Quantifying relationships between bird and butterfly community shifts and environmental change

Ecological Applications

Volumn 16, Issue 1, 2006, Pages 380-393

  Debinski, Diane M ^a   VanNimwegen, Ron E ^a   Jakubauskas, Mark E ^b  

^a IOWA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF KANSAS   (United States)

Author keywords

Butterflies; Climate change; Community structure; Discriminant analysis; Environmental variation; Normalized difference vegetation index (NDVI); Ordination; Songbirds; Species composition

Indexed keywords

BIOLOGICAL MARKER;

BUTTERFLY; COMMUNITY RESPONSE; COMMUNITY STRUCTURE; ENVIRONMENTAL CHANGE; NDVI; QUANTITATIVE ANALYSIS; SONGBIRD;

ANIMAL; ARTICLE; BIRD; BUTTERFLY; CLASSIFICATION; CLIMATE; COMPARATIVE STUDY; DEMOGRAPHY; DISASTER; ENVIRONMENT; ENVIRONMENTAL MONITORING; FORECASTING; GEOGRAPHY; PHYSIOLOGY; POPULATION DYNAMICS; SEASON; SPECIES DIFFERENCE; TIME;

ANIMALS; BIOLOGICAL MARKERS; BIRDS; BUTTERFLIES; CLIMATE; ENVIRONMENT; ENVIRONMENTAL MONITORING; FORECASTING; GEOGRAPHY; NATURAL DISASTERS; POPULATION DYNAMICS; RESIDENCE CHARACTERISTICS; SEASONS; SPECIES SPECIFICITY; TIME FACTORS;

AVES; PAPILIONOIDEA; PASSERI;

EID: 33645098508     PISSN: 10510761     EISSN: 10510761     Source Type: Journal    
DOI: 10.1890/04-1896     Document Type: Article

References (68)

