The foraging benefits of information and the penalty of ignorance

Patch use theory and the marginal value theorem predict that a foraging patch should be abandoned when the costs and benefits of foraging in the patch are equal. This has generally been interpreted as all patches being abandoned when their instantaneous intake rate equals the foraging costs. Bayesian foraging – patch departure is based on a prior estimate of patch qualities and sampling information from the current patch – predicts that instantaneous quitting harvest rates sometimes are not constant across patches but increase with search time in the patch. That is, correct Bayesian foraging theory has appeared incompatible with the widely accepted cost–benefit theories of foraging. In this paper we reconcile Bayesian foraging with cost–benefit theories. The general solution is that a patch should be left not when instantaneous quitting harvest rate reaches a constant level, but when potential quitting harvest rate does. That is, the forager should base its decision on the value now and in the future until the patch is left. We define the difference between potential and instantaneous quitting harvest rates as the foraging benefit of information, FBI. For clumped prey the FBI is positive, and by including this additional benefit of patch harvest the forager is able to reduce its penalty of ignorance.     

State‐dependent Bayesian foraging on spatially autocorrelated food distributions

When prey are cryptic and are distributed in discrete clumps (patches), Bayesian foragers revise their prior expectation about a patch's prey density by using their foraging success in the patch as a source of information. Prey densities are often spatially autocorrelated, meaning that rich patches are often surrounded by other rich patches, while poor patches are often in the midst of other poor patches. In that case, foraging success is informative about prey densities in the current patch and in the surrounding patches. In a spatially explicit environment where prey are cryptic and their densities autocorrelated, I modelled two types of Bayesian foragers that aim to maximize their survival rate: (1) the spatially ignorant forager which does not take account of the spatial structure in its food supply and (2) the spatially informed forager which does take this into account. Not surprisingly, the spatially informed forager has a higher survivorship than the spatially ignorant forager, simply because it is able to obtain more reliable prey density estimates than the spatially ignorant forager. Surprisingly though, the emerging policy used by the spatially informed forager is to leave patches at a lower (expected) giving‐up density (GUD) the further away from its latest prey capture. This is because this forager is willing to wait for good news: a prey capture far from the latest prey capture drastically changes the forager's expectations about prey densities in the patches that it will exploit in the near future, whereas a prey capture near its latest prey capture hardly affects these expectations. Thus, by sacrificing current intake rate for information gain, the spatially informed forager ultimately maximizes its long‐term pay‐off. Finally, as the value of food is less the more energy is stored, both types make state‐dependent giving‐up decisions: the higher their energy store levels, the higher their GUDs.     

On the value of information: studying changes in patch assessment abilities through learning

Little is known about how animals acquire and use prior information, particularly for Bayesian patch assessment strategies. Because different patch assessment strategies rely upon distinct capabilities to obtain information, we analyzed whether foragers can alter their foraging strategy when they exploit predictable patches with periodic renewal. For this, we evaluated if learning contribute to increase foraging efficiency by improving patch assessment abilities in degus (Octodon degus), a diurnal caviomorph rodent from central Chile. Single degus exploited pairs of depleting patches that were renewed daily. During the initial two days of the experiment, degus exploited patches in agreement with a fixed‐time strategy, i.e. at the population level, giving‐up densities (GUD) were not distinguishable from density‐independence (i.e. consumption proportional to initial patch densities), and richer patches were under‐exploited. After day five, degus improved significantly their assessment strategy, showing agreement with Bayesian information updating. However, on day 15 and afterwards, degus foraged patches in agreement with a prescient strategy, because GUDs across patches indicated positive density‐dependence and equalization of GUDs. Although highly variable, the GUD ratio between rich and poor patches decreased significantly throughout time. Within‐subject data showed that as subjects learned patch qualities they showed a tendency toward GUD equalization and differentiation from density‐independence. By the end of the experiment, degus allocated more time to richer patches during the initial period of each trial, and allocated similar amounts of time by the end of trials. Further, the first visit of a session was significantly biased toward the rich patch by the final days of the experiment. The results suggest that assessment abilities can change when exploiting novel but predictable patches. When degus can incorporate adequate environmental information, prior and current information may become accurate enough to make animals exploit patches efficiently.     

