Polar Biology

, Volume 32, Issue 12, pp 1717–1729 | Cite as

Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas

  • Bettina Ohse
  • Falk Huettmann
  • Stefanie M. Ickert-Bond
  • Glenn P. Juday
Original Paper

Abstract

Most wilderness areas still lack accurate distribution information on tree species. We met this need with a predictive GIS modeling approach, using freely available digital data and computer programs to efficiently obtain high-quality species distribution maps. Here we present a digital map with the predicted distribution of white spruce (Picea glauca) in Alaska (4 km resolution, accuracy over 90%). Our presented concept represents a role-model for predicting tree species distribution for remote areas world-wide. Although this model intends to be accurate in making predictions rather than to give detailed biological mechanistic explanations, it can also be used as a baseline for further research and testable hypothesis on the importance of the environmental variables used to build a generalizable model. Further, we emphasize that work like presented here is a pre-condition for assessing human impacts and impacts of climate change on species distribution in a quantitative and transparent fashion, allowing for improved sustainable decision-making world-wide.

Keywords

Species distribution models (SDM) Alaska Tree species Predictive modeling Open access (OA) White spruce Picea glauca 

Abbreviations

AGDC

Alaska Geospatial Data Clearinghouse

CODATA

Committee on Data for Science and Technology

ESRI

Environmental Systems Research Institute

FGDC

Federal Geographic Data Committee

FIA

Forest inventory and analysis

GIS

Geographic information system

ICSU

International Council for Science

IDW

Inverse distance weighting

IPY

International Polar Year

NAD83

North American Datum of 1983

NBII

National Biological Information Infrastructure

NDVI

Normalized difference vegetation index

NSF

National Science Foundation

OA

Open access

OECD

Organisation for Economic Collaboration and Development

PRISM

Parameter-elevation regressions on independent slopes model

ROC

Receiver operating characteristic

SDM

Species distribution models

Supplementary material

300_2009_671_MOESM1_ESM.doc (179 kb)
Supplementary material 1 (DOC179 kb)

