Abstract
Landscape ecology often adopts a patch mosaic model of ecological patterns. However, many ecological attributes are inherently continuous and classification of species composition into vegetation communities and discrete patches provides an overly simplistic view of the landscape. If one adopts a niche-based, individualistic concept of biotic communities then it may often be more appropriate to represent vegetation patterns as continuous measures of site suitability or probability of occupancy, rather than the traditional abstraction into categorical community types represented in a mosaic of discrete patches. The goal of this paper is to demonstrate the high effectiveness of species-level, pixel scale prediction of species occupancy as a continuous landscape variable, as an alternative to traditional classified community type vegetation maps. We use a Random Forests ensemble learning approach to predict site-level probability of occurrence for four conifer species based on climatic, topographic and spectral predictor variables across a 3,883 km2 landscape in northern Idaho, USA. Our method uses a new permutated sample-downscaling approach to equalize sample sizes in the presence and absence classes, a model selection method to optimize parsimony, and independent validation using prediction to 10% bootstrap data withhold. The models exhibited very high accuracy, with AUC and kappa values over 0.86 and 0.95, respectively, for all four species. The spatial predictions produced by the models will be of great use to managers and scientists, as they provide vastly more accurate spatial depiction of vegetation structure across this landscape than has previously been provided by traditional categorical classified community type maps.
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References
Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157:101–118
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140
Brieman L (2001a) Statistical modeling: the two cultures. Stat Sci 16(3):199–231. doi:10.1214/ss/1009213726
Breiman L (2001b) Random forests. Mach Learn 45:5–32. doi:10.1023/A:1010933404324
Chavez PS (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24:459–479. doi:10.1016/0034-4257(88)90019-3
Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTEboost: improving prediction of the minority class in boosting. In: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp 107–119
Chen C, Liaw A, Breiman L (2004) Using random forest to learn imbalanced data. http://oz.berkeley.edu/users/chenchao/666.pdf
Crookston NL, Finley AO (2008) yaImpute: an R package for kNN imputation. J Stat Softw 23:1–16
Curtis JT, McIntosh RP (1951) An upland forest continuum in the prairie-forest border region of Wisconsin. Ecol Monogr 32:476–496
Cushman SA, McKenzie D, Peterson DL, Littell J, McKelvey KS (2007) Research agenda for integrated landscape modelling. USDA Forest Service General Technical Report RMRS-GTR-194
Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, Lawler J (2007) Random forests for classification in ecology. Ecology 88:2783–2792. doi:10.1890/07-0539.1
Déath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81:3178–3192
DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the area under tow or more correlated receiver operating characteristics curves: a nonparametric approach. Biometrics 59:837–845. doi:10.2307/2531595
Evans IS (1972) General geomorphometry, derivatives of altitude, and descriptive statistics. In: Chorley RJ (ed) Spatial analysis in geomorphology. Harper & Row, New York, pp 17–90
Forman RTT (1995) Land mosaics: the ecology of landscapes and regions. Cambridge University Press, Cambridge
Forman RTT, Godron M (1986) Landscape ecology. John Wiley & Sons, New York
Freeman EA, Moisen G (2008) Presence absence: an R package for presence absence analysis. J Stat Softw 23(11):31
Fu P, Rich PM (1999) Design and implementation of the solar analyst: an ArcView extension for modeling solar radiation at landscape scales. Proceedings of the 19th Annual ESRI User Conference, San Diego, USA, http://www.esri.com/library/userconf/proc99/proceed/papers/pap867/p867.htm
Gleason HA (1926) The individualistic concept of the plant association. Bull Torrey Bot Club 53:7–26. doi:10.2307/2479933
Guisan A, Zimmermann NE (2000) Predictive habitat distribution model in ecology. Ecol Model 135:147–186. doi:10.1016/S0304-3800(00)00354-9
