FN Archimer Export Format
PT J
TI Three problems with the conventional delta-model for biomass sampling data, and a computationally efficient alternative
BT 
AF THORSON, James T.
AS 1:1;
FF 1:;
C1 NOAA, Fisheries Resource Assessment & Monitoring Div, Northwest Fisheries Sci Ctr, Natl Marine Fisheries Serv, Seattle, WA 98112 USA.
C2 NOAA, USA
IF 2.567
TC 57
UR https://archimer.ifremer.fr/doc/00405/51671/90022.pdf
LA English
DT Article
CR EVHOE EVALUATION HALIEUTIQUE DE L'OUEST DE L'EUROP
AB 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 useful alternative to the conventional delta-model with similar properties to the CPG distribution. 
PY 2018
PD SEP
SO Canadian Journal Of Fisheries And Aquatic Sciences
SN 0706-652X
PU Canadian Science Publishing, Nrc Research Press
VL 75
IS 9
UT 000442595300002
BP 1369
EP 1382
DI 10.1139/cjfas-2017-0266
ID 51671
ER

EF