Deriving optimal fishing effort for managing Australia's Moreton Bay multispecies trawl fishery with aggregated effort data

Deriving an estimate of optimal fishing effort or even an approximate estimate is very valuable for managing fisheries with multiple target species. The most challenging task associated with this is allocating effort to individual species when only the total effort is recorded. Spatial information on the distribution of each species within a fishery can be used to justify the allocations, but often such information is not available. To determine the long-term overall effort required to achieve maximum sustainable yield (MSY) and maximum economic yield (MEY), we consider three methods for allocating effort: (i) optimal allocation, which optimally allocates effort among target species; (ii) fixed proportions, which chooses proportions based on past catch data; and (iii) economic allocation, which splits effort based on the expected catch value of each species. Determining the overall fishing effort required to achieve these management objectives is a maximizing problem subject to constraints due to economic and social considerations. We illustrated the approaches using a case study of the Moreton Bay Prawn Trawl Fishery in Queensland (Australia). The results were consistent across the three methods. Importantly, our analysis demonstrated the optimal total effort was very sensitive to daily fishing costs-the effort ranged from 9500-11 500 to 6000-7000, 4000 and 2500 boat-days, using daily cost estimates of $0, $500, $750, and $950, respectively. The zero daily cost corresponds to the MSY, while a daily cost of $750 most closely represents the actual present fishing cost. Given the recent debate on which costs should be factored into the analyses for deriving MEY, our findings highlight the importance of including an appropriate cost function for practical management advice. The approaches developed here could be applied to other multispecies fisheries where only aggregated fishing effort data are recorded, as the literature on this type of modelling is sparse.

Optimal allocation of conservation effort among subpopulations of a threatened species: How important is patch quality?

Money is often a limiting factor in conservation, and attempting to conserve endangered species can be costly. Consequently, a framework for optimizing fiscally constrained conservation decisions for a single species is needed. In this paper we find the optimal budget allocation among isolated subpopulations of a threatened species to minimize local extinction probability. We solve the problem using stochastic dynamic programming, derive a useful and simple alternative guideline for allocating funds, and test its performance using forward simulation. The model considers subpopulations that persist in habitat patches of differing quality, which in our model is reflected in different relationships between money invested and extinction risk. We discover that, in most cases, subpopulations that are less efficient to manage should receive more money than those that are more efficient to manage, due to higher investment needed to reduce extinction risk. Our simple investment guideline performs almost as well as the exact optimal strategy. We illustrate our approach with a case study of the management of the Sumatran tiger, Panthera tigris sumatrae, in Kerinci Seblat National Park (KSNP), Indonesia. We find that different budgets should be allocated to the separate tiger subpopulations in KSNP. The subpopulation that is not at risk of extinction does not require any management investment. Based on the combination of risks of extinction and habitat quality, the optimal allocation for these particular tiger subpopulations is an unusual case: subpopulations that occur in higher-quality habitat (more efficient to manage) should receive more funds than the remaining subpopulation that is in lower-quality habitat. Because the yearly budget allocated to the KSNP for tiger conservation is small, to guarantee the persistence of all the subpopulations that are currently under threat we need to prioritize those that are easier to save. When allocating resources among subpopulations of a threatened species, the combined effects of differences in habitat quality, cost of action, and current subpopulation probability of extinction need to be integrated. We provide a useful guideline for allocating resources among isolated subpopulations of any threatened species. © 2010 by the Ecological Society of America.

Subpopulation triage: How to allocate conservation effort among populations

Threatened species often exist in a small number of isolated subpopulations. Given limitations on conservation spending, managers must choose from strategies that range from managing just one subpopulation and risking all other subpopulations to managing all subpopulations equally and poorly, thereby risking the loss of all subpopulations. We took an economic approach to this problem in an effort to discover a simple rule of thumb for optimally allocating conservation effort among subpopulations. This rule was derived by maximizing the expected number of extant subpopulations remaining given n subpopulations are actually managed. We also derived a spatiotemporally optimized strategy through stochastic dynamic programming. The rule of thumb suggested that more subpopulations should be managed if the budget increases or if the cost of reducing local extinction probabilities decreases. The rule performed well against the exact optimal strategy that was the result of the stochastic dynamic program and much better than other simple strategies (e.g., always manage one extant subpopulation or half of the remaining subpopulation). We applied our approach to the allocation of funds in 2 contrasting case studies: reduction of poaching of Sumatran tigers (Panthera tigris sumatrae) and habitat acquisition for San Joaquin kit foxes (Vulpes macrotis mutica). For our estimated annual budget for Sumatran tiger management, the mean time to extinction was about 32 years. For our estimated annual management budget for kit foxes in the San Joaquin Valley, the mean time to extinction was approximately 24 years. Our framework allows managers to deal with the important question of how to allocate scarce conservation resources among subpopulations of any threatened species. © 2008 Society for Conservation Biology.