1. Achard, F., and F. Blasco. 1990. Analysis of vegetation seasonal evolution and mapping of forest cover in West Africa with the use of NOAA AVHRR HRPT data. Photogrammetric Engineering and Remote Sensing 56:1359-1365.
2. Becker, F., and B. J. Choudhury. 1988. Relative sensitivity of Normalized Difference Vegetation Index (NDVI) and Microwave Polarization Difference Index (MPDI) for vegetation and desertification monitoring. Remote Sensing of Environment 24:297-311.
3. Brown, J. H., T. H. Valone, and C. G. Curtin. 1997. Reorganization of an arid ecosystem in response to recent climate change. Proceedings of the National Academy of Science USA 94:9729-9733.
4. Cairns, J., Jr. 1986. The myth of the most sensitive species. BioScience 36:670-672.
5. Cardillo, M., D. W. Macdonald, and S. P. Rushton. 1999. Predicting mammal species richness and distributions: testing the effectiveness of satellite-derived land cover data. Landscape Ecology 14:423-435.
6. De'ath, G. 1999. Principal curves: a new technique for indirect and direct gradient analysis. Ecology 80:2237-2253.
7. Debinski, D. M., M. E. Jakubauskas, and K. Kindscher. 1999. A remote sensing and GIS-based model of habitats and biodiversity in the Greater Yellowstone Ecosystem. International Journal of Remote Sensing 20:3281-3292.
8. Debinski, D. M., M. E. Jakubauskas, and K. Kindscher. 2000. Montane meadows as indicators of environmental change. Environmental Monitoring and Assessment 64:213-225.
9. Debinski, D. M., M. E. Jakubauskas, K. Kindscher, E. H. Saveraid, and M. Borgognone. 2002. Predicting meadow communities and species occurrences in the Greater Yellowstone Ecosystem. Pages 499-506 in J. M. Scott, P. J. Heglund, M. Morrison, M. Raphael, J. Haufler, and B.Wall, editors. Predicting species occurrences: issues of scale and accuracy. Island Press, Covello, California, USA.
10. Debinski, D. M., and J. A. Pritchard. 2002. A field guide to butterflies of the Greater Yellowstone Ecosystem. Roberts Rinehart, Boulder, Colorado, USA.
11. Debinski, D. M., C. Ray, and E. H. Saveraid. 2001. Species diversity and the scale of the landscape mosaic: do scales of movement and patch size affect diversity? Biological Conservation 98:179-190.
12. Dennis, R. L. 1993. Butterflies and climate change. Manchester University Press, New York, New York, USA.
13. Dixon, P. 2003. VEGAN, a package of R functions for community ecology. Journal of Vegetation Science 14:927-930.
14. Du, Y., P. M. Teillet, and J. Cihlar. 2002. Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection. Remote Sensing of Environment 82:123-134.
15. Faith, D. P., P. R. Minchin, and L. Belbin. 1987. Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69:57-68.
16. Fitter, A. H., and R. S. R. Fitter. 2002. Rapid changes in flowering time in British plants. Science 296:1689-1691.
17. Fleishman, E., G. T. Austin, and A. D. Weiss. 1998. An empirical test of Rappaport's rule: elevational gradients in montane butterfly communities. Ecology 79:2482-2493.
18. Fleishman, E., J. P. Fay, and D. D. Murphy. 2000. Upsides and downsides: contrasting topographic gradients in species richness and associated scenarios for climate change. Journal of Biogeography 27:1209-1219.
19. Fleishman, E., R. Mac Nally, and J. P. Fay. 2003. Validation tests of predictive models of butterfly occurrence based on environmental variables. Conservation Biology 17:806-817.
20. Fleishman, E., R. Mac Nally, J. P. Fay, and D. D. Murphy. 2001. Modeling and predicting species occurrence using broad-scale environmental variables: an example with butterflies of the Great Basin. Conservation Biology 15:1674-1685.
21. Furby, S. L., and N. A. Campbell. 2001. Calibrating images from different dates to 'like-value' digital counts. Remote Sensing of Environment 77:186-196.
22. Gallo, K. P., and J. Eidenshink. 1988. Differences in visible and near-IR responses and derived vegetation indices for the NOAA-9 and NOAA-10 AVHRRs: a case study. Photogrammetric Engineering and Remote Sensing 54:485-490.
23. Graetz, R. D. 1990. Remote sensing of terrestrial ecosystem structure: an ecologist's pragmatic view. Pages 5-30 in R. J. Hobbs and H. A. Mooney, editors. Remote sensing of biosphere functioning. Ecological Studies 79. Springer-Verlag, New York, New York, USA.
24. Hepinstall, J. A., W. B. Krohn, and S. A. Sader. 2002. Predicting meadow communities and species occurrences in the Greater Yellowstone Ecosystem. Pages 499-506 in J. M. Scott, P. J. Heglund, M. Morrison, M. Raphael, J. Haufler, and B. Wall, editors. Predicting species occurrences: issues of scale and accuracy. Island Press, Covello, California, USA.
25. Jakubauskas, M. E., and D. M. Debinski. 1995. An integrated spectral and ecological approach to mapping forest and meadow communities of the Greater Yellowstone Ecosystem. Association of American Geographers Annual Meeting, Chicago, Illinois, USA.
26. Jakubauskas, M. E., K. Kindscher, and D. M. Debinski. 1998. Multitemporal characterization and mapping of montane sagebrush communities using Indian IRS LISS-II imagery. Geocarto International 13:65-74.
27. Jensen, J. R. 1983. Urban/suburban land use analysis. Pages 1571-1666 in J. R. Jensen, editor. Manual of remote sensing. Second edition. Volume 2. American Society of Photogrammetry, Falls Church, Virginia, USA.
28. Jensen, J. R. 2000. Remote sensing of the environment: an earth resource perspective. Prentice-Hall, Upper Saddle River, New Jersey, USA.
29. Kerr, J. T., T. R. E. Southwood, and J. Cihlar. 2001. Remotely sensed habitat diversity predicts butterfly species richness and community similarity in Canada. Proceedings of the National Academy of Science 98:11365-11370.
30. Kindscher, K., A. Frasier, M. E. Jakubauskas, and D. M. Debinski. 1998. Identifying wetland meadows in Grand Teton National Park using remote sensing and average wetland values. Wetlands Ecology and Management 5:265-273.
31. Knopf, F. L., and J. A. Sedgwick. 1992. An experimental study of nest-site selection by Yellow Warblers. Condor 94:734-742.
