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Active adaptive conservation of threatened species in the face of uncertainty

ECOLOGICAL APPLICATIONS 2010年第5期20卷 1476-1489页

作者： McDonald-Madden, Eve Probert, William J. M. Hauser, Cindy E. Runge, Michael C. Possingham, Hugh P. Jones, Menna E. Moore, Joslin L. Rout, Tracy M. Vesk, Peter A. Wintle, Brendan A. CSIRO Sustainable Ecosyst St Lucia Qld 4067 Australia Univ Queensland Dept Math St Lucia Qld 4069 Australia Univ Melbourne Australian Ctr Excellence Risk Anal Melbourne Vic 3010 Australia US Geol Survey Patuxent Wildlife Res Ctr Laurel MD 20708 USA Univ Tasmania Sch Zool Hobart Tas 7001 Australia Dept Primary Ind & Water Hobart Tas 7001 Australia Univ Melbourne Ctr Appl Environm Decis Anal Sch Bot Parkville Vic 3010 Australia

Adaptive management has a long history in the natural resource management literature, but despite this, few practitioners have developed adaptive strategies to conserve threatened species. Active adaptive management provides a framework for valuing learning by measuring the degree to which it improves long-run management outcomes. The challenge of an active adaptive approach is to find the correct balance between gaining knowledge to improve management in the future and achieving the best short-term outcome based on current knowledge. We develop and analyze a framework for active adaptive management of a threatened species. Our case study concerns a novel facial tumor disease affecting the Australian threatened species Sarcophilus harrisii: the Tasmanian devil. We use stochastic dynamic programming with Bayesian updating to identify the management strategy that maximizes the Tasmanian devil population growth rate, taking into account improvements to management through learning to better understand disease latency and the relative effectiveness of three competing management options. Exactly which management action we choose each year is driven by the credibility of competing hypotheses about disease latency and by the population growth rate predicted by each hypothesis under the competing management actions. We discover that the optimal combination of management actions depends on the number of sites available and the time remaining to implement management. Our approach to active adaptive management provides a framework to identify the optimal amount of effort to invest in learning to achieve long-run conservation objectives.

关键词： active adaptive management Bayesian updating decision theory learning Markov decision process Sarcophilus harrisii stochastic dynamic programming Tasmania, Australia Tasmanian devil facial tumor disease

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Accounting for habitat loss rates in sequential reserve selection: Simple methods for large problems

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BIOLOGICAL CONSERVATION 2007年第3期136卷 470-482页

作者： Moilanen, Atte Cabeza, Mar Univ Helsinki Dept Biol & Environm Sci Metapopulat Res Grp FIN-00014 Helsinki Finland

We develop reserve selection methods for maximizing either species retention in the landscape or species representation in reserve areas. These methods are developed in the context of sequential reserve selection, where site acquisition is done over a number of years, yearly budgets are limited and habitat loss may cause some sites to become unavailable during the planning period. The main methodological development of this study is what we call a site-ordering algorithm, which maximizes representation within selected sites at the end of the planning period, while accounting for habitat loss rates in optimization. Like stochastic dynamic programming, which is an approach that guarantees a globally optimal solution, the ordering algorithm generates a sequence in which sites are ideally acquired. As a distinction from stochastic dynamic programming, the ordering is generated via a relatively fast approximate process, which involves hierarchic application of the principle of maximization of marginal gain. In our comparisons, the ordering algorithm emerges a clear winner, it does well in terms of retention and is superior to simple heuristics in terms of representation within reserves. Unlike stochastic dynamic programming, the ordering algorithm is applicable to relatively large problem sizes, with reasonable computation times expected for problems involving thousands of sites. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： site selection representation retention site ordering algorithm optimization stochastic dynamic programming

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Urban water management: optimal price and investment policy under climate variability

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AUSTRALIAN JOURNAL OF AGRICULTURAL AND RESOURCE ECONOMICS 2009年第2期53卷 175-192页

作者： Hughes, Neal Hafi, Ahmed Goesch, Tim Australian Bur Agr & Resource Econ Canberra ACT 2601 Australia

Australian urban water utilities face a significant challenge in designing appropriate demand management and supply augmentation policies in the presence of significant water scarcity and climate variability. This article considers the design of optimal demand management and supply augmentation policies for urban water. In particular, scarcity pricing is considered as a potential alternative to the predominant demand management policy of water restrictions. A stochastic dynamic programming model of an urban water market is developed based on data from the ACT region. Given a specification of the demand and supply for urban water state dependent optimal price and investment policies are estimated. The results illustrate how the optimal urban water price varies inversely with the prevailing storage level and how the optimal timing of investment differs significantly between rain dependent and rain independent augmentation options.