Bayesian foraging with only two patch types

I model the optimal Bayesian foraging strategy in environments with only two patch qualities. That is, all patches either belong to one rich type, or to one poor type. This has been a situation created in several foraging experiments. In contrast, previous theories of Bayesian foraging have dealt with prey distributions where patches may belong to one out of a large range of qualities (binomial, Poisson and negative binomial distributions). This study shows that two‐patch systems have some unique properties. One qualitative difference is that in many cases it will be possible for a Bayesian forager to gain perfect information about patch quality. As soon as it has found more than the number of prey items that should be available in a poor patch, it “knows” that it is in a rich patch. The model generates at least three testable predictions. 1) The distribution of giving‐up densities, GUDs, should be bimodal in rich patches, when rich patches are rare in the environment. This is because the optimal strategy is then devoted to using the poor patches correctly, at the expense of missing a large fraction of the few rich patches available. 2) There should be a negative relation between GUD and search time in poor patches, when rich patches are much more valuable than poor. This is because the forager gets good news about potential patch quality from finding some food. It therefore accepts a lower instantaneous intake rate, making it more resistant against runs of bad luck, decreasing the risk of discarding rich patches. 3) When the energy gains required to remain in the patch are high (such as under high predation risk), the overuse of poor patches and the underuse of rich increases. This is because less information about patch quality is gained if leaving at high intake rates (after short times). The predictions given by this model may provide a much needed and effective conceptual framework for testing (both in the lab and the field) whether animals are using Bayesian assessment.     

Optimal movement between patches under incomplete information about the spatial distribution of food items

If the food distribution contains spatial pattern, the food density in a particular patch provides a forager with information about nearby patches. Foragers might use this information to exploit patchily distributed resources profitably. We model the decision on how far to move to the next patch in linear environments with different spatial patterns in the food distribution (clumped, random, and regular) for foragers that differ in their degree of information. An ignorant forager is uninformed and therefore always moves to the nearest patch (be it empty or filled). In contrast, a prescient forager is fully informed and only exploits filled patches, skipping all empty patches. A Bayesian assessor has prior knowledge about the content of patches (i.e. it knows the characteristics of the spatial pattern) and may skip neighbouring patches accordingly by moving to the patch where the highest gain rate is expected. In most clumped and regular distributions there is a benefit of assessment, i.e. Bayesian assessors achieve substantially higher long-term gain rates than ignorant foragers. However, this is not the case in distributions with less strong spatial pattern, despite the fact that there is a large potential benefit from a sophisticated movement rule (i.e. a large penalty of ignorance). Bayesian assessors do also not achieve substantially higher gain rates in environments that are relatively rich or poor in food. These results underline that an incompletely informed forager that is sensitive to spatial pattern should not always respond to existing pattern. Furthermore, we show that an assessing forager can enhance its long-term gain rate in highly clumped and some specific near-regular food distributions, by sampling the environment in slightly larger spatial units.     

Optimal movement between patches under incomplete information about the spatial distribution of food items

If the food distribution contains spatial pattern, the food density in a particular patch provides a forager with information about nearby patches. Foragers might use this information to exploit patchily distributed resources profitably. We model the decision on how far to move to the next patch in linear environments with different spatial patterns in the food distribution (clumped, random, and regular) for foragers that differ in their degree of information. An ignorant forager is uninformed and therefore always moves to the nearest patch (be it empty or filled). In contrast, a prescient forager is fully informed and only exploits filled patches, skipping all empty patches. A Bayesian assessor has prior knowledge about the content of patches (i.e. it knows the characteristics of the spatial pattern) and may skip neighbouring patches accordingly by moving to the patch where the highest gain rate is expected. In most clumped and regular distributions there is a benefit of assessment, i.e. Bayesian assessors achieve substantially higher long-term gain rates than ignorant foragers. However, this is not the case in distributions with less strong spatial pattern, despite the fact that there is a large potential benefit from a sophisticated movement rule (i.e. a large penalty of ignorance). Bayesian assessors do also not achieve substantially higher gain rates in environments that are relatively rich or poor in food. These results underline that an incompletely informed forager that is sensitive to spatial pattern should not always respond to existing pattern. Furthermore, we show that an assessing forager can enhance its long-term gain rate in highly clumped and some specific near-regular food distributions, by sampling the environment in slightly larger spatial units.     

