Maximum sustainable yield from interacting fish stocks in an uncertain world: two policy choices and underlying trade-offs

The case of fisheries management illustrates how the inherent structural instability of ecosystems can have deep-running policy implications. We contrast ten types of management plans to achieve maximum sustainable yields (MSY) from multiple stocks and compare their effectiveness based on a management strategy evaluation (MSE) that uses complex food webs in its operating model. Plans that target specific stock sizes ($B_{\text{MSY}}$) consistently led to higher yields than plans targeting specific fishing pressures ($F_{\text{MSY}}$). A new self-optimising control rule, introduced here for its robustness to structural instability, led to intermediate yields. Most plans outperformed single-species management plans with pressure targets set without considering multispecies interactions. However, more refined plans to "maximise the yield from each stock separately", in the sense of a Nash equilibrium, produced total yields comparable to plans aiming to maximise total harvested biomass, and were more robust to structural instability. Our analyses highlight trade-offs between yields, amenability to negotiations, pressures on biodiversity, and continuity with current approaches in the European context. Based on these results, we recommend directions for developments of EU fisheries policy.Comment: 21 pages, 1 figure, 2 tables, plus supplementary material (substantial textual revision of v5

A Self-Adaptive Evolutionary Negative Selection Approach for Anomaly Detection

Forrest et al. (1994; 1997) proposed a negative selection algorithm, also termed the exhaustive detector generating algorithm, for various anomaly detection problems. The negative selection algorithm was inspired by the thymic negative selection process that is intrinsic to natural immune systems, consisting of screening and deleting self-reactive T-cells, i.e., those T-cells that recognize self-cells. The negative selection algorithm takes considerable time (exponential to the size of the self-data) and produces redundant detectors. This time/size limitation motivated the development of different approaches to generate the set of candidate detectors. A reasonable way to find suitable parameter settings is to let an evolutionary algorithm determine the settings itself by using self-adaptive techniques. The objective of the research presented in this dissertation was to analyze, explain, and demonstrate that a novel evolutionary negative selection algorithm for anomaly detection (in non-stationary environments) can generate competent non redundant detectors with better computational time performance than the NSMutation algorithm when the mutation step size of the detectors is self-adapted

Reducing Random Fluctuations in Mutative Self-Adaptation

A simple method of reducing random fluctuations experienced in step-size control under mutative self-adaptation is discussed. The approac