Landscape Ecology

, Volume 24, Issue 3, pp 405–418 | Cite as

Conservation of northern bobwhite on private lands in Georgia, USA under uncertainty about landscape-level habitat effects

  • Jay E. Howell
  • Clinton T. Moore
  • Michael J. Conroy
  • Richard G. Hamrick
  • Robert J. Cooper
  • Reggie E. Thackston
  • John P. Carroll
Research Article


Large-scale habitat enhancement programs for birds are becoming more widespread, however, most lack monitoring to resolve uncertainties and enhance program impact over time. Georgia’s Bobwhite Quail Initiative (BQI) is a competitive, proposal-based system that provides incentives to landowners to establish habitat for northern bobwhites (Colinus virginianus). Using data from monitoring conducted in the program’s first years (1999–2001), we developed alternative hierarchical models to predict bobwhite abundance in response to program habitat modifications on local and regional scales. Effects of habitat and habitat management on bobwhite population response varied among geographical scales, but high measurement variability rendered the specific nature of these scaled effects equivocal. Under some models, BQI had positive impact at both local farm scales (1, 9 km2), particularly when practice acres were clustered, whereas other credible models indicated that bird response did not depend on spatial arrangement of practices. Thus, uncertainty about landscape-level effects of management presents a challenge to program managers who must decide which proposals to accept. We demonstrate that optimal selection decisions can be made despite this uncertainty and that uncertainty can be reduced over time, with consequent improvement in management efficacy. However, such an adaptive approach to BQI program implementation would require the reestablishment of monitoring of bobwhite abundance, an effort for which funding was discontinued in 2002. For landscape-level conservation programs generally, our approach demonstrates the value in assessing multiple scales of impact of habitat modification programs, and it reveals the utility of addressing management uncertainty through multiple decision models and system monitoring.


Adaptive management Colinus virginianus Habitat Hierarchical models Monitoring Northern bobwhite Uncertainty 



Data used in this research were collected with funding from the WRD through the BQI; additional support was provided by the University of Georgia Warnell School of Forestry and Natural Resources and McIntire-Stennis Projects GEO-0100-MS and GEO-0136-MS. Funding for spatial analysis and model development was provided by US Geological Survey through the Eastern Region State Partnership Program. The WRD provided logistical support for workshops and data assembly, and we specifically wish to acknowledge the assistance of C. D. Baumann of WRD. We thank J. R. Sauer, S. J. Converse, and three anonymous reviewers for helpful criticism of the manuscript. The Georgia Cooperative Fish and Wildlife Research Unit is jointly sponsored by USGS, the University of Georgia, US Fish and Wildlife Service, Georgia Department of Natural Resources, and the Wildlife Management Institute. Use of trade or product names does not imply endorsement by the US Government.


