How Spatial Information Contributes to the Conservation and Management of Biodiversity

  • Dawn Robin Magness
  • John M. Morton
  • Falk Huettmann


Reliable ecological information is a necessary component of sustainable management practices (Walters 1986). Land managers need to understand the spatial distribution and population status of species and habitats in regional landscapes. The Millennium Assessment, a global assessment of human well-being, identified biodiversity as a crucial ecosystem service that increases the capacity of ecosystems to adapt to environmental change and maintain productivity (http://www.millenniumassessment. org/en/index.aspx). Biodiversity is widely defined as the variety of compositional, structural, and functional biological components available across multiple scales including landscapes, ecosystems, species, and genetics (Noss 2001). As biodiversity occurs at a multitude of scales, species conservation and sustainable management requires that planning also occur at these scales. Planning for biodiversity conservation is critical because regional landscapes are increasingly compromised by global anthropogenic influences (Vitousek et al. 1997). More than 75% of habitable, ice-free land is already altered by human residence and land-use (Ellis and Ramankutty 2008; Usher et al. 2005; Vitousek et al. 1997).


Global Position System Geographic Information System Species Distribution Model Prairie Pothole Region Habitat Suitability Index 


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  1. Alidina HM, Fisher D, Stienback C, Ferdana Z, Lombana A, Huettmann F (2008) Assessing and managing data. In: Ardron J, Possingham H, Klein C (eds) Marxan good practices handbook. Vancouver, Canada
  2. Austin MP, Heyligers PC (1989) Vegetation survey design for conservation: gradsect sampling of forests in north-eastern New South Wales. Biol Conserv 50:13–32CrossRefGoogle Scholar
  3. Bella D, Li H, et al (1992) Ecological indicators of global climate change: proceeding of a US Fish and Wildlife Service global climate change workshop held at Oregon State University, Corvallis, Oregon 13–15 November 1990Google Scholar
  4. Berry KH (1986) Introduction: development, testing, and application of wildlife-habitat models. In: Verner J, Morrison ML, Ralph CJ Wildlife 2000: modeling habiat relationships of terrestrial vertebrates. The University of Wisconsin Press, Madison, WIGoogle Scholar
  5. Bowser ML, Morton JM (2009) Modeling terrestrial arthropod diversity on the Kenai National Wildlife Refuge. In: McWilliams W, Moisen G, Czaplewski R Forest inventory and analysis (FIA) Symposium, 21–23 October 2008, Park City, UT. Proc RMRS-P-56CD USDA Forest Service, Rocky Mountain Research StationGoogle Scholar
  6. Boyce MS, McDonald LL (1999) Relating populations to habitats using resouce selection functions. Trends Ecol Evol 14:268–272CrossRefPubMedGoogle Scholar
  7. Breiman L (2001) Statistical modeling: the two cultures. Stat Sci 16:199–231CrossRefGoogle Scholar
  8. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
  9. Busby JR (1991) BIOCLIM — a bioclimatic analysis and prediction system. In: Austin MP, Margules CR Conservation: cost effective biological surveys and data analysis. CISIRO, Melbourne, AustraliaGoogle Scholar
  10. Canhos VP, Souza S, et al (2004) Global biodiversity informatics: setting the scene for a “new world” of ecological modeling. Biodiver Inform 1:1–13Google Scholar
  11. Chapin FS III, Peterson G, et al (2004) Resilience and vulnerability of northern regions to social and environmental change. Ambio 33:344–349PubMedGoogle Scholar
  12. Chapman AD, Busby JR (1994) Linking plant species information to continental bidoversity inventory, climate and environmental monitoring. In: Miller RI Mapping the diversity of nature. Chapman & Hall, LondonGoogle Scholar
  13. Chapman AD, Wieczorek J (2006) Guide to best practices for georeferencing. Copenhagen: Global Biodiversity Information FacilityGoogle Scholar
  14. Csuti B, Crist P (2000) Methods for developing terrestrial vertebrate distribution maps for gap analysis. In: Scott JM, Jennings MD A handbook for gap analysis. Version 2.0 Idaho Cooperative Fish and Wildlife Research Unit, University of Idaho. MoscowGoogle Scholar
