Environmental Modeling & Assessment

, Volume 18, Issue 2, pp 159–170 | Cite as

A Data-Based Conservation Planning Tool for Florida Panthers

  • Jennifer L. Murrow
  • Cindy A. Thatcher
  • Frank T. van Manen
  • Joseph D. Clark
Article

Abstract

Habitat loss and fragmentation are the greatest threats to the endangered Florida panther (Puma concolor coryi). We developed a data-based habitat model and user-friendly interface so that land managers can objectively evaluate Florida panther habitat. We used a geographic information system (GIS) and the Mahalanobis distance statistic (D2) to develop a model based on broad-scale landscape characteristics associated with panther home ranges. Variables in our model were Euclidean distance to natural land cover, road density, distance to major roads, human density, amount of natural land cover, amount of semi-natural land cover, amount of permanent or semi-permanent flooded area–open water, and a cost–distance variable. We then developed a Florida Panther Habitat Estimator tool, which automates and replicates the GIS processes used to apply the statistical habitat model. The estimator can be used by persons with moderate GIS skills to quantify effects of land-use changes on panther habitat at local and landscape scales. Example applications of the tool are presented.

Keywords

Puma concolor coryi Florida panther Habitat model Mahalanobis distance 

References

  1. 1.
    Adriaensen, F., Chardon, J. P., De Blust, G., Swinnen, E., Villalba, S., Gulinck, H., et al. (2003). The application of ‘least-cost’ modelling as a functional landscape model. Landscape and Urban Planning, 64, 233–247.CrossRefGoogle Scholar
  2. 2.
    Buehler, D. A., Welton, M. J., & Beachy, T. J. (2006). Predicting cerulean warbler habitat use in the Cumberland Mountains of Tennessee. Journal of Wildlife Management, 70, 1763–1769.CrossRefGoogle Scholar
  3. 3.
    Boyce, M. S., Vernier, P. R., Nielsen, S. E., & Schmiegelow, F. K. A. (2002). Evaluating resource selection functions. Ecological Modelling, 157, 281–300.CrossRefGoogle Scholar
  4. 4.
    Browning, D. M., Beaupre, S. J., & Duncan, L. (2005). Using partitioned Mahalanobis D 2 to formulate a GIS-based model of timber rattlesnake hibernacula. Journal of Wildlife Management, 69, 33–44.CrossRefGoogle Scholar
  5. 5.
    Clark, J. D., Dunn, J. E., & Smith, K. G. (1993). A multivariate model of female black bear habitat use for a geographic information system. Journal of Wildlife Management, 57, 519–526.CrossRefGoogle Scholar
  6. 6.
    Comiskey, E. J., Bass, O. L., Jr., Gross, L. J., McBride, R. T., Salinas, R. (2002). Panthers and forests in South Florida: An ecological perspective. Conservation Ecology, 6:18. <http://www.consecol.org/vol6/iss1/art18>. Accessed 1 August 2002.Google Scholar
  7. 7.
    Corsi, F., Dupré, E., & Boitani, L. (1999). A large-scale model of wolf distribution in Italy for conservation planning. Conservation Biology, 13, 150–159.CrossRefGoogle Scholar
  8. 8.
    Cowardin, L. M., Carter, V., Golet, F. C., & LaRoe, E. T. (1979). Classification of wetlands and deepwater habitats of the United States. Washington: U.S. Fish and Wildlife Service.Google Scholar
  9. 9.
    Crooks, K. R. (2002). Relative sensitivities of mammalian carnivores to habitat fragmentation. Conservation Biology, 16, 488–502.CrossRefGoogle Scholar
  10. 10.
    Davis Jr., J. H. (1943). The natural features of southern Florida, especially the vegetation and the Everglades. Florida Geological Survey Bulletin, 25. Tallahassee, Florida, USA.Google Scholar
  11. 11.
    Dickson, B. G., Jenness, J. S., & Beier, P. (2005). Influence of vegetation, topography, and roads on cougar movement in southern California. Journal of Wildlife Management, 69, 264–276.CrossRefGoogle Scholar