Honeybee (Apis mellifera ligustica) Response to Differences in Handling Time, Rewards and Flower Colours

Free flying honeybees were tested outdoors on blue–white and blue–yellow dimorphic artificial flower patches to examine the influence of reward difference, flower handling‐time difference and flower colour choice on foraging decisions. We employed different flower‐well depths to vary handling times (costs), and differences in sucrose molarity to vary reward quality. Tests were performed with 2 and 6 μl rewards to vary quantity. We show that when handling time is correlated with flower‐colour morphs on a pedicellate artificial flower patch, a honeybee's foraging behaviour is dependent on the flower colours used in the choice tests. This supports a honeybee foraging model where constraints are a significant factor in decision making. Bees visiting blue–yellow flower patches exhibited flower constancy to colour, where they restricted most visits to a single flower colour, some bees to blue and others to yellow, irrespective of handing time differences. When offered a choice of equally rewarding blue or white flowers, bees were not constrained by flower colour and chose to visit flowers with a lower handling time. When reward molarity varied with well depth between blue and white flowers, foragers chose shallow‐well flowers (short‐handling time) with a smaller net harvest rate over deep‐well flowers (long‐handling time) with a greater net harvest rate. Results using the blue–white dimorphic flower patch suggest that when foraging options simultaneously involve reward and handling‐time choices, honeybee forager behaviour is inconsistent with an absolute method of evaluating profit.     

Depletion of seed patches by Merriam’s kangaroo rats: are GUD assumptions met?

Depletion of experimental seed patches by granivorous animals often is used as a qualitative assay of foraging activity. An optimal foraging model suggests that seed amounts remaining when foragers leave patches ("giving-up-density", GUD) also provide quantitative measures of foraging economics, diet strategies and foraging abilities. Such quantitative uses of GUDs rest on several largely untested assumptions. We tested two of these with Merriam's kangaroo rats: that gain curves are smoothly decelerating, and that foragers leave patches at a constant harvest rate. Harvest rates indeed declined with patch residence time, but in the piecewise linear fashion expected of systematic search. Animals also revisited areas within patches less frequently than expected with random search. In the field, they depleted patches in multiple visits and did not use a constant-rate leaving rule. These deviations from model assumptions cast doubt on inferences about foraging ecology that have been based on quantitative GUD theory.     

Foraging for intermittently refuged prey: theory and field observations of a parasitoid

Many insect herbivores feed in concealed locations but become accessible intermittently, creating windows of greater vulnerability to attack, and generating a proportion of the prey population that is readily accessible to foraging natural enemies. We incorporated accessible prey into an extant optimal foraging model, and found that this addition allowed opportunistic exploitation of prey that have already emerged from refugia (the leaving strategy) as a viable strategy, in addition to waiting at refugia for prey to emerge (the waiting strategy). We parameterized the model empirically for the parasitoid Macrocentrus grandii and its host, Ostrinia nubilalis, under field conditions. The model predicted that M. grandii should adopt a leaving strategy when host patch density is high (travel time between patches is short), but a waiting strategy when host patch density is low (travel time between patches is long). Field observations of M. grandii patch tenure were consistent with model predictions, indicating that M. grandii exhibited flexible behaviour based on experience within a foraging bout, and that these behavioural shifts improved foraging efficiency. Behaviour of M. grandii was responsive to heterogeneity in host emergence rates, and appeared to be driven by the relatively small proportion of the host population that became accessible at a fast rate. Therefore understanding forager responses to intermittently refuged prey may require characterization of the behaviour of a subset of the prey population, rather than the average prey individual. The model can potentially be used as a framework for comparative studies across forager taxa, to understand when foragers on intermittently accessible prey should adopt fixed waiting or leaving strategies vs. a flexible strategy that is responsive to the current environment.     

The survival-rate-maximizing policy for Bayesian foragers: wait for good news

We present a model of the survival-maximizing foraging behaviorof an animal searching in patches for hidden prey with a clumpeddistribution. We assume the forager to be Bayesian: it updatesits statistical estimate of prey number in the current patchwhile foraging. When it arrives at the parch, it has an expectationof the patch's quality, which equals the average patch qualityin the environment While foraging, the forager uses its informationabout the time spent searching in the patch and how many preyhas been caught during this time. It can estimate both the instantaneousintake rate and the potential intake rate during the rest ofthe parch visit. When prey distribution is clumped, potentialintake rate may increase with time spent in the parch if preyis caught in the near future. Being optimal, a Bayesian foragershould therefore base its patch-leaving decision on the estimatedpotential patch value, not on the instantaneous parch value.When patch value is measured in survival rate and mortalitymay occur either as starvation or predation, the patch shouldbe abandoned when the forager estimates that its potential survivalrate dining the rest of the patch visit equals the long termsurvival rate in the environment This means that the instantaneousintake rate, when the patch is left, is nor constant but isan increasing function of searching time in the patch. Therefore,the giving-up densities of prey in the patches will also behigher the longer the search times. The giving-up densitiesare therefore expected to be an increasing, but humped, functionof initial prey densities. These are properties of Bayesianforaging behavior not included in previous empirical studiesand model tests.     

