Skip to main content

Integrated Population Modelling

  • Chapter
  • First Online:
Modelling Population Dynamics

Abstract

In Chap. 5, we recommended that formulation of population dynamics models should be guided by aims to answer specific scientific questions or assess or predict the effects of management actions. Management actions might target a specific life stage. For example, we might ask “How does removing wetland plants (such as bulrush or cattail) that have started to cover ponds and reduce the amount of open water in a waterfowl breeding area affect reproductive success?” The consequences of actions, however, typically ripple throughout the entire population life history and effective management requires more detailed ecological study. This in turn requires information about demographic processes and abundances for multiple life stages to characterize the population dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Baillie, S. R., & Green, R. E. (1987). The importance of variation in recovery rates when estimating survival rates from ringing recoveries. Acta Ornithologica, 23, 41–60.

    Google Scholar 

  • Barry, S. C., Brooks, S. P., Catchpole, E. A., & Morgan, B. J. T. (2003). The analysis of ring-recovery data using random effects. Biometrics, 59, 54–65.

    Article  MATH  MathSciNet  Google Scholar 

  • Besbeas, P., Borysiewicz, R. S., Morgan, B. J. T. (2009). Completing the ecological jigsaw. In D. Thomson, E. G. Cooch, & M. J. Conroy (Eds.), Modeling demographic processes in marked populations. Environmental and ecological statistics (Vol. 3, pp. 513–539). New York: Springer

    Google Scholar 

  • Besbeas, P., & Freeman, S. N. (2006). Methods for joint inference from panel survey and demographic data. Ecology, 87, 1138–1145.

    Article  Google Scholar 

  • Besbeas, P., Freeman, S. N., & Morgan, B. J. T. (2005). The potential of integrated population modelling. Australian and New Zealand Journal of Statistics, 47, 35–48.

    Article  MATH  MathSciNet  Google Scholar 

  • Besbeas, P., Freeman, S. N., Morgan, B. J. T., & Catchpole, E. A. (2002). Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics, 58, 540–547.

    Article  MATH  MathSciNet  Google Scholar 

  • Besbeas, P., Lebreton, J.-D., & Morgan, B. J. T. (2003). The efficient integration of abundance and demographic data. Applied Statistics, 52, 95–102.

    MATH  MathSciNet  Google Scholar 

  • Besbeas, P., & Morgan, B. J. T. (2012a). A threshold model for heron productivity. Journal of Agricultural, Biological, and Environmental Statistics, 17, 128–141.

    Article  MathSciNet  Google Scholar 

  • Besbeas, P., & Morgan, B. J. T. (2012b). Kalman filter initialization for integrated population modelling. Applied Statistics, 61, 151–162.

    MathSciNet  Google Scholar 

  • Brooks, S. P., King, R., Morgan, B. J. T. (2004). A Bayesian approach to combining animal abundance and demographic data. Animal Biodiversity and Conservation, 27, 515–529.

    Google Scholar 

  • Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (2001). Introduction to distance sampling: Estimating abundance of biological populations. Oxford: Oxford University Press.

    Google Scholar 

  • Buckland, S. T., Newman, K. B., Fernández, C., Thomas, L., & Harwood, J. (2007). Embedding population dynamics models in inference. Statistical Science, 22, 44–58.

    Article  MATH  MathSciNet  Google Scholar 

  • Buckland, S. T., Newman, K. B., Thomas, L., & Koesters, N. B. (2004). State-space models for the dynamics of wild animal populations. Ecological Modelling, 171, 157–175.

    Article  Google Scholar 

  • Caswell, H. (2001). Matrix population models: Construction, analysis, and interpretation (2nd ed.). Sunderland: Sinauer Associates.

    Google Scholar 

  • Catchpole, E. A., Morgan, B. J. T., Freeman, S. N., & Peach, W. J. (1999). Modelling the survival of British lapwings, Vanellus vanellus using ring-recovery data and weather covariates. Bird Study, 46(Suppl), S5–S13.

    Article  Google Scholar 

  • Coulson, T. N., Catchpole, E. A., Albon, S. D., Morgan, B. J. T., Pemberton, J. M., Clutton-Brock, T. H., Crawley, M. J., & Grenfell, B. T. (2001). Age, sex, density, winter weather and population crashes in Soay sheep. Science, 292, 1528–1531.