Optimal allocation of a cross-connection and sectionalizers in distribution systems

In this paper, the placement of sectionalizers, as well as, a cross-connection is optimally determined so that the objective function is minimized. The objective function employed in this paper consists of two main parts, the switch cost and the reliability cost. The switch cost is composed of the cost of sectionalizers and cross-connection and the reliability cost is assumed to be proportional to a reliability index, SAIDI. To optimize the allocation of sectionalizers and cross-connection problem realistically, the cost related to each element is considered as discrete. In consequence of binary variables for the availability of sectionalizers, the problem is extremely discrete. Therefore, the probability of local minimum risk is high and a heuristic-based optimization method is needed. A Discrete Particle Swarm Optimization (DPSO) is employed in this paper to deal with this discrete problem. Finally, a testing distribution system is used to validate the proposed method.

Is implementing criterion-referenced assessment worth the effort with gen Y?

Well implemented criterion-referenced assessment (CRA) requires dedicated time and effort, especially in describing realistic expectations of evidence of achievement to students in the form of criteria sheets (or grading rubrics). It is also takes time out of delivering content to teach students how to judge their own work using criteria sheets. In 2007, to engage third year Microbiology students in using criteria sheets for the first time in their degree, we devised an innovative assessment tutorial supported by online resources. We were sceptical of much of the literature that reported ‘agreed’ characteristics of our predominantly gen Y cohort, because of the older ages of the majority of authors. These authors claim gen Y has a propensity for digital media, overconfidence in their own abilities and a collaborative orientation. We rejected this stereotype when developing the tutorial. Evaluations by students were positive and there was no dramatic change to grades for the unit. These results are similar to those in the literature for non gen Y cohorts. This lends support to our claim that giving students control over their own learning, irrespective of their generational label, is worth the time and effort.

Optimal allocation and sizing of capacitors to minimize the transmission line loss and to improve the voltage profile

To allocate and size capacitors in a distribution system, an optimization algorithm, called Discrete Particle Swarm Optimization (DPSO), is employed in this paper. The objective is to minimize the transmission line loss cost plus capacitors cost. During the optimization procedure, the bus voltage, the feeder current and the reactive power flowing back to the source side should be maintained within standard levels. To validate the proposed method, the semi-urban distribution system that is connected to bus 2 of the Roy Billinton Test System (RBTS) is used. This 37-bus distribution system has 22 loads being located in the secondary side of a distribution substation (33/11 kV). Reducing the transmission line loss in a standard system, in which the transmission line loss consists of only about 6.6 percent of total power, the capabilities of the proposed technique are seen to be validated.

Optimal allocation and sizing of DGs in distribution networks

In this paper, the placement and sizing of Distributed Generators (DG) in distribution networks are determined optimally. The objective is to minimize the loss and to improve the reliability. The constraints are the bus voltage, feeder current and the reactive power flowing back to the source side. The placement and size of DGs are optimized using a combination of Discrete Particle Swarm Optimization (DPSO) and Genetic Algorithm (GA). This increases the diversity of the optimizing variables in DPSO not to be stuck in the local minima. To evaluate the proposed algorithm, the semi-urban 37-bus distribution system connected at bus 2 of the Roy Billinton Test System (RBTS), which is located at the secondary side of a 33/11 kV distribution substation, is used. The results finally illustrate the efficiency of the proposed method.