References

  1. Araújo MB, Thuiller W, Williams PH, Reginster I (2005) Downscaling European species atlas distributions to a finer resolution: implications for conservation planning. Glob Ecol Biogeogr 14:1–17CrossRefGoogle Scholar
  2. Barry S, Elith J (2006) Error and uncertainty in habitat models. J Appl Ecol 43:413–423CrossRefGoogle Scholar
  3. Boyce MS, Vernier PR, Nielsen SE, Schmiegelow FKA (2002) Evaluating resource selection functions. Ecol Modell 157:281–300CrossRefGoogle Scholar
  4. Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30:1145–1159CrossRefGoogle Scholar
  5. Breiman L (2001) Statistical modelling: the two cultures. Statistical Sci 16:199–215CrossRefGoogle Scholar
  6. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth, Belmont, CAGoogle Scholar
  7. Brink CH, Dean FC (1966) Spruce seed as a food of red squirrels and flying squirrels in interior Alaska. J Wildl Manag 30:503–512CrossRefGoogle Scholar
  8. Burnham KP, Anderson DR (1998) Model selection and inference: a practical information-theoretic approach. Springer-Verlag, New York, USAGoogle Scholar
  9. Buskirk SW (1984) Seasonal use of resting sites by marten in south-central Alaska. J Wildl Manag 48:950–953CrossRefGoogle Scholar
  10. Calef MP, McGuire AD, Epstein HE, Rupp TS, Shugart HH (2005) Analysis of vegetation distribution in Interior Alaska and sensitivity to climate change using a logistic regression approach. J Biogeogr 32:863–878CrossRefGoogle Scholar
  11. 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: Hsiao-fan Wang (ed) Intelligent data analysis: developing new methodologies through pattern discovery and recovery. IGI Global, Hershey, PA, USAGoogle Scholar
  12. Dunning JB Jr, Stewart DJ, Danielson BJ, Noon BR, Root TL, Lamberson RH, Stevens EE (1995) Spatially explicit population models: current forms and future uses. Ecol Appl 5:3–11CrossRefGoogle Scholar
  13. 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, JMcC Overton, Peterson AT, Phillips SJ, Richardson KS, Scachetti-Pereira R, Schapire RE, Soberon J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecogr 29:129–151CrossRefGoogle Scholar
  14. Ellenberg H (1988) Vegetation ecology of Central Europe, 4th edn. Cambridge University Press, CambridgeGoogle Scholar
  15. Engler R, Guisan A, Rechsteiner L (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J Appl Ecol 41:263–274CrossRefGoogle Scholar
  16. Farr A, Harris AS (1979) Site index of Sitka Spruce along the Pacific Coast related to latitude and temperatures. For Sci 25:145–153Google Scholar
  17. Ferrier S, Guisan A (2006) Spatial modelling of biodiversity at the community level. J Appl Ecol 43:393–404CrossRefGoogle Scholar
  18. Ferrier S, Drielsma M, Manion G, Watson G (2002) Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New SouthWales. II. Community-level modelling. Biodivers Conserv 11:2309–2338CrossRefGoogle Scholar
  19. Fielding AH (2002) What are the appropriate characteristics of an accuracy measure? In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, Covelo, CA, pp 271–280Google Scholar
  20. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49CrossRefGoogle Scholar
  21. Fleming M (1997) A statewide vegetation map of Alaska using phenological classification of AVHRR data. In: Walker DA, Lillie AC (eds) The second circumpolar arctic vegetation mapping workshop, Arendal, Norway, 18–24 May 1996 and the CAVM-North American Workshop, Anchorage, Alaska, US, 14–16 January 1997. Institute of Arctic and Alpine Research, Boulder, CO, pp 25–26Google Scholar
  22. Franklin J (1995) Predictive vegetation mapping: geographical modelling of biospatial patterns in relation to environmental gradients. Proc Phys Geogr 19:474–499CrossRefGoogle Scholar
  23. Franklin J (1998) Predicting the distribution of shrub species in southern California from climate and terrain-derived variables. J Veg Sci 9:733–748CrossRefGoogle Scholar
  24. Friedman JH, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28:337–407CrossRefGoogle Scholar
  25. Gallant AL, Binnian EF, Omernik JM, Shasby MB (1995) Ecoregions of Alaska US Geological Survey Professional Paper 1567. US Government Printing Office, Washington, DCGoogle Scholar
  26. Graham CH, Ferrier S, Huettmann F, Moritz C, Peterson AT (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol Evol 19:497–503CrossRefPubMedGoogle Scholar
  27. Graham CH, Elith J, Hijmans RJ, Guisan A, Peterson AT, Loiselle BA, The Nceas Predicting Species Distributions Working Group (2008) The influence of spatial errors in species occurrence data used in distribution models. J Appl Ecol 45:239–247CrossRefGoogle Scholar