关键词： demand management scarcity pricing stochastic dynamic programming supply augmentation urban water

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Optimal Strategies for Managing Wildlife Harvest Under System Change

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JOURNAL OF WILDLIFE MANAGEMENT 2021年第5期85卷 847-854页

作者： Tucker, Anna M. Runge, Michael C. US Geol Survey Eastern Ecol Sci Ctr Patuxent Res Refuge 12100 Beech Forest Rd Laurel MD 20708 USA Iowa State Univ Dept Nat Resources Ecol & Management US Geol Survey Iowa Cooperat Fish & Wildlife Res Unit Ames IA 50011 USA

Wildlife populations are experiencing shifting dynamics due to climate and landscape change. Management policies that fail to account for non-stationary dynamics may fail to achieve management objectives. We establish a framework for understanding optimal strategies for managing a theoretical harvested population under non-stationarity. Building from harvest theory, we develop scenarios representing changes in population growth rate (r) or carrying capacity (K) and derive time-dependent optimal harvest policies using stochastic dynamic programming. We then evaluate the cost of falsely assuming stationarity by comparing the outcomes of forward projections in which either the optimal policy or a stationary policy is applied. When K declines over time, the stationary policy leads to an underharvest of the population, resulting in less harvest over the short term but leaving the population in a higher-value state. When r declines over time, the stationary policy leads to overharvest, resulting in greater harvest returns in the short term but leaving the population in a lower and potentially more vulnerable state. This work demonstrates the basic properties of time-dependent harvest management and provides a framework for evaluating the many outstanding questions about optimal management strategies under climate change. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.

关键词： climate change harvest management non‐ stationary dynamics stochastic dynamic programming

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Energetic state and the performance of dawn chorus in silvereyes (Zosterops lateralis)

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BEHAVIORAL ECOLOGY AND SOCIOBIOLOGY 2007年第4期61卷 579-587页

作者： Barnett, Craig A. Briskie, James V. Univ Canterbury Sch Biol Sci Christchurch New Zealand

stochastic dynamic programming (SDP) models predict that males singing to attract a mate should concentrate singing in what has been termed the dawn chorus. This is because male birds should have a variable surplus of fat in the morning that can be used to fuel singing, with the amount of fat available dependent upon such factors as his quality, foraging success and risk of predation. In this manner, the dawn chorus can act as an indicator of male quality in the context of female mate choice. We test a key prediction of SDP models of singing behaviour that males with greater fat levels should sing more. We conducted an experiment where we recorded the dawn chorus of male silvereyes (Zosterops lateralis) on three consecutive days. Each male received supplementary food on the second day, which enabled us to sample his dawn chorus before, during and after food supplementation. We also collected data on the effect of supplementary food on the body mass of silvereyes. As predicted by SDP models, we found that silvereyes sang for a greater proportion of the time after receiving supplementary food. Supplementary food also had a significant effect on the complexity of a male song, indicating that males not only increased the quantity of their song but also the quality of their song when they received extra food. As the provision of supplementary food significantly increased the mass of fed birds, our results support a causal link between male energy reserves and his ability to perform the dawn chorus.

关键词： dawn chorus mate quality stochastic dynamic programming songbirds

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Determining the optimal strategy for the live-attenuated herpes zoster vaccine in adults

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VACCINE 2018年第41期36卷 6237-6247页

作者： Harvey, M. J. Denton, B. T. Prosser, L. A. Hutton, D. W. Univ Michigan Dept Hlth Management & Policy Ann Arbor MI 48109 USA Aquarius Populat Hlth London N6 5HX England Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA Univ Michigan Dept Pediat & Communicable Dis Child Hlth Evaluat & Res Ctr Ann Arbor MI 48109 USA

The optimal strategy for the vaccinating against herpes zoster (HZ) vaccine remains unknown. Cost-effectiveness analyses provide insight to the most cost-effective age groups but results vary across studies. The optimal strategy is important given that vaccine efficacy and duration vary depending on vaccination age. Therefore, small changes from the optimal age can affect long-term outcomes and produce sub-optimal results. The objective of this research was to determine the optimal timing policy for HZ vaccination. We simulated cohorts of men and women and use stochastic dynamic programming to evaluate the decision to vaccinate or defer each year from age 50 to 100. If the decision was to defer, the cohort risked developing HZ. If HZ occurred, the cohort was subjected to cost and quality-adjusted life year (QALY) loss for a typical HZ infection (including complications) at that age. If HZ did not occur, the decision was evaluated at the next age. Then, we extend the model to consider the case in which a booster vaccine is available. A set of probabilistic sensitivity analyses were conducted to check model robustness. Results show the optimal policy for women is to vaccinate between ages 66 and 77, and for men between ages 66 and 74, assuming a willingness to pay (WTP) of $100,000 per QALY. It becomes optimal to vaccinate earlier if a booster vaccine is available, and women have a wider range of ages than men. This research is the first to examine exactly when the HZ vaccine should be administered. It is also the first study, to our knowledge, that used stochastic dynamic programming to examine the question of a second dose for any vaccine. This research provides the first simple policy on when to vaccinate and re-vaccinate against HZ. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Herpes zoster stochastic dynamic programming Optimization Health policy

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Financial optimisation and risk management in refining activities

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2006年第1-2期26卷 62-82页

作者： Fiorenzani, Stefano Risk Management Off Edison Spa Foro Buonaparte 31 I-20121 Milan Italy

The real options approach has become a benchmark in real assets evaluation and optimal management problems, especially in liberalised and competitive markets such as the oil and hydrocarbon markets. This paper describes how the same approach can be a useful tool for both risk management decisions and the financial optimisation problem. Refineries are black boxes, which can be used for the transformation of crude oil into more refined hydrocarbon products. These black boxes are characterised by operational flexibilities and constraints, which should be optimally managed in order to maximise the refiner's economic goals. stochastic dynamic programming represents the right mathematical instrument employed to solve the decision-making problem in such an economic environment.