  12. 12.
    ESRI. (2006). ArcGIS desktop help. Redlands: ESRI.Google Scholar
  13. 13.
    Farber, O., & Kadmon, R. (2003). Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecological Modelling, 160, 115–130.CrossRefGoogle Scholar
  14. 14.
    Fleming, M. (1994). Distribution, abundance, and demography of white-tailed deer in the Everglades. In D. Jordan (Ed.), Proceedings of the Florida Panther Conference (pp. 494–503). Fort Myers: U.S. Fish and Wildlife Service.Google Scholar
  15. 15.
    Florida Fish and Wildlife Conservation Commission. (2003). Florida vegetation and land cover. http://myfwc.com/oes/habitat_sec/gis/fl_veg03.htm. Accessed 12 July 2005.
  16. 16.
    Florida Geographic Data Library. (2005). Public lands. <http://www.geoplan.ufl.edu/education.html>. Accessed 14 February 2006.
  17. 17.
    Foster, M. L., & Humphrey, S. R. (1995). Use of highway underpasses by Florida panthers and other wildlife. Wildlife Society Bulletin, 23, 95–100.Google Scholar
  18. 18.
    Fritts, S. H., & Carbyn, L. N. (2006). Population viability, nature reserves, and the outlook for wolf conservation in North America. Restoration Ecology, 3, 26–38.CrossRefGoogle Scholar
  19. 19.
    Frontier, S. (1976). Étude de la décroissance des valeurs propres dans une analyse en composantes principales: Comparaison avec le modèle du bâton brisé. Journal of Experimental Marine Biology and Ecology, 25, 67–75.CrossRefGoogle Scholar
  20. 20.
    Griffin, S. C., Taper, M. L., Hoffman, R., & Mills, L. S. (2010). Ranking Mahalanobis distance models for predictions of occupancy from presence-only data. Journal of Wildlife Management, 74, 1112–1121.CrossRefGoogle Scholar
  21. 21.
    Gu, W., & Swihart, R. K. (2004). Absence or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biological Conservation, 116, 195–203.CrossRefGoogle Scholar
  22. 22.
    Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C., & Guisan, A. (2006). Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199, 142–152.CrossRefGoogle Scholar
  23. 23.
    Hoctor, T. S., Carr, M. H., & Zwick, P. D. (2000). Identifying a linked reserve system using a regional landscape approach: The Florida ecological network. Conservation Biology, 14, 984–1000.CrossRefGoogle Scholar
  24. 24.
    Hooge, P. N., & Eichenlaub, B. (1997). Animal movement extension to ArcView. Version 2.04 beta. Alaska Biological Science Center, U.S. Geological Survey, Anchorage, Alaska, USA.Google Scholar
  25. 25.
    Horne, J. S., & Garton, E. O. (2006). Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter of kernel home-range analysis. Journal of Wildlife Management, 70, 641–648.CrossRefGoogle Scholar
  26. 26.
    Jackson, D. A. (1993). Stopping rules in principal components analysis: A comparison of heuristical and statistical approaches. Ecology, 74, 2204–2214.CrossRefGoogle Scholar
  27. 27.
    Jackson, J. E. (1991). A user’s guide to principal components. New York: Wiley-Interscience: JohnWiley and Sons.CrossRefGoogle Scholar
  28. 28.
    Janis, M. W., & Clark, J. D. (2002). Responses of Florida panthers to recreational deer and hog hunting. Journal of Wildlife Management, 66, 839–848.CrossRefGoogle Scholar
  29. 29.
    Katnik, D. D., & Wielgus, R. B. (2005). Landscape proportions versus Monte Carlo simulated home ranges for estimating habitat availability. Journal of Wildlife Management, 69, 20–32.CrossRefGoogle Scholar
  30. 30.
    Kauhala, K., & Tiilikainen, T. (2002). Radio location error and the estimates of home-range size, movements, and habitat use: A simple field test. Annales Zoologici Fennici, 39, 317–324.Google Scholar
