http://osenberglab.ecology.uga.edu/wp-content/uploads/2015/09/Charnov-1976-TPB.pdf

Optimal foraging, the marginal value theorem.

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 patch...

/pdf/optimal-foraging-a-selective-review-of-theory-and-tests-3ar3ls90ph.pdf

Optimal foraging: A selective review of theory and tests

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

Food webs in nature have multiple, reticulate connections between a diversity of consumers and resources. Such complexity affects web dynamics: it first spreads the direct effects of consumption and productivity throughout the web rather than focusing them at particular "trophic levels." Second, consumer densities are often donor controlled with food from across the trophic spectrum, the herbivore and detrital channels, other habitats, life-history omnivory, and even trophic mutualism. Although consumers usually do not affect these resources, increased numbers often allow consumers to depress other resources to levels lower than if donor-controlled resources were absent. We propose that such donor-controlled and "multichannel" omnivory is a general feature of consumer control and central to food web dynamics. This observation is contrary to the normal practice of inferring dynamics by simplifying webs into a few linear "trophic levels," as per "green world" theories. Such theories do not accommodate commo...

Food Web Complexity and Community Dynamics

The use of recommender systems is an emerging trend today, when user behavior information is abundant. There are many large datasets available for analysis because many businesses are interested in future user opinions. Sophisticated algorithms that predict such opinions can simplify decision-making, improve customer satisfaction, and increase sales. However, modern datasets contain millions of records, which represent only a small fraction of all possible data. Furthermore, much of the information in such sparse datasets may be considered irrelevant for making individual recommendations. As a result, there is a demand for a way to make personalized suggestions from large amounts of noisy data.
Current recommender systems are usually all-in-one applications that provide one type of recommendation. Their inflexible architectures prevent detailed examination of recommendation accuracy and its causes. We introduce a novel architecture model that supports scalable, distributed suggestions from multiple independent nodes. Our model consists of two components, the input matrix generation algorithm and multiple platform-independent combination algorithms. A dedicated input generation component provides the necessary data for combination algorithms, reduces their size, and eliminates redundant data processing. Likewise, simple combination algorithms can produce recommendations from the same input, so we can more easily distinguish between the benefits of a particular combination algorithm and the quality of the data it receives. Such flexible architecture is more conducive for a comprehensive examination of our system.
We believe that a user's future opinion may be inferred from a small amount of data, provided that this data is most relevant. We propose a novel algorithm that generates a more optimal recommender input. Unlike existing approaches, our method sorts the relevant data twice. Doing this is slower, but the quality of the resulting input is considerably better. Furthermore, the modular nature of our approach may improve its performance, especially in the cloud computing context. We implement and validate our proposed model via mathematical modeling, by appealing to statistical theories, and through extensive experiments, data analysis, and empirical studies.
Our empirical study examines the effectiveness of accuracy improvement techniques for collaborative filtering recommender systems. We evaluate our proposed architecture model on the Netflix dataset, a popular (over 130,000 solutions), large (over 100,000,000 records), and extremely sparse (1.1%) collection of movie ratings. The results show that combination algorithm tuning has little effect on recommendation accuracy. However, all algorithms produce better results when supplied with a more relevant input. Our input generation algorithm is the reason for a considerable accuracy improvement.

/pdf/a-distributed-architecture-centric-approach-to-computing-31zmh6iinx.pdf

A distributed, architecture-centric approach to computing accurate recommendations from very large and sparse datasets

A common but not universal consequence of the foraging activities of a predator is a lowering of capture rates with prey in its immediate vicinity. This may result from a number of different processes and need not require actual harvesting of any prey items by the predator. We term this phenomenon "depression" and its ecological consequences are the focus of this paper. A great deal of attention has been devoted to implications of changes in prey abundances as a result of the activity of predators. We will focus on changes in prey availability strictly in terms of the perspective of actual or potential predators. Potential here refers to predators who may visit the site shortly after "the prey have been depressed." Depression phenomena are familiar to most field ecologists but are seldom incorporated into formal ecological theory. While they are not really separate, it will be useful to divide our discussion into several sections, each focusing on a different question. (1) What are the processes by which prey might be depressed? (2) What are the characteristics of prey which affect their depressibility? Their recovery from the depression? (3) Are there prey that cannot be depressed? (4) What are the implications of depression from the viewpoint of a single predator individual? (5) Since prey depressed for one kind of predator are not necessarily depressed for another kind, what are the implications for competition theory? Here we are concerned with the possibility of enhancement of availability, as well as competition.

/pdf/ecological-implications-of-resource-depression-52pzwawmly.pdf

Kim Hyatt

Papers

Ecological Implications of Resource Depression