Employee work motivation and discretionary work effort

The focus of this thesis is discretionary work effort, that is, work effort that is voluntary, is above and beyond what is minimally required or normally expected to avoid reprimand or dismissal, and is organisationally functional. Discretionary work effort is an important construct because it is known to affect individual performance as well as organisational efficiency and effectiveness. To optimise organisational performance and ensure their long term competitiveness and sustainability, firms need to be able to induce their employees to work at or near their peak level. To work at or near their peak level, individuals must be willing to supply discretionary work effort. Thus, managers need to understand the determinants of discretionary work effort. Nonetheless, despite many years of scholarly investigation across multiple disciplines, considerable debate still exists concerning why some individuals supply only minimal work effort whilst others expend effort well above and beyond what is minimally required of them (Le. they supply discretionary work effort). Even though it is well recognised that discretionary work effort is important for promoting organisational performance and effectiveness, many authors claim that too little is being done by managers to increase the discretionary work effort of their employees. In this research, I have adopted a multi-disciplinary approach towards investigating the role of monetary and non-monetary work environment characteristics in determining discretionary work effort. My central research questions were "What non-monetary work environment characteristics do employees perceive as perks (perquisites) and irks (irksome work environment characteristics)?" and "How do perks, irks and monetary rewards relate to an employee's level of discretionary work effort?" My research took a unique approach in addressing these research questions. By bringing together the economics and organisational behaviour (OB) literatures, I identified problems with the current definition and conceptualisations of the discretionary work effort construct. I then developed and empirically tested a more concise and theoretically-based definition and conceptualisation of this construct. In doing so, I disaggregated discretionary work effort to include three facets - time, intensity and direction - and empirically assessed if different classes of work environment characteristics have a differential pattern of relationships with these facets. This analysis involved a new application of a multi-disciplinary framework of human behaviour as a tool for classifying work environment characteristics and the facets of discretionary work effort. To test my model of discretionary work effort, I used a public sector context in which there has been limited systematic empirical research into work motivation. The program of research undertaken involved three separate but interrelated studies using mixed methods. Data on perks, irks, monetary rewards and discretionary work effort were gathered from employees in 12 organisations in the local government sector in Western Australia. Non-monetary work environment characteristics that should be associated with discretionary work effort were initially identified through a review of the literature. Then, a qualitative study explored what work behaviours public sector employees perceive as discretionary and what perks and irks were associated with high and low levels of discretionary work effort. Next, a quantitative study developed measures of these perks and irks. A Q-sorttype procedure and exploratory factor analysis were used to develop the perks and irks measures. Finally, a second quantitative study tested the relationships amongst perks, irks, monetary rewards and discretionary work effort. Confirmatory factor analysis was firstly used to confirm the factor structure of the measurement models. Correlation analysis, regression analysis and effect-size correlation analysis were used to test the hypothesised relationships in the proposed model of discretionary work effort. The findings confirmed five hypothesised non-monetary work environment characteristics as common perks and two of three hypothesised non-monetary work environment characteristics as common irks. Importantly, they showed that perks, irks and monetary rewards are differentially related to the different facets of discretionary work effort. The convergent and discriminant validities of the perks and irks constructs as well as the time, intensity and direction facets of discretionary work effort were generally confirmed by the research findings. This research advances the literature in several ways: (i) it draws on the Economics and OB literatures to redefine and reconceptualise the discretionary work effort construct to provide greater definitional clarity and a more complete conceptualisation of this important construct; (ii) it builds on prior research to create a more comprehensive set of perks and irks for which measures are developed; (iii) it develops and empirically tests a new motivational model of discretionary work effort that enhances our understanding of the nature and functioning of perks and irks and advances our ability to predict discretionary work effort; and (iv) it fills a substantial gap in the literature on public sector work motivation by revealing what work behaviours public sector employees perceive as discretionary and what work environment characteristics are associated with their supply of discretionary work effort. Importantly, by disaggregating discretionary work effort this research provides greater detail on how perks, irks and monetary rewards are related to the different facets of discretionary work effort. Thus, from a theoretical perspective this research also demonstrates the conceptual meaningfulness and empirical utility of investigating the different facets of discretionary work effort separately. From a practical perspective, identifying work environment factors that are associated with discretionary work effort enhances managers' capacity to tap this valuable resource. This research indicates that to maximise the potential of their human resources, managers need to address perks, irks and monetary rewards. It suggests three different mechanisms through which managers might influence discretionary work effort and points to the importance of training for both managers and non-managers in cultivating positive interpersonal relationships.