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Physiological adaptations to climate change in pro-ovigenic parasitoids

JOURNAL OF THEORETICAL BIOLOGY 2012年 309卷 67-77页

作者： Denis, Damien Pierre, Jean-Sebastien van Baaren, Joan van Alphen, Jacques J. M. Univ Rennes 1 UMR ECOBIO 6553 F-35042 Rennes France Univ Amsterdam IBED NL-1090 GE Amsterdam Netherlands Netherlands Ctr Biodivers Nat NL-2300 RA Leiden Netherlands

Temperature increase can affect physiological and behavioural constraints. Here, we use a stochastic dynamic modelling approach to predict changes in physiological adaptations and behaviour in response to temperature increase of pro-ovigenic parasitoids (i.e., parasitoids that mature all of their eggs before emergence). Adults of most species of parasitoids, are not capable of de novo lipogenesis. The allocation of lipids accumulated during the larval stage determines adult lifespan and fecundity. In females, lipids can be allocated either to egg production or to adult lipid reserves leading to a trade-off between fecundity and lifespan. Our results show that selection by an increase in ambient temperature, favours a smaller initial egg load and a larger amount of lipids for maintenance. The cost of habitat exploitation increases with temperature because the rate of lipid consumption increases. Hence, lifetime reproductive success decreases. When the optimal activity rate shifts to match the higher ambient temperature, these effects become less pronounced. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Parasitoids Pro-ovigenic Climate change Egg load stochastic dynamic programming

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A simple method for dealing with large state spaces

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METHODS IN ECOLOGY AND EVOLUTION 2012年第6期3卷 949-957页

作者： Schapaugh, Adam W. Tyre, Andrew J. Univ Nebraska Sch Nat Resources Lincoln NE 68510 USA

Most sequential decision-making problems in conservation can be viewed conceptually and modelled as a Markov decision process. The goal in this context is to construct a policy that associates each state of the system with a particular action. This policy should offer optimal performance in the sense of maximizing or minimizing a specified conservation objective dynamic programming algorithms rely on explicit enumeration to derive the optimal policy. This is problematic from a computational perspective as the size of the state space grows exponentially with the number of state variables. We present a state aggregation method where the idea is to capture the most important aspects of the original Markov decision process, find an optimal policy over this reduced space and use this as an approximate solution to the original problem. Applying the aggregation method to a species reintroduction problem, we demonstrate how we were able to reduce the number of states by 75% and reduce the size of the transition matrices by almost 94% (324 vs. 5184), and the abstract action matched the optimal action more than 86% of the time. We conclude that the aggregation method is not a panacea for the curse of dimensionality, but it does advance our ability to construct approximately optimal policies in systems with large state spaces.

关键词： abstraction curse of dimensionality Markov decision process state space stochastic dynamic programming

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Setting Realistic Recovery Targets for Two Interacting Endangered Species, Sea Otter and Northern Abalone

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CONSERVATION BIOLOGY 2012年第6期26卷 1016-1025页

作者： Chades, Iadine Curtis, Janelle M. R. Martin, Tara G. Ecosci Precinct CSIRO Ecosyst Sci Dutton Pk Qld 4001 Australia Fisheries & Oceans Canada Pacific Biol Stn Nanaimo BC V9S 3E1 Canada

Failure to account for interactions between endangered species may lead to unexpected population dynamics, inefficient management strategies, waste of scarce resources, and, at worst, increased extinction risk. The importance of species interactions is undisputed, yet recovery targets generally do not account for such interactions. This shortcoming is a consequence of species-centered legislation, but also of uncertainty surrounding the dynamics of species interactions and the complexity of modeling such interactions. The northern sea otter (Enhydra lutris kenyoni) and one of its preferred prey, northern abalone (Haliotis kamtschatkana), are endangered species for which recovery strategies have been developed without consideration of their strong predator-prey interactions. Using simulation-based optimization procedures from artificial intelligence, namely reinforcement learning and stochastic dynamic programming, we combined sea otter and northern abalone population models with functional-response models and examined how different management actions affect population dynamics and the likelihood of achieving recovery targets for each species through time. Recovery targets for these interacting species were difficult to achieve simultaneously in the absence of management. Although sea otters were predicted to recover, achieving abalone recovery targets failed even when threats to abalone such as predation and poaching were reduced. A management strategy entailing a 50% reduction in the poaching of northern abalone was a minimum requirement to reach short-term recovery goals for northern abalone when sea otters were present. Removing sea otters had a marginally positive effect on the abalone population but only when we assumed a functional response with strong predation pressure. Our optimization method could be applied more generally to any interacting threatened or invasive species for which there are multiple conservation objectives.

