State dependent behavior and the Marginal Value Theorem

The Marginal Value Theorem (MVT) is the dominant paradigm inpredicting patch use and numerous tests support its qualitativepredictions. Quantitative tests under complex foraging situationscould be expected to be more variable in their support becausethe MVT assumes behavior maximizes only net energy-intake rate.However across a survey of 26 studies, foragers rather consistently"erred" in staying too long in patches. Such a consistent directionto the errors suggests that the simplifying assumptions ofthe MVT introduce a systematic bias rather than just imprecision. Therefore, I simulated patch use as a state-dependent responseto physiological state, travel cost, predation risk, prey densities,and fitness currencies other than net-rate maximization (e.g.,maximizing survival, reproductive investment, or mating opportunities).State-dependent behavior consistently results in longer patchresidence times than predicted by the MVT or another foragingmodel, the minimize µ/g rule, and these rules fail to closely approximate the best behavioral strategy over a widerange of conditions. Because patch residence times increasewith state-dependent behavior, this also predicts mass regulationbelow maximum energy capacities without direct mass-specificcosts. Finally, qualitative behavioral predictions from theMVT about giving-up densities in patches and the effects oftravel costs are often inconsistent with state-dependent behavior.Thus in order to accurately predict patch exploitation patterns,the model highlights the need to: (1) consider predator behavior(sit-and-wait versus actively foraging); (2) identify activitiesthat can occur simultaneously to foraging (i.e., mate searchor parental care); and (3) specify the range of nutritional states likely in foraging animals. Future predictive modelsof patch use should explicitly consider these parameters.     

Performance evaluation of a new algorithm for the detection of remote homologs with sequence comparison

A detailed analysis of the performance of hybrid, a new sequence alignment algorithm developed by Yu and coworkers that combines Smith Waterman local dynamic programming with a local version of the maximum-likelihood approach, was made to access the applicability of this algorithm to the detection of distant homologs by sequence comparison. We analyzed the statistics of hybrid with a set of nonhomologous protein sequences from the SCOP database and found that the statistics of the scores from hybrid algorithm follows an Extreme Value Distribution with lambda approximately 1, as previously shown by Yu et al. for the case of artificially generated sequences. Local dynamic programming was compared to the hybrid algorithm by using two different test data sets of distant homologs from the PFAM and COGs protein sequence databases. The studies were made with several score functions in current use including OPTIMA, a new score function originally developed to detect remote homologs with the Smith Waterman algorithm. We found OPTIMA to be the best score function for both both dynamic programming and the hybrid algorithms. The ability of dynamic programming to discriminate between homologs and nonhomologs in the two sets of distantly related sequences is slightly better than that of hybrid algorithm. The advantage of producing accurate score statistics with only a few simulations may overcome the small differences in performance and make this new algorithm suitable for detection of homologs in conjunction with a wide range of score functions and gap penalties.     

Modelling spatial dynamics of fish

Our ability to model spatial distributions of fish populations is reviewed by describing the available modelling tools. Ultimate models of the individual's motivation for behavioural decisions are derived from evolutionary ecology. Mechanistic models for how fish sense and may respond to their surroundings are presented for vision, olfaction, hearing, the lateral line and other sensory organs. Models for learning and memory are presented, based both upon evolutionary optimization premises and upon neurological information processing and decision making. Functional tools for modelling behaviour and life histories can be categorized as belonging to an optimization or an adaptation approach. Among optimization tools, optimal foraging theory, life history theory, ideal free distribution, game theory and stochastic dynamic programming are presented. Among adaptation tools, genetic algorithms and the combination with artificial neural networks are described. The review advocates the combination of evolutionary and neurological approaches to modelling spatial dynamics of fish.     