    Article  Google Scholar 

  • Eaton, M. A., Brown, A. F., Noble, D. G., Musgrove, A. J., Hearn, R., Aebischer, N. J., Gibbons, D. W., Evans, A., & Gregory, R. D. (2009). Birds of conservation concern 3: The population status of birds in the United Kingdom, Channel Islands and the Isle of Man. British Birds, 102, 296–341.

    Google Scholar 

  • Fournier, D., & Archibald, C. P. (1982). A general theory for analyzing catch at age data. Canadian Journal of Fisheries and Aquatic Sciences, 39, 1195–1207.

    Article  Google Scholar 

  • Freeman, S. N., & Morgan, B. J. T. (1992). A modelling strategy for recovery data from birds ringed as nestlings. Biometrics, 48, 217–236.

    Article  Google Scholar 

  • Gimenez, O., Morgan, B. J. T., & Brooks, S. P. (2009b). Weak identifiability in models for mark-recapture-recovery data. In D. L. Thomson, E. G. Cooch, & M. J. Conroy (Eds.), Modelling demographic processes in marked populations. Environmental and ecological statistics (Vol. 3, pp. 1057–1070). Springer, New York.

    Google Scholar 

  • Harvey, A. C. (1989). Forecasting, structural time series models and the Kalman filter. Cambridge: Cambridge University Press.

    Google Scholar 

  • Hoyle, S. D., & Maunder, M. N. (2004). A Bayesian integrated population dynamics model to analyze data for protected species. EURING proceedings. Animal Biodiversity and Conservation, 27, 247–266.

    Google Scholar 

  • Kanyamibwa, S., & Lebreton, J.-D. (1992). Variation des effectifs de Cigogne blanche et facteurs de milieu: Un modèle démographique. In J.-L. Mériaux, A. Schierer, C. Tombal, & J.-C. Tombal (Eds.), Les Cigognes d’Europe (pp. 259–264). Metz: Institut Européen d’Ecologie.

    Google Scholar 

  • King, R. (2011). Statistical ecology. In S. P. Brooks, A. Gelman, G. L. Jones, & X.-L. Meng (Eds.), Handbook of Markov chain Monte Carlo (pp. 419–447). Boca Raton: Chapman & Hall/CRC.

    Google Scholar 

  • King, R., Brooks, S. P., Mazzetta, C., Freeman, S. N., & Morgan, B. J. T. (2008). Identifying and diagnosing population declines: A Bayesian assessment of Lapwings in the UK. Applied Statistics, 57, 609–632.

    MathSciNet  Google Scholar 

  • King, R., Morgan, B. J. T., Gimenez, O., & Brooks, S. P. (2009). Bayesian analysis for population ecology. London: Chapman & Hall/CRC.

    Book  Google Scholar 

  • Knape, J., Besbeas, P., de Valpine, P. (2013). Using uncertainty estimates in analysis of population time series. Ecology, 94, 2097–2107.

    Article  Google Scholar 

  • Lebreton, J-D., Burnham, K. P., Clobert, J., & Anderson, D. R. (1992). Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecological Monographs, 62, 67–118.

    Article  Google Scholar 

  • Marchant, J. H., Hudson, R., Carter, S. P., & Whittington, P. A. (1990). Population trends in British breeding birds. Tring: British Trust for Ornithology.

    Google Scholar 

  • Maunder, M. N. (1998). Integration of tagging and population dynamics models in fisheries stock assessment. (Ph.D. thesis, University Washington, Seattle).

    Google Scholar 

  • Maunder, M. N. (2001). Integrated tagging and catch-at-age analysis (ITCAAN). In G. H. Kruse, N. Bez, A. Booth, M. W. Dorn, S. Hills, R. N. Lipcius, D. Pelletier, C. Roy, S. J. Smith, & D. Witherell (Eds.), Spatial processes and management of fish populations (pp. 123–146, Alaska Sea Grant College Program Report No. AK-SG-01-02). University of Alaska Fairbanks.

    Google Scholar 

  • Maunder, M. N. (2003). Paradigm shifts in fisheries stock assessment: From integrated analysis to Bayesian analysis and back again. Natural Resource Modeling, 16, 465–475.

    Article  MATH  Google Scholar 

  • Maunder, M. N. (2004). Population viability analysis based on combining Bayesian, integrated, and hierarchical analyses. Acta Oecologica, 26, 85–94.

    Article  Google Scholar 

  • Maunder, M. N., & Punt, A. E. (2013). A review of integrated analysis in fisheries stock assessment. Fisheries Research, 142(SI), 61–74.