Downy woodpecker foraging behavior: foraging by expectation and energy intake rate

Summary I describe an artificial patch system that was used to study the foraging behavior of free-roaming downy woodpeckers (Picoides pubescens) in a woodlot in southeastern Michigan. The artificial patches used were thin logs into which were drilled small holes to hold food items (bits of sunflower seed kernels). Downy woodpeckers would systematically search the holes of a patch for food items and thus by manipulating the food distribution within the patches, the birds could be made to experience differing rates of energy intake while foraging.Simple deterministic theories of optimal foraging in patchy environments indicate that an optimal forager, who experiences a decreasing rate of energy intake while foraging in a patch, should leave a patch when its rate of energy intake falls below the average intake rate for the overall environment. In other words, an optimal forager is continually assessing the quality of a patch and makes decisions as to when to leave a patch via its energy intake rate. When the downy woodpeckers studied could encounter any one of several types of patches each with differing, decreasing rates of energy intake, they followed a patch quality assessment strategy similar to that suggested by theory. Upon encountering a single type of patch for a number of consecutive days, however, the birds appeared to forage according to prior expectations of patch quality and not according to a quality assessment strategy based on energy intake rates. The observed expectations were not related to the number of food items per patch but they appeared to be based on expectations of when or where to leave a patch.     

Tolerance of understory plants subject to herbivory by roe deer

Classical foraging theory states that animals feeding in a patchy environment can maximise their long term prey capture rates by quitting food patches when they have depleted prey to a certain threshold level. Theory suggests that social foragers may be better able to do this if all individuals in a group have access to the prey capture information of all other group members. This will allow all foragers to make a more accurate estimation of the patch quality over time and hence enable them to quit patches closer to the optimal prey threshold level. We develop a model to examine the foraging efficiency of three strategies that could be used by a cohesive foraging group to initiate quitting a patch, where foragers do not use such information, and compare these with a fourth strategy in which foragers use public information of all prey capture events made by the group. We carried out simulations in six different prey environments, in which we varied the mean number of prey per patch and the variance of prey number between patches. Groups sharing public information were able to consistently quit patches close to the optimal prey threshold level, and obtained constant prey capture rates, in groups of all sizes. In contrast all groups not sharing public information quit patches progressively earlier than the optimal prey threshold value, and experienced decreasing prey capture rates, as group size increased. This is more apparent as the variance in prey number between patches increases. Thus in a patchy environment, where uncertainty is high, although public information use does not increase the foraging efficiency of groups over that of a lone forager, it certainly offers benefits over groups which do not, and particularly where group size is large.     

Neuroeconomics in parasitoids: computing accurately with a minute brain

In the so‐called ‘patch problem’, at any given moment, the forager must decide whether to leave the current patch or to remain there and continue foraging. Optimal foraging theory and subsequent theoretical works have identified theoretical optimal policies governing this decision. In a stochastic environment, the Bayesian framework has proved to be effective. A set of mechanistic proximal mechanisms explaining how parasitoid wasps may take decisions has been proposed. These mechanisms are based in on changes in the degree of motivation to continue foraging during a particular foraging episode. Using a simple, straightforward model, we show here that the psychological mechanism proposed mimics precisely the theoretical Bayesian solution, provided that motivation displays exponential decay, rather than the linear pattern of decay initially assumed. Changes in motivation thus function as a sort of analogue computer, and may be seen as more than purely heuristic rules of thumb. This link between psychological processes and ultimate optimisation places foraging theory in the domain of neuroeconomics.     

Optimal Bayesian foraging policies and prey population dynamics-some comments on Rodriguez-Girones and Vasquez