  31. 31.
    Kautz, R., Kawula, R., Hoctor, T., Comiskey, J., Jansen, D., Jennings, D., et al. (2006). How much is enough? Landscape-scale conservation for the Florida panther. Biological Conservation, 130, 118–133.CrossRefGoogle Scholar
  32. 32.
    Knick, S. T., & Dyer, D. L. (1997). Distribution of black-tailed jackrabbit habitat determined by GIS in southwestern Idaho. Journal of Wildlife Management, 61, 75–85.CrossRefGoogle Scholar
  33. 33.
    Knick, S. T., & Rotenberry, J. T. (1998). Limitations to mapping habitat use areas in changing landscapes using the Mahalanobis distance statistic. Journal of Agricultural, Biological, and Environmental Statistics, 3, 311–322.CrossRefGoogle Scholar
  34. 34.
    McGarigal K. & Marks B. J. (1995). FRAGSTATS: Spatial pattern analysis program for quantifying landscape structure. USDA Forest Service General Technical Report PNW-351.Google Scholar
  35. 35.
    Morrison, M. L., Marcot, B. G., & Mannan, R. W. (1992). Wildlife–habitat relationships: Concepts and applications. Madison: University of Wisconsin Press.Google Scholar
  36. 36.
    Moser, B. W., & Garton, E. O. (2007). Effects of telemetry location error on space-use estimates using a fixed-kernel density estimator. Journal of Wildlife Management, 71, 2421–2426.CrossRefGoogle Scholar
  37. 37.
    Peterson, A. T., Stockwell, D. R. B., & Kluza, D. A. (2002). Distributional prediction based on ecological niche modelling of primary occurrence data. In J. M. Scott, P. J. Heglund, M. L. Morrison, J. B. Haufler, M. G. Raphael, W. A. Wall, & F. B. Samson (Eds.), Predicting species occurrences: Issues of accuracy and scale (pp. 617–623). Washington: Island Press.Google Scholar
  38. 38.
    Rao, C. R. (1952). Advanced statistical methods in biometric research. New York: John Wiley and Sons.Google Scholar
  39. 39.
    Seaman, D. E., & Powell, R. A. (1996). An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology, 77, 2075–2085.CrossRefGoogle Scholar
  40. 40.
    Seaman, D. E., Millspaugh, J. J., Kernohan, B. J., Brundige, G. C., Raedeke, K. J., & Gitzen, R. A. (1999). Effects of sample size on kernel home range estimates. Journal of Wildlife Management, 63, 739–747.CrossRefGoogle Scholar
  41. 41.
    Thatcher, C. A., van Manen, F. T., & Clark, J. D. (2009). A habitat assessment for Florida panther population expansion into central Florida. Journal of Mammalogy, 90, 918–925.CrossRefGoogle Scholar
  42. 42.
    Thompson, L. M., van Manen, F. T., Schlarbaum, S. E., & DePoy, M. (2006). A spatial modeling approach to identify potential butternut restoration sites in Mammoth Cave National Park. Restoration Ecology, 14, 289–296.CrossRefGoogle Scholar
  43. 43.
    U.S. Department of Agriculture Natural Resources Conservation Service. (2006). http://www.ncgc.nrcs.usda.gov/products/datasets/climate/data/index.html. Accessed 2 May 2006.
  44. 44.
    U.S. Fish and Wildlife Service. (2006). Third revision Florida panther recovery plan, Atlanta, Georgia, USA.Google Scholar
  45. 45.
    Worton, B. J. (1989). Kernel methods for estimating the utilization distribution in home-range studies. Ecology, 70, 165–168.CrossRefGoogle Scholar
  46. 46.
    Worton, B. J. (1995). Using Monte Carlo simulation to evaluate kernel-based home range estimators. Journal of Wildlife Management, 59, 794–800.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. (outside the USA) 2012

Authors and Affiliations

  • Jennifer L. Murrow
    • 1
  • Cindy A. Thatcher
    • 1
  • Frank T. van Manen
    • 1
  • Joseph D. Clark
    • 1
  1. 1.U.S. Geological Survey, Southern Appalachian Research BranchUniversity of TennesseeKnoxvilleUSA

Personalised recommendations