Contributions of long-term research and time-series observations to marine ecology and biogeochemistry

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Annual Reviews for personal use, not for redistribution. The definitive version was published in Annual Review of Marine Science 1 (2009): 279-302, doi:10.1146/annurev.marine.010908.163801.Time-series observations form a critical element of oceanography. New interdisciplinary efforts launched in the past two decades complement the few earlier, longer-running time series in building a better, though still poorly-resolved, picture of lower-frequency ocean variability, the climate processes driving it, and its implications for foodweb dynamics, carbon storage and climate feedbacks. Time-series also enlarge our understanding of ecological processes and are integral for improving models of physical-biogeochemical-ecological ocean dynamics. The major time-series observatories go well beyond simple monitoring of core ocean properties, although that important activity forms the critical center of all time-series efforts. Modern ocean time series have major process and experimental components, entrain ancillary programs and have integrated modeling programs for deriving better understanding of the observations and the changing, three-dimensional ocean in which the observatories are embedded.HWD was supported by NSF grant OPP-0217282. SCD was supported by the Center for Microbial Oceanography Research and Education (C-MORE; NSF CCF-424599). DKS was supported by NSF grant OCE-0628444

A heuristic model of socially learned migration behaviour exhibits distinctive spatial and reproductive dynamics

We explore a “Go With the Older Fish” (GWOF) mechanism of learned migration behaviour for exploited fish populations, where recruits learn a viable migration path by randomly joining a school of older fish. We develop a non-age-structured biomass model of spatially independent spawning sites with local density dependence, based on Pacific herring (Clupea pallasii). We compare a diffusion (DIFF) strategy, where recruits adopt spawning sites near their natal site without regard to older fish, with GWOF, where recruits adopt the same spawning sites, but in proportion to the abundance of adults using those sites. In both models, older individuals return to their previous spawning site. The GWOF model leads to higher spatial variance in biomass. As total mortality increases, the DIFF strategy results in an approximately proportional decrease in biomass among spawning sites, whereas the GWOF strategy results in abandonment of less productive sites and maintenance of high biomass at more productive sites. A DIFF strategy leads to dynamics comparable to non-spatially structured populations. While the aggregate response of the GWOF strategy is distorted, non-stationary and slow to equilibrate, with a production curve that is distinctly flattened and relatively unproductive. These results indicate that fishing will disproportionately affect populations with GWOF behaviour

When are estimates of spawning stock biomass for small pelagic fishes improved by taking spatial structure into account?

Consideration should be taken of including spatial structure in the models on which stock assessments are based. Abstract A simulation-estimation approach is used to evaluate the efficacy of stock assessment methods that incorporate various levels of spatial complexity. The evaluated methods estimate historical and future biomass for a situation that roughly mimics Pacific herring Clupea pallasii at Haida Gwaii, British Columbia, Canada. The baseline operating model theorizes ten areas arranged such that there is post-recruitment dispersal among all areas. Simulated data (catches, catch age-composition, estimates of spawning stock biomass and its associated age structure) generated for each area are analyzed using estimation methods that range in complexity from ignoring spatial structure to explicitly modelling ten areas. Estimation methods that matched the operating model in terms of spatial structure performed best for hindcast performance and short-term forecasting, i.e., adding spatial structure to assessments improved estimation performance. Even with similar time trajectories among sub-stocks, accounting for spatial structure when conducting the assessment leads to improved estimates of spawning stock biomass. In contrast, assuming spatial variation in productivity when conducting assessments did not appreciably improve estimation performance, even when productivity actually varied spatially. Estimates of forecast biomass and of spawning stock biomass relative to the unfished level were poorer than estimates of biomass for years with data, i.e., hindcasts. Overall, the results of this study further support efforts to base stock assessments for small pelagic fishes on spatially-structured population dynamics models when there is a reasonable likelihood of identifying the sub-stocks that should form the basis for the assessment

When are estimates of spawning stock biomass for small pelagic fishes improved by taking spatial structure into account?

Consideration should be taken of including spatial structure in the models on which stock assessments are based. Abstract A simulation-estimation approach is used to evaluate the efficacy of stock assessment methods that incorporate various levels of spatial complexity. The evaluated methods estimate historical and future biomass for a situation that roughly mimics Pacific herring Clupea pallasii at Haida Gwaii, British Columbia, Canada. The baseline operating model theorizes ten areas arranged such that there is post-recruitment dispersal among all areas. Simulated data (catches, catch age-composition, estimates of spawning stock biomass and its associated age structure) generated for each area are analyzed using estimation methods that range in complexity from ignoring spatial structure to explicitly modelling ten areas. Estimation methods that matched the operating model in terms of spatial structure performed best for hindcast performance and short-term forecasting, i.e., adding spatial structure to assessments improved estimation performance. Even with similar time trajectories among sub-stocks, accounting for spatial structure when conducting the assessment leads to improved estimates of spawning stock biomass. In contrast, assuming spatial variation in productivity when conducting assessments did not appreciably improve estimation performance, even when productivity actually varied spatially. Estimates of forecast biomass and of spawning stock biomass relative to the unfished level were poorer than estimates of biomass for years with data, i.e., hindcasts. Overall, the results of this study further support efforts to base stock assessments for small pelagic fishes on spatially-structured population dynamics models when there is a reasonable likelihood of identifying the sub-stocks that should form the basis for the assessment