Wetlands

, Volume 32, Issue 4, pp 725–736 | Cite as

Comparing Habitat Models Using Ground-Based and Remote Sensing Data: Saltmarsh Sparrow Presence Versus Nesting

  • Susan Meiman
  • Daniel Civco
  • Kent Holsinger
  • Chris S. Elphick
Article

Abstract

Remote sensing data can represent various habitat characteristics, and thus can substitute for detailed ground sampling when constructing habitat models. To predict saltmarsh sparrow (Ammodramus caudacutus) distribution and nesting activity, we compared Bayesian hierarchical models in which variables were generated from field or remote sensing data, at a scale of 1-ha plots and at the landscape scale. Field data consisted of plant structure and plant composition variables. Data derived from remote sensing included high and low marsh classifications, LiDAR elevation data, and a classification derived from spectral characteristics specifically associated with saltmarsh sparrow habitat use. The best sparrow presence model used a variable derived from spectral reflectance values associated with plots where sparrows did not occur, indicating that the remote sensing data included additional information about conditions associated with saltmarsh sparrow occurrence than was provided by plant composition, structure, or community classes. In contrast, nest presence was modeled best using vegetation structure variables that required data collection on the ground, although the best remote-sensing model was almost as good. These results reinforce the value of remote-sensing data in habitat modeling, and highlight the need to distinguish between sites that contribute to reproduction and sites where a species is merely present.

Keywords

Ammodramus caudacutus Bayesian hierarchical models Probability of presence Remote sensing Saltmarsh sparrow 

Supplementary material

13157_2012_306_MOESM1_ESM.pdf (142 kb)
ESM 1(PDF 141 kb)

