Recent Papers

Lost property

The world’s fisheries are in an even worse state than feared

The Economist, Feb 25, 2012

A cold oceanographic regime with high exploitation rates in the Northeast Pacific forecasts a collapse of the sardine stock

Juan P. Zwolinski and David A. Demer.

http://www.pnas.org/content/early/2012/02/24/1113806109.abstract

The oceanographic conditions in the north Pacific have shifted to a colder period, Pacific sardine (Sardinops sagax) biomass has declined precipitously in the California Current, the international sardine fishery is collapsing, and mackerel (Trachurus symmetricus and Scomber japonicus) are thriving. This situation occurred in the mid-1900s, but indices of current oceanographic conditions and the results of our acoustic-trawl surveys indicate it likely is recurring now, perhaps with similar socioeconomic and ecological consequences. Also alarming is the repetition of the fishery's response to a declining sardine stock—progressively higher exploitation rates targeting the oldest, largest, and most fecund fish. Furthermore, our data indicate the recent reproductive condition of sardine is poor, and their productivity is below modeled estimates used to derive the current fishery-exploitation rates. Consequently, the sardine population has been reduced to two cohorts that are unlikely to produce an appreciable new cohort. Thus, a near-term recovery of this important stock is unlikely, depending on the return of warmer oceanographic conditions, reduced pressure from mackerel species, and perhaps the adoption of a more precautionary strategy for managing the residual sardine population.

Reconsidering the Consequences of Selective Fisheries

S. M. Garcia et al.

http://www.sciencemag.org/content/335/6072/1045.summary

Concern about the impact of fishing on ecosystems and fisheries production is increasing (1, 2). Strategies to reduce these impacts while addressing the growing need for food security (3) include increasing selectivity (1, 2): capturing species, sexes, and sizes in proportions that differ from their occurrence in the ecosystem. Increasing evidence suggests that more selective fishing neither maximizes production nor minimizes impacts (4–7). Balanced harvesting would more effectively mitigate adverse ecological effects of fishing while supporting sustainable fisheries. This strategy, which challenges present management paradigms, distributes a moderate mortality from fishing across the widest possible range of species, stocks, and sizes in an ecosystem, in proportion to their natural productivity (8), so that the relative size and species composition is maintained.

Causal Discovery for Climate Research Using Graphical Models

Imme Ebert-Uphoff and Yi Deng

http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-11-00387.1

Causal discovery seeks to recover cause-effect relationships from statistical data using graphical models. One goal of this paper is to provide an accessible introduction to causal discovery methods for climate scientists, with a focus on constraint based structure learning. Secondly, in a detailed case study constraint based structure learning is applied to derive hypotheses of causal relationships between four prominent modes of atmospheric low-frequency variability in boreal winter including the Western Pacific Oscillation (WPO), Eastern Pacific Oscillation (EPO), Pacific North America Pattern (PNA) and North Atlantic Oscillation (NAO). The results are shown in the form of static and temporal independence graphs, also known as Bayesian Networks. It is found that WPO and EPO are nearly indistinguishable from the cause-effect perspective as strong simultaneous coupling is identified between the two. In addition, changes in the state of EPO (NAO) may cause changes in the state of NAO (PNA) approximately 18 (3-6) days later. These results are not only consistent with previous findings on dynamical processes connecting different low-frequency modes (e.g., interaction between synoptic and low-frequency eddies), but also provide basis for formulating new hypotheses regarding the time-scale and temporal-sequencing of dynamical processes responsible for these connections. Lastly we propose to use structure learning for climate networks, which are currently based primarily on correlation analysis. While correlation-based climate networks focus on similarity between nodes, independence graphs would provide an alternative viewpoint by focusing on information flow in the network.