Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

Citation classic - optimal foraging - a selective review of theory and tests

The Millennium Ecosystem Assessment (MA) introduced a new framework for analyzing social-ecological systems that has had wide influence in the policy and scientific communities. Studies after the MA are taking up new challenges in the basic science needed to assess, project, and manage flows of ecosystem services and effects on human well-being. Yet, our ability to draw general conclusions remains limited by focus on discipline-bound sectors of the full social-ecological system. At the same time, some polices and practices intended to improve ecosystem services and human well-being are based on untested assumptions and sparse information. The people who are affected and those who provide resources are increasingly asking for evidence that interventions improve ecosystem services and human well-being. New research is needed that considers the full ensemble of processes and feedbacks, for a range of biophysical and social systems, to better understand and manage the dynamics of the relationship between humans and the ecosystems on which they rely. Such research will expand the capacity to address fundamental questions about complex social-ecological systems while evaluating assumptions of policies and practices intended to advance human well-being through improved ecosystem services.

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Science for managing ecosystem services: Beyond the Millennium Ecosystem Assessment

The State of Food and Agriculture

PROMETHEE: A comprehensive literature review on methodologies and applications

Performance measures (PMs) and corresponding performance evaluation criteria (PEC) are important aspects of calibrating and validating hydrologic and water quality models and should be updated with advances in modeling science. We synthesized PMs and PEC from a previous special collection, performed a meta-analysis of performance data reported in recent peer-reviewed literature for three widely published watershed-scale models (SWAT, HSPF, WARMF), and one field-scale model (ADAPT), and provided guidelines for model performance evaluation. Based on the synthesis, meta-analysis, and personal modeling experiences, we recommend coefficient of determination (R2; in conjunction with gradient and intercept of the corresponding regression line), Nash Sutcliffe efficiency (NSE), index of agreement (d), root mean square error (RMSE; alongside the ratio of RMSE and standard deviation of measured data, RSR), percent bias (PBIAS), and several graphical PMs to evaluate model performance. We recommend that model performance can be judged satisfactory for flow simulations if monthly R2 0.70 and d 0.75 for field-scale models, and daily, monthly, or annual R2 0.60, NSE 0.50, and PBIAS ≤ ±15% for watershed-scale models. Model performance at the watershed scale can be evaluated as satisfactory if monthly R2 0.40 and NSE 0.45 and daily, monthly, or annual PBIAS ≤ ±20% for sediment; monthly R20.40 and NSE 0.35 and daily, monthly, or annual PBIAS ≤ ±30% for phosphorus (P); and monthly R2 0.30 and NSE 0.35 and daily, monthly, or annual PBIAS ≤ ±30% for nitrogen (N). For RSR, we recommend that previously published PEC be used as detailed in this article. We also recommend that these PEC be used primarily for the four models for which there were adequate data, and used only with caution for other models. These PEC can be adjusted within acceptable bounds based on additional considerations, such as quality and quantity of available measured data, spatial and temporal scales, and project scope and magnitude, and updated based on the framework presented herein. This initial meta-analysis sets the stage for more comprehensive meta-analysis to revise PEC as new PMs and more data become available.

Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria

Within wicked environmental challenges, problems that exist in the nexus of environmental science and environmental values, neatly and elegantly optimized solutions are difficult to find and rarely accepted by stakeholders. Different role players must explore the challenge adaptively and through viewpoints to contribute to their understanding of the situation and to learn about the dynamics and values of other relevant stakeholders. The Questions and Decisions (QnD) system (Kiker, G.A., et al., Springer Science, 2006, 11, 151–186) was created to provide an effective and efficient tool to integrate ecosystem, management, economics, and sociopolitical factors into a user-friendly game/model framework. QnD is written in object-oriented Java and can be deployed in stand-alone or Web-based (browser-accessed) modes. The QnD model links spatial components within geographic information system (GIS) files to the abiotic (climatic) and biotic interactions that exist in an environmental system. QnD can be used in a rigorous modeling role to mimic system elements obtained from scientific data or it can be used to create a “cartoon” style depiction of the system to promote greater learning and discussion from decision participants. Elephant and vegetation dynamics in Africa provide an excellent example of a wicked environmental challenge as conservation objectives and societal values (both local and international) often have conflicting goals concerning appropriate elephant densities and population control options in protected areas. In attempting to capture many dynamic aspects of elephant–vegetation relationships, previous models depicting the savanna ecosystem of the Kruger National Park (KNP), South Africa, can become quite complex and demanding in terms of detailed parameter inputs. Therefore, the purpose of this modeling project was to create a simplified, management-focused, visual simulation of the KNP in order to chart future elephant, tree, and grass scenarios. QnD:EleSim has been designed to spatially simulate elephant–vegetation dynamics in 195 areas at 10-km resolution at a monthly time-step. As the effects of elephant populations on the tree–grass equilibrium of the savanna are documented, future management decisions can be advised after analysis of potential scenarios.

