Foraging Theory

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Advancement in Information Foraging Theory

Advancement in Information Foraging Theory

2) The traditional theory of information retrieval will retrieve the results, which are divided into related and not related as per the recall and precision evaluation of user search [8], but the reality is: When the user faces a large number of results, often only part of the contents is read. This is not because other information is irrelevant, but because of the repetition of the contents of this informa- tion with previous information, the contents on the back have the lower value to users. In fact, users do not know when to stop feeding the information in a particular en- vironment; it’s often difficult to determine whether you can find valuable information at the right time. When users finds it difficult to continue to collect valuable in- formation, that means that users are not satisfied with information in the environment or has spent a great price to get which in turn will trigger them to stop foraging behavior, or move to another environment to continue the search. Information foraging theory assumes that the user benefits can be quantified, by building information bene- fit curve using the idea of marginal gain to explain the average efficiency of access to information problem [9].
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Exploring Prehistoric Salmon Subsistence in the Willamette Valley using Zooarchaeological Records and Optimal Foraging Theory

Exploring Prehistoric Salmon Subsistence in the Willamette Valley using Zooarchaeological Records and Optimal Foraging Theory

Valley. Finally, targeted research questions can be generated when incongruities are found between expected subsistence practices and archaeological or ethnographic records for subsistence practices. Using the diet breadth model, this project will test whether salmon exploitation would have been a viable option for the people of the Willamette Valley given the availability of a variety of resources. If the diet breadth analysis predicts that salmon should have been exploited, I analyze whether there are regional factors which lower the relative rank of salmon, such as lack of availability or increased search or processing time associated with salmon capture. To do this, I describe the resources that are known to have been used in the region, then briefly summarize optimal foraging theory. After this, I present return rate data based on ethnographic records relating to subsistence and the hypothetical addition of salmon. Once these data are presented, I assess whether salmon would have been a viable resource, based on the results of the diet breadth analysis and other factors. Finally, the results of this analysis shows that while salmon exploitation was probably not as profitable as in other areas in the Pacific Northwest, the resource should still have been exploited given its relatively high rank.
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When and why do people avoid unknown probabilities in decisions under uncertainty? Testing some predictions from optimal foraging theory

When and why do people avoid unknown probabilities in decisions under uncertainty? Testing some predictions from optimal foraging theory

Before addressing this question, let us pose it more precisely. Optimal foraging theory is an explicit account of some of the selection pressures that should have shaped mechanisms for making judgments under uncertainty in foraging animals. Based on this theory, animal behavior researchers have found evidence of mechan- isms in nonhuman animals that generate the judgments one would expect if these mechanisms were functionally specialized for solving the adaptive problems described by optimal foraging theory. Based on both considerations Ð the selection pressures and the evidence from the literature on risk-sensitive foraging in other animals Ð we predicted that humans, who were also shaped by a selective history of foraging, would also have evolved mechanisms that are functionally specialized for making such decisions. Just as your retina can compute the second derivative of the local distribution of light intensity (regardless of whether you have ever taken a course in calculus; Gallistel, 1990), we predicted that the mechanisms that generate these judgements are designed to combine data about means, variances, and need level in the mathematically appropriate ways to generate a well-calibrated judgment (regardless of whether the subject has ever had explicit instruction in probability theory). Our hypothesis is that the mechanism is functionally specialized for this purpose, in the same way that the language faculty is thought to be functionally specialized for the acquisition of language (Pinker, 1994).
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An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval

An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval

Abstract. This paper presents the design and results of a task-based user study, based on Information Foraging Theory, on a novel user inter- action framework - uInteract - for content-based image retrieval (CBIR). The framework includes a four-factor user interaction model and an in- teractive interface. The user study involves three focused evaluations, 12 simulated real life search tasks with different complexity levels, 12 comparative systems and 50 subjects. Information Foraging Theory is applied to the user study design and the quantitative data analysis. The systematic findings have not only shown how effective and easy to use the uInteract framework is, but also illustrate the value of Information Foraging Theory for interpreting user interaction with CBIR.
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Sensory Ecology of Foraging in Bumblebees