  28. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009CrossRefGoogle Scholar
  29. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Modell 135:147–186CrossRefGoogle Scholar
  30. Guisan A, Theurillat J-P, Kienast F (1998) Predicting the potential distribution of plant species in an alpine environment. J Veg Sci 9:65–74CrossRefGoogle Scholar
  31. Guisan A, Lehmann A, Ferrier S, Austin M, Overton JMcC, Aspinall R, Hastie T (2006) Making better biogeographical predictions of species’ distributions. J Appl Ecol 43:386–392CrossRefGoogle Scholar
  32. Guisan A, Graham C, Elith J, Huettmann F, NCEAS modelling Group (2007) Sensitivity of predictive species distribution models to change in grain size: insights from an international experiment across five continents. Divers Distrib 13:332–340CrossRefGoogle Scholar
  33. Hastie T, Tibshirani R, Friedman JH (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New YorkGoogle Scholar
  34. Hennon PE, Trummer LM (2001) Yellow Cedar (Chamaecyparis nootkatensis) at the Northwest Limits of its Natural Range in Prince Williams Sound, Alaska. Northwest Sci 75:61–71Google Scholar
  35. Hirzel AH, Le Lay G, Helfer V, Randin C, Guisan A (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol Modell 199:142–152CrossRefGoogle Scholar
  36. Holsten EH, Thier RW, Schmid JM (1991) The sprucebeetle. USDA Forest Service Forest Insect and Disease Leaflet 127Google Scholar
  37. Huettmann F (2007) Modern adaptive management: adding digital opportunities towards a sustainable world with new values. Forum Public Policy 3:337–342Google Scholar
  38. Huettmann F, Diamond AW (2001) Seabird colony locations and environmental determination of seabird distribution: a spatially explicit seabird breeding model in the Northwest Atlantic. Ecol Modell 141:261–298CrossRefGoogle Scholar
  39. Hultén E (1968) Flora of Alaska and neighbouring territories: a manual of vascular plants. University Press Stanford, StanfordGoogle Scholar
  40. Interagency Working Group on Digital Data (2009) Harnessing the power of digital data for science and society. Report to the Committee on Science of the National Science and Technology Council. Washington DCGoogle Scholar
  41. Juday GP, Barber V, Berg E, Valentine D (1999) Recent dynamics of white spruce treeline forests across Alaska in relation to climate. In: Kankaanpaa S, Tasanen T, Sutinen M-L (eds) Sustainable development in Northern Timberline Forests. Proceedings of the Timberline Workshop, May 10–11, 1998 in Whitehorse, Canada. Finnish Forest Research Institute. Research Papers 734, pp 165–187Google Scholar
  42. Kadmon R, Farber O, Danin A (2004) Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecol Appl 14:401–413CrossRefGoogle Scholar
  43. LaBau VJ, Alden JN (2000) Unalaska, Alaska: Revisiting North America’s Oldest Afforestation Effort. J For 98:24–29Google Scholar
  44. LaBau VJ, van Hees WS (1990) An inventory of Alaska’s boreal forests: their extent, condition, and potential use. In: Condition, dynamics, anthropological influences. Proceedings of the International Symposium, 16–26 July, 1990. Archangelsk, Russia. State Forest Committee of the USSR. Part 6, pp 30–39Google Scholar
  45. Lawler JJ, White D, Neilson RP, Blaustein AR (2006) Predicting climate-induced range shifts: model differences and model reliability. Glob Chang Biol 12:1568–1584CrossRefGoogle Scholar
  46. Leathwick JR, Whitehead D, McLeod M (1996) Predicting changes in the composition of New Zealand’s indigenous forests in response to global warming: a modelling approach. Environ Softw 11:81–90CrossRefGoogle Scholar
  47. MacCracken JG, Viereck LA (1990) Browse regrowth and use by moose after fire in interior Alaska. Northwest Sci 64:11–18Google Scholar
  48. Maggini R, Lehmann A, Zimmermann NE, Guisan A (2006) Improving generalized regression analysis for the spatial prediction of forest communities. J Biogeogr 33:1729–1749CrossRefGoogle Scholar
  49. Margules CR, Austin MP (1994) Biological models for monitoring species decline: the construction and use of data bases. Phil Trans Roy Soc B 344:69–75CrossRefGoogle Scholar
  50. Masek JG (2001) Stability of Boreal forest stands during recent climate change: evidence from Landsat Satellite Imagery. J Biogeogr 28:967–976CrossRefGoogle Scholar
  51. Murray DF (1980) Balsam Poplar in Northern Alaska. Can J Anthropol 1:29–32Google Scholar