How2QnD: Design and Construction of a Game-Style, Environmental Simulation Engine and Interface Using UML, XML, and Java

Water managers face the daunting task of balancing limited water resources with over-subscribed water users among competing demands. They face the additional challenge of taking water planning decisions in an uncertain environment with limited and sometimes inaccurate observed and simulated hydrological data. Within South African watersheds, spatial parameterization data for hydrological models are now available at two different basin management resolutions (termed quaternary and quinary). Currently, water management decisions in the Crocodile River watershed are often made at a more coarse resolution, which may exclude crucial insights into the data. This research has the following aims (1) to explore whether model performance is improved by parameterization using a more detailed quinary-scale watershed data and (2) to explore whether quinary-scale models reduce uncertainty in allocation or restriction decisions to provide better informed water resources management and decision outcomes. This study used the Agricultural Catchments Research Unit (ACRU) agro-hydrological watershed model, to evaluate the effects of spatial discretization at the quaternary and quinary scales on watershed hydrological response and runoff within the Crocodile River basin. Model performance was evaluated using statistical comparisons of results using traditional goodness-of-fit measures such as the coefficient of efficiency (C
 eff), root mean square of the error and the coefficient of determination (R
 2) to compare simulated monthly flows and observed flows in six subcatchments. Traditional interpretation of these goodness-of-fit measures may be inadequate as they can be subjectively interpreted and easily influenced by the number of data points, outliers and model bias. This research utilizes a recently released model evaluation program (FITEVAL) which presents probability distributions of R
 2and C
 eff derived by bootstrapping, graphical representation of observed and simulated stream flows, incorporates statistical significance to detect the sufficiency of the R
 2and C
 eff and determines the presence of outliers and bias. While analyses indicate that the ACRU model performs marginally better when parameterized and calibrated at the quinary scale, the measurements at both scales show significant variability in predictions for both high and low flows that are endemic to southern African hydrology. The improved evaluation methods also allow for the analysis of data collection errors at monitoring sites and help determine the effect of data quality on adaptive water planning management decisions. Given that many water resource challenges are complex adaptive systems, these expanded performance analysis tools help provide deeper insights into matching watershed decision metrics and model-derived predictions.

Improving watershed decisions using run-off and yield models at different simulation scales

The Dallas-Fort Worth International Airport is used as an example of how traditional approaches of hardening isolated components of critical infrastructure against specific threats leaves critical assets exposed to significant, expensive, and unacceptable levels of cascading failure. Optimizing the entire airport supply chain requires the development of an interdisciplinary approach that incorporates the complexities of the airports' supply chain including numerous dependent, interdependent and independent relationships. Far greater return-on-investment will be yielded through resilience-focused approaches that address the entire life cycle of disruptions including planning, absorption, recovery and adaptation. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1468-5973.12401

International airports as agents of resilience

ABSTRACT The application of fuzzy multi-objective decision-making (F-MODM) to the optimization problems of renewable energy (RE) planning with uncertainties has become increasingly popular, although it is still rare in contrast to the use of traditional multi-attribute decision-making (MADM) methods. This study contributes to the domain of F-MODM modeling and the empirical applications of RE. Methodologically, as a recent trend to enhance the solution quality of fuzzy goal programming (FGP) in F-MODM is to introduce the “tolerance” (or admissible violation) concept to membership functions (MFs), this study proposes a novel ‘FGP-MT’ (multi-tolerance FGP) model to overcome the limitations of ‘single tolerance’ during FGP modeling (i.e. being unable to fit complicated ‘multitolerance’ decision contexts and running the risk of missing better optimality). The model is first verified using a numerical example from the F-MODM literature. Empirically, an application of the FGP-MT model is then considered for a SREPO (sustainable renewable energy portfolio optimization) decision, searching for the optimal electricity generation portfolio from multiple RE resources in Algeria for sustainable RE development, subject to the special preference structure of the decision-maker (DM) that involves manifesting two or more tolerances for a fuzzy goal. As the model performs well or even better in determining the most suitable SREPO decision under this condition, insights are gained for the studied case, and future applications of the model are therefore expected.

Optimising sustainable renewable energy portfolios using a multi-tolerance fuzzy goal programming approach

. Spatial and temporal information on cattle location within a pasture can be instrumental in estimating potential nutrient loading to surface waters. To account for cattle variability over time and space, a population distribution model was added as a module to the Java-based, object-oriented ACRU2000 modeling system. The algorithms are composed of attractants of cattle (shade, water, and forage) and their weighting factors derived from multi-criteria decision analysis. The algorithms were developed using the techniques of habitat suitability index (HSI), and criteria weighting was developed with expert opinion using the analytical hierarchy process. The HSI model was calibrated and tested on beef cattle pastures in south Florida. Model verification efforts revealed that the modelâ€™s performance was in good agreement with observed GPS collar data. The HSI model has enhanced the capability of ACRU2000 to represent the spatial variability and nutrient effects of cattle distribution within complex agro-ecosystems of south Florida.

Gregory A. Kiker

Papers

How2QnD: Design and Construction of a Game-Style, Environmental Simulation Engine and Interface Using UML, XML, and Java

Improving watershed decisions using run-off and yield models at different simulation scales

International airports as agents of resilience

Optimising sustainable renewable energy portfolios using a multi-tolerance fuzzy goal programming approach

Modeling Cattle Distribution in Southern Florida Pasture Agro-Ecosystems: Implications Based on Forage, Shade, and Water Availability