Sensory Ecology of Foraging in Bumblebees

Choosing flower types that involve minimal search times is critical in flower visitors for several reasons. Flight is energetically the most costly activity in insects (Wolf et al., 1999), and even though pollinating insects often operate at the limit of sustaining their flight activity, their fitness depends on the surplus forage brought home to provision their young (Heinrich, 1979; Schaffer et al., 1979). Most flowers offer only small quantities of nectar reward, to keep pollinators moving between plants and so maximize pollen transfer. Activities of many competing flower visitors further reduce those rewards. Bees have been widely used to study foraging decisions, and behavioral ecologists have made intriguing predictions on how pollinators should behave in complex situations where flowers of different species differ in detectability (Dukas and Clark, 1995). But the perceptual dimensions that underlie search times, and the floral parameters involved, have been little addressed. Possibly for this reason, predictions of optimal foraging theory are often inconsistent with observations of natural foraging behavior (Heinrich, 1983; Schmid-Hempel, 1993; Varjú and Núñez, 1991; Wells et
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Risso's dolphins plan foraging dives

Risso's dolphins plan foraging dives

Optimal foraging theory predicts that breath-hold divers would maximize the time spent foraging at depth and minimize the time spent in transit (Mori, 1998). This led us to presume that dolphins will forage for as long as possible in their chosen foraging layer and cease searching for prey on the dive ascent once they have left the layer. Alternatively, dolphins may echolocate all the way up for orientation, or because they expect to gain from capturing shallower prey during the ascent, or to gain new information about prey for planning the next dive. To test these hypotheses, we examined the depth of the last click in dives and the sampling strategy used by the dolphins on dive ascents in relation to prey features and buzz occurrences. Pooling data from the two dolphins for which prey data were available, the mean depth of the last click in a dive was 37±36 m, indicating they echolocated almost all the way up to the surface, irrespective of the type of dive. Furthermore, the buzz rates, i.e. number of buzzes per minute, during dive ascents were up to six times higher than those during descents (signed-rank P=0.005, N=14 paired comparisons of buzz rate during descent versus ascent phases). Dolphins emitted buzzes during the ascent in 40% of dives. In those dives, clicking continued after the last buzz recorded on the ascent. All midwater and deep dives with no buzz on ascent and one with buzz on ascent had buzzes on descent (4 of 14 dives). Average ascent duration was 58±31 and 76 ±42 s for dives without and with buzzes on the ascent, respectively, whereas bottom time was reduced from 158±61 to 110±50 s during the same dives. To test whether, on ascent, dolphins were seeking more efficient foraging in upper layers, we explored whether the buzz rate during the bottom phase was higher than that during the ascent.
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Risso's dolphins plan foraging dives

Risso's dolphins plan foraging dives

Optimal foraging theory predicts that breath-hold divers would maximize the time spent foraging at depth and minimize the time spent in transit (Mori, 1998). This led us to presume that dolphins will forage for as long as possible in their chosen foraging layer and cease searching for prey on the dive ascent once they have left the layer. Alternatively, dolphins may echolocate all the way up for orientation, or because they expect to gain from capturing shallower prey during the ascent, or to gain new information about prey for planning the next dive. To test these hypotheses, we examined the depth of the last click in dives and the sampling strategy used by the dolphins on dive ascents in relation to prey features and buzz occurrences. Pooling data from the two dolphins for which prey data were available, the mean depth of the last click in a dive was 37±36 m, indicating they echolocated almost all the way up to the surface, irrespective of the type of dive. Furthermore, the buzz rates, i.e. number of buzzes per minute, during dive ascents were up to six times higher than those during descents (signed-rank P=0.005, N=14 paired comparisons of buzz rate during descent versus ascent phases). Dolphins emitted buzzes during the ascent in 40% of dives. In those dives, clicking continued after the last buzz recorded on the ascent. All midwater and deep dives with no buzz on ascent and one with buzz on ascent had buzzes on descent (4 of 14 dives). Average ascent duration was 58±31 and 76 ±42 s for dives without and with buzzes on the ascent, respectively, whereas bottom time was reduced from 158±61 to 110±50 s during the same dives. To test whether, on ascent, dolphins were seeking more efficient foraging in upper layers, we explored whether the buzz rate during the bottom phase was higher than that during the ascent.
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Foraging : an ecology model of consumer behaviour?