关键词： Enhydra lutris Haliotis kamtschatkana predator-prey relations reinforcement learning species interaction stochastic dynamic programming

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Simple rules to contain an invasive species with a complex life cycle and high dispersal capacity

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JOURNAL OF APPLIED ECOLOGY 2012年第1期49卷 52-62页

作者： Pichancourt, Jean-Baptiste Chades, Iadine Firn, Jennifer van Klinken, Rieks D. Martin, Tara G. CSIRO Ecosyst Sci EcoSci Precinct Dutton Pk Qld 4102 Australia Queensland Univ Technol Brisbane Qld 4001 Australia

1. Designing practical rules for controlling invasive species is a challenging task for managers, particularly when species are long-lived, have complex life cycles and high dispersal capacities. Previous findings derived from plant matrix population analyses suggest that effective control of long-lived invaders may be achieved by focusing on killing adult plants. However, the cost-effectiveness of managing different life stages has not been evaluated. 2. We illustrate the benefits of integrating matrix population models with decision theory to undertake this evaluation, using empirical data from the largest infestation of mesquite (Leguminosae: Prosopis spp) within Australia. We include in our model the mesquite life cycle, different dispersal rates and control actions that target individuals at different life stages with varying costs, depending on the intensity of control effort. We then use stochastic dynamic programming to derive cost-effective control strategies that minimize the cost of controlling the core infestation locally below a density threshold and the future cost of control arising from infestation of adjacent areas via seed dispersal. 3. Through sensitivity analysis, we show that four robust management rules guide the allocation of resources between mesquite life stages for this infestation: (i) When there is no seed dispersal, no action is required until density of adults exceeds the control threshold and then only control of adults is needed;(ii) when there is seed dispersal, control strategy is dependent on knowledge of the density of adults and large juveniles (LJ) and broad categories of dispersal rates only;(iii) if density of adults is higher than density of LJ, controlling adults is most cost-effective;(iv) alternatively, if density of LJ is equal or higher than density of adults, management efforts should be spread between adults, large and to a lesser extent small juveniles, but never saplings. 4. Synthesis and applications. In this study,

关键词： cost-effective control decision theory detection eradication matrix population model mesquite search source-sink stochastic dynamic programming weed

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Optimal asset allocation for DC pension plans under inflation

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INSURANCE MATHEMATICS & ECONOMICS 2012年第1期51卷 172-181页

作者： Han, Nan-wei Hung, Mao-wei Natl Taiwan Univ Dept Int Business Coll Management Taipei 10764 Taiwan Takming Univ Sci & Technol Coll Finance Dept Banking & Finance Taipei Taiwan

In this paper, the stochastic dynamic programming approach is used to investigate the optimal asset allocation for a defined-contribution pension plan with downside protection under stochastic inflation. The plan participant invests the fund wealth and the stochastic interim contribution flows into the financial market. The nominal interest rate model is described by the Cox-Ingersoll-Ross (Cox et al., 1985) dynamics. To cope with the inflation risk, the inflation indexed bond is included in the asset menu. The retired individuals receive an annuity that is indexed by inflation and a downside protection on the amount of this annuity is considered. The closed-form solution is derived under the CRRA utility function. Finally, a numerical application is presented to characterize the dynamic behavior of the optimal investment strategy. (C) 2012 Published by Elsevier B.V.

关键词： Defined-contribution pension plan Inflation risk Inflation-indexed bond stochastic dynamic programming Minimum guarantee

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Bayesian networks and the quest for reserve adequacy

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BIOLOGICAL CONSERVATION 2012年 152卷 178-186页

作者： Schapaugh, Adam W. Tyre, Andrew J. Univ Nebraska Sch Nat Resources Lincoln NE 68510 USA