References

  1. Andrén H (1994) Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: A review. Oikos 71:355–366CrossRefGoogle Scholar
  2. Bahn V, Krohn WB, O’Connor RJ (2008) Dispersal leads to spatial autocorrelation in species distributions: A simulation model. Ecol Model 213:285–292CrossRefGoogle Scholar
  3. Bayard TS, Elphick CS (2011) Planning for sea level rise: Quantifying patterns of Saltmarsh Sparrow nest flooding under current sea level conditions. Auk 128:393–403CrossRefGoogle Scholar
  4. Benoit LK, Askins RA (2002) Relationship between habitat area and the distribution of tidal marsh birds. Wilson Bulletin 114:314–323CrossRefGoogle Scholar
  5. Bertness MD, Ellison AM (1987) Determinants of pattern in a New England salt marsh plant community. Ecol Monogr 57:129–147CrossRefGoogle Scholar
  6. Bertness M, Ewanchuk PJ, Silliman BR (2002) Anthropogenic modification of New England salt marsh landscapes. Proc Natl Acad Sci 99:1395–1398PubMedCrossRefGoogle Scholar
  7. Chambers RM, Meyerson LA, Saltonstall K (1999) Expansion of Phragmites australis into tidal wetlands of North America. Aquat Bot 64:261–273CrossRefGoogle Scholar
  8. Diez JM, Pulliam HR (2007) Hierarchical analysis of species distributions and abundance across environmental gradients. Ecology 88:3144–3152PubMedCrossRefGoogle Scholar
  9. DiQuinzio DA, Paton PWC, Eddleman WR (2002) Nesting ecology of saltmarsh sharp-tailed sparrows in a tidally restricted salt marsh. Wetlands 22:179–185CrossRefGoogle Scholar
  10. Elphick CS, Bayard T, Meiman S, Hill JM, Rubega MA (2009) A comprehensive assessment of the distribution of saltmarsh sharp-tailed sparrows in Connecticut. Final report to the Long Island Sound License Plate Program, Connecticut Department of Environmental Protection. University of Connecticut, StorrsGoogle Scholar
  11. Fahrig L (1997) Relative effects of habitat loss and fragmentation on population extinction. J Wildl Manag 61:603–610CrossRefGoogle Scholar
  12. Garrison BA, Lupo T (2002) Accuracy of bird range maps based on habitat maps and habitat relationship models. In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrence: Issues of accuracy and scale. Island Press, Washington, DC, pp 367–375Google Scholar
  13. Gedan KB, Silliman BR, Bertness MD (2009) Centuries of human-driven change in salt marsh ecosystems. Annual Review of Marine Science 1:117–141PubMedCrossRefGoogle Scholar
  14. Gilmore MS, Wilson EH, Barrett N, Civco DL, Prisloe S, Hurd JD, Chadwick C (2008) Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh. Remote Sens Environ 112:4048–4060CrossRefGoogle Scholar
  15. Gjerdrum C, Elphick CS, Rubega M (2005) Nest site selection and nesting success in saltmarsh breeding sparrows: the importance of nest habitat, timing, and study site differences. Condor 107:849–862CrossRefGoogle Scholar
  16. Gjerdrum C, Sullivan-Wiley K, King E, Rubega MA, Elphick CS (2008a) Egg and chick fates during tidal flooding of saltmarsh sharp–tailed sparrow nests. Condor 110:579–584CrossRefGoogle Scholar
  17. Gjerdrum C, Elphick CS, Rubega MA (2008b) How well can we model saltmarsh sharp–tailed sparrow numbers and productivity using habitat features? Auk 125:608–617CrossRefGoogle Scholar
  18. Gottschalk TK, Huettmann F, Ehlers M (2005) Thirty years of analysing and modelling avian habitat relationships using satellite imagery data: a review. Int J Remote Sens 26:2631–2656CrossRefGoogle Scholar
  19. Gottschalk TK, Ekschmitt K, Bairlein F (2007) A GIS-based model of Serengeti grassland bird species. Ostrich 78:259–263CrossRefGoogle Scholar
  20. Greenlaw JS, Rising JD (1994) Sharp-tailed sparrow (Ammodramus caudacutus) No 112. In: Poole A, Gill F (eds) Birds of North America. The Academy of Natural Sciences, Philadelphia, and The American Ornithologist’s Union, Washington, DCGoogle Scholar
  21. Greenberg R, Elphick C, Nordby J, Gjerdrum C, Spautz H, Shriver WG, Schmeling B, Olsen B, Marra P, Nur N, Winter M (2006) Flooding and predation: Trade-offs in the nesting ecology of tidal-marsh sparrows. Studies in Avian Biology 32:96–109Google Scholar
