Modeling and mapping forest diversity in the boreal forest of interior Alaska
Patterns of forest diversity are less well known in the boreal forest of interior Alaska than in most ecosystems of North America. Proactive forest planning requires spatially accurate information about forest diversity. Modeling is a cost-efficient way of predicting key forest diversity measures as a function of human and environmental factors.
Investigate and predict the patterns and processes in tree species and tree size-class diversity within the boreal forest of Alaska for a first mapped quantitative baseline.
For the boreal forest of Alaska, USA, we employed Random Forest Analysis (machine learning) and the Boruta algorithm in R to predict tree species and tree size-class diversity for the entire region using a combination of forest inventory data and a suite of 30 predictors from public open-access data archives that included climatic, distance, and topographic variables. We developed prediction maps in a GIS for the current levels (Year 2012) of tree size-class and species diversity.
The method employed here yielded good accuracy for the huge Alaskan landscape despite the exclusion of spectral reflectance data. It’s the first quantified GIS prediction baseline. The results indicate that the geographic pattern of tree species diversity differs from the pattern of tree size-class diversity across this forest type.
The results suggest that human factors combined with topographical factors had a large impact on predicting the patterns of diversity in the boreal forest of interior Alaska.
KeywordsPredictive mapping Tree species diversity Tree size-class diversity Machine learning Random forest Alaska
We thank Thomas Malone and Dan Rees along with their field assistants for collecting and compiling all the forest inventory data. We would also like to thank Daniel Kashian, Steve Cumming and, one anonymous reviewer for their thoughtful comments which greatly improved the overall quality of this manuscript. Support for this work was provided by the National Science Foundation, through its Integrative Graduate Education and Research Traineeship (IGERT, NSF 0114423) to the Resilience and Adaptation Program (RAP) at the University of Alaska Fairbanks; Alaska EPSCoR NSF award #EPS-0701898; and the State of Alaska Department of Natural Resources Division of Forestry.
- Angelstam P, Boutin S, Schmiegelow F, Villard MA, Drapeau P, Host G, Innes J, Isachenko G, Kuuluvainen T, Mönkkönen M (2004) Targets for boreal forest biodiversity conservation—a rationale for macroecological research and adaptive management. Ecol Bull 51:487–509Google Scholar
- Beers TW, Dress PE, Wensel LC (1966) Aspect transformation in site productivity research. J For 64:691–692Google Scholar
- Bergeron Y, Leduc A, Harvey BD, Gauthier S (2002) Natural fire regime: a guide for sustainable management of the Canadian boreal forest. Silv Fen 36(1):81–95Google Scholar
- Bivand RS, Anselin L, Berke O, Bernat A, Carvalho M, Chun Y, Dormann CF, Dray S, Halbersma R, Lewin-Koh N (2007) spdep: Spatial dependence: weighting schemes, statistics and models. R package version 0.4–9Google Scholar
- Bivand RS, Pebesma EJ, Gómez-Rubio V (2008) Applied spatial data analysis with R. Springer, New YorkGoogle Scholar
- Burton PJ, Messier C, Smith DW, Adamowicz WL (eds) (2003) Towards sustainable management of the boreal forest. NRC Research Press, OttawaGoogle Scholar
- Chapin FS III, Hollingsworth T, Murray DF, Viereck LA, Walker MD (2006a) Floristic diversity and vegetation distribution in the Alaskan boreal forest. In: Chapin FS III, Oswood M, Van Cleve K, Viereck LA, Verbyla D (eds) Alaska’s changing boreal forest. Oxford University Press, New York, pp 81–99Google Scholar
- Chapin FS 3rd, Lovecraft AL, Zavaleta ES, Nelson J, Robards MD, Kofinas GP, Trainor SF, Peterson GD, Huntington HP, Naylor, RL (2006b) Policy strategies to address sustainability of Alaskan boreal forests in response to a directionally changing climate. Proc Natl Acad Sci USA 103(45):16637–16643CrossRefPubMedPubMedCentralGoogle Scholar
- Chapin FS, Oswood MW, Van Cleve K, Viereck LA, Chapin MC, Verbyla DL (eds) (2006c) Alaska’s changing boreal forest. Oxford University Press, New YorkGoogle Scholar