  1. Best LB, Freemark KE, Dinsmore JJ (1995) A review and synthesis of habitat use by breeding birds in agricultural landscapes of Iowa. Am Midl Nat 134:1–29CrossRefGoogle Scholar
  2. Brooks SP, Gelman A (1998) General methods for monitoring convergence of iterative simulations. J Comput Graph Stat 7:434–455CrossRefGoogle Scholar
  3. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New YorkGoogle Scholar
  4. Congdon P (2001) Bayesian statistical modelling. Wiley, New YorkGoogle Scholar
  5. Conroy MJ, Allen CR, Peterson JT, Pritchard LJ, Moore CT (2003) Landscape change in the southern Piedmont: challenges, solutions, and uncertainty across scales. Conserv Ecol 8(2):3. Available at Accessed Dec 2008Google Scholar
  6. Conroy MJ, Runge JP, Barker RJ, Schofield MR, Fonnesbeck CJ (2008) Efficient estimation of abundance for patchily distributed populations via 2-phase, adaptive sampling. Ecology 89:3362–3370PubMedCrossRefGoogle Scholar
  7. Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analyis, 2nd edn. Chapman and Hall, Boca RatonGoogle Scholar
  8. Hamrick RG (2002) Evaluation of northern bobwhite (Colinus virginianus) population monitoring methods and population trends in agricultural systems in the Upper Coastal Plain of Georgia. M.S. Thesis, University of GeorgiaGoogle Scholar
  9. Harper MJ, McCarthy MA, van der Ree R (2008) Resources at the landscape scale influence possum abundance. Austral Ecol 33:243–252CrossRefGoogle Scholar
  10. Howell JE, Peterson JT, Conroy MJ (2008) Building hierarchical models of avian distributions for the state of Georgia. J Wildl Manag 72:168–178CrossRefGoogle Scholar
  11. Hunter WC, Pashley DN, Escano REF (1992) Neotropical migratory landbird species and their habitats of special concern within the Southeast Region. In: Finch DM, Stangel PW (eds) Status and management of neotropical migratory birds. USDA Forest Service General Technical Report RM–229, pp 159–171Google Scholar
  12. Johnson FA, Moore CT, Kendall WL, Dubovsky JA, Caithamer DF, Kelley JR Jr, Williams BK (1997) Uncertainty and the management of mallard harvests. J Wildl Manag 61:202–216CrossRefGoogle Scholar
  13. Johnson FA, Williams BK (1999) Protocol and practice in the adaptive management of waterfowl harvests. Conserv Ecol 3(1):8. Available at Accessed Dec 2008Google Scholar
  14. Kéry M (2008) Estimating abundance from bird counts: binomial mixture models uncover complex covariate relationships. Auk 125:336–345CrossRefGoogle Scholar
  15. Kéry M, Royle JA (2008) Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys. J Appl Ecol 45:589–598CrossRefGoogle Scholar
  16. Link WA, Cam E, Nichols JD, Cooch EG (2002) Of BUGS and birds: Markov chain Monte Carlo for hierarchical modeling in wildlife research. J Wildl Manag 66:277–291CrossRefGoogle Scholar
  17. Lunn D (2003) WinBUGS development interface (WBDev). ISBA Bull 10(3). Accessed Dec 2008
  18. Python Software Foundation (2008). Python Programming Language—Official Website. Accessed Dec 2008
  19. Rich TD, Beardmore CJ, Berlanga H, Blancher PJ, Bradstreet MSW, Butcher GS, Demarest DW, Dunn EH, Hunter WC, Iñigo-Elias EE, Kennedy JA, Martell AM, Panjabi AO, Pashley DN, Rosenberg KV, Rustay CM, Wendt JS, Will TC (2004) Partners in Flight North American Landbird Conservation Plan. Cornell Laboratory of Ornithology, Ithaca, NY. Available at Accessed Dec 2008
  20. Rivot E, Prevost E, Cuzol A, Bagliniere JL, Parent E (2008) Hierarchical Bayesian modelling with habitat and time covariates for estimating riverine fish population size by successive removal method. Can J Fish Aquat Sci 65:117–133CrossRefGoogle Scholar
  21. Rodenhouse NL, Best LB, O’Connor RJ, Bollinger EK (1995) Effects of agricultural practices and farmland structures. In: Martin TE, Finch DM (eds) Ecology and management of Neotropical migratory birds: a synthesis and review of critical issues. Oxford University Press, New York, pp 269–293Google Scholar
  22. Rosene W Jr (1969) The bobwhite quail: its life and management. Rutgers University Press, New BrunswickGoogle Scholar
