The Optimal Recovery of a Semelparous Fish Population with Delayed Recruitment

Annual recruitment of the New Zealand longfin eel (Anguilla dieffenbachii) has decreased by around 75 percent since heavy levels of commercial fishing began in the early 1970s. Given the unsustainability of existing regulatory policy, a deterministic multiple-cohort bioeconomic model is developed and applied to this system to gain insight into the optimal age structure and harvest profile for this species. This model illustrates that the rapid recovery of this fishery would rely on the ability of managers to sufficiently limit the harvest of more valuable year classes to allow for the rebuilding of spawning biomass. The importance of decreasing the degree of exploitation within the fishable population and lessening the time that longfins are available to harvest through higher minimum weight restrictions is also highlighted. The need for conservative management is reinforced when uncertainty regarding the effect of exploitation on fecundity and barriers to the successful instigation of these policies are considered.Keywords: Theoretical and Empirical Bio-Economic Modelling, Fisheries Economic

Modelling the regulatory system for diabetes mellitus with a threshold window

Piecewise (or non-smooth) glucose-insulin models with threshold windows for type 1 and type 2 diabetes mellitus are proposed and analyzed with a view to improving understanding of the glucose-insulin regulatory system. For glucose-insulin models with a single threshold, the existence and stability of regular, virtual, pseudo-equilibria and tangent points are addressed. Then the relations between regular equilibria and a pseudo-equilibrium are studied. Furthermore, the sufficient and necessary conditions for the global stability of regular equilibria and the pseudo-equilibrium are provided by using qualitative analysis techniques of non-smooth Filippov dynamic systems. Sliding bifurcations related to boundary node bifurcations were investigated with theoretical and numerical techniques, and insulin clinical therapies are discussed. For glucose-insulin models with a threshold window, the effects of glucose thresholds or the widths of threshold windows on the durations of insulin therapy and glucose infusion were addressed. The duration of the effects of an insulin injection is sensitive to the variation of thresholds. Our results indicate that blood glucose level can be maintained within a normal range using piecewise glucose-insulin models with a single threshold or a threshold window. Moreover, our findings suggest that it is critical to individualize insulin therapy for each patient separately, based on initial blood glucose levels

The Role of Motor Learning in Spatial Adaptation near a Tool

Some visual-tactile (bimodal) cells have visual receptive fields (vRFs) that overlap and extend moderately beyond the skin of the hand. Neurophysiological evidence suggests, however, that a vRF will grow to encompass a hand-held tool following active tool use but not after passive holding. Why does active tool use, and not passive holding, lead to spatial adaptation near a tool? We asked whether spatial adaptation could be the result of motor or visual experience with the tool, and we distinguished between these alternatives by isolating motor from visual experience with the tool. Participants learned to use a novel, weighted tool. The active training group received both motor and visual experience with the tool, the passive training group received visual experience with the tool, but no motor experience, and finally, a no-training control group received neither visual nor motor experience using the tool. After training, we used a cueing paradigm to measure how quickly participants detected targets, varying whether the tool was placed near or far from the target display. Only the active training group detected targets more quickly when the tool was placed near, rather than far, from the target display. This effect of tool location was not present for either the passive-training or control groups. These results suggest that motor learning influences how visual space around the tool is represented

Importance of user cost to the optimal management of multiplecohort fish populations

Exploitation frequently reduces the mean age of a fish population, particularly where a lack of property rights stimulates producers to ignore the user cost of harvest. The authors demonstrate that harvesting a young fish bears a significant efficiency cost when the multiple benefits accruing to its protection are recognised. This is magnified when the full complement of year classes within a fish population is considered. These findings identify the importance of protecting older year classes using rights-based management and age/size restrictions, although their successful application can be problematic. In addition, the importance of incorporating more detail in bioeconomic models of multiple-cohort fisheries is highlighted, as underestimating the magnitude of user costs associated with the cropping of younger fish will promote recommendations for inefficient harvest levels. These factors are demonstrated in an application of an optimal control model to the New Zealand longfin eel (Anguilla dieffenbachii) fishery

Motor learning results for the test phase.

<p>Panel A: Movement time (ms) as a function of training group, Panel B: mean end-point variability (mm) as a function of training group, Panel C: mean signed error (mm) as a function of training group. Error bars represent 95% confidence intervals.</p

Reaction time (RT; ms) as a function of training condition, tool location, and target location.

<p>The active training group responded to targets more quickly when the tool was held near the display rather than far from it (training condition x tool location interaction: p = .014). This effect did not interact with target location (3-way interaction, p = .114). Error bars represent 95% confidence intervals.</p

Reaction time (RT; ms) as a function of cue location, target location and training condition.

<p>RT was lower when the target appeared in the cued location than in the uncued location, p <.001 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028999#pone.0028999-Cohen1" target="_blank">[61]</a>. This effect did not interact with training condition (p = .861) or tool location (p = .145). Error bars represent 95% confidence intervals.</p

Measures of performance accuracy (percent correct), sensitivity (d'), and response bias (β) in the visual detection task.

<p>Measures of performance accuracy (percent correct), sensitivity (d'), and response bias (β) in the visual detection task.</p

Experimental set up.

<p>The layout of the start position and targets for the motor learning task is shown in Panel A. The arrangement of the fixation cross and cue-target placeholders for the visual detection task is shown in Panel B.</p