In this paper we show the density-dependent harvest rates of optimal Bayesian foragers exploiting prey occurring with clumped spatial distribution. Rodríguez-Gironés and Vásquez (1997) recently treated the issue, but they used a patch-leaving rule (current value assessment rule) that is not optimal for the case described here. An optimal Bayesian forager exploiting prey whose distribution follows the negative binomial distribution should leave a patch when the potential (and not instantaneous) gain rate in that patch equals the best long-term gain rate in the environment (potential value assessment rule). It follows that the instantaneous gain rate at which the patches are abandoned is an increasing function of the time spent searching in the patch. It also follows that the proportion of prey harvested in a patch is an increasing sigmoidal function of the number of prey initially present. In this paper we vary several parameters of the model to evaluate the effects on the forager's intake rate, the proportion of prey harvested per patch, and the prey's average mortality rate in the environment. In each case, we study an intake rate maximizing forager's optimal response to the parameter changes. For the potential value assessment rule we find that at a higher average prey density in the environment, a lower proportion of the prey is taken in a patch with a given initial prey density. The proportion of prey taken in a patch of a given prey density also decreases when the variance of the prey density distribution is increased and if the travel time between patches is reduced. We also evaluate the effect of using predation minimization, rather than rate maximization, as the currency. Then a higher proportion of the prey is taken for each given initial prey density. This is related to the assumption that traveling between patches is the most risky activity. Compared to the optimal potential value assessment rule, the current value assessment rule performs worse, in terms of long-term intake rate achieved. The difference in performance is amplified when prey density is high or highly aggregated. These results pertain to the foraging patch spatial scale and may have consequences for the spatial distribution of prey in the environment.     

Spatial learning and discrimination of food patches in the European badger (Meles meles L.)

Theories of foraging often assume that concentrations of prey (‘patches’) can be discriminated by a forager, but there have been few studies of how this is achieved when patches are not recognizable by means of an obvious proximal cue. We observed the search trajectories of two badgers (Meles meles L.) foraging for peanuts in artificial patches to see how efficiently they could map a new patch in the first place, and whether they would remember the location and extent of a previously visited patch. The results suggest that when a patch is encountered for the first time, a strategy of area-restricted searching keeps the animal's trajectory largely within the patch boundary. After a single exposure to a novel patch, however, badgers show evidence of being able to remember its location and extent, apparently with reference to distal landmarks.     

A guide to central place effects in foraging

We develop a general patch-use model of central place foraging, which subsumes and extends several previous models. The model produces a catalog of central place effects predicting how distance from a central place influences the costs and benefits of foraging, load-size, quitting harvest rates, and giving-up densities. In the model, we separate between costs that are load-size dependent, i.e. a direct effect of the size of the load, and load-size independent effects, such as correlations between distance and patch qualities. We also distinguish between predictions of between- and within-environment comparisons. Foraging costs, giving-up densities and quitting harvest rates should almost always increase with distance with these effects amplified by increases in metabolic costs, predation risk and load-costs. With respect to load-size: when comparing foraging in patches within an environment, we should often expect smaller loads to be taken from distant patches (negative distance–load correlation). However, when comparing between environments, there should be a positive correlation between average distance and load-size.     

Varied tastes: home range implications of foraging‐patch selection

Despite evidence of home range behaviour across many taxa, the mechanisms underlying the development of home ranges are still unknown. Recently, models have been developed to explore these mechanisms for both territorial and non‐ territorial species. One such model for a generic forager suggests animal memory and optimal foraging theory as underlying mechanisms driving forager movement and the development of stable home ranges. Although this is a promising model for ungulate home range development, assumptions of the model have yet to be evaluated. Using GPS relocation data from two populations of elk, we explored how foraging patch selection might influence the structure and development of home ranges in elk Cervus elaphus. During the summer growing season, we identified and sampled foraging patches used by elk. Points along elk paths not used for foraging were sampled identically for comparison. We contrasted ‘patch’ and ‘nonpatch’ data points, to identify foraging selection differences across herd, sex and season using a combination of directly sampled and remotely sensed covariates. In general, elk selected patches with higher biomass, cover, slope and lower traffic on the nearest road. These patch‐selection results speak directly to differences between foraging areas and other areas used by elk and demonstrate that both physiographic and anthropocentric features influence foraging patch selection. Our results offer insight as to what defines a valuable foraging patch for elk and how these patches might influence the development and structure of home ranges in a free‐ranging ungulate.     