  15. Cutler DR, Edwards TC Jr, et al (2007) Random forests for classification in ecology. Ecology 88:2783–2792CrossRefPubMedGoogle Scholar
  16. Droege S, Cyr A, et al (1998) Checklists: and under-used tool for the inventory and monitoring of plants and animals. Conserv Biol 12:1134–1138CrossRefGoogle Scholar
  17. Edwards TC Jr, Cutler DR, Zimmermann NE, Geiser L, Moisen GG (2006) Effects of sample survey design on the accuracy of classification tree models in species distribution models. Ecological Modelling 199:132–141CrossRefGoogle Scholar
  18. Elith J, Graham CH, et al (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29:129–151CrossRefGoogle Scholar
  19. Ellis EC, Ramankutty V (2008) Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecol Environ 6:439–447CrossRefGoogle Scholar
  20. Franklin SE (2001) Remote sensing for sustainable forest management. Lewis, Boca Raton, FLGoogle Scholar
  21. GAO (2007) Climate change: agencies should develop guidance for addressing the effects on federal land and water resources. US Government Accountability Office Report to Congressional RequestersGoogle Scholar
  22. 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
  23. Gu W, Swihart RK (2004) Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biol Conserv 116:195–203Google Scholar
  24. Hirzel AH, Hausser J, et al (2002) Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83:2027–2036CrossRefGoogle Scholar
  25. Hochachka WM, Caruana R, et al (2007) Data-mining discovery of pattern and process in ecological systems. J Wildl Manag 71:2427–2437CrossRefGoogle Scholar
  26. Huettmann F (2005) Databases and science-based management in the context of wildlife and habitat: toward a certified ISO standard for objective decision-making for the global community by using the internet. The Journal of Wildlife Management 69 (2):466–472CrossRefGoogle Scholar
  27. Inkley DB, Anderson MG, et al (2004) Global climate change and wildlife in North America. Wildl Soc: 34Google Scholar
  28. Iverson LR, Schwartz MW, et al (2004) How fast and far might tree species migrate in the eastern United States due to climate change? Global Ecol Biogeogr 13:209–219CrossRefGoogle Scholar
  29. Jennings MD (2000) Gap analysis: concepts, methods, and resent results. Landsc Ecol 15:5–20CrossRefGoogle Scholar
  30. Kappelle M, Vuuren MMIV, et al (1999) Effects of climate change on biodiversity: a review and identification of key research issues. Biodiver Conserv 8:1383–1397CrossRefGoogle Scholar
  31. Kodric-Brown A, Brown JH (1998) Incomplete datasets in community ecology and biogeography: a cautionary tale. Ecol Appl 3:736–742CrossRefGoogle Scholar
  32. Koeln GT, Cowardin LM, et al (1994) Geographic information systems. In: Bookhout TA Research and management techniques for wildlife and habitats. The Wildlife Society, Bethesda, MDGoogle Scholar
  33. Lobo JM (2008) More complex distribution models or more representative data? Biodiver Inform 5:14–19Google Scholar
  34. Lunetta RS (1998) Applications, project formulation, and analytical approach. In: Lunetta RS, Elvidge CD Remote sensing change detection: environmental monitoring methods and applications. Ann Arbor, Chelsea, MichiganGoogle Scholar
  35. Lunetta RS, Lyon JG, et al (1998) North American Landscape Characterization: triplicate data sets and data fusion products. In: Lunetta RS, Elvidge CD Remote sensing change detection: environmental monitoring methods and applications. Ann Arbor, Chelsea, MichiganGoogle Scholar
  36. MacKenzie DI, Nichols JD, et al (2006) Estimation and modeling: inferring patterns and dynamics of species occurrence. Elsevier, San Diego, CAGoogle Scholar
  37. Magness DR, Huettmann F, et al (2008) Using random forests to provide predicted species distribution maps as a metric for ecological inventory & monitoring programs. In: Smolinski TG, Milanova MG, Hassanien A-E Applications of computational intelligence in biology: current trends and open problems. Studies in computational intelligence, Springer, BerlinGoogle Scholar