Foraging : an ecology model of consumer behaviour?

Behavioural ecology and foraging theory, provides a framework for answering questions about strategic feeding and consumption behavior of animals (Stephens and Krebs 1986), including behaviors such as search, identification, procurement, handling, utilisation and digestion (Mellgren and Brown 1987). It combines ideas from evolution, ecology and behaviour studies and has developed from a number of schools of thought (Krebs and Davies 1997). Foraging theory has traditionally been used to study the behavior of animals in naturalistic settings, via both quantitative and qualitative methodologies, and has been expanded to the operant experimental laboratory via behavioral psychology (termed behavioral ecology)(Williams and Fantino 1994). In the tradition of the natural sciences the study of animal foraging behaviour has involved a substantial research building precise quantitative predictions which have been tested and refined through extensive replication. Foraging theory has also been used to analyse both ancient and modern hunter-gatherer populations in anthropological settings exploring human foraging behavior via observation (Fitzhugh and Habu 2003, Kelly 1995, Winterhalder and Smith 1981, Smith and Winterhalder 1981, Winterhalder 1981) and more recently modern aspects of human behaviour such as the behavior of serial killers by comparison to bees behaviour (Carpenter 2008, Raine, Rossmo and le Comber 2009).
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Sex-specific foraging

Sex-specific foraging

Classical foraging theory predicts that animals select their food rationally, i.e. in such ways that maximum fitness gains are achieved (Stephens & Krebs 1986). Such ‘optimal’ foraging decisions vary with ecological context, and their rationale underlies relation- ships between population level processes and changes in food quality and abundance (e.g. Goss-Custard 1977; van Gils et al. 2006; Piersma 2012). However, any understanding of the relevant food-predator relationships starts off with solid descriptions of diet (e.g. Dekinga & Piersma 1993; Moreira 1994a; Quaintenne et al. 2010). Diets can be recon- structed in direct and indirect ways. Direct methods are: (1) examining the digestive tracts of the birds, (2) taking regurgitation samples, or (3) the lavage method (Verkuil 1996). All these methods have limitations, as the birds have to be caught and sometimes euthanized (e.g. Barrett et al. 2007). Direct visual observations of foraging birds often yield large amounts of unidentified prey (e.g. Scheiffarth 2001a), so the alternative is to study the diet based on indirect methods, such as pellet- and dropping analysis (e.g. Aler- stam et al. 1992; Sanchez et al. 2005). Hard parts from prey, such as jaws and chaetae of worms, or hinges of bivalves are indigestible and often remain in birds droppings, which can be used to reconstruct the diet. The advantages of these methods are that they are non-invasive and simple to perform (e.g. Alerstam et al. 1992; Dekinga & Piersma 1993).
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Foraging: an ecology model of consumer behaviour?

Foraging: an ecology model of consumer behaviour?

