Estimating migratory game-bird productivity by integrating age ratio and banding data
G. S. Zimmerman A E , W. A. Link B , M. J. Conroy C , J. R. Sauer B , K. D. Richkus D and G. Scott Boomer AA US Fish and Wildlife Service, Division of Migratory Bird Management, 11510 American Holly Drive, Laurel, MD 20708, USA.
B US Geological Survey, 12100 Beech Forest Road, Laurel, MD 20708, USA.
C Warnell School of Forestry and Natural Resources, University of Georgia, 3-427 Forestry Building, Athens, GA 30602, USA.
D US Fish and Wildlife Service, Division of Migratory Bird Management, 10815 Loblolly Pine Drive, Laurel, MD 20708, USA.
E Corresponding author. Email: Guthrie_Zimmerman@fws.gov
Wildlife Research 37(7) 612-622 https://doi.org/10.1071/WR10062
Submitted: 2 April 2010 Accepted: 3 November 2010 Published: 17 December 2010
Abstract
Context: Reproduction is a critical component of fitness, and understanding factors that influence temporal and spatial dynamics in reproductive output is important for effective management and conservation. Although several indices of reproductive output for wide-ranging species, such as migratory birds, exist, there has been no theoretical justification for their estimators or associated measures of variance.
Aims: The aims of our research were to develop statistical justification for an estimator of reproduction and associated variances on the basis of an existing national wing-collection survey and banding data, and to demonstrate the applicability of this estimator to a migratory game bird.
Methods: We used a Bayesian hierarchical modelling approach to integrate wing-collection data, which provides information on population age ratios, and band-recovery data, which provides information on recovery probabilities of various age classes, for American woodcock (Scolopax minor) to estimate productivity and associated measures of variance. We present two models of relative vulnerability between age classes: one model assumed that adult recovery probabilities were higher, but that annual fluctuations were synchronous between the two age classes (i.e. an additive effect of age and year). The second model assumed that adults, on average, had higher recovery probabilities than did juveniles and that annual fluctuations were asynchronous through time (i.e. an interaction between age and year).
Key results: Fitting our models within a hierarchical Bayesian framework efficiently incorporates the two data types into a single estimator and derives appropriate variances for the productivity estimator. Further, use of Bayesian methods enabled us to derive credible intervals that avoid the reliance on asymptotic assumptions. When applied to American woodcock data, the additive model resulted in biologically realistic and more precise age-ratio estimates each year and is adequate when the relative vulnerability to sampling only slightly varies or does not vary among components of a population (e.g. age, sex class) among years. Therefore, we recommend using woodcock indices from our analysis based on this model.
Conclusions: We provide a flexible modelling framework for estimating productivity and associated variances that can incorporate ecological covariates to explore various factors that could drive annual dynamics in productivity. Applying our model to the American woodcock data indicated that assumptions about the variability in relative recovery probabilities could greatly influence the precision of our productivity estimator. Therefore, researchers should carefully consider the assumption of temporally variable relative recovery probabilities (i.e. ratio of juvenile to adults’ recovery probability) for different age classes when applying this estimator.
Implications: Several national and international management strategies for migratory game birds in North America rely on measures of productivity from harvest survey parts collections, without a justification of the estimator or providing estimates of precision. We derive an estimator of productivity with realistic measures of uncertainty that can be directly incorporated into management plans or ecological studies across large spatial scales.
References
Brownie, C., Anderson, D. R., Burnham, K. P., and Robson, D. S. (1985). ‘Statistical Inference from Band Recovery Data – A Handbook.’ 2nd edn. (US Fish and Wildlife Service: Washington, DC.)Cooch, E., and White, G. (Eds) (2009). ‘Program MARK. A Gentle Introduction.’ 8th edn. Available at http://www.phidot.org/software/mark/docs/book/ [accessed July 2010].
Cooper, T. R., Parker, K., and Rau, R. D. (2008). ‘American Woodcock Population Status, 2008.’ (US Fish and Wildlife Service: Laurel, MD.)
Cowardin, L. M., and Blohm, R. J. (1992). Breeding population inventories and measures of recruitment. In ‘Ecology and Management of Breeding Waterfowl’. (Eds B. D. J. Batt, A. D. Afton, M. G. Anderson, C. D. Ankney, D. H. Johnson, J. A. Kadlec and G. L. Krapu.) pp. 423–445. (University of Minnesota Press: Minneapolis, MN.)