  22. Hill CE, Gjerdrum C, Elphick CS (2010) Extreme levels of multiple mating characterize the mating system of the saltmarsh sparrow (Ammodramus caudacutus). Auk 127:300–307CrossRefGoogle Scholar
  23. Hill JM (2008) Postfledging movement behavior and habitat use of adult female saltmarsh sharp-tailed sparrows. M. S. Thesis. University of Connecticut, Storrs, ConnecticutGoogle Scholar
  24. Hoover MD (2009) Connecticut’s changing salt marshes: A remote sensing approach to sea level rise and possible salt marsh migration. M. S. Thesis. University of Connecticut, Storrs, ConnecticutGoogle Scholar
  25. Humphreys S, Elphick CS, Gjerdrum C, Rubega MA (2007) Testing the function of nest domes in Saltmarsh Sharp-tailed Sparrows. Journal of Field Ornithology 78:152–158CrossRefGoogle Scholar
  26. Hurd J (2009) 2006 Land Cover, Greater Connecticut Connecticut’s Changing Landscape Land Cover (version 2.02) Center for Land use Education And Research (CLEAR) Storrs, Connecticut, USA http://clear.uconn.edu/projects/landscape/download.asp. Accessed 06 March 2009
  27. IUCN (2009) IUCN Red List of Threatened Species.Version 2009.2. www.iucnredlist,org. Accessed 03 November 2009
  28. Jenkins CN, Powell RD, Bass OL Jr, Pimm SL (2003) Demonstrating the destruction of the habitat of the Cape Sable seaside sparrow (Ammodramus maritimus mirabilis). Anim Conserv 6:29–38CrossRefGoogle Scholar
  29. Johnson MD (2007) Measuring habitat quality: A review. Condor 109:489–504CrossRefGoogle Scholar
  30. Kearney MS, Stutzer D, Turpie K, Stevenson JC (2009) The effects of tidal inundation on the reflectance characteristics of coastal marsh vegetation. J Coast Res 25:1177–1186CrossRefGoogle Scholar
  31. Mack EL, Firbank LG, Bellamy PE, Hinsley SA, Veitch N (1997) The comparison of remotely sensed and ground–based habitat area data using species-area models. J Appl Ecol 34:1222–1228CrossRefGoogle Scholar
  32. McIntire EJB, Fajardo A (2009) Beyond description: the active and effective way to infer processes from spatial patterns. Ecology 90:46–56PubMedCrossRefGoogle Scholar
  33. Morris JT, Porter D, Neet M, Noble PA, Schmidt L, Lapine LA, Jensen JR (2005) Integrating LIDAR elevation data, multi-spectral imagery and neural network modelling for marsh characterization. Int J Remote Sens 26:5221–5234CrossRefGoogle Scholar
  34. Naidoo G, McKee KL, Mendelssohn IA (1992) Anatomic and metabolic responses to waterlogging in Spartina alterniflora and S patens (Poaceae). Am J Bot 79:765–770CrossRefGoogle Scholar
  35. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center and the United States Geological Survey (USGS) (2004) Coastal Connecticut 2004 color infrared orthophoto. State of Connecticut, Department of Environmental Protection. http://cteco.uconn.edu/guides/ortho_2004_coast_infrared.htm
  36. National Oceanic and Atmospheric Administration (NOAA), National Ocean Service, Office of Response and Restoration, Hazardous Materials Response Division, and the State of Connecticut, Department of Environmental Protection (2004) Connecticut Coastal 2002 Environmental Sensitivity Index Mapping (Polygons). State of Connecticut, Department of Environmental Protection. http://www.cteco.uconn.edu/metadata/dep/document/ESI_2002_POLY_FGDC_Plus.htm. Accessed 29 March 2009
  37. Niering WA, Warren RS (1980) Vegetation patterns and processes in New England salt marshes. BioScience 30:301–307CrossRefGoogle Scholar
  38. Pulliam HR (2002) On the relationship between niche and distribution. Ecol Lett 3:349–361CrossRefGoogle Scholar
  39. Rhodes JR, Wiegand T, McAlpine CA, Callaghan J, Lunney D, Bowen M, Possingham HP (2006) Modeling species’ distributions to improve conservation in semiurban landscapes: Koala case study. Conserv Biol 20:449–459PubMedCrossRefGoogle Scholar
  40. Roman CT, Niering WA, Warren RS (1984) Salt marsh vegetation change in response to tidal restriction. Environ Manag 8:141–150CrossRefGoogle Scholar
  41. Rotenberry JT (1981) Why measure bird habitat? In: Capen DE (ed) The use of multivariate statistics in studies of wildlife habitat. General Technical Report RM-87. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, pp 29–32Google Scholar
