Paying for nectar with wingbeats: a new model of honeybee foraging

Honeybees acquire wing damage as they age and older foraging honeybees accept lavender inflorescences with fewer flowers. These indicate the operation of some kind of optimal response, but this cannot be based on energy because energy expenditure does not change as the wings get damaged. However, wingbeat frequency increases with wing damage. A deterministic analytical model was constructed, based on the assumptions that bees have a limited total number of wingbeats that the flight motor can perform and that they maximize lifetime energy profit by conserving the number of wingbeats used in foraging. The optimal response to wing damage is to reduce the threshold number of flowers needed to accept an inflorescence. The predicted optimal gradient between wing damage (wingbeat frequency) and acceptance threshold (number of flowers on an inflorescence) was close to the observed gradient from field data. This model demonstrates that wear and tear is a significant factor in optimal foraging strategies.     

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.     

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.     

Inheritance of optimal foraging behaviour in Columbian ground squirrels

Summary I examined the potential inheritance of the ability of Columbian ground squirrels (Spermophilus columbianus) to select an optimal diet. I calculated the diet that would maximize daily energy intake for each of 21 adult females and their litters, using a linear programming optimization model for each individual. The absolute value of the difference between an individual's predicted optimal diet and observed diet (deviation from an optimal diet) was used as a measure of an individual's foraging ability. The foraging ability of individuals was consistent over time and in different foraging environments, so I considered foraging ability to be a potentially heritable trait.Inheritance was determined from correlations of mother and offspring foraging ability. I experimentally removed some mothers just as they weaned their offspring so that offspring could not be influenced by their mother while learning to forage, while leaving the other mothers to raise their litters normally. In both cases, offspring strongly resembled their mother in foraging ability. However, offspring with mothers absent exhibited significantly larger deviations from their optimal diet. Offspring with mothers absent appeared to imitate their mother's diet during lactation, and this tendency partially explained their greater deviation. Consequently, offspring appear to inherit the ability to forage optimally from their mother, perhaps through observational learning or imitation. There may also be a genetic basis to foraging ability, but uncontrolled maternal effects in the experiment prevent a proper test for it.     

Optimal foraging in bumblebees: Hunting by expectation

The foraging behaviour literature contains three hypotheses concerned with hunting by expectation. These suggest possible rules animals use to decide when to leave particular feeding sites and search in other places for food. Predictions of the three hypotheses were tested experimentally by varying the quality of plants (amount and distribution of nectar) encountered by bumblebees (Bombus appositus). Results support only a rate expectation hypothesis. Bees left multiflowered plants when the amount of nectar found in the first flower was below a threshold volume. Bees stayed on plants if they received greater than the threshold volume. This threshold nectar volume is close to the amount predicted if a bee forages to maximize its rate of net energy intake.     

Model of microtine cycles caused by lethal toxins in non-preferred food plants

A recent model of microtine cycles has hypothesized that plant chemical defences can drive the precipitous decline phase, through periodic lethal toxin production (PLTP) by non-preferred plant foods. Here we enumerate possible mechanisms using a previously published model of optimal foraging by one consumer (microtine rodent) of two types of food plant (1 preferred and 1 non-preferred). Rate constants for each of the model parameters were sought from the extensive literature on vole cycles. For a range of likely values of input parameters, we evaluated model fit by applying five empirically derived criteria for cyclic behaviour. These were: cycles with a period length of 2-5 yr, peak densities of 100-350 voles per ha and trough densities of 0-25 ha(-1), ratio of peak to trough densities of 10-100, and the occurrence of a catastrophic collapse in the vole population followed by a prolonged low phase. In contrast to previous models of food-induced microtine cycles, the optimal foraging model successfully reproduced the first four criteria and the prolonged low phase. The criterion of population collapse was met if the non-preferred food began producing lethal toxins at a threshold grazing intensity, as predicted by PLTP. Fewer criteria could be met in variations on the model, in which the non-preferred food was equally as nutritious as the preferred food or was continuously toxic.     

A dynamic model of hypothermia as an adaptive response by small birds to winter conditions

We present a dynamic programming model which is used to investigate hypothermia as an adaptive response by small passerine birds in winter. The model predicts that there is a threshold function of reserves during the night, below which it is optimal to enter hypothermia, and above which it is optimal to rest. This threshold function decreases during the night, with a particularly sharp drop at the end of the night, representing the time and energy costs associated with returning to normal body temperature. The results of the model emphasise the trade-off between energy and predation, not just between foraging options, but also between foraging during the day and entering hypothermia at night. The value of being able to use hypothermia represents not just energy savings, but also reduced predation risk due to changes in the optimal foraging strategy. Conditions which give a high value of hypothermia are short photoperiod, variable food supply, low temperatures, poor and scarce food supplies.     