Behavioural ecology and foraging theory, provides a framework for answering questions about strategic feeding and consumption behavior of animals (Stephens and Krebs 1986), including behaviors such as search, identification, procurement, handling, utilisation and digestion (Mellgren and Brown 1987). It combines ideas from evolution, ecology and behaviour studies and has developed from a number of schools of thought (Krebs and Davies 1997). Foraging theory has traditionally been used to study the behavior of animals in naturalistic settings, via both quantitative and qualitative methodologies, and has been expanded to the operant experimental laboratory via behavioral psychology (termed behavioral ecology)(Williams and Fantino 1994). In the tradition of the natural sciences the study of animal foraging behaviour has involved a substantial research building precise quantitative predictions which have been tested and refined through extensive replication. Foraging theory has also been used to analyse both ancient and modern hunter-gatherer populations in anthropological settings exploring human foraging behavior via observation (Fitzhugh and Habu 2003, Kelly 1995, Winterhalder and Smith 1981, Smith and Winterhalder 1981, Winterhalder 1981) and more recently modern aspects of human behaviour such as the behavior of serial killers by comparison to bees behaviour (Carpenter 2008, Raine, Rossmo and le Comber 2009).
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A Foraging Mandala for Aquatic Microorganisms

A Foraging Mandala for Aquatic Microorganisms

The foraging mandala The outcome of microbial foraging, in the water column as in other environments, hinges on the resource landscape: the quantity, composition, and spatiotemporal distribution of resources. It is a challenge to quantify the quality of the aquatic resource landscape for microorganisms and further to parameterize it in order to interpret the different nutrient acquisition strategies exhibited by microorganisms. Intui- tively, a resource landscape can be described in terms of two general metrics, related to the spatiotemporal ‘fre- quency ’ of occurrence (how many patches there are per volume) and the quality of resources in them. For example, in a water column containing particulate organic matter, one could quantify the concentration of particles (the ‘fre- quency ’ of the patches) and the average amount of available carbon in each particle (the quality of the patches). These two resource landscape metrics are not new: because of their generality, they are cornerstones of macroecology and feature, for example, in the intermediate disturbance hypothesis [ 7 ], the patch dynamics concept [ 8 ] and in optimal foraging theory [ 9 ]. However, these two loosely de fined metrics, which are purely related to features of the resource landscape, cannot encompass the foraging perfor- mance of diverse microorganisms, which are further deter- mined by the organisms ’ adaptive behaviors (Fig. 1 a). For example, it would be a challenge to pin down a practical metric for resource ‘quality’ that encompasses all the dif- ferent sources of carbon and other nutrients that micro- organisms can utilize. For this reason, the foraging mandala views the features of the resource landscape through the lens of the organisms ’ adaptive behaviors, resulting in foraging metrics that allow the consistent and comparative interpretation of a variety of environments and foraging adaptations.
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The foraging economics of honey bees in almonds

The foraging economics of honey bees in almonds

This last hypothesis derives from the pervasive notion that the activity and benefits from the foraging of honey bees are costly to measure. A similar idea underlies Meade’s famous example of “unpaid factors” as a type of externality existing between beekeepers and apple-growers. Yet, prohibitive costs of acquiring information on individual production functions are not sufficient to explain the behavior of growers. If anything, it displaces the question to one about the economics of collective information acquisition. If honey bee densities are determined by the recommendations of experts, understanding the response of pollinator use to prices hinges on understanding the production of information by these experts. The results of the spatial model of foraging behavior developed below show and quantify the extent to which the diffusive nature of pollination makes it difficult for individual growers to learn about the relationship between hive use and yield (see section . The next three sections present the model of foraging behavior of hives in commercial almonds orchards. First, we develop a model of hive behavior in a landscape where a given stock of pollen is distributed homogeneously in space at the beginning of the foraging period after which it is progressively depleted the foraging of bees. The following section extends the model to allow the pollen to be law is true when the elasticity of substitution between factors is larger in absolute value than the elasticity of demand for the output.
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Breeding limits foraging time : evidence of interrupted foraging response from body mass variation in a tropical environment

Breeding limits foraging time : evidence of interrupted foraging response from body mass variation in a tropical environment

In temperate environments mass gain in female birds during breeding is often attributed to egg formation and mass loss after incubation to flight adaptation or the effect of reproductive[r]

