Nereus alumnus Colleen Petrik (Texas A&M University) and Charles Stock (NOAA) are co-authors on a new paper in Progress in Oceanography. The authors investigate how dominant factors (e.g. cross-ecosystem differences in zooplankton and benthic organism availability) influence the global distribution and functionality of certain commercially important fishes, thereby improving the ability to predict “the changing structure of fish communities and their productive capacity under global change and continued exploitation”.

To do this, the authors built a “spatially explicit mechanistic model” and applied it to three functional types of commercial fishes – forage (in the upper water column, e.g. sardines and anchovies), large pelagic (upper water column and deep depths, e.g. tunas) and demersal (seafloor dwelling, e.g. Atlantic cod and Greenland halibut). Their model “simulates the competitive and predatory trophic interactions between the fishes and with their [open water] and [bottom dwelling] food resources and replicates fundamental aspects of fish life cycles” and can be coupled with global earth system model. The authors found that “total system productivity [and] the type of productivity (zooplankton vs. benthos) determines the broad-scale spatial patterns in abundance and dominance of the commercially harvested fish.” You can read the full abstract and access the article for more information below.

Abstract: Large-scale spatial heterogeneity in fisheries production is predominantly controlled by the availability of zooplankton and benthic organisms, which have a complex relationship with primary production. To investigate how cross-ecosystem differences in these drivers determine fish assemblages and productivity, we constructed a spatially explicit mechanistic model of three fish functional types: forage, large pelagic, and demersal fishes. The model is based on allometric scaling principles, includes basic life cycle transitions, and has trophic interactions between the fishes and with their pelagic and benthic food resources. The model was applied to the global ocean, with plankton food web estimates and ocean conditions from a high-resolution earth system model. Further, a simple representation of fishing was included, and led to moderate matches with total, large pelagic, and demersal catches, including re-creation of observed variations in fish catch spanning two orders of magnitude. Our results highlight several ecologically meaningful model sensitivities. First, coexistence between forage and large pelagic fish in productive regions occurred when forage fish survival is promoted via both favorable metabolic allometry and enhanced predator avoidance in adult forage fish. Second, the prominence of demersal fish is highly sensitive to the efficiency of energy transfer to benthic invertebrates. Third, the latitudinal distribution of the total catch is modulated by the temperature dependence of metabolic rates, with increased sensitivity pushing fish biomass toward the poles. Fourth, forage fish biomass is suppressed by strong top-down controls on temperate and subpolar shelves, where mixed assemblages of large pelagic and demersal fishes exerted high predation rates. Last, spatial differences in the dominance of large pelagics vs. demersals is strongly related to the ratio of pelagic zooplankton production to benthic production. We discuss the potential linkages between model misfits and unresolved processes including movement, spawning phenology, seabird and marine mammal predators, and socioeconomically driven fishing pressure, which are identified as priorities for future model development. Ultimately, the model and analyses herein are intended as a baseline for a robust, mechanistic tool to understand, quantify, and predict global fish biomass and yield, now and in a future dominated by climate change and improved fishing technology.

Reference

Petrik, C.M., Stock, C.A., Andersen, K.H., Van Denderen, P.D. & Watson, J.R. (2019). Bottom-up drivers of global patterns of demersal, forage, and pelagic fishes. Progress in Oceanography, 176, 102124. https://doi.org/10.1016/j.pocean.2019.102124 link