Learning to forage in a regenerating patchy environment: Can it fail to be optimal?

The behaviour of animals foraging along closed traplines of regenerating patches of food has been simulated using a learning rule that determines when an animal should leave the patch at which it is currently feeding to search for another one. The rule causes the animal to stay at the patch as long as it is feeding faster than it remembers doing. The foraging behaviour of one animal, and of two or more animals together, feeding in traplines containing patches of the same and of differing types has been simulated, and in all cases the foraging behaviour generated by the rule allowed the animals to exploit the food very efficiently. The learning model is also responsible for indirect social interactions among animals sharing the same trapline because the feeding of each animal reduces the availability of food for the others. This causes a population of animals to disperse themselves, on average, among patches of food according to the ideal free distribution. The relationship between the learning model and conventional optimal foraging models is examined and it is shown that it is pointless to try to account for learned behaviour in the context of optimal foraging theory.     

Foraging efficiency and the fitness consequences of spatial marking by ladybeetle larvae

Marking and avoiding poor‐quality resources can be an important mechanism by which animals lacking a spatial memory can maximize their foraging efficiency. Here, we investigate the behaviour of larval Harmonia axyridis ladybeetles that leave chemical tracks as they forage. We built a model of an individual larva foraging for aphids, parameterized it using experimental data, and used the model to predict the effect of larval track production and detection on foraging efficiency, an important component of fitness. The model predicted that there is an optimal sensitivity of larvae to tracks which maximizes foraging efficiency; if the larva is too sensitive to tracks, it will avoid areas that might still contain resources, whereas if it is too insensitive, it will forage in areas that have depleted resources. Furthermore, the increased efficiency conferred by detecting tracks depends on the spatial arrangement of resources, with more aggregated resource distributions allowing greater benefits of detecting tracks. We tested the predictions of the model experimentally by measuring predation on aggregated versus dispersed soybean aphids by H. axyridis larvae whose ability to produce tracks was experimentally manipulated. The experiments corroborated the results of the model: larvae that could produce tracks consumed more aphids than those that could not, and this difference was greatest when aphids were aggregated among plants. Our results suggest that larval tracks play an important role in foraging efficiency, and we discuss implications for the evolution of larval track production and detection in ladybeetles.     

Co-evolution of learning complexity and social foraging strategies

Variation in learning abilities within populations suggests that complex learning may not necessarily be more adaptive than simple learning. Yet, the high cost of complex learning cannot fully explain this variation without some understanding of why complex learning is too costly for some individuals but not for others. Here we propose that different social foraging strategies can favor different learning strategies (that learn the environment with high or low resolution), thereby maintaining variable learning abilities within populations. Using a genetic algorithm in an agent-based evolutionary simulation of a social foraging game (the producer-scrounger game) we demonstrate how an association evolves between a strategy based on independent search for food (playing a producer) and a complex (high resolution) learning rule, while a strategy that combines independent search and following others (playing a scrounger) evolves an association with a simple (low resolution) learning rule. The reason for these associations is that for complex learning to have an advantage, a large number of learning steps, normally not achieved by scroungers, are necessary. These results offer a general explanation for persistent variation in cognitive abilities that is based on co-evolution of learning rules and social foraging strategies.     

Learning Differences between Feral Pigeons and Zenaida Doves: The Role of Neophobia and Human Proximity

Learning differences predicted from ecological variables can be confounded with differences in wariness of novel stimuli (neophobia). Previous work on feral pigeons ( Columba livia ), as well as on group-feeding and territorial zenaida doves ( Zenaida aurita ), reported individual and social learning differences predicted from social foraging mode. In the present study, we show that speed of learning a foraging task covaries with neophobia and latency to feed from a familiar dish in the three types of columbids. Pigeons were much faster than either territorial or group-feeding zenaida doves on all tests conducted in captivity, but showed unexpectedly strong neophobia in some urban flocks during field tests. Human proximity strongly affected performance in group-feeding doves both in the field and in captivity. They were slightly faster at learning than their territorial conspecifics in cage tests. In multiple regressions, species identity, but not social foraging mode, significantly predicted individual variation in learning, as did individual variation in neophobia. Wariness of novel stimuli and species differences associated with artificial selection appear to be more important than foraging mode and wariness of humans in accounting for learning differences between these columbids.     

