Contributions of Spatially Explicit Landscape Models To Conservation Biology

  • Eli Meir
  • Peter M. Kareiva


The practice of conservation is often a form of land management. One of the most powerful approaches for connecting the needs of a particular species with land usage is the linking of biologically-detailed models of that species dispersal and demography with geographic information systems (GIS). For example, juvenile spotted owls must depart their birthplace in search of unoccupied expanses of old growth forest. Maps that detail the scarcity, fragmentation, and location of remnant old growth stands dramatize how difficult a search these juvenile owls may face in heavily logged portions of the Pacific Northwest. By connecting these spatially detailed maps with a model of how owls disperse and reproduce, managers can construct logging plans that make the best of what little old growth might remain. We call such approaches spatially explicit population models (or SEPMs) because they assign habitats and owls to particular locations in space, and depending upon the number and placement of individuals, they predict population change as a result of dispersal, mortality, and reproduction. The emergence of user-friendly GIS software, the maturing of ecological theory pertaining to population dynamics in fragmented habitats, and the increased popularity of individual behavior simulation models have combined to produce a tremendous enthusiasm for SEPM’s (see Ecological Applications, issue #1, volume 7, 1995).


Geographic Information System Suitable Habitat Habitat Patch Conservation Biology Explicit Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literature Cited

  1. Doak, D. 1996. Source-sink models and the problem of habitat degradation: General models and applications to the Yellowstone grizzly. Conservation Biology 9:1370–1379.CrossRefGoogle Scholar
  2. Dunning, J.B., D.J. Stewart, B.J. Danielson, B.R. Noon, T.L. Root, R.H. Lamberson, and E.E. Stevens. 1995. Spatially explicit population models: Current forms and future uses. Ecological Applications 5:3–11.CrossRefGoogle Scholar
  3. Groom, M. and N. Schumaker. 1993. Evaluating landscape change: Patterns of worldwide deforestation and local fragmentation. In Biotic interactions and global change, eds. P. Kareiva, J. Kingsolver, and R. Huey, 25–40, Sunderland, MA: Sinauer Associates.Google Scholar
  4. Hanski, I., A. Moilanen, T. Pakkala, and M. Kusari. 1996. The quantitative incidence function model and persistence of an endangered butterfly metapopulation. Conservation Biology 10: 578–590.CrossRefGoogle Scholar
  5. Hanski, I. 1997. Predictive and practical metapopulation models: the incidence function approach. In Spatial ecology: the role of space in population dynamics and interspecific interactions, eds. D. Tilman and P. Kareiva. Princeton, NJ: Princeton University Press.Google Scholar
  6. Kareiva, P., D. Skelly, and M. Ruckelshaus. 1997. Reevaluating the use of models to predict the consequences of habitat loss and fragmentation. In Enhancing the ecological basis of conservation: heterogenity, ecosystem function, and biodiversity, eds. S.T.A. Pickett, R.S. Ostfeld, H. Schchak, and G.E. Likens. New York: Chapman and Hall.Google Scholar
  7. Lamberson, R., R. McKelvey, B. R. Noon, C. Voss. 1992. A dynamic analysis of Northern Spotted owl viability in a fragmented forest landscape. Conservation Biology 6:505–512.CrossRefGoogle Scholar
  8. Liu, J., B. Dunning, and H. Pulliam. 1995. Potential effects of a forest management plan on Bachman’s sparrows: linking a spatially explicit model with GIS. Conservation Biology 9:62–75.CrossRefGoogle Scholar
  9. McKelvey, K., B. Noon, and R. Lamberson. 1993. Conservation planning for species occupying fragmented landscapes: the case of the Northern Spotted Owl. In Biotic interactions and global change, eds. P. Kareiva, J. Kingsolver and R. Huey, 424–450. Sunderland, MA: Sinauer Associates.Google Scholar
  10. Pulliam, R. 1988. Sources, sinks, and population regulation. American Naturalist 132:652–661.CrossRefGoogle Scholar
  11. Ruckelshaus, M., C. Hartway, and P. Kareiva 1997. Assessing the data requirements of spatially explicit dispersal models. Conservation Biology, in press.Google Scholar
  12. Skelly, D. and E. Meir. 1997. Rule-based models for evaluating mechanisms of distributional change. Conservation Biology, in press.Google Scholar
  13. Steinberg, E. and P. Kareiva. 1997. Challenges and opportunities for empirical evaluation of “spatial theory.” In Spatial ecology: The role of space in population dynamics and interspecific interactions, eds. D. Tilman and P. Kareiva. Princeton: Princeton University Press.Google Scholar
  14. Wahlberg, N., A. Moilanen, and I. Hanski. 1996. Predicting the occurrence of endangered species in fragmented landscapes. Science 273:1536–1538.CrossRefGoogle Scholar
  15. Wootton, J. and D. Bell. 1992. A metapopulation model of the peregrine falcon in California: viability and management strategies. Ecological Applications 2:307–321.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Eli Meir
  • Peter M. Kareiva

There are no affiliations available

Personalised recommendations