Hastie T, Tibshirani R, Friedman JH (2001) The elements of statistical learning. Springer, New York
Hutchinson GE (1957) Concluding remarks. Cold Spring Harb Symp Quant Biol 22:415–427
Liaw A, Wiener M (2002) Classification and regression by random forest. R news: the newsletter of the R project (http://cran.r-project.org/doc/Rnews/) 2(3):18–22
Manning AD, Lindenmayer DB, Nix HA (2004) Continua and umwelt: novel perspectives on viewing landscapes. Oikos 104:621–628. doi:10.1111/j.0030-1299.2004.12813.x
McCune B, Keon D (2002) Equations for potential annual direct incident radiation and heat load index. J Veg Sci 13:603–606. doi:10.1658/1100-9233(2002)013[0603:EFPADI]2.0.CO;2
McGarigal K, Cushman SA (2005) The gradient concept of landscape structure. In: Wiens J, Moss M (eds) Issues and perspectives in landscape ecology. Cambridge University Press, Cambridge, pp 112–119
McGarigal K, Tagil S, Cushman SA (2009) Surface metrics: an alternative to patch metrics for the quantification of landscape structure. Landscape Ecol 24:433–450
McIntyre S, Barrett GW (1992) Habitat variegation, an alternative to fragmentation. Conserv Biol 6:146–147. doi:10.1046/j.1523-1739.1992.610146.x
Moore ID, Gessler PE, Nielsen GA, Petersen GA (1993) Terrain attributes: estimation methods and scale effects. In: Jakeman AJ, Beck MB, McAleer M (eds) Modeling change in environmental systems. Wiley, London, pp 189–214
Morrison D (2002) Multivariate statistical methods. McGraw-Hill series in probability and statistics, 4th edn. McGraw-Hill, New York
Murphy MA, Evans JS, Storfer AS (2009) Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology (in press)
Nemani R, Pierce L, Running S, Brand L (1993) Forest ecosystem processes at the watershed scale; sensitivity to remotely-sensed leaf-area index estimates. Int J Remote Sens 14:2519–2534. doi:10.1080/01431169308904290
Park Y-S, Chon T-S (2006) Biologically inspired machine learning implemented to ecological informatics. Ecol Inform 203:1–7
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. doi:10.1016/j.ecolmodel.2005.03.026
Prasad AM, Iverson LR, Liaw A (2006) Random forests for modeling the distribution of tree abundances. Ecosystems (N Y, Print) 9:181–199. doi:10.1007/s10021-005-0054-1
R Development Core Team (2007) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org
Rabus B, Eineder M, Roth A, Bamler R (2003) The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar. Photogramm Eng Remote Sens 57:241–262. doi:10.1016/S0924-2716(02)00124-7
Rehfeldt GE, Crookston NL, Warwell MV, Evans JS (2006) Empirical analyses of plant–climate relationships for the western United States. Int J Plant Sci 167(6):1123–1150. doi:10.1086/507711
Roberts DW, Cooper SV (1989) Concepts and techniques of vegetation mapping, land classifications based on vegetation: applications for resource management. GTR-INT-257, USDA Forest Service Intermountain Research Station, Ogden, UT, pp 90–96
Schapire R (1990) Strength of weak learnability. J Mach Learn 5:197–227
Stage AR (1976) An expression of the effects of aspect, slope, and habitat type on tree growth. For Sci 22(3):457–460
Stockwell DRB, Peters DP (1999) The GARP modeling system: problems and solutions to automated spatial prediction. Int J Geogr Inf Syst 13:143–158. doi:10.1080/136588199241391
Svetnik V, Liaw A, Tong C, Wang T (2004) Application of Breiman’s random forest to modeling structure–activity relationships of pharmaceutical molecules. In: Roli F, Kittler J, Windeatt T (eds) Lecture notes in computer science, vol 3077. Springer, Berlin, pp 334–343
Tarboton DG (1997) A new method for the determination of flow directions and contributing areas in grid digital elevation models. Water Resour Res 33(2):309–319. doi:10.1029/96WR03137
Turner MG, Gardner RH, O’Neill RV (2001) Landscape ecology in theory and practice. Springer-Verlag, New York
Whittaker RH (1967) Gradient analysis of vegetation. Biol Rev Camb Philos Soc 42:207–264. doi:10.1111/j.1469-185X.1967.tb01419.x
Acknowledgments
We would like to thank M. Murphy, W. Godsoe, J. Rehfeldt, R. Dezzani, N. Crookston, and J. Kiesecker for helpful discussion of methods and concepts presented in this paper.
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Evans, J.S., Cushman, S.A. Gradient modeling of conifer species using random forests. Landscape Ecol 24, 673–683 (2009). https://doi.org/10.1007/s10980-009-9341-0
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DOI: https://doi.org/10.1007/s10980-009-9341-0