Early oviposition experience affects patch residence time in a foraging parasitoid

Parasitoids learn olfactory and visual cues that are associated with their hosts, and use these cues to forage more efficiently. Classical conditioning theory predicts that encounters with high-quality hosts will lead to better learning of host-associated cues than encounters with low-quality hosts. We tested this prediction in a two-phase laboratory experiment with the parasitoid Trichogramma thalense Pinto & Oatman (Hymenoptera: Trichogrammatidae) and the host Anagasta kuehniella Zeller (Lepidoptera: Pyralidae).Host quality during the first exposure to hosts affected later foraging behavior for some experimental treatments, as predicted. We used a learning model, followed by patch-time optimization, to interpret our findings. We first simulated the parasitoids' host encounters during the experiment, and predicted their estimate of patch quality after each encounter. We then used dynamic optimization to predict the parasitoids' optimal patch residence times. The model reproduces the trends of the experimental results.     

An energy-based model of optimal feeding-territory size

An energy-based model of feeding territoriality is described. The model predicts an optimal territory size where the territory holder's net energy intake is maximized, on a daily or seasonal time scale. Simulated effects on optimal territory size of animal size, food availability, and competitor density are in general agreement with observed relationships in a wide variety of animals. When salmonid data are used to estimate the model parameter values, predicted territory sizes are similar to those actually observed. When emigration is allowed, the model predicts that territoriality, through individual selection alone, can regulate population size, but that this regulation breaks down when initial densities exceed some threshold value. Sensitivity analysis shows optimal territory size to be most affected by those parameters influencing food intake and energy expenditure. Some alternative criteria for optimization are also discussed. An animal maximizing net foraging efficiency has a smaller territory than one maximizing net energy, but the effects of animal size, food availability, and competitor density on territory size are the same in either case.     

Foraging Ecology Predicts Learning Performance in Insectivorous Bats

Bats are unusual among mammals in showing great ecological diversity even among closely related species and are thus well suited for studies of adaptation to the ecological background. Here we investigate whether behavioral flexibility and simple- and complex-rule learning performance can be predicted by foraging ecology. We predict faster learning and higher flexibility in animals hunting in more complex, variable environments than in animals hunting in more simple, stable environments. To test this hypothesis, we studied three closely related insectivorous European bat species of the genus Myotis that belong to three different functional groups based on foraging habitats: M. capaccinii, an open water forager, M. myotis, a passive listening gleaner, and M. emarginatus, a clutter specialist. We predicted that M. capaccinii would show the least flexibility and slowest learning reflecting its relatively unstructured foraging habitat and the stereotypy of its natural foraging behavior, while the other two species would show greater flexibility and more rapid learning reflecting the complexity of their natural foraging tasks. We used a purposefully unnatural and thus species-fair crawling maze to test simple- and complex-rule learning, flexibility and re-learning performance. We found that M. capaccinii learned a simple rule as fast as the other species, but was slower in complex rule learning and was less flexible in response to changes in reward location. We found no differences in re-learning ability among species. Our results corroborate the hypothesis that animals’ cognitive skills reflect the demands of their ecological niche.     

Models of Eucalypt phenology predict bat population flux

Fruit bats (Pteropodidae) have received increased attention after the recent emergence of notable viral pathogens of bat origin. Their vagility hinders data collection on abundance and distribution, which constrains modeling efforts and our understanding of bat ecology, viral dynamics, and spillover. We addressed this knowledge gap with models and data on the occurrence and abundance of nectarivorous fruit bat populations at 3 day roosts in southeast Queensland. We used environmental drivers of nectar production as predictors and explored relationships between bat abundance and virus spillover. Specifically, we developed several novel modeling tools motivated by complexities of fruit bat foraging ecology, including: (1) a dataset of spatial variables comprising Eucalypt‐focused vegetation indices, cumulative precipitation, and temperature anomaly; (2) an algorithm that associated bat population response with spatial covariates in a spatially and temporally relevant way given our current understanding of bat foraging behavior; and (3) a thorough statistical learning approach to finding optimal covariate combinations. We identified covariates that classify fruit bat occupancy at each of our three study roosts with 86–93% accuracy. Negative binomial models explained 43–53% of the variation in observed abundance across roosts. Our models suggest that spatiotemporal heterogeneity in Eucalypt‐based food resources could drive at least 50% of bat population behavior at the landscape scale. We found that 13 spillover events were observed within the foraging range of our study roosts, and they occurred during times when models predicted low population abundance. Our results suggest that, in southeast Queensland, spillover may not be driven by large aggregations of fruit bats attracted by nectar‐based resources, but rather by behavior of smaller resident subpopulations. Our models and data integrated remote sensing and statistical learning to make inferences on bat ecology and disease dynamics. This work provides a foundation for further studies on landscape‐scale population movement and spatiotemporal disease dynamics.     

