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Graham H. Pyke - One of the best experts on this subject based on the ideXlab platform.
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Optimal Foraging Theory
Encyclopedia of Social Insects, 2020Co-Authors: Graham H. Pyke, Christopher K. StarrAbstract:All of life forages for resources that are needed for survival, development, and reproduction. Optimal Foraging theory (OFT) aims to understand Foraging behavior by hypothesizing that animals forage...
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plant pollinator co evolution it s time to reconnect with Optimal Foraging theory and evolutionarily stable strategies
Perspectives in Plant Ecology Evolution and Systematics, 2016Co-Authors: Graham H. PykeAbstract:Abstract Pollination syndromes (correlations between floral and pollinator traits), have long interested ecologists, but remain inadequately explained. For example, plant species pollinated by relatively large animals cannot have evolved correspondingly high rates of nectar-energy production simply because such animals need relatively more energy; evolution does not work that way. The inverse correlation between pollinator body-size and nectar concentration is similarly difficult to explain. To remedy this, I propose that Optimal Foraging Theory (OFT) and the Evolutionarily Stable Strategy approach (ESS) be combined and applied to pollination syndromes. Both hypothesise that, through evolution, average biological fitness of individuals has been maximised. OFT predicts Foraging consequences for pollinators varying in body size, and other attributes, allowing the ESS approach to be applied to co-adapted plant–pollinator traits. This should lead to predicted relationships between plants and their pollinators. The steps involved in this process are conceptually straightforward, but empirically difficult, which may explain why the approach has been very little pursued in the past. However such difficulties can be overcome, thus pointing to the future. We surely need to understand pollination systems, in order to conserve and manage them. It is therefore time to reconnect OFT and plant–pollinator co-evolution, within the general ESS approach, and hence increasing our understanding of pollination syndromes and other plant–pollinator relationships.
Jonathan Roughgarden - One of the best experts on this subject based on the ideXlab platform.
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food hoarding future value in Optimal Foraging decisions
Ecological Modelling, 2004Co-Authors: Leah R Gerber, O J Reichman, Jonathan RoughgardenAbstract:Abstract Traditionally, Optimal Foraging theory has been applied to situations in which a forager makes decisions about current resource consumption based on tradeoffs in resource attributes (e.g. caloric intake versus handling time). Food storage, which permits animals to manage the availability of food in space and time, adds a complex dimension to Foraging decisions, and may influence the predictions of traditional Foraging theory. One key question about the role of caching behavior in Optimal Foraging theory is the degree to which information about future value might influence Foraging decisions. To investigate this question, we use a simple prey selection model that minimizes the time spent Foraging and is modified to include food storage and changes in nutritional value through time. We used simulations to evaluate time spent in Foraging activities per prey item and Optimal Foraging strategies (e.g. cache versus consume immediately) for 3125 parameter combinations, representing different abundance levels, handling times, and nutritional values. Using discriminant function analysis it was possible to distinguish situations where “caching” versus “immediate consumption” were Optimal strategies with abundance as the single predictive variable. The circumstances where caching was Optimal were characterized by a decline in prey abundance and an increase in nutritional value through time. These results provide a framework for identifying subtle differences in Foraging behavior when future value is accounted for thereby improving our predictive understanding of how caching animals forage.
Reny B Tyson - One of the best experts on this subject based on the ideXlab platform.
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does Optimal Foraging theory predict the Foraging performance of a large air breathing marine predator
Animal Behaviour, 2016Co-Authors: Reny B Tyson, Ari S Friedlaender, Douglas P NowacekAbstract:Optimal Foraging theory (OFT) suggests that air-breathing diving animals should minimize costs associated with feeding under water (e.g. travel time, oxygen loss) while simultaneously maximizing benefits gained from doing so (e.g. Foraging time, energy gain). Humpback whales, Megaptera novaeangliae , Foraging along the Western Antarctic Peninsula appear to forage according to OFT, but the direct costs and benefits in terms of their behaviours (e.g. allocation of time) have not been examined. We compared the Foraging behaviour of humpback whales in this region inferred from multisensor high-resolution recording tags to their behaviour predicted by OFT time allocation models assuming the following currencies were being maximized: (1) the proportion of time spent Foraging, (2) the net rate of energetic gain and/or (3) the ratio of energy gained to energy expended (i.e. efficiency). Model predictions for all three currencies were similar, suggesting any of these OFT models were suitable for comparison with the observed data. However, agreement between observed and Optimal behaviours varied widely depending on the physiological and behavioural values used to derive Optimal predictions, highlighting the need for an improved understanding of cetacean physiology. Despite this, many of the theoretical OFT predictions were supported: shallow dives (i.e.
Peter M Todd - One of the best experts on this subject based on the ideXlab platform.
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Optimal Foraging in semantic memory
Psychological Review, 2012Co-Authors: Thomas T Hills, Michael P Jones, Peter M ToddAbstract:Do humans search in memory using dynamic local-to-global search strategies similar to those that animals use to forage between patches in space? If so, do their dynamic memory search policies correspond to Optimal Foraging strategies seen for spatial Foraging? Results from a number of fields suggest these possibilities, including the shared structure of the search problems-searching in patchy environments-and recent evidence supporting a domain-general cognitive search process. To investigate these questions directly, we asked participants to recover from memory as many animal names as they could in 3 min. Memory search was modeled over a representation of the semantic search space generated from the BEAGLE memory model of Jones and Mewhort (2007), via a search process similar to models of associative memory search (e.g., Raaijmakers & Shiffrin, 1981). We found evidence for local structure (i.e., patches) in memory search and patch depletion preceding dynamic local-to-global transitions between patches. Dynamic models also significantly outperformed nondynamic models. The timing of dynamic local-to-global transitions was consistent with Optimal search policies in space, specifically the marginal value theorem (Charnov, 1976), and participants who were more consistent with this policy recalled more items.