关键词： real option distillation process downstream process stochastic dynamic programming earning/profit at risk

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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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Optimal management of molds in stored corn

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AGRICULTURAL SYSTEMS 2008年第3期98卷 220-227页

作者： Yigezu, Yigezu A. Alexander, Corinne E. Preckel, Paul V. Maier, D. E. Woloshuk, C. P. Mason, L. J. Lawrence, J. Moog, D. J. Purdue Univ Dept Agr Econ W Lafayette IN 47907 USA Kansas State Univ Dept Grain Sci & Ind Manhattan KS 66506 USA Purdue Univ Dept Bot & Plant Pathol W Lafayette IN 47907 USA Purdue Univ Dept Entomol W Lafayette IN USA Purdue Univ Dept Agr & Biol Engn W Lafayette IN 47907 USA

Long term storage of corn is becoming more common due to the recent increase in the demand for corn by ethanol plants. Infection of maize kernels by toxigenic fungi remains a challenging storage problem despite decades of research. Experts in storage management propose the use of a combination of preventive and monitoring-based responsive strategies in response to mold risks. In this paper, a stochastic dynamic programming model is solved to determine the expected profitability and optimal combination. timing, and intensity of the proposed mold management strategies using farmers' existing infrastructure. The results show that even with relatively high monitoring costs, maintaining high quality grain using a monitoring-based optimal mold management strategy costs less than the benefit it fetches. The current typical practice by Indiana farmers of aerating the grain until the end of December and doing nothing thereafter bears a high risk of economic losses if grain is to be stored until later during the summer. Generally, the optimal mold management strategy depends on monitoring the biophysical conditions of the grain and the time period under consideration. If the in-bin temperature is high and less than 5% of kernels are mold damaged, then aerating when the outside temperature is at least 3 degrees C less than the in-bin temperature and continuing to store the grain is the optimal strategy. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Mold management Stored corn Expected profitability Integrated pest management Monitoring Aeration stochastic dynamic programming

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Data-driven optimization for Dallas Fort Worth International Airport deicing activities

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ANNALS OF OPERATIONS RESEARCH 2018年第1-2期263卷 361-384页

作者： Fan, Huiyuan Tarun, Prashant K. Chen, Victoria C. P. Shih, Dachuan T. Rosenberger, Jay M. Kim, Seoung Bum Horton, Robert A. Johnson Controls 631 South Richland Ave York PA 17403 USA Missouri Western State Univ Steven L Craig Sch Business St Joseph MO 64507 USA Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76019 USA Tenet PFS 13737 Noel RdSuite 100 Dallas TX 75240 USA Korea Univ Dept Ind Management Engn Seoul 02841 South Korea Dallas Ft Worth Int Airport 3200 East Airfield Dr Dfw Airport TX 75261 USA

Airplane deicing is a safety measure to eliminate/prevent icing on airplanes that can lead to airflow disruption and emergency conditions. Aircraft deicing/anti-icing fluids (ADF) are high in glycol content. At Dallas Fort Worth International Airport (DFW), the major aircraft deicing activities are conducted at designated deicing pads called Source Isolation Deicing Sites, where ADF run-off can be captured and conveyed into the airport's glycol collection system. A portion of ADF drips from the aircraft during taxiing and shears off the aircraft during take-off, entering nearby waterways without treatment. Glycol acts as a nutrient for bacteria in the airport's receiving waterways, resulting in an increase in bacterial growth and a subsequent reduction in dissolved oxygen (DO), potentially endangering aquatic life. This paper proposes a prototype data-driven deicing activities management framework for DFW to address the complexity of airport deicing operations and its impacts. The proposed framework uses stochastic dynamic programming (SDP) to assign airplanes in each hour to deicing pad locations, so as to maximize DO in the receiving waters, subject to airport constraints. Some data were artificially generated using the available knowledge of airport operations. The state transition equations in SDP were estimated. The proposed framework was demonstrated using three cases during major deicing events. Improvements in DO compared with actual DO recorded in the data were mixed;however, the results motivated DFW to implement a new data collection process to replace the artificially-generated data, so that a more accurate optimization could be conducted in the future.

关键词： Data-driven optimization stochastic dynamic programming Airport deicing Water quality Glycol Dissolved oxygen

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