Authors
Shizen Wang Gary Morishima Rishi SharmaLarry Gilbertson
Report Reference
#North
American Journal of Fisheries Management 29:423–433,
2009
Publication Date
20 April
2009
|
 |
The
Use of Generalized Additive Models for Forecasting the Abundance of Queets
River Coho Salmon
Abstract |
| We examined
three types of models for preseason forecasting of the abundance of
Queets River coho salmon Oncorhynchus kisutch: (1) a simple
model in which estimates of smolt production are multiplied by projected
marine survival rates, (2) a Ricker spawner–recruitment model,
and (3) a regression model relating log-transformed adult recruitment
to smolt production. Each type of model was formulated with and without
environmental variables that influence production and survival. We
attempted to use a nonparametric generalized additive model (GAM)
to guide the selection of the environmental variables and the form
of the regression model. The GAM model was derived through a stepwise
selection strategy based on the Akaike information criterion. Parametric
approximate models were developed for each selected GAM model, and
their performance was compared with postseason estimates of abundance
using three criteria: the mean absolute percentage error, the largest
absolute percentage error, and the probability of being included in
the 90% prediction interval. This paper shows that the GAM approach
is useful in constructing forecasting models by identifying promising
relationships with predictor variables and improving abundance forecasts
through the incorporation of environmental variables. |
|
|
|
| sitemap | © 2011
(188
KB )