Sustainability Indicators at the Firm Level

After reviewing recent attempts to develop sustainability indicators, this article shows how the principles of productive efficiency can be used to elaborate such indicators at the firm level. The theory of productive efficiency is somewhat expanded to incorporate fundamental issues of sustainable development: environment, equity, and futurtty. Efficiency, in the expanded notion of productive efficiency, is viewed as a necessary condition for sustainability. Working with aggregate performance indicators, it is important not to lose track of the relevant basic information. Therefore, instead of elaborating one unique indicator; we propose to implement several kinds of indicators, each of which stresses one particular focus (e.g., environmental vs. social concems). The definition of sustainable development indicators is illustrated with reference to a small data set of U.S. fossil fuel-fired electric utilities. In a sustainabiltty perspective, two important aspects are stressed, namely, the use of nonmewable resources and the inclusion of employment as a variable to maximize rather than an input to minimize. The article ends with a discussion of the significance of, and limits to, the proposed indicators.     

The influence of predation risk on diet selectivity: A theoretical analysis

Studies that have examined the effect of experimental increases in predation risk on diet selectivity have shown both decreased and increased diet selectivity. A possible explanation for these disparate results emerges from an examination of the prey sets used in these studies, which differed in the relationship between the values of risk components associated with the capture of different prey types (‘danger’) and their profitabilities. When less profitable prey were more dangerous, selectivity increased with predation risk. When prey were equally dangerous, selectivity did not change. Finally, when the more profitable prey were also more dangerous, selectivity decreased with risk. Here, we examine theoretically the influence of a forager's estimate of the probability that a predator is present (φ) on the selection of diets from prey sets with varying danger–profitability relationships. A dynamic programming model is used to determine the maximum attack time (or distance) for each of two types of prey, differing in their energetic content, for a range of forager energy state and φ levels. The diets which would result if foragers attacked prey according to the rules provided by the dynamic model are then determined. The model results indicate that the prey danger–profitability relationship determines the diet selectivity response to φ, confirming that variation in this relationship could be responsible for the range of experimental results. This revised version was published online in July 2006 with corrections to the Cover Date.     

An Approach to Integrating Environmental Considerations Within Managerial Decision‐Making

Recent environmental trends, including (1) an expansion of existing command and control directives, (2) the introduction of market‐based policy instruments, and (3) the adoption of extended producer responsibility, have created a need for new tools to help managerial decision‐making. To address this need, we develop a nonlinear mathematical programming model from a profit‐maximizing firm's perspective, which can be tailored as a decision‐support tool for firms facing environmental goals and constraints. We typify our approach using the specific context of diesel engine manufacturing and remanufacturing. Our model constructs are based on detailed interviews with top managers from two leading competitors in the medium and heavy‐duty diesel engine industry. The approach allows the incorporation of traditional operations‐planning considerations—in particular, capacity, production, and inventory—together with environmental considerations that range from product design through production to product end of life. A current hurdle to implementing such a model is the availability of input data. We therefore highlight the need not only to involve all departments within businesses but also for industrial ecologists and business managers to work together to implement meaningful decision models that are based on accurate and timely data and can have positive economic and environmental impact.     

Factors influencing route choice by avian migrants: a dynamic programming model of Pacific brant migration

We used stochastic dynamic programming to investigate a spectacular migration strategy in the black brant Branta bernicla nigricans, a species of goose. Black brant migration is well suited for theoretical analysis since there are a number of existing strategies that easily can be compared. In early autumn, almost the entire population of the black brant gathers at Izembek Lagoon on the Alaska Peninsula to stage and refuel before the southward migration. There are at least three distinct strategies, with most geese making a spectacular direct migration more than 5000km across the Gulf of Alaska to their wintering grounds in southern Baja California or mainland Mexico. This is a potentially dangerous strategy since foraging is not possible during the overseas passage. Some individuals instead use shorter flights to make a detour along the coast, a longer route that all individuals use for northwards migration in spring. Since flight costs accelerate with increasing body mass, migration by short flights is energetically cheaper than long-distance flights. A small but increasing part of the population has recently begun to winter at Izembek. We investigated this migration under two different suppositions using a dynamic state variable model. First, if the geese are free to make a strategic choice, under what assumptions should they prefer direct migration and under what assumptions should they prefer detour migration/winter residency? Second, provided that the dominating direct migration strategy is optimal, what conditions will force the geese to go for detour migration/winter residency? In the second case the geese may try to follow an optimal direct migration strategy, but stochastic events may force them to choose a suboptimal policy. We also simulated possible effects of global warming. The model suggests that the fuel level at arrival in Izembek and fuel gain rates are key factors and that tail winds must have been reliable in the past, otherwise direct migration could not have evolved. It also suggests that a change to milder winters may promote an unexpectedly abrupt change from long-distance to short-distance migration or winter residency. Finally, it produced a number of predictions that might be testable in the field.     

