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Three problems with the conventional delta-model for biomass sampling data, and a computationally efficient alternative



Details

  • Journal Title:
    Canadian Journal of Fisheries and Aquatic Sciences
  • Personal Author:
    Thorson, James T.
  • NOAA Program & Office:
  • Description:
    Ecologists often analyse biomass sampling data that result in many zeros, where remaining samples can take any positive real number. Samples are often analysed using a “delta-model” that combines two separate generalized linear models, GLMs (for encounter probability and positive catch rates), or less often using a compound Poisson-gamma (CPG) distribution that is computationally expensive. I discuss three theoretical problems with the conventional delta-model: difficulty interpreting covariates for encounter probability, the assumed independence of the two GLMs, and the biologically implausible form when eliminating covariates for either GLM. I then derive an alternative “Poisson-link model” that solves these problems. To illustrate, I use biomass samples for 113 fish populations to show that the Poisson-link model improves fit (and decreases residual spatial variation) for >80% of populations relative to the conventional delta-model. A simulation experiment illustrates that CPG and Poisson-link models estimate covariate effects that are similar and biologically interpretable. I therefore recommend the Poisson-link model as a useful alternative to the conventional delta-model with similar properties to the CPG distribution.
  • Keywords:
  • Source:
    Canadian Journal of Fisheries and Aquatic Sciences, 75(9), 1369-1382
  • DOI:
  • ISSN:
    0706-652X ; 1205-7533
  • Format:
    PDF
  • Publisher:
    Canadian Science Publishing
  • Document Type:
    Journal Article
  • Rights Information:
    Other
  • Compliance:
    Library
  • Main Document Checksum:
    urn:sha256:997668354551c589e35058067040ad1f0297cbfab87a2f1ee6f4b69ede612efd
  • Download URL:
  • File Type:
    Filetype[PDF - 1.77 MB ]
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