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A few long versus many short foraging trips: different foraging strategies of lesser kestrel sexes during breeding

A few long versus many short foraging trips: different foraging strategies of lesser kestrel sexes during breeding

has also been related to sex-biased competition abilities [89]. In species in which sexes differ in size, the larger sex normally outcompetes the smaller one and displaces it to suboptimal foraging areas [70, 90, 91]. From an individual perspective, it would be more advantageous for both lesser kestrel sexes to forage in areas close to the colony because of the smaller costs in energy and time invested in commuting flights [92]. In a scenario of competitive exclusion, the larger kestrel females would forage closer to the colony and would displace the smaller males to areas located farther. Nevertheless, we observe the opposite pattern with the smaller males for- aging closer to the colony than the larger females. The fact that the spatial segregation between sexes is smaller during the establishment period than in the following periods leads us to think that it is not caused by a competitive exclusion, and role specialization might be involved. The male, which is the sex responsible for nest provisioning, may forage close to the colony in order to reduce foraging trip duration and consequently maximize prey delivering rate. Meanwhile, females may fly towards foraging areas farther away in order to reduce competition for food with males, which they could do by thermal soar- ing with low flight cost. This is important since prey de- pletion in the surroundings of the colony has been reported as a common negative density-dependent effect in colonial species, including the lesser kestrel [31, 93, 94]. Indeed, during the nestling period when availability of pre- ferred prey is highest [73], and both sexes contribute to feed the chicks, kestrels forage closer to the colony than in previous periods (Fig. 3). Our findings suggest that the sexual spatial segregation could be caused by lesser kestrel breeders aiming to increase offspring survival through re- ducing prey depletion close to the colony and intersexual competition between members of the breeding pair. Therefore, the sexual spatial segregation of the lesser kes- trel might well be a result of an adaptive foraging strategy based on role specialization in order to improve breeding success.
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Colony size and foraging range in seabirds

Colony size and foraging range in seabirds

Ref. 50. Nettleship, D.N. and Gaston, A.J. (1978) Patterns of pelagic distribution of seabirds in western Lancaster Sound and Barrow Strait, Northwest Territories, in August and September 1976. Canadian Wildlife Service Occasional Papers No. 39. Ref. 51. Obst, B. S., Russell, R. W., Hunt, G. L., Jr, Eppley, Z. A., and Harrison, N. M. 1995. Foraging radii and energetics of least auklets (Aethia pusilla) breeding on three Bering Sea islands. Physiological Zoology, 68: 647–672

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Levy foraging patterns of rural humans

Levy foraging patterns of rural humans

We conjecture that a potential parsimonious explanation for our findings is that the foragers are frequently making excursions from the trails and that these excursions take the form of random walks (coming back to the trail). We do not have direct evidence for such excursions but we suspect that they do occur being triggered by opportunistic foraging, curiosity about the surroundings, or because the farmers have temporally lost contact with the trail. If the farmers tend to drift in the direction of the trail when making such excursions then the distribution of net displacements made during long excursions will have a 3/2 power-law tail. This is a consequence of the Sparre Andersen theorem [29,30]. Net displacements made during short excursions will be distributed differently having tails that deviate from 3/2 power- laws. The projection method, used in our data analysis, would identify these net
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Assessment of pollen rewards by foraging bees

Assessment of pollen rewards by foraging bees

the phototaxis of pollen and nectar foraging honey bees are related to their 929  . octopamine brain titers[r]

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Nocturnal Foraging by Chatham-Island Skuas

Nocturnal Foraging by Chatham-Island Skuas

were common. From dusk (at 2000h) until 2240h, the birds were together on the roost above the nest site, except for two short flights by the alpha male and 25 minutes foraging among petrel burrows along the forest edge, without success, by the beta male. From 2240h until 0140h all three birds foraged for petrels in the long grass and shrubs of the upper territory, capturing at least three petrels, and making three flights back to the chicks with food. The alpha male fed the chicks twice, the beta male once. The skuas roosted again for the rest of the night, with a single flight by the beta male, until general flights about the territory began again at first light at 0420h.
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Foraging behaviour of parasitoids in multi-herbivore communities

