 845.
13. Tan B, Morisette JT, Wolfe RE, Gao F, Ederer GA, Nightingale J et al. Vegetation phenology metrics derived from temporally smoothed and gap-filled MODIS data. IGARSS, 2008 III : 593 595.
14. st century. Frontiers in Ecology and Environment, 2009 7 (5 253 260.
15. Nix HA. A biogeographic analysis of Australian elapid snakes. Chapter 7 in Longmore R (ed). Australian Flora and Fauna Series 8. Canberra, Australia : Australian Government Publishing Service, 1986.
16. Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A et al. Novel methods improve prediction of species' distributions from occurrence data. Ecography, 2006 29 (2 129 151.
17. Guisan A, Zimmermann NE, Elith J, Graham CH, Phillips S, Peterson AT. What matters for predicting the occurrences of trees: Techniques, data or species' characteristics Ecological Monographs, 2007 77 (4 615 630.
18. Evangelista PH, Kumar S, Stohlgren TJ, Jarnevich CS, Crall AW, Norman JB et al. Modelling invasion for a habitat generalist and a specialist plant species. Diversity and Distribution, 2008 14 (5 808 817.
19. Kumar S, Spaulding SA, Stohlgren TJ, Herman KA, Schmidt TS, Bahls LL. Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US. Frontiers in Ecology and Environment, 2009 7 (8 415 420.
20. Friedman JH, Hastie T, Tibshirani R. Additive logistic regression: A statistical view of boosting. Annals of Statistics, 2000 28 (2 337 407.
21. Friedman JH. Greedy function approximation: A gradient boosting machine. Ann. Stat, 2001 29 (2 1189 1232.
22. Friedman JH. Stochastic gradient boosting. Computational Statistics & Data Analysis, 2002 38 (4 367 378.
23. Schapire R. The boosting approach to machine learning - An overview. In MSRI Workshop on Nonlinear Estimation and Classification, Berkeley, CA : 2003.
24. Elith J, Leathwick, JR Hastie T. A working guide to boosted regression trees. Journal of Animal Ecology, 2008 77 (4 802 813.
25. McCullagh P, Nelder JA. Generalized Linear Models. New York : Chapman and Hall, 1989.
26. Pearce J, Ferrier S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling, 2000 133 (3 225 245.
27. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 2006 190 (3-4 231 259.
28. Phillips SJ, Dudik M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography, 2008 31 (2 161 175.
29. Phillips SJ, Dudik M, Elith J, Graham CH, Lehmann A, Leathwick J et al. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecological Applications, 2009 19 (1 181 197.
30. Friedman JH. Multivariate adaptive regression splines (with discussion). Annals of Statistics, 1991 19 (1 1 141.
31. Hastie T, Tibshirani RJ. Discriminant analysis by Gaussian mixtures. Journal of Royal Statistical Society. Series B, 1996 58 (1 155 176.
32. Breiman L. Random forests. Machine Learning, 2001 45 (1 5 32.
33. Prasad AM, Iverson LR, Liaw A. Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems, 2006 9 (2 181 199.
34. Guisan A, Hofer U. Predicting reptile distributions at the mesoscale: Relation to climate and topography. Journal of Biogeography, 2003 30 (8) 1233 1243.
35. Pearson RG, Dawson TP, Liu C. Modelling species distributions in Britain: A hierarchical integration of climate and land-cover data. Ecography, 2004 27 (3 285 298.
36. Liu CR, Berry PM, Dawson TP, Pearson RG. Selecting thresholds of occurrence in the prediction of species distributions. Ecography, 2005 28 (3 85 393.
37. Fielding AH, Bell JF. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environment Conservation, 1997 24 (1 38 49.
38. Graham CH, Elith J, Hijmans RJ, Guisan A, Peterson AT, Loiselle BA et al. The influence of spatial errors in species occurrence data used in distribution models. Journal of Applied Ecology, 2008 45 (1 239 247.
39. Swets JA. Measuring the accuracy of diagnostic systems. Science, 1988. 240 (3) 1285 1293.
40. Lobo JM, Jiménez-Valverdel A, Real R. AUC: A misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 2008 17 (2 145 151.
41. Li M-Y, Ju Y-W, Kumar S, Stohlgren TJ. Modeling potential habitats for alien species Dreissena polymorpha (Zebra mussel) in the continental USA. Acta Ecologica Sinica, 2008 28 (9 4253 4258.
42. Segurado P, Araujo MB, Kunin WE. Consequences of spatial autocorrelation for niche-based models. Journal of Applied Ecology, 2006 43 (2 433 444.
43. Thuiller W, Brotons L, Araujo MB, Lavorel S. Effects of restricting environmental range of data to project current and future species distributions. Ecography, 2004 27 (2 165 172.
44. Peters J, De Baets B, Verhoest NEC, Samson R, Degroeve S, De Becker P et al. Random forests as a tool for ecohydrological distribution modelling. Ecological Modelling, 2007 207 (2-4 304 318.
45. Schisterman EF, Faraggi D, Reiser B, Trevisan, M. Statistical inference for the area under the receiver operating characteristic curve in the presence of random measurement error. American Journal of Epidemiology, 2001 154 (2 174 179.
46. Thuiller W. Patterns and uncertainties of species' range shifts under climate change. Global Change Biology, 2004 10 (12 2020 2027.
47. Zimmermann NE, Edwards TC, Moisen GG, Frescino TS, Blackard JA. Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah. Journal of Applied Ecology, 2007 44 (5 1057 1067.
48. Jarnevich CS, Stohlgren TJ. Near term climate projections for invasive species distributions. Biological Invasions, 2009 11 (6 1373 1379.
49. McInerny GJ, Turner JRG, Wong HY, Travis JMJ, Benton TG. How range shifts induced by climate change affect neutral evolution. Proceedings of the Royal Society-Biological Sciences, 2008 276 (1661 1527 1534.
50. Beaumont LJ, Gallagher RV, Thuiller W, Downey PO, Leishman MR, Hughes L. Different climatic envelopes among invasive populations may lead to underestimations of current and future biological invasions. Diversity and Distributions, 2009 15 (3 409 420.
51. Bradley BA, Oppenheimer M, Wilcove DS. Climate change and plant invasion: Restoration opportunities ahead Global Change Biology, 2009 15 (6 1511 1521.
52. Keller RP, Lodge DM, Lewis MA, Shogren JF (eds). Bioeconomics of Invasive Species: Integrating Ecology, Economics, Policy, and Management. New York : Oxford, 2009.
53. Schroeder B. ROC/AUC-calculation, 2006. http://brandenburg.geoecology. uni-potsdam.de/users/schroeder/download.html, Accessed on July 1, 2009.