Prior knowledge about spatial pattern affects patch assessment rather than movement between patches in tactile-feeding mallard

1. Heterogeneity in food abundance allows a forager to concentrate foraging effort in patches that are rich in food. This might be problematic when food is cryptic, as the content of patches is unknown prior to foraging. In such case knowledge about the spatial pattern in the distribution of food might be beneficial as this enables a forager to estimate the content of surrounding patches. A forager can benefit from this pre-harvest information about the food distribution by regulating time in patches and/or movement between patches. 2. We conducted an experiment with mallard Anas platyrhynchos foraging in environments with random, regular, and clumped spatial configurations of full and empty patches. An assessment model was used to predict the time in patches for different spatial distributions, in which a mallard is predicted to remain in a patch until its potential intake rate drops to the average intake rate that can be achieved in the environment. A movement model was used to predict lengths of interpatch movements for different spatial distributions, in which a mallard is predicted to travel to the patch where it expects the highest intake rate. 3. Consistent with predictions, in the clumped distribution mallard spent less time in an empty patch when the previously visited neighbouring patch had been empty than when it had been full. This effect was not observed for the random distribution. This shows that mallard use pre-harvest information on spatial pattern to improve patch assessment. Patch assessment could not be evaluated for the regular distribution. 4. Movements that started in an empty patch were longer than movements that started in a full patch. Contrary to model predictions this effect was observed for all spatial distributions, rather than for the clumped distribution only. In this experiment mallard did not regulate movement in relation to pattern. 5. An explanation for the result that pre-harvest information on spatial pattern affected patch assessment rather than movement is that mallard move to the nearest patch where the expected intake rate is higher than the critical value, rather than to the patch where the highest intake rate is expected.     

The effects of habitat manipulation on population distribution and foraging behavior in meadow voles

Individuals, free to choose between different habitat patches, should settle among them such that fitness is equalized. Alternatives to this ideal free distribution result into fitness differences among the patches. The concordance between fitnesses and foraging costs among inhabitants of different quality patches, demonstrated in recent studies, suggests that the mode of habitat selection and the resulting fitness patterns may have important implications to the resource use of a forager and to the survival of its prey. We studied how coarse scale selection between habitat patches of different quality and quitting harvest rate in these patches are related to each other and to fine scale patch use in meadow voles (Microtus pennsylvanicus). To demonstrate these relationships, we manipulated habitat patches within large field enclosures by mowing vegetative cover and adding supplemental food according to a 2×2 factorial design. We tracked vole population densities, collected giving‐up densities (GUDs, a measure of patch quitting harvest rate), and monitored the removal of seeds from lattice grids with 1.5 m intervals (an index of fine‐scale space use) in the manipulated habitat patches. Changes in habitat quality induced changes in habitat use at different spatial scales. In preferred habitats with intact cover, voles were despotic and GUDs were low, but increased with the addition of food. In contrast, voles in less‐preferred mowed habitats settled into an ideal free distribution, GUDs were high and uninfluenced by the addition of food. Seed removal was enhanced by the presence of cover but inhibited by supplemental food. Across all treatments, vole densities and GUDs were strongly correlated making it impossible to separate their effects on seed removal rates. However, this relationship broke down in unmowed habitats, where GUDs rather than vole density primarily influenced seed removal by voles. GUDs and seed removal correlated with predation on tree seedlings formerly planted into the enclosures, demonstrating the mechanisms between coarse‐scale habitat manipulations and community level consequences on a forager's prey.     

Maintaining social cohesion is a more important determinant of patch residence time than maximizing food intake rate in a group-living primate,Japanese macaque (Macaca fuscata)

Animals have been assumed to employ an optimal foraging strategy (e.g., rate-maximizing strategy). In patchy food environments, intake rate within patches is positively correlated with patch quality, and declines as patches are depleted through consumption. This causes patch-leaving and determines patch residence time. In group-foraging situations, patch residence times are also affected by patch sharing. Optimal patch models for groups predict that patch residence times decrease as the number of co-feeding animals increases because of accelerated patch depletion. However, group members often depart patches without patch depletion, and their patch residence time deviates from patch models. It has been pointed out that patch residence time is also influenced by maintaining social proximity with others among group-living animals. In this study, the effects of maintaining social cohesion and that of rate-maximizing strategy on patch residence time were examined in Japanese macaques (Macaca fuscata). I hypothesized that foragers give up patches to remain in the proximity of their troop members. On the other hand, foragers may stay for a relatively long period when they do not have to abandon patches to follow the troop. In this study, intake rate and foraging effort (i.e., movement) did not change during patch residency. Macaques maintained their intake rate with only a little foraging effort. Therefore, the patches were assumed to be undepleted during patch residency. Further, patch residence time was affected by patch-leaving to maintain social proximity, but not by the intake rate. Macaques tended to stay in patches for short periods when they needed to give up patches for social proximity, and remained for long periods when they did not need to leave to keep social proximity. Patch-leaving and patch residence time that prioritize the maintenance of social cohesion may be a behavioral pattern in group-living primates.