The fundamental goal of conservation planning is biodiversity persistence, yet most reserve selection methods prioritize sites using occurrence data. We describe a method that integrates correlates of persistence for multiple species into a single currency - site quality. Site quality is, in turn, an explicit measure of performance used in optimization. We develop a Bayesian network to assess site quality, which assigns an expected value to a property based on criteria arrayed into a causal diagram. We then use stochastic dynamic programming to determine whether an organization should acquire or reject a site placed on the public market. Our framework for assessing sites and making land acquisition decisions represents a compromise between the use of generic spatial design criteria and more intensive computational tools, like spatially-explicit population models. There is certainly a loss of precision by using site quality as a surrogate for more direct measures of persistence. However, we believe this simplification is defensible when sufficient data, expertise, or other resources are lacking. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Bayesian network Interior least tern Persistence Piping plover Reserve adequacy Reserve selection stochastic dynamic programming Whooping crane

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Strategic bidding of ancillary services for a hydro power producer

Strategic bidding of ancillary services for a hydro power pr...

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International Conference on the European Energy Market

作者： Hubert Abgottspon Goran Andersson Power Systems Laboratory ETH Zurich

ISBN: (纸本)9781479925582

This paper presents an agent-based simulator for examination of a secondary control market dominated by hydro power producer as decision support for one of the market participants. Proposed is a Q-learning algorithm for determining possible strategic behavior. Adaptive learning is made possible by application of certain characteristics to agents quantity price pairs bids. Considered are for each agent its portfolio of different hydro power plants with their water values estimated by a stochastic dynamic programming scheme. The simulator is applied to the Swiss system where strategic behavior will be shown. Additionally it is analyzed how single agents could make use of strategic behavior in case of special occurrences in the market.

关键词： Ancillary services market Price-maker Hydro power Multiagent systems stochastic dynamic programming Self-scheduling

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Assessment of Parameter Uncertainty in Autoregressive Streamflow Models for stochastic Long-term Hydrothermal Scheduling

Assessment of Parameter Uncertainty in Autoregressive Stream...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Bezerra, Bernardo Veiga, Alvaro Barroso, Luiz Augusto Pereira, Mario Pontificia Univ Catolica Rio de Janeiro Rio de Janeiro Brazil PSR Rio De Janeiro Brazil

ISBN: (纸本)9781467327299

Hydrothermal systems optimal scheduling requires the representation of uncertainties in future inflows in order to hedge against adverse future low inflows by committing thermal plants, and also to store water in reservoirs while avoiding spillage when high future inflows occur. stochastic optimization technics has been widely used as a tool for long-term hydrothermal scheduling. These models rely on Monte Carlo simulation in order to capture the inflow uncertainty during the planning horizon. Since the parameters of these models are typically estimated from historical data, it is not surprising that the actual performance of a chosen reservoirs strategy often significantly differs from the designer's initial expectations due to unavoidable modeling ambiguity. The objective of this work is to assess the impact of inflow parameter uncertainty on the stochastic hydrothermal scheduling. The results presented in this work may be useful for the improvement of stochastic optimization techniques. The results presented show that the uncertainty on the parameters of the stochastic model consists on a supplementary source of risk that should be taken into account in the scheduling model.

关键词： stochastic dynamic programming parameter uncertainty hydro scheduling bootstrap

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Optimal Electric Energy Storage Operation

Optimal Electric Energy Storage Operation

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Qin, Junjie Sevlian, Raffi Varodayan, David Rajagopal, Ram Stanford Univ Inst Computat & Math Engn Stanford CA 94305 USA

ISBN: (纸本)9781467327299

Estimating the arbitrage value of storage is an important problem in power systems planning. Various studies have reported different values based numerical solutions of variations of a basic model. In this paper, we instead rely on a closed form solution for storage control. The closed form highlights the right type of forecasting that is required and allows large horizon problems to be solved. We study various scenarios and provide a simple methodology for evaluating the arbitrage value of storage.

关键词： Energy storage operation stochastic dynamic programming warehousing problem

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Injection Mold Replacement Analysis in Automotive Industry

Injection Mold Replacement Analysis in Automotive Industry

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Suwannabool, T. Sutivong, D. Chulalongkorn Univ Dept Ind Engn Bangkok 10330 Thailand

ISBN: (纸本)9781467329453

Injection mold replacement decisions in the automotive industry are usually made by managers and engineers, who based primarily on their own experience. This paper presents an injection mold replacement analysis through the use of stochastic dynamic programming and benchmark from the case study. The algorithm determines a optimal production volume and age that give the minimum expected cost under time constraint. The solutions are presented as a decision chart for easy application and interpretation.

关键词： Injection mold replacement analysis stochastic dynamic programming maintenance decision chart

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