Foraging behaviour of parasitoids in multi-herbivore communities

Extensive research has been executed to reveal how species diversity affects ecosystem functions and services. Yet, consequences of diversity loss for ecosystems as a whole as well as for single community members are still difficult to predict. A suitable system for studies into diversity effects is that consisting of arthropod herbivores and their natural enemies; parasitoids. Parasitoids forage for herbivorous hosts by using herbivore-induced plant volatiles (indirect cues) and cues produced by their host (direct cues). However, in addition to hosts, non-suitable herbivores are also present in a parasitoid’s environment and they may complicate the foraging process for the parasitoid. Therefore, whenever the species diversity of herbivores, including both hosts and non-hosts, changes, this could affect the behaviour of parasitoids. Either by means of species numbers per se, or by specific species traits, or by both. To investigate how diversity and identity of non-host herbivores influence the behaviour of parasitoids, we created environments with different levels of non-host diversity, where hosts were complemented with herbivores from one to four non-host species. We subsequently studied the behaviour of the gregarious endoparasitoid Cotesia glomerata while foraging for its gregarious host Pieris brassicae throught indirect plant cues or direct host cues. Our results show that neither non-host species diversity, nor non-host identity influences the preference of the parasitoid for herbivore-infested plants. However, after landing on the plant, non-host species identity does affect parasitoid behaviour whereas non-host diversity does not. One of the non-host species investigated, i.e. Trichoplusia ni, negatively affected the time the parasitoid spent on the plant and the number of hosts it parasitized. We conclude that non-host herbivore species identity has a larger influence on C. glomerata foraging behaviour than non- host species diversity. Our study shows the importance of species identity over species diversity in a multi-trophic interaction of plants, herbivores and parasitoids.
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Collective foraging: Cleaning, energy harvesting and trophallaxis

Collective foraging: Cleaning, energy harvesting and trophallaxis

In recent studies, we examined several honeybee-derived algorithms for swarm robotics: The vector-based swarm algorithm draws inspiration from the dance language which is used by honeybees to communicate the location of feed- ing sites [Valdastri et al. (2006); Corradi et al. (2009)]. The BEECLUST algo- rithm is inspired by the self-organized aggregation behaviour of young honey- bees in the hive [Schmickl et al. (2009); Kernbach et al. (2009a); Schmickl et al. (2009)]. The trohpallaxis-inspired algorithm is mimicking the frequent food ex- change observable in honeybees [Schmickl and Crailsheim (2006); Schmickl et al. (2007a,c,b); Schmickl and Crailsheim (2008c)]. The algorithm that was analysed most intensively and which we present in the following sections is the called the ”trophallaxis-inspired” robot algorithm. It is tested and analysed in a foraging- collectively-for-dirt scenario, which is closely related to the foraging-collectively- for-food task, which is prominently exhibited by in all eusocial insect colonies: in our focal scenario, a swarm of robots has to explore collectively the arena. If a robot finds one of those dirt particles, which are aggregated around some spot in the arena, it picks up that particle and transports it towards a designated dump area. All robots are able to perform only close-neighbour communication within a radius of 2-3 robot diameters. They are able to sense dirt particles and the dump area only directly below themselves on the ground, thus they have no far-range de- tection of dirt particles. The goal in the evaluated cleaning scenario was to control the swarm in a way that dirt collection is performed efficiently (fast) and robustly, which is indicated by a complete removal of dirt particles from the environment. Due to the limited sensory abilities of these robots, the only way to achieve this goal was by exploiting communication among neighbouring robots in a swarm- intelligent way.
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