Climate change is altering the rate and distribution of primary production in the world's oceans. Primary production is critical to maintaining biodiversity and supporting fishery catches, but predicting the response of populations to primary production change is complicated by predation and competition interactions. We simulated the effects of change in primary production on diverse marine ecosystems across a wide latitudinal range in Australia using the marine food web model Ecosim. We link models of primary production of lower trophic levels (phytoplankton and benthic producers) under climate change with Ecosim to predict changes in fishery catch, fishery value, biomass of animals of conservation interest, and indicators of community composition. Under a plausible climate change scenario, primary production will increase around Australia and generally this benefits fisheries catch and value and leads to increased biomass of threatened marine animals such as turtles and sharks. However, community composition is not strongly affected. Sensitivity analyses indicate overall positive linear responses of functional groups to primary production change.Responses are robust to the ecosystem type and the complexity of the model used. However, model formulations with more complex predation and competition interactions can reverse the expected responses for some species, resulting in catch declines for some fished species and localized declines of turtle and marine mammal populations under primary productivity increases. We conclude that climate-driven primary production change needs to be considered by marine ecosystem managers and more specifically, that production increases can simultaneously benefit fisheries and conservation. Greater focus on incorporating predation and competition interactions into models will significantly improve the ability to identify species and industries most at risk from climate change.

To evaluate the impacts of fishing on marine ecosystems, the total extraction of fish must be known. Putting a figure on total extraction entails the difficult task of estimating, in addition to reported landings, discards, illegal and unmandated catches. Unreported catches cast various types of shadow, which may be tracked and estimated quantitatively. Some shadows of unreported catches are reviewed, for example, an innovative, well‐funded NGO publicizes illegal catch in the Southern Ocean. For various reasons, official figures often have the implicit but unacceptable assumption that such categories are null. We present an estimation procedure based on adjustment factors taken from observer reports, correspondents and published information that track changes in a regulatory regime, and hence reflect incentives and disincentives to misreport. Monte Carlo simulations address uncertainty using multiple sources of information to provide upper and lower estimates. Once in place, this method provides preliminary estimates that may be refined without disruption. The method is demonstrated for fisheries in Iceland and Morocco. We use a ‘by‐species’ approach for Icelandic cod and haddock, while the Moroccan catch is divided into demersal and pelagic categories. Results suggest that Icelandic cod catches may have been underestimated by between 1 and 14% at different times, and haddock by between 1 and 28%. Underestimation of Moroccan catches appears to have been as much as by 50%. These case studies show that it is possible to obtain estimates of misreporting, even when direct data are lacking. Our method encourages transparency because sources of information are presented so that uncertain values are easily identified, offering a basis for comment, collaboration and refinement in estimating illegal and unreported fishing.

We provide a perspective on steepness, reference points for fishery management, and stock assessment. We first review published data and give new results showing that key reference points are fixed when steepness and other life history parameters are fixed in stock assessments using a Beverton–Holt stock–recruitment relationship. We use both production and age-structured models to explore these patterns. For the production model, we derive explicit relationships for steepness and life history parameters and then for steepness and major reference points. For the age-structured model, we are required to generally use numerical computation, and so we provide an example that complements the analytical results of the production model. We discuss what it means to set steepness equal to 1 and how to construct a prior for steepness. Ways out of the difficult situation raised by fixing steepness and life history parameters include not fixing them, using a more complicated stock–recruitment relationship, and being more explicit about the information content of the data and what that means for policy makers. We discuss the strengths and limitations of each approach.

No abstract

Hierarchical Bayesian meta-analysis can be a useful method for improving estimation of key parameters for harvested fish populations. In hierarchical models, data from multiple populations are used simultaneously to obtain estimates of parameters for individual populations and characterize the variability among populations. Many populations of Pacific rockfishes ( Sebastes spp.) have declined off the US West Coast since the 1980s, and there is also concern for their conservation in Canada. We develop a hierarchical Bayesian meta-analysis to improve estimates of stock–recruit parameters, characterize management-related parameters (e.g., optimal harvest rate), and address uncertainties in the structural form of the stock–recruit function for Pacific rockfishes. We estimate steepness and optimal harvest rates for 14 populations of Pacific rockfishes under alternative assumptions about the underlying stock–recruit function (Beverton–Holt and Ricker). We provide a posterior predictive distribution of steepness for rockfishes that can be used as a prior in future assessments for similar populations. We also evaluate whether F40% is an appropriate proxy for FMSY for Pacific rockfishes and show that uncertainty in the natural mortality rate can have a significant effect on management advice derived from meta-analyses of stock–recruit data.