  23. Royle JA (2008) Modeling individual effects in the Cormack-Jolly-Seber model: a state-space formulation. Biometrics 64:364–370PubMedCrossRefGoogle Scholar
  24. Royle JA, Dorazio RM (2008) Hierarchical modeling and inference in ecology: the analysis of data from populations, metapopulations and communities. Academic, New YorkGoogle Scholar
  25. Royle JA, Young KV (2008) A hierarchical model for spatial capture–recapture data. Ecology 89:2281–2289PubMedCrossRefGoogle Scholar
  26. Sauer JR, Hines JE, Fallon J (2001) The North American Breeding Bird Survey, results and analysis 1966–2000. Version 2001.2, USGS Patuxent Wildlife Research Center, Laurel, MD. Accessed Dec 2008
  27. Snijders T, Bosker R (1999) Multilevel analysis: an introduction to basic and advanced multilevel modeling. Sage, Thousand OaksGoogle Scholar
  28. Spiegelhalter DJ, Best NG, Carlin BR, van der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B-Stat Methodol 64:583–616CrossRefGoogle Scholar
  29. Spiegelhalter D, Thomas A, Best N, Lunn D (2003) WinBUGS user manual. Version 1.4. Accessed Dec 2008
  30. Stoddard HR (1931) The bobwhite quail: its habits, preservation, and increase. Charles Scribner’s Sons, New YorkGoogle Scholar
  31. Thackston RE, Baumann CD, Bond BT, Whitney MD (2008) Summary of Georgia’s Bobwhite Quail Initiative 2000–2005. In: Proceedings of Gamebird 2006, Joint Conference of Quail VI and Perdix XII, Athens, 30 May–4 June 2006 (in press)Google Scholar
  32. Trapp JL (1995) Migratory nongame birds of management concern in the United States: the 1995 list. US Department of Interior, Fish and Wildlife Service, Office of Migratory Bird Management, Washington, D.C. Available at Accessed Dec 2008
  33. USDA (1995) Farms and land in farms: final estimates 1988–92. Agricultural Statistics Board, National Agricultural Statistics Service, US Department of Agriculture. Statistical Bulletin No. 895. Accessed Dec 2008
  34. Vesterby M, Krupa KS (2001) Major uses of land in the United States, 1997. Resource Economics Division, Economic Research Service, US Department of Agriculture. Statistical Bulletin No. 973. Accessed Dec 2008
  35. Walters CJ (1986) Adaptive management of renewable resources. Macmillan, New YorkGoogle Scholar
  36. Walters CJ, Hilborn R (1978) Ecological optimization and adaptive management. Annu Rev Ecol Syst 9:157–188CrossRefGoogle Scholar
  37. Wibster RA, Pollock KH, Ghosh SK, Hankin DG (2008) Bayesian spatial modeling of data from unit-count surveys of fish in streams. Trans Am Fish Soc 137:438–453CrossRefGoogle Scholar
  38. Williams BK (1996) Adaptive optimization and the harvest of biological populations. Math Biosci 136:1–20PubMedCrossRefGoogle Scholar
  39. Wong GY, Mason WM (1985) The hierarchical logistic-regression model for multilevel analysis. J Am Stat Assoc 80:513–524CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Jay E. Howell
    • 1
    • 2
  • Clinton T. Moore
    • 3
  • Michael J. Conroy
    • 4
  • Richard G. Hamrick
    • 5
    • 6
  • Robert J. Cooper
    • 7
  • Reggie E. Thackston
    • 8
  • John P. Carroll
    • 7
  1. 1.Georgia Cooperative Fish and Wildlife Research UnitWarnell School of Forestry and Natural Resources, University of GeorgiaAthensUSA
  2. 2.Virginia Department of Game and Inland FisheriesRichmondUSA
  3. 3.USGS Patuxent Wildlife Research CenterUS Geological Survey, Warnell School of Forestry and Natural Resources, University of GeorgiaAthensUSA
  4. 4.USGS Georgia Cooperative Fish and Wildlife Research UnitWarnell School of Forestry and Natural Resources, US Geological Survey and University of GeorgiaAthensUSA
  5. 5.Warnell School of Forestry and Natural ResourcesUniversity of GeorgiaAthensUSA
  6. 6.Mississippi Department of Wildlife, Fisheries, and ParksJacksonUSA
  7. 7.Warnell School of Forestry and Natural ResourcesUniversity of GeorgiaAthensUSA
  8. 8.Wildlife Resources DivisionGeorgia Department of Natural ResourcesForsythUSA

Personalised recommendations