- Craig E, Huettmann F (2008) Using “blackbox” algorithms such as treenet and random forests for data-mining and for finding meaningful patterns, relationships and outliers in complex ecological data: an overview, an example using golden eagle satellite data and an outlook for a promising future. In: Wang HF (ed) Intelligent data analysis: developing new methodologies through pattern discovery and recovery. IGI Global, HersheyGoogle Scholar
- Cressie NAC (1993) Statistics for spatial data. Wiley, New YorkGoogle Scholar
- Curtis RO (1983) Procedures for establishing and maintaining permanent plots for silvicultural and yield research. Page 56. Gen. Tech. Rep. PNW-155. U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station, PortlandGoogle Scholar
- Cushman S, Huettmann F (eds) (2010) Spatial complexity, informatics, and wildlife conservation. Springer, TokyoGoogle Scholar
- Drew CA, Wiersma YF, Huettmann F (eds) (2011) Predictive species and habitat modeling in landscape ecology: concepts and applications. Springer, New YorkGoogle Scholar
- ESRI (2011) ArcGIS desktop: release 10. Environmental Systems Research Institute, RedlandsGoogle Scholar
- Franklin JF (1988) Structural and functional diversity in temperate forests. In: Wilson EO (ed) Biodiversity. National Academy Press, Washington, DC, pp 166–175Google Scholar
- Franklin JF, Spies TA, Pelt RV, Carey AB, Thornburgh DA, Berg DR, Lindenmayer DB, Harmon ME, Keeton WS, Shaw DC (2002) Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. For Ecol Manag 155(1):399–423CrossRefGoogle Scholar
- Fu P, Rich PM (1999) Design and implementation of the solar analyst: an arcview extension for modeling solar radiation at landscape scales. In: Proceedings of the 19th annual ESRI user conference. San Diego, USAGoogle Scholar
- Hawkins BA, Montoya D, Rodriguez MA, Olalla-Tarraga MA, Zavala MA (2007) Global models for predicting woody plant richness from climate: comment. Ecology 88(1): 255–259; discussion 259–62Google Scholar
- Hubbell SP (2001) The unified neutral theory of biodiversity and biogeography. Princeton University Press, PrincetonGoogle Scholar
- Jenness J (2006) Topographic position index (tpi_jen.avx) extension for Arcview 3.x, v.1.3a. http://www.jennessent.com/arcview/tpi.htm, Jenness Enterprises [EB/OL]
- Johnson KD, Harden J, McGuire AD, Bliss NB, Bockheim JG, Clark M, Nettleton-Hollingsworth T, Jorgenson MT, Kane ES, Mack M, O’Donnell J, Ping CL, Schuur EAG, Turetsky MR, Valentine DW (2011) Soil carbon distribution in Alaska in relation to soil-forming factors. Geoderma 167–68:71–84CrossRefGoogle Scholar
- Johnson NC, Malk AJ, Szaro RC, Sexton WT (eds) (1999) Ecological stewardship: a common reference for ecosystem management. Elsevier Science, OxfordGoogle Scholar
- Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22Google Scholar
- Magness D, Huettmann F, Morton J (2008) Using random forests to provide predicted species distribution maps as a metric for ecological inventory & monitoring programs. In: Smolinski T, Milanova M, Hassanien A-E (eds) Applications of computational intelligence in biology, studies in computational intelligence, vol 122. Springer, Berlin, pp 209–229CrossRefGoogle Scholar
- Malone T, Liang J, Packee EC (2009) Cooperative Alaska Forest Inventory. Gen. Tech. Rep. PNW-GTR-785. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, PortlandGoogle Scholar
- Pastor J, Mladenoff D, Haila Y, Bryant J, Payette S (1996) Biodiversity and ecosystem processes in boreal regions. Scope-Scientific Committee on problems of the Environment International Council of Scientific Unions 55:33–69Google Scholar
- Roessler JS, Packee EC (2000) Disturbance history of the Tanana River Basin in Alaska: management implications. In: Proceedings of the Annual Tall Timbers Fire Ecology Conference. Fire and forest ecology: innovative silviculture and vegetation management Tallahassee, 2000. Tall Timbers Research Station, pp. 46–57Google Scholar
- Stage AR, Salas C (2007) Interactions of elevation, aspect, and slope in models of forest species composition and productivity. For Sci 53(4):486–492Google Scholar
- USDA Forest service SaPF, Forest Health Protection, Alaska Department of Natural Resources DoF (2005) Forest insect and disease conditions in Alaska. http://agdc.usgs.gov/data/projects/fhm/#K
- Viereck L, Little E (2007) Alaska trees and shrubs. University of Alaska Press, FairbanksGoogle Scholar
- Wurtz TL, Ott RA, Maisch JC (2006) Timber harvest in interior Alaska. In: Chapin FS III, Oswood M, Van Cleve K, Viereck L, Verbyla D (eds) Alaska’s changing boreal forest. Oxford University Press, New York, pp 302–308Google Scholar