  52. National Research Council of the National Academies (2003) Sharing Publication-related Data and Materials: Responsibilities of Authorship in the Life Sciences. The National Academic Press, Washington DC, www.nap.edu
  53. Parviainen M, Luoto M, Ryttäri T, Heikkinen RK (2008) Modelling the occurrence of threatened plant species in taiga landscapes: methodological and ecological perspectives. J Biogeogr. doi:10.1111/j.1365-2699.2008.01922.x
  54. Pojar J, Mackinnon A (1994) Plants of the Pacific Northwest Coast: Washington, Oregon, British Columbia, and Alaska. Lone Pine Publishing, Redmond, WAGoogle Scholar
  55. Prasad AM, Iverson LR, Matthews S, Peters M (2007-ongoing) A climate change Atlas for 134 Forest Tree Species of the Eastern United States [database]. Northern Research Station, USDA Forest Service, Delaware, OH. http://www.nrs.fs.fed.us/atlas/tree
  56. Risenhoover KL (1989) Composition and quality of moose winter diets in interior Alaska. J Wildl Manag 53:568–577CrossRefGoogle Scholar
  57. Rupp TS, Chapin FS, Starfield AM (2001) Modelling the influence of topographic barriers on treeline advance at the forest-tundra ecotone in northwestern Alaska. Clim Chang 48:399–416CrossRefGoogle Scholar
  58. Sinclair ARE, Jogia MK, Andersen RJ (1988) Camphor from juvenile white spruce as an antifeedant for snowshoe hares. J Chem Ecol 14:1505–1514CrossRefGoogle Scholar
  59. Slough BD (1989) Movement and habitat use by transplanted marten in the Yukon Territory. J Wildl Manag 53:991–997CrossRefGoogle Scholar
  60. Smith M (1968) Red squirrel responses to spruce cone failure in interior Alaska. J Wildl Manag 32:305–317CrossRefGoogle Scholar
  61. Stockwell DRB, Peterson AT (2002) Controlling bias in biodiversity data. In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, pp 537–546Google Scholar
  62. Thompson RS, Anderson KH, Strickland LE, Shafer SL, Pelltier RT, Bartlein PJ (2006) Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America—Alaskan Species and Ecoregions. USGS Professional Paper 1650-DGoogle Scholar
  63. Tsoar A, Allouche O, Steinitz O, Rotem D, Kadmon R (2007) A comparative evaluation of presence-only methods for modelling species distribution. Divers Distrib 13:397–405CrossRefGoogle Scholar
  64. Van Cleve K, Dyrness CT, Viereck LA, Fox J, Chapin FS III (1983) Taiga ecosystems in interior Alaska. Biosci 33:39–44CrossRefGoogle Scholar
  65. Viereck LA, Foote JM (1970) The Status of Populus balsamifera and P. trichiocarpa in Alaska. Can Field Nat 84:169–173Google Scholar
  66. Viereck LA, Little EL (2007) Alaska trees and shrubs. Snowy Owl Books, FairbanksGoogle Scholar
  67. Viereck LA, Van Cleve K, Dyrness CT (1986) Forest ecosystem distribution in the taiga environment. In: Van Cleve K, Chapin FS III, Flanagan PW, Viereck LA, Dyrness CT (eds) Forest ecosystems in the Alaskan taiga. Ecological studies 57. Springer-Verlag, New York, NY, pp 22–43Google Scholar
  68. Viereck LA, Dyrness CT, Batten AR, Wenzlick KJ (1992) The Alaska vegetation classification. General Technical Report No. 286. US Forest Service, Pacific Northwest Research Station, Portland, ORGoogle Scholar
  69. Walker LR, Zasada JC, Chapin FS III (1986) The role of life history processes in primary succession on an Alaskan floodplain. Ecol 67:1243–1253CrossRefGoogle Scholar
  70. Walter H (1985) Vegetation of the earth and ecological systems of geobiosphere, 3rd edn. Springer, HeidelbergGoogle Scholar
  71. Walters CJ (1986) Adaptive management of renewable resources. McGraw Hill, New YorkGoogle Scholar
  72. Whittaker RH (1967) Gradient analysis of vegetation. Biol Rev Camb Philos Soc 42:207–264CrossRefPubMedGoogle Scholar
  73. Wolff JO (1978) Food habits of snowshoe hares in interior Alaska. J Wildl Manag 42:148–153CrossRefGoogle Scholar
  74. Zimmermann NE, Kienast F (1999) Predictive mapping of alpine grasslands in Switzerland: species versus community approach. J Veg Sci 10:469–482CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Bettina Ohse
    • 1
    • 2
  • Falk Huettmann
    • 3
  • Stefanie M. Ickert-Bond
    • 4
  • Glenn P. Juday
    • 2
  1. 1.Institute of Botany and Landscape EcologyUniversity of GreifswaldGreifswaldGermany
  2. 2.Forest Science DepartmentUniversity of Alaska FairbanksFairbanksUSA
  3. 3.EWHALE Lab, Biology and Wildlife Department, Institute of Arctic BiologyUniversity of Alaska FairbanksFairbanksUSA
  4. 4.Herbarium and Department of Biology and Wildlife, Institute of Arctic BiologyUniversity of Alaska Museum of the NorthFairbanksUSA

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