32. Kremen, C. 1992. Assessing the indicator properties of species assemblages for natural areas monitoring. Ecological Applications 2:203-217.
33. Kruskal, J. B. 1964. Nonmetric multidimensional scaling: a numerical method. Psychometrika 29:115-129.
34. Lancaster, J., D. Mouat, R. Kuehl, W. Whitford, and D. Rapport. 1996. Time series satellite data to identify vegetation response to ecosystem stress as an indicator of ecosystem health. Proceedings, Shrubland Ecosystem Dynamics in a Changing Environment. General Technical Report INT-GTR-338. U.S. Department of Agriculture, Forest Service, Intermountain Research Station, Ogden, Utah, USA.
35. Landres, P. B., J. Verner, and J. W. Thomas. 1988. Ecological uses of vertebrate indicator species: a critique. Conservation Biology 2:316-329.
36. Lighthill, J., G. Holland, W. Gray, C. Landsea, G. Craig, J. Evans, Y. Kurihara, and C. Guard. 1994. Global climate-change and tropical cyclones. Bulletin of the American Meteorological Society 75:2147-2157.
37. McLaughlin, J. F., J. J. Hellman, C. L. Boggs, and P. R. Ehrlich. 2002. Climate change hastens population extinctions. Proceedings of the National Academy of Sciences USA 99:6070-6074.
38. Morrison, M. L. 1986. Bird populations as indicators of environmental change. Current Ornithology 3:429-451.
39. Nearing, M. A. 2001. Potential changes of rainfall erosivity in the U.S. with climate change during the 21st century. Journal of Soil and Water Conservation 56:229-232.
40. Nearing, M. A., F. F. Pruski, and M. R. O'Neal. 2004. Expected climate change impacts on soil erosion rates: a review. Journal of Soil and Water Conservation 59:43-48.
41. Økland, R. H. 1996. Are ordination and constrained ordination alternative or complementary strategies in general ecological studies? Journal of Vegetation Science 7:289-292.
42. O'Neill, R. V., C. T. Hunsaker, K. B. Jones, K. H. Riitters, J. D. Wickham, P. Schwartz, I. A. Goodman, B. Jackson, and W. S. Baillargeon. 1997. Monitoring environmental quality at the landscape scale. BioScience 47:513-520.
43. Parmesan, C., et al. 1999. Poleward shifts in geographical ranges of butterfly species associated with regional warming. Nature 399:579-583.
44. Parmesan, C., and G. Yohe. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37-42.
45. Pavlidis, Y. A., and F. A. Shcherbakov. 2002. Possible ways of the development of coasts and shelves in the Russian inland seas under the condition of climate warming and sea level rise. Oceanology 42:868-876.
46. Pollard, E., and T. J. Yates. 1995. Monitoring butterflies for ecology and conservation. Chapman and Hall, London, UK.
47. Pullin, A. S. 1995. Ecology and conservation of butterflies. Chapman and Hall, London, UK.
48. R Development Core Team. 2004. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
49. Roller, N. E., and J. E. Colwell. 1986. Coarse-resolution satellite data for ecological surveys. BioScience 36:468-475.
50. Romme, W. H., and M. G. Turner. 1991. Implications of global climate change for biogeographic patterns in the Greater Yellowstone Ecosystem. Conservation Biology 5:373-386.
51. Root, T. L., J. T. Price, K. R. Hall, S. H. Schneider, C. Rosenzweig, and J. A. Pounds. 2003. Fingerprints of global warming on wild animals and plants. Nature 421:57-60.
52. Rotenberry, J. T. 1985. The role of habitat in avian community composition: physiognomy or floristics? Oecologia 67:213-217.
53. Salt, G. W. 1957. An analysis of avifaunas in the Teton mountains and Jackson Hole, Wyoming. Condor 59:373-393.
54. SAS Institute. 1999. SAS/STAT user's guide. Version 8.2. SAS Institute, Cary, North Carolina, USA.
55. Saveraid, E. H., D. M. Debinski, K. Kindscher, and M. E. Jakubauskas. 2001. A comparison of satellite data and landscape variables in predicting bird species occurrences in the Greater Yellowstone Ecosystem. Landscape Ecology 16:71-83.
56. Schultz, P. A., and M. S. Halpert. 1993. Global correlation of temperature, NDVI and precipitation. Advances in Space Research 13:277-280.
57. Steele, B. B., R. L. Bayn, Jr., and C. V. Grant. 1984. Environmental monitoring using populations of birds and small mammals: analysis of sampling effort. Biological Conservation 30:157-172.
58. Taper, M. L., K. B. Gaese, and J. H. Brown. 1995. Individualistic responses of bird species to environmental change. Oecologia 101:478-486.
59. Teng, W. L. 1990. AVHRR monitoring of U.S. crops during the 1988 drought. Photogrammetric Engineering and Remote Sensing 56:1143-1146.
60. Ter Braak, C. J. F. 1986. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67:1167-1179.
61. Thomas, C. D., et al. 2004. Extinction risk from climate change. Nature 427:145-148.
62. Ustin, S. L., M. O. Smith, and J. Adams. 1993. Remote sensing of ecological processes: a strategy for developing and testing ecological models using spectral mixture analysis. Pages 339-357 in J. R. Ehleringer and C. B. Field, editors. Scaling ecological processes: leaf to globe. Academic Press, San Diego, California, USA.
63. Warren, M. 1999. Poleward shift of butterfly species' ranges associated with regional warming. Nature 399:579-583.
64. Wiens, J. A., and J. T. Rotenberry. 1981. Habitat associations and community structure of birds in shrubsteppe environments. Ecological Monographs 51:21-41.
65. Wiens, J. A., J. T. Rotenberry, and B. Van Horne. 1987. Habitat occupancy patterns of North American shrubsteppe birds: the effects of spatial scale. Oikos 48:132-147.
66. Yang, L., B. K. Wylie, L. L. Tieszen, and B. C. Reed. 1998. An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. Northern and Central Great Plains. Remote Sensing of Environment 65:25-37.
67. Yang, W., L. Yang, and J. W. Merchant. 1997. An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, U.S.A. International Journal of Remote Sensing 18:2161-2180.
68. Yuan, D., and C. D. Elvidge. 1996. Comparison of relative radiometric normalization techniques. Journal of Photogrammetry and Remote Sensing 51:117-126.