© 2005 International Council for the Exploration of the Sea
Comparative analysis of statistical tools to identify recruitment–environment relationships and forecast recruitment strength
a National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Alaska Fisheries Science Center 7600 Sand Point Way NE, Seattle, WA 98115, USA
b Joint Institute for the Study of the Atmosphere and the Oceans PO Box 354235, University of Washington, Seattle, WA 98195, USA
c National Oceanic and Atmospheric Administration, Pacific Marine Environmental Laboratory 7600 Sand Point Way NE, Seattle, WA 98115, USA
*Correspondence to B. A. Megrey: tel: +1 206 526 4147; fax: +1 206 526 6723. e-mail: bern.megrey{at}noaa.gov.
Many of the factors affecting recruitment in marine populations are still poorly understood, complicating the prediction of strong year classes. Despite numerous attempts, the complexity of the problem often seems beyond the capabilities of traditional statistical analysis paradigms. This study examines the utility of four statistical procedures to identify relationships between recruitment and the environment. Because we can never really know the parameters or underlying relationships of actual data, we chose to use simulated data with known properties and different levels of measurement error to test and compare the methods, especially their ability to forecast future recruitment states. Methods examined include traditional linear regression, non-linear regression, Generalized Additive Models (GAM), and Artificial Neural Networks (ANN). Each is compared according to its ability to recover known patterns and parameters from simulated data, as well as to accurately forecast future recruitment states. We also apply the methods to published Norwegian spring-spawning herring (Clupea harengus L.) spawner–recruit–environment data. Results were not consistently conclusive, but in general, flexible non-parametric methods such as GAMs and ANNs performed better than parametric approaches in both parameter estimation and forecasting. Even under controlled data simulation procedures, we saw evidence of spurious correlations. Models fit to the Norwegian spring-spawning herring data show the importance of sea temperature and spawning biomass. The North Atlantic Oscillation (NAO) did not appear to be an influential factor affecting herring recruitment.
Keywords: Artificial Neural Networks, environment–recruitment models, GAM, herring, NAO, spawner–recruit models
Received 2 July 2004; accepted 8 May 2005.
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