Optimal resource allocation to survival and reproduction in parasitic wasps foraging in fragmented habitats

Expansion and intensification of human land use represents the major cause of habitat fragmentation. Such fragmentation can have dramatic consequences on species richness and trophic interactions within food webs. Although the associated ecological consequences have been studied by several authors, the evolutionary effects on interacting species have received little research attention. Using a genetic algorithm, we quantified how habitat fragmentation and environmental variability affect the optimal reproductive strategies of parasitic wasps foraging for hosts. As observed in real animal species, the model is based on the existence of a negative trade-off between survival and reproduction resulting from competitive allocation of resources to either somatic maintenance or egg production. We also asked to what degree plasticity along this trade-off would be optimal, when plasticity is costly. We found that habitat fragmentation can indeed have strong effects on the reproductive strategies adopted by parasitoids. With increasing habitat fragmentation animals should invest in greater longevity with lower fecundity; yet, especially in unpredictable environments, some level of phenotypic plasticity should be selected for. Other consequences in terms of learning ability of foraging animals were also observed. The evolutionary consequences of these results are discussed.     

A mathematical framework for optimal foraging of herbivores

The aim of this paper is to study a model of optimal foraging of herbivores (with special reference to ungulates) assuming that food distribution is arbitrary. Usually the analysis of foraging of herbivores in the framework of optimal foraging theory is based on the assumption of a patchy food distribution. We relax this assumption and we construct more realistic models. The main constraint of our model is the total amount of food which the animal may eat and the currency is the total foraging time. We represent total foraging time as a variational expression depending on food eaten and the length of the path. We prove that there exists a threshold for food acquisition. More explicitly, it exists a positive real number such that, at any point x of the path, the animal either eats till the density of food is decreased to the value or, if the density of food at x is less than , there it does not eat. We discuss the results and emphasize some biologically important relationships among model parameters and variables. Finally, we try to give a sound biological interpretation of our results.     

Context-dependent behavior and the benefits of communal nesting

We present a model for the behavior of communally nesting insects. Females may forage for food to provision offspring or may remain in the nest, with the option of eating and replacing nest mates' eggs. Orphaned brood are at risk of predation. The optimal behavior of solitary females is determined using stochastic dynamic programming; static and dynamic evolutionarily stable strategies (ESSs) are then calculated for colonies of various sizes. A solitary female should forage if her brood is smaller than a time-dependent threshold. Females in small colonies should forage. In colonies above some threshold size, the static ESS is for one female to forage and the rest to cheat. The dynamic ESS in large colonies is for no females to forage until some time close to the end of the foraging season and for all females to forage thereafter. Mixed dynamic ESSs, with some foragers and some cheats, may arise if individuals differ in their chances of surviving a foraging interval or if females with new offspring vary their guarding behavior, depending on the numbers of cheats and new cells in the nest. We discuss these predictions in the light of published observations and preliminary data on the halictine bee Lasioglossum (Chilalictus) hemichalceum.     

Foraging rate versus sociality in the starling Sturnus vulgaris

It is well established that social conditions often modify foraging behaviour, but the theoretical interpretation of the changes produced is not straightforward. Changes may be due to alterations of the foraging currency (the mathematical expression that behaviour maximizes) and/or of the available resources. An example of the latter is when both solitary and social foragers maximize rates of gain over time, but competition alters the behaviour required to achieve this, as assumed by ideal free distribution models. Here we examine this problem using captive starlings Sturnus vulgaris. Subjects had access to two depleting patches that replenished whenever the alternative patch was visited. The theoretical rate-maximizing policy was the same across all treatments, and consisted of alternating between patches following a pattern that could be predicted using the marginal value theorem (MVT). There were three treatments that differed in the contents of an aviary adjacent to one of the two patches (called the 'social' patch). In the control treatment, the aviary was empty, in the social condition it contained a group of starlings, and in a non-specific stimulus control it contained a group of zebra finches. In the control condition both patches were used equally and behaviour was well predicted by the MVT. In the social condition, starlings foraged more slowly in the social than in the solitary patch. Further, foraging in the solitary patch was faster and in the social patch slower