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Optimal Foraging in semantic memory
Proceedings of the Annual Meeting of the Cognitive Science Society, 2009Co-Authors: Thomas T Hills, Michael P Jones, Peter M ToddAbstract:Optimal Foraging in Semantic Memory Thomas T. Hills (thomas.hills@unibas.ch) Center for Cognitive and Decision Sciences University of Basel, Switzerland Peter M. Todd (pmtodd@indiana.edu), Michael N. Jones (jonesmn@indiana.edu) Department of Psychological and Brain Sciences Indiana University, Bloomington, IN, 47405 USA A number of studies have also found relationships between animal and human Foraging strategies when patch boundaries are determined by the external environment (Wilke et al., 2009; Hutchinson et al., 2008; Payne et al., 2007; Pirolli, 2007). Here, we investigate whether retrieval from semantic memory in a fluency task also follows an Optimal Foraging policy—called the marginal value theorem—when patches are defined strictly internally, by the structure of semantic memory. The marginal value theorem has been found to describe the search policies of a number of animals (Charnov, 1976; Stephens & Krebs, 1987) as well as the search strategies of humans in external information Foraging (Pirolli, 2007). The basic assumptions of the marginal value theorem are that resources are distributed in patches, and that moving from one patch to another involves a travel time. The marginal value theorem seeks to maximize the return from Foraging defined as the average rate of energy intake, R, over all patches: Abstract When searching for items in memory, people explore internal representations in much the same way that animals forage in space. Results from a number of fields support this notion at a deeper level of evolutionary homology, with evidence that goal-directed cognition is an evolutionary descendent of animal Foraging behavior (Hills, 2006). Is it possible then that humans forage in memory using similar search policies to the way that animals forage in space? To investigate this, we examine how people retrieve items from memory in the category fluency task: Participants were asked to retrieve as many types of animals from memory as they could in 3 minutes. Clusters or patches of these items, along with their semantic similarity and frequency, were found with an automatic Wikipedia corpus analysis using the BEAGLE semantic memory model (Jones & Mewhort, 2007), and via hand-coded category membership from Troyer et al. (1997). Participants did not seem to use static patch boundaries, such as ‘pets’, to search memory, but instead used fluid patch boundaries that were updated with each new item retrieved. We found that participants leave patches in memory when the marginal (i.e., current) rate of finding items is near the average rate for the entire task, as predicted by Optimal Foraging theory. Furthermore, participants appear to search within patches using item similarity, but decide where to “land” when moving between patches using item frequency. R = ∑ p E(Y ) ∑ p E( τ ) + T i i i i i i In the numerator, the summed product of the relative frequency p i of a given patch i and its expected payoff value E(Y i ) defines the cumulative payoff over all patches. The denominator represents the total time spent Foraging, which is a sum of the average travel time between patches T and the summed product of the expected time spent Foraging in each patch type E( τ i ) with the frequency of encounter with a patch of that type. The central result from the marginal value theorem is that the Optimal Foraging policy is to leave patches when the instantaneous rate (or marginal value) of resource intake is equal to the long-term average resource intake R over all patches. The marginal value theorem can be applied to Foraging in human memory, given two prerequisites: First, that we have a task in which individuals forage among patch-structured memory representations; and second, that we have an a priori method for determining what those patches in memory are. The task we use here is called the “semantic fluency” or “category fluency” task, and is commonly used in both clinical (Troster et al., 1989) and experimental settings (Bousfield & Sedgewick, 1944). Participants or patients are simply asked to produce as many instances of some Introduction Animals often search for resources that occur in spatial patches, such as the berries on a bush or a cluster of clams at the beach. Humans also search for cognitive resources that can be seen as patchy with respect to some other metric, such as memory representations of words grouped by semantic similarity, or sets of solutions that can be navigated by working memory processes in a problem- solving task. Several lines of evidence have given rise to the idea that search in such cognitive spaces shares fundamental properties with Foraging in physical spaces, based on an argument from evolutionary homology. That is, these search properties share conserved neural substrates, with similar neuro-molecular processes guiding spatial search in animals and modulating the control of human attention (Hills, 2006). Furthermore, they appear to involve a generalized cognitive search process, based on evidence that humans can be primed to search differently in lexical problem spaces following experience searching in different distributions of spatial resources (Hills et al., 2008b).
Robert Michaud - One of the best experts on this subject based on the ideXlab platform.
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Optimal Foraging theory predicts diving and feeding strategies of the largest marine predator
Behavioral Ecology, 2011Co-Authors: Thomas Doniolvalcroze, Veronique Lesage, Janie Giard, Robert MichaudAbstract:Accurate predictions of predator behavior remain elusive in natural settings. Optimal Foraging theory predicts that breath-hold divers should adjust time allocation within their dives to the distance separating prey from the surface. Quantitative tests of these models have been hampered by the difficulty of documenting underwater feeding behavior and the lack of systems, experimental or natural, in which prey depth varies over a large range. We tested these predictions on blue whales (Balaenoptera musculus), which track the diel vertical migration of their prey. A model using simple allometric arguments successfully predicted diving behavior measured with data loggers. Foraging times within each dive increased to compensate longer transit times and optimize resource acquisition. Shallow dives were short and yielded the highest feeding rates, explaining why feeding activity was more intense at night. An Optimal framework thus provides powerful tools to predict the behavior of free-ranging marine predators and inform conservation studies. Copyright 2011, Oxford University Press.