Bayesian flux balance analysis applied to a skeletal muscle metabolic model

In this article, the steady state condition for the multi-compartment models for cellular metabolism is considered. The problem is to estimate the reaction and transport fluxes, as well as the concentrations in venous blood when the stoichiometry and bound constraints for the fluxes and the concentrations are given. The problem has been addressed previously by a number of authors, and optimization-based approaches as well as extreme pathway analysis have been proposed. These approaches are briefly discussed here. The main emphasis of this work is a Bayesian statistical approach to the flux balance analysis (FBA). We show how the bound constraints and optimality conditions such as maximizing the oxidative phosphorylation flux can be incorporated into the model in the Bayesian framework by proper construction of the prior densities. We propose an effective Markov chain Monte Carlo (MCMC) scheme to explore the posterior densities, and compare the results with those obtained via the previously studied linear programming (LP) approach. The proposed methodology, which is applied here to a two-compartment model for skeletal muscle metabolism, can be extended to more complex models.     

Prenatal lipopolysaccharide exposure causes mesenteric vascular dysfunction through the nitric oxide and cyclic guanosine monophosphate pathway in offspring

Cardiovascular diseases, such as hypertension, could be programmed in fetal life. Prenatal lipopolysaccharide (LPS) exposure in utero results in increased blood pressure in offspring, but the vascular mechanisms involved are unclear. Pregnant Sprague–Dawley rats were intraperitoneally injected with LPS (0.79 mg/kg) or saline (0.5 ml) on gestation days 8, 10, and 12. The offspring of LPS-treated dams had higher blood pressure and decreased acetylcholine (ACh)-induced relaxation and increased phenylephrine (PE)-induced contraction in endothelium-intact mesenteric arteries. Endothelium removal significantly enhanced the PE-induced contraction in offspring of control but not LPS-treated dams. The arteries pretreated with l-NAME to inhibit nitric oxide synthase (eNOS) in the endothelium or ODQ to inhibit cGMP production in the vascular smooth muscle had attenuated ACh-induced relaxation but augmented PE-induced contraction to a larger extent in arteries from offspring of control than those from LPS-treated dams. In addition, the endothelium-independent relaxation caused by sodium nitroprusside was also decreased in arteries from offspring of LPS-treated dams. The functional results were accompanied by a reduction in the expressions of eNOS and soluble guanylate cyclase (sGC) and production of NO and cGMP in arteries from offspring of LPS-treated dams. Furthermore, LPS-treated dam’s offspring arteries had increased oxidative stress and decreased antioxidant capacity. Three-week treatment with TEMPOL, a reactive oxygen species (ROS) scavenger, normalized the alterations in the levels of ROS, eNOS, and sGC, as well as in the production of NO and cGMP and vascular function in the arteries of the offspring of LPS-treated dams. In conclusion, prenatal LPS exposure programs vascular dysfunction of mesenteric arteries through increased oxidative stress and impaired NO–cGMP signaling pathway.     

Econometric models for biohydrogen development

Biohydrogen is considered as an attractive clean energy source due to its high energy content and environmental-friendly conversion. Analyzing various economic scenarios can help decision makers to optimize development strategies for the biohydrogen sector. This study surveys econometric models of biohydrogen development, including input-out models, life-cycle assessment approach, computable general equilibrium models, linear programming models and impact pathway approach. Fundamentals of each model were briefly reviewed to highlight their advantages and disadvantages. The input-output model and the simplified economic input-output life-cycle assessment model proved most suitable for economic analysis of biohydrogen energy development. A sample analysis using input-output model for forecasting biohydrogen development in the United States is given.