  42. Rozsa R (1995) Human impacts on tidal wetlands: History and regulations. In: Dreyer GD, Niering WA (eds) Tidal marshes of Long Island Sound: Ecology, history and restoration. Connecticut College, New London, pp 42–50Google Scholar
  43. Scott JM, Davis F, Csuti B, Noss R, Butterfield B, Groves C, Anderson H, Caicco S, D’Erchia F, Edwards TC Jr, Ulliman J, Wright RG (1993) Gap analysis: A geographic approach to protection of biological diversity. Wildl Monogr 123:1–41Google Scholar
  44. Seoane J, Bustamante J, Diaz-Delgado R (2004) Are existing vegetation maps adequate to predict bird distributions? Ecol Model 175:137–149CrossRefGoogle Scholar
  45. Shriver WG, Hodgman TP, Gibbs JP, Vickery PD (2004) Landscape context influences salt marsh bird diversity and area requirements in New England. Biol Conserv 119:545–553CrossRefGoogle Scholar
  46. Spiegelhalter D, Thomas A, Best N (2000) WinBUGS user manual. MRC Biostatistics Unit, CambridgeGoogle Scholar
  47. Teal JM, Howes BL (1996) Interannual variability of a salt-marsh ecosystem. Limnol Oceanogr 41:802–809CrossRefGoogle Scholar
  48. Tiner RW Jr (1987) A field guide to coastal wetland plants of the northeastern United States. University of Massachusetts Press, AmherstGoogle Scholar
  49. Tuxen K, Schile L, Stralberg D, Siegel S, Parker T, Vasey M, Callaway J, Kelly M (2011) Mapping changes in tidal wetland vegetation composition and pattern across a salinity gradient using high spatial resolution imagery. Wetl Ecol Manag 19:141–157CrossRefGoogle Scholar
  50. Underwood AJ, Chapman MG, Crowe TP (2004) Identifying and understanding ecological preferences for habitat or prey. J Exp Mar Biol Ecol 300:161–187CrossRefGoogle Scholar
  51. Van Horne B (1983) Density as a misleading indicator of habitat quality. J Wildl Manag 47:893–901CrossRefGoogle Scholar
  52. Van Horne B (2002) Approaches to habitat modeling. In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrence: Issues of accuracy and scale. Island Press, Washington, DC, pp 63–72Google Scholar
  53. Vickery PD, Hunter ML Jr, Wells JV (1992) Is density an indicator of breeding success? Auk 109:706–710Google Scholar
  54. Warren RS, Niering WA (1993) Vegetation change on a northeast tidal marsh: interaction of sea-level rise and marsh accretion. Ecology 74:96–103CrossRefGoogle Scholar
  55. Warren RS, Fell PE, Grimsby JL, Buck EL, Rilling GC, Fertik RA (2001) Rates, patterns, and impacts of Phragmites australis expansion and effects of experimental Phragmites control on vegetation, macroinvertebrates, and fish within tidelands of the lower Connecticut River. Estuaries 24:90–107CrossRefGoogle Scholar
  56. Warren RS, Fell PE, Rozsa R, Brawley AH, Orsted AC, Olson ET, Swamy V, Niering WA (2002) Salt marsh restoration in Connecticut: 20 years of science and management. Restor Ecol 10:497–513CrossRefGoogle Scholar
  57. Wiegand K, Schmidt H, Jeltsch F, Ward D (2000) Linking a spatially-explicit model of acacias to GIS and remotely-sensed data. Folia Geobotanica 35:211–230CrossRefGoogle Scholar
  58. Wiegand T, Jeltsch F, Hanski I, Grimm V (2003) Using pattern-oriented modelling for revealing hidden information: a key for reconciling ecological theory and application. Oikos 100:209–222CrossRefGoogle Scholar
  59. Wigand C, McKinney RA, Charpentier MA, Chintala MM, Thursby GB (2003) Relationships of nitrogen loadings, residential development, and physical characteristics with plant structure in New England salt marshes. Estuaries 26:1494–1504CrossRefGoogle Scholar

Copyright information

© Society of Wetland Scientists 2012

Authors and Affiliations

  • Susan Meiman
    • 1
  • Daniel Civco
    • 2
  • Kent Holsinger
    • 1
  • Chris S. Elphick
    • 1
    • 3
  1. 1.Department of Ecology and Evolutionary BiologyUniversity of ConnecticutStorrsUSA
  2. 2.Department of Natural Resources and the EnvironmentUniversity of ConnecticutStorrsUSA
  3. 3.Center for Conservation and BiodiversityUniversity of ConnecticutStorrsUSA

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