Derleth, E. L., and Sepik, G. F. (1990). Summer–fall survival of American woodcock in Maine. The Journal of Wildlife Management 54, 97–106.
| Summer–fall survival of American woodcock in Maine.Crossref | GoogleScholarGoogle Scholar |
Gelman, A., and Hill, J. (2007). ‘Data Analysis using Regression and Multilevel/Hierarchical Models.’ (Cambridge University Press: Cambridge, UK.)
Hagen, C. A., and Loughin, T. M. (2008). Productivity estimates from upland bird harvests: estimating variance and necessary sample sizes. The Journal of Wildlife Management 72, 1369–1375.
| Productivity estimates from upland bird harvests: estimating variance and necessary sample sizes.Crossref | GoogleScholarGoogle Scholar |
Hone, J., and Sibly, R. M. (2003). Demographic, mechanistic and density-dependent determinants of a population growth rate: a case study in an avian predator. In ‘Wildlife Population Growth Rates’. (Eds R. M. Sibly, J. Hone and T. H. Clutton-Brock.) pp. 44–60. (Cambridge University Press: Cambridge, UK.)
Keppie, D. M., and Whiting, R. M., Jr (1994). American woodcock, Scolopax minor. In ‘The Birds of North America. No. 100’. (Eds A. Poole and F. Gill.) pp. 1–28. (The Academy of Natural Sciences: Philadelphia and The American Ornithologists’ Union: Washington, DC.)
Krementz, D. G., and Berdeen, J. B. (1997). Survival rates of American woodcock wintering in the Georgia piedmont. The Journal of Wildlife Management 61, 1328–1332.
| Survival rates of American woodcock wintering in the Georgia piedmont.Crossref | GoogleScholarGoogle Scholar |
Link, W. L., and Barker, R. J. (2010). ‘Bayesian Inference with Ecological Applications.’ (Academic Press: London.)
Longcore, J. R., McAuley, D. G., Sepik, G. F., and Pendleton, G. W. (1996). Survival of breeding male American woodcock in Maine. Canadian Journal of Zoology 74, 2046–2054.
| Survival of breeding male American woodcock in Maine.Crossref | GoogleScholarGoogle Scholar |
Lunn, D. J., Thomas, A., Best, N., and Spiegelhalter, D. (2000). WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 10, 325–337.
Martin, F. W. (1964). Woodcock age and sex determination from wings. The Journal of Wildlife Management 28, 287–293.
| Woodcock age and sex determination from wings.Crossref | GoogleScholarGoogle Scholar |
Martin, E. M., and Carney, S. M. (1977). ‘Population Ecology of the Mallard. IV. A Review of Duck Hunting Regulations, Activity, and Success, with Special Reference to the Mallard.’ Publication 130. (US Fish and Wildlife Service Resource: Washington, DC.)
Padding, P. I., Moore, M. T., Richkus, K. D., and Martin, E. M. (In press). Estimating woodcock hunter activity and harvest in the United States. In ‘Proceedings of the 10th Woodcock Symposium’. (Ed. A. Stewart.)(Michigan Department of Natural Resources and Environment: Lansing, MI.)
Reynolds, R. E. (1987). Breeding duck populations, production and habitat surveys, 1979–85. Transactions of the North American Wildlife and Natural Resources Conference 52, 186–205.
Royle, J. A., and Dorazio, R. M. (2008). ‘Hierarchical Modeling and Inference in Ecology. The Analysis of Data from Populations, Metapopulations and Communities.’ (Academic Press: London.)
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society. Series B. Methodological 64, 583–639.
| Bayesian measures of model complexity and fit (with discussion).Crossref | GoogleScholarGoogle Scholar |
USFWS (US Fish and Wildlife Service) (2006). Migratory bird hunting activity and harvest during the 1999 and 2000 hunting seasons – Final report. US Department of the Interior, Washington, DC.
Williams, B. K., Nichols, J. D., and Conroy, M. J. (2002). ‘Analysis and Management of Animal Populations.’ (Academic Press: London.)
Zimpfer, N. L., and Conroy, M. J. (2006). Models of production rates in American black duck populations. The Journal of Wildlife Management 70, 947–954.
| Models of production rates in American black duck populations.Crossref | GoogleScholarGoogle Scholar |
