Interannual changes in sablefish (Anoplopoma fimbria) recruitment in relation to oceanographic conditions within the California Current System

Abstract

Sablefish (Anoplopoma fimbria) supports substantial fisheries in both the eastern and western Pacific Oceans. Juvenile recruitment along the west coast of the continental United States has been highly variable over the past three decades. Using a generalized additive model, we demonstrate that physical oceanographic variables within the California Current System have significant effects on sablefish recruitment. Significant relationships were found between juvenile recruitment and northward Ekman transport, eastward Ekman transport, and sea level during key times and at key locations within the habitat of this species. The model explains nearly 70% of the variability in sablefish recruitment between the years 1974 and 2000. The predictive power of the model was demonstrated by refitting without the last 5 yr of data and subsequent prediction of those years. Bootstrap assessments of bias associated with parameter estimates and jackknife-after-bootstrap assessments of the influence of individual data on parameter estimates are presented and discussed. Using this model, it is possible to draw preliminary conclusions concerning yearclass strength of cohorts not yet available to the survey gear as well as historic year-class strengths. We discuss changes in zooplankton abundance and shifts in species of copepods associated with fluctuations in the physical variables that appear to have a major influence on sablefish recruitment.

Untangling the relationships among climate, prey and top predators in an ocean ecosystem

“…These results are similar to Schirripa & Colbert (2006), wherein sablefish production along the California Current was found to be negatively correlated to northern Ekman transport at 48°N. Within central California it is possible that reduced offshore wind stress enables advection of nutrients, zooplankton and larvae into relaxed waters equatorward of upwelling jets (Strub et al 1991, Graham et al 1992, Wing et al 1995.…”

“…As a result, we can now describe, for the first time, why and how a variable such as coastal sea level, which apparently relates to ecosystem variability on multiple trophic levels (e.g. Logerwell et al 2003, Schirripa & Colbert 2006, determines productivity. The final model provides what might be described as a quantitative conceptual model of how variables drive productivity.…”

“…Such information is increasingly applied. For example, the California sardine Sardinops sagax harvest guidelines are based on sea surface temperature (Jacobson & MacCall 1995), and projections of sablefish Anoplopoma fimbria recruitment are based, in part, on indices of coastal sea level (Schirripa & Colbert 2006). Yet, explicit understanding of causal physical processes on production has been elusive and thus the potential for relation-ships to break down in different steady environmental states (regimes, Bond et al 2003) threatens applications and predictive power.…”

Population genetic structure, coloration, and morphometrics of yellowhead jawfish Opistognathus aurifrons (Perciformes: Opistognathidae) in the Caribbean region

“…Although the abilities and behaviors of larval fishes (reviewed by Leis 2006), such as detection of terrestrial noise and chemical cues (Simpson et al 2005, Dixson et al 2008, extensive swimming abilities (Stobutzki & Bellwood 1997), and vertical distribution (Cowen & Castro 1994), can have large influ-ences on recruitment, oceanographic patterns on both the geographic and temporal scales can also contribute to recruitment processes (Cowen et al 2000, Bradbury & Snelgrove 2001, Schirripa & Colbert 2006, Galarza et al 2009). Longer PLDs can lead to extensive dispersal (Shanks 2009), and this could cause extensive gene flow and diminished levels of population genetic structure.…”

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

“…Such research has evolved from considering only the biomass of spawners, to including also environmental factors that can modulate recruitment (e.g. Planque and Buffaz, 2008;Schirripa and Colbert, 2006). The main limitation to achieve good forecasts, from a data analysis perspective is the sparse and 'noisy' nature of the available data (Fernandes et al, 2010;Francis, 2006).…”