Predicting fish recruitment from juvenile abundance and environmental indices

Abstract

Prediction of year-class strength is a critical challenge for fisheries managers. Theoretically, predictions of recruitment should be better when they are based on estimates of cohort size taken close to the age of recruitment and may improve if the effects of environmental factors that influence pre-recruit mortality are accounted for. In practice, measurement error and difficulties in establishing robust recruitment -environment relationships complicate the picture. For 5 fish stocks of 4 species in 3 ecosystems, we examined the usefulness of indices of juvenile abundance relative to larval abundance for predicting recruitment. Further, we examined whether the use of environmental covariates improved predictions. For 2 of 4 stocks with sufficient data (1 stock did not have larval data), juvenile abundance was a better predictor of recruitment compared to larval indices. For the 2 other stocks, we found that juvenile indices were not superior to larval indices, possibly because of error in the measurement of juvenile abundance. In all 5 of these stocks, regression analysis showed that inclusion of environmental correlates contributed significantly to explaining recruitment variation compared to models based on juvenile indices alone. Further, cross validation showed that forecasts of future recruitment were either improved or qualitatively unchanged by including environmental correlates. This was despite apparent nonstationarity in the recruitment -environment relationships; most of the environmental variables and pre-recruit abundance indices were significantly correlated with recruitment for only parts of the studied period. Such complex responses to environmental changes are difficult to anticipate, yet the environmental information should not be ignored altogether. FREE REE

Reproduction, feeding and migration patterns of Prochilodus nigricans (Characiformes: Prochilodontidae) in northeastern Ecuador

“…In other words, recruitment to the local population takes place at a specific time (falling water season, when current velocities are decreasing, and thus, relatively less energy may be needed to swim upstream). Given the discrete size mode for the YOY fishes, it may be feasible to establish a long-term monitoring program focused on applying an annual index of recruitment, which could be useful to determine when harvest rates may be getting excessive and to better understand environmental effects on recruitment (e.g., Stige et al, 2013).…”

Harvested fish stocks in a changing environment

“…The situation is further complicated by the temperature−recruitment relation also displaying spatial differences within the same sea . Still, Stige et al (2013) showed that environmental information can aid the prediction of recruitment. Furthermore, while much of the year class strength is determined during egg and larval stages, this pattern may be overshadowed by density-dependent mortality factors operating at later life stages .…”

“…They found that abundance indices of later life stages do not always provide better estimates than those based on earlier life stages, and suggest that this unexpected result was due to the poor quality of the time series collected for older life stages. Stige et al (2013) showed that the inclusion of environmental indices often improved the accuracy of 'honest' predictions of recruitment, i.e. predictions based upon data not used when fitting the recruitment models.…”

“…predictions based upon data not used when fitting the recruitment models. Stige et al (2013) finally stressed that even with complex marine systems, the environment−recruitment correlations were found to be unstable, and the available timeseries were short, consideration of environmental information may still improve recruitment predictions.…”

Forecasting Pink Salmon Production in Southeast Alaska Using Ecosystem Indicators in Times of Climate Change

“…For example, compared to simple models based on juvenile abundance only, environmental correlates may contribute signifi cantly in explaining fi sh recruitment variation (Stige et al 2013). Also, larger ocean basin-scale factors refl ective of climate, such as the El Niño Southern Oscillation (ENSO) and the Pacifi c Decadal Oscillation (PDO) multivariate indexes, may also aff ect salmon production (Beamish and Bouillon 1993;Hare and Francis 1995;Mantua et al 1997;Francis et al 1998;Hare et al 1999).…”