Management strategy evaluation: best practices

Abstract

Management strategy evaluation (MSE) involves using simulation to compare the relative effectiveness for achieving management objectives of different combinations of data collection schemes, methods of analysis and subsequent processes leading to management actions. MSE can be used to identify a 'best' management strategy among a set of candidate strategies, or to determine how well an existing strategy performs. The ability of MSE to facilitate fisheries management achieving its aims depends on how well uncertainty is represented, and how effectively the results of simulations are summarized and presented to the decision-makers. Key challenges for effective use of MSE therefore include characterizing objectives and uncertainty, assigning plausibility ranks to the trials considered, and working with decisionmakers to interpret and implement the results of the MSE. This paper explores how MSEs are conducted and characterizes current 'best practice' guidelines, while also indicating whether and how these best practices were applied to two casestudies: the Bering-Chukchi-Beaufort Seas bowhead whales (Balaena mysticetus; Balaenidae) and the northern subpopulation of Pacific sardine (Sardinops sagax caerulea; Clupeidae).

Harvest Regulations and Implementation Uncertainty in Small Game Harvest Management

“…When field studies or experiments are not feasible, setting up mathematical simulation models to assess how the system behaves under a range of conditions, and how sensitive management objectives are to certain parameters is often a useful way forward. In the management of natural resources, MSE models are particularly suitable because they allow complex relationships between the biological resource and the harvest management (Bunnefeld et al, 2011a;Punt et al, 2016). Eriksen et al (2017) recently showed that increased implementation uncertainty can have a substantial effect on the risk of overharvest in the management of willow ptarmigan in Norway.…”

Initial tests of the robustness of the provisional harvest control rule in Canada’s Sustainable Fisheries Policy to process and measurement errors using simulated depleted fish populations

“…Simulation testing of fishery management strategies is widely considered to be good practice to ensure robustness to uncertainty (Deroba and Bence, 2008;2012;Zhang et al, 2011;Wiedenmann et al, 2013;Punt et al, 2014). However, there has only been limited testing of management strategies on Canadian fish stocks (e.g.…”

“…Performance criteria for evaluating the PHCR were developed from the DMF's management objectives with regard to SSB, F and catch. This study is considered preliminary because it was not stock-specific and did not implement a full closed-loop management strategy evaluation (MSE) that includes simulating the actual stock assessment process; widely acknowledged as the preferred approach, but one that would have to be stock-specific (Cox and Kronlund, 2008;Punt et al, 2014).…”

Investigating trade-offs in alternative catch share systems: an individual-based bio-economic model applied to the Bay of Biscay sole fishery

“…In addition, 79 policies for adjusting fleet capacity have relied on limited entry and public-aided decommissioning 80 schemes (Quillérou and Guyader 2012). However, the European Commission identified heavy 81 subsidies as one of the main problems of the CFP and thus tends to promote the use of ITQs rather 82 than public-aided decommissioning schemes to achieve necessary reduction of fleet capacity (CEC 83 D r a f t 5 A means of overcoming this drawback is to develop innovative bio-economic tools that include the 97 core processes of catch share management so as to augment the management model and the 98 harvest control rule (HCR) implementation components of the typical MSE loop (Holland 2010; 99 Bunnefeld et al 2011;Punt et al 2016). 100…”