    Google Scholar 

  • McCrea, R. S., Morgan, B. J. T., Brown, D. I., & Robinson, R. A. (2012b). Conditional modelling of ring-recovery data. Methods in Ecology and Evolution, 3, 823–831.

    Article  Google Scholar 

  • McCrea, R. S., Morgan, B. J. T., Gimenez, O., Besbeas, P., Lebreton, J.-D., & Bregnballe, T. (2010). Multi-site integrated population modelling. Journal of Agricultural, Biological, and Environmental Statistics, 15, 539–561.

    Article  MathSciNet  Google Scholar 

  • Methot, R. D., & Wetzel, C. R. (2013). Stock synthesis: A biological and statistical framework for fish stock assessment and fishery management. Fisheries Research, 142, 86–99.

    Article  Google Scholar 

  • Meyer, R., & Millar, R. B. (1999). BUGS in Bayesian stock assessments. Canadian Journal of Fisheries and Aquatic Science, 56, 1078–1086.

    Article  Google Scholar 

  • Millar, R. B., & Meyer, R. (2000a). Bayesian state-space modeling of age-structured data: Fitting a model is just the beginning. Canadian Journal of Fisheries and Aquatic Science, 57, 43–50.

    Article  Google Scholar 

  • Millar, R. B., & Meyer, R. (2000b). Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling. Applied Statistics, 49, 327–342.

    MATH  MathSciNet  Google Scholar 

  • Morgan, B. J. T., & Freeman, S. N. (1989). A model with first-year variation for ring-recovery data. Biometrics, 45, 1087–1102.

    Article  MATH  Google Scholar 

  • Rifflart, R., Marchand, F., Rivot, E., & Baglinière, J. L. (2006). Scale reading validation for estimating age from tagged fish recapture in a brown trout (Salmo trutta) population. Fisheries Research, 78, 380–384.

    Article  Google Scholar 

  • Rivot, E., & Prévost, E. (2002). Hierarchical Bayesian analysis of capture-mark-recapture data. Canadian Journal of Fisheries and Aquatic Sciences, 59, 1768–1784.

    Article  Google Scholar 

  • Rivot, E., Prévost, E., & Parent, E. (2001). How robust are Bayesian posterior inferences based on a Ricker model with regards to measurement errors and prior assumptions about parameters? Canadian Journal of Fisheries and Aquatic Sciences, 58, 2284–2297.

    Article  Google Scholar 

  • Rivot, E., Prévost, E., Parent, E., & Baglinière, J. L. (2004). A Bayesian state-space modelling framework for fitting a salmon stage-structured population model to multiple time series of field data. Ecological Modelling, 179, 463–485.

    Article  Google Scholar 

  • Schaub, M., & Abadi, F. (2011). Integrated population models: A novel analysis framework for deeper insights into population dynamics. Journal of Ornithology, 152(Suppl 1), S227–S237.

    Article  Google Scholar 

  • Sullivan, P. J. (1992). A Kalman filter approach to catch-at-length analysis. Biometrics, 48, 237–258.

    Article  MATH  Google Scholar 

  • Tavecchia, G., Besbeas, P., Coulson, T., Morgan, B. J. T., & Clutton-Brock, T. H. (2009). Estimating population size and hidden demographic parameters with state-space modelling. American Naturalist, 173, 722–733.

    Article  Google Scholar 

  • ter Braak, C. J. F., van Strien, A. J., Meyer, R., & Verstrael, T. J. (1994). Analysis of monitoring data with many missing values: Which method? In W. Hagemeijer & T. Verstrael (Eds.), Bird numbers 1992. Distribution, monitoring and ecological aspects (pp. 663–673). Proceedings of the 12th International Conference of the International Bird Census Council and European Ornithological Atlas Committee. Beek-Ubbergeon, Sovon, The Netherlands.

    Google Scholar 

  • White, G. C., & Burnham, K. P. (1999). Program MARK: Survival estimation from populations of marked animals. Bird Study, 46(Suppl), 120–139.

    Article  Google Scholar 

  • Wilson, A. M., Vickery, J. A., & Browne S. J. (2001). Numbers and distribution of northern lapwings Vanellus vanellus breeding in England and Wales in 1998. Bird Study, 48, 2–17.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Newman, K.B. et al. (2014). Integrated Population Modelling. In: Modelling Population Dynamics. Methods in Statistical Ecology. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0977-3_9

Download citation

Publish with us

Policies and ethics