Statistical modeling of seedling mortality

Article

Abstract

Seedling mortality in tree populations limits population growth rates and controls the diversity of forests. To learn about seedling mortality, ecologists use repeated censuses of forest quadrats to determine the number of tree seedlings that have survived from the previous census and to find new ones. Typically, newly found seedlings are marked with flags. But flagging is labor intensive and limits the spatial and temporal coverage of such studies. The alternative of not flagging has the advantage of ease but suffers from two main disadvantages. It complicates the analysis and loses information. The contributions of this article are (i) to introduce a method for using unflagged census data to learn about seedling mortality and (ii) to quantify the information loss so ecologists can make informed decisions about whether to flag. Based on presented results, we believe that not flagging is often the preferred alternative. The labor saved by not flagging can be used to better advantage in extending the coverage of the study.

Key words

Bayesian inference Ecological statistics Experimental design Fisher information Gibbs sampling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Beckage, B. (2000), “A Long-Term Study of Red Maple (Acer rubrum L.) Seedling Survival in Southern Appalachian Forests: The Effects of Canopy Gaps and Shrub Understories,” Ph.D. thesis, Duke University, Durham, NC.Google Scholar
  2. Brown, L. D. (1986), Fundamentals of Statistical Exponential Families With Applications in Statistical Decision Theory, Hayward, CA: Institute of Mathematical Statisties.MATHGoogle Scholar
  3. Casella, G., and George, E. I. (1992), “Explaining the Gibbs Sampler,” The American Statistican, 46, 167–174.CrossRefMathSciNetGoogle Scholar
  4. Clark, J. S., and Beckage, B., and Camill, P., and Cleveland, B., and HilleRisLambers, J., Lichter, J., and MacLachlan, J., and Mohan, J., and Wyckoff, P. (1999), “Interpreting Recruitment Limitation in Forests,” American Journal of Botany, 86, 1–16.CrossRefGoogle Scholar
  5. Clark, J. S., and Macklin, E., and Wood, L. (1998), “Stages and Spatial Scales of Recruitment Limitation in Southern Appalachian Forests,” Ecological Monographs 68, 213–235.CrossRefGoogle Scholar
  6. Gelfand, A. E., and Smith, A. F. M. (1990), “Sampling Based Approaches to Calculating Marginal Densities,” Journal of the American Statistical Association, 85, 398–409.MATHCrossRefMathSciNetGoogle Scholar
  7. Grubb, P. J. (1977), “The Maintenance of Species-Richness in Plant Communities: The Importance of the Regeneration Niche,” Biological Reviews, 52, 107–145.CrossRefGoogle Scholar
  8. Hubbell, S. P., and Foster, R. B., and O’Brien, S. T., and Harms, K. E., and Condit, R., Wechsler, B., and Wright, S. J., and de Lao, S. L. (1999), “Light-Gap Disfurbances, Recruitment Limitation, and Tree Diversity in a Neotropical Forest,” Science, 283, 554–557.CrossRefGoogle Scholar
  9. Jones, R., and Sharitz, R., and Dixon, P., and Segal, D., and Schneider, R. (1994), “Woody Plant Regeneration in Four Flood plain Forests,” Ecological Monographs, 64, 345–367.CrossRefGoogle Scholar
  10. Kahn, W. D. (1987), “A Cautionary Note for Bayesian Estimation of the Binomial Parameter n”, The American Statistician, 41, 38–39.CrossRefMathSciNetGoogle Scholar
  11. Lavine, M., and Wasserman, L. (1992), “Can We Estimate N?” Discussion Paper 92A-08, Institute of Statistics and Decision Sciences, Duke University, Durham, NC.Google Scholar
  12. Pacala, S. W., and Tilman, D. (1994), “Limiting Similarity in Mechanistic and Spatial Models of Plant Competition in Heterogeneous Environments,” The American Naturalist, 143, 222–257.CrossRefGoogle Scholar
  13. Raftery, A. E. (1988), “Inference for the N Parameter: A Hierarchical Bayes Approach,” Biometrika, 75, 223–228.MATHGoogle Scholar
  14. Streng, D., and Glitzenstein, J., and Harcombe, P. (1989), “Woody Seedling Dynamics in an East Texas Flood plain Forest,” Ecological Monographis, 59, 177–204.CrossRefGoogle Scholar
  15. Sundberg, R. (1974), “Maximum Likelihood Theory for Incomplete Data From an Exponential Family,” Scandina vian Journal of Statistics, 1, 49–58.MATHMathSciNetGoogle Scholar
  16. Watt, A. S. (1947), “Pattern and Process in the Plant Community,” Journal of Ecology, 35, 1–22.CrossRefGoogle Scholar

Copyright information

© International Biometric Society 2002

Authors and Affiliations

  • Michael Lavine
    • 1
  • Brian Beckage
    • 3
  • James S. Clark
    • 2
  1. 1.Institute of Statistics and Decision SciencesDuke UniversityDurham
  2. 2.Department of BiologyDuke UniversityDurham
  3. 3.Department of Biological SciencesLouisiana State UniversityBaton Rouge

Personalised recommendations