  38. Manly B, McDonald L, et al (1993) Resource selection by animals: statistical design for field studies. Chapman & Hall, LondonGoogle Scholar
  39. McHarg IL (1969) Design with nature. Doubleday & Company, Garden City, NJGoogle Scholar
  40. Morrison ML, Marcot BG, et al (1992) Wildlife-habitat relationships: concepts and applications. The University of Wisconsin Press, MadisonGoogle Scholar
  41. Morton J, Bowser M, et al (2009) Long term ecological monitoring program on the Kenai National Wildlife Refuge: an FIA adjunct inventory. In: McWilliams W, Moisen G, Czaplewski R Forest inventory and analysis (FIA) symposium, 21–23 October 2008, Park City, UT, USDA Forest Service, Rocky Mountain Research Station RMRS-P-56CDGoogle Scholar
  42. Nichols JD, Williams BR (2006) Monitoring for Conservation. Trends Ecol Evol 21:668–673CrossRefPubMedGoogle Scholar
  43. Niemuth ND, Reynolds RE, et al (2008) Landscape-level planning for conservation of wetland birds in the US Prairie Pothole Region. In: Millspaugh JJ, Thompson FR III Models for planning wildlife conservation in large landscapes. Elsevier, AmsterdamGoogle Scholar
  44. Noss RF (2001) Beyond Kyoto: forest management in a time of rapid climate change. Conserv Biol 15:578–590CrossRefGoogle Scholar
  45. Nusser SM, Goebel JJ (1997) The national resources inventory: a long-term multi-resource monitoring programme. Environ EcolStat 4:181–204CrossRefGoogle Scholar
  46. Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Ann Rev Ecol Evol Syst 37:637–669CrossRefGoogle Scholar
  47. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42CrossRefPubMedGoogle Scholar
  48. Phillips SJ, Anderson RP, et al (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  49. Prasad AM, Iverson LR, et al (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–199CrossRefGoogle Scholar
  50. Rodriguez JP, Brotons L, et al (2007) The application of predictive modeling of species distribution to biodiversity conservation. Diversity Distributions 13:243–251CrossRefGoogle Scholar
  51. Root TL, Schneider SH (2001) Climate change: overview and implications for wildlife. In: Schneider SH, Root TL Wildlife responses to climate change: North American case studies. Island, Washington, DCGoogle Scholar
  52. Scott JM, Davis F, et al (1993) Gap analysis: a geographic approach to the protection of biological diversity. Wildl Monogr 123Google Scholar
  53. Scott JM, Heglund PJ, et al (2002) Predicting species occurrences: issues of accuracy and scale. Island, Washington, DCGoogle Scholar
  54. Smith WB (2002) Forest inventory and analysis: a national inventory and monitoring program. Enviro Pollut 116:S233–S242CrossRefGoogle Scholar
  55. Stockwell D, Peters D (1999) The GARP modelling system: problems and solutions to automated spatial prediction. Internat J Geograph Info Sci 13:143–158CrossRefGoogle Scholar
  56. Stoms DM (2007. Actual vegetation layer. In: Scott JM, Jennings MD A handbook for gap analysis. Version 2.0 Idaho Cooperative Fish and Wildlife Research Unit, University of Idaho, MoscowGoogle Scholar
  57. Stow DA, Hope A, et al (2004) Remote sensing of vegetation and land-cover change in Arctic tundra ecosystems. Remote Sensing of Environment 89:281–308CrossRefGoogle Scholar
  58. Tremblay J-P, Hester A, et al (2004) Choice and development of decision support tools for the sustainable management of deer-forest systems. For Ecol Manag 191:1–16CrossRefGoogle Scholar
  59. Vitousek PM, Lubchenco J, et al (1997) Human domination of Earth's ecosystems. Science 277:494–499CrossRefGoogle Scholar
  60. Walters C (1986) Adaptive management of renewable resources. Blackburn, Caldwell, NJGoogle Scholar
  61. Yen PPW, Huettmann F, et al (2004) A large-scale model for the at-sea distribution and abundance of Marbled Murrlets (Brachyramphus marmoratus) during the breeding season in coastal British Columbis, Canada. Ecol Model 171:395–413CrossRefGoogle Scholar

Copyright information

© Springer 2010

Authors and Affiliations

  • Dawn Robin Magness
    • 1
  • John M. Morton
    • 1
  • Falk Huettmann
    1. 1.Kenai National Wildlife RefugeU.S. Fish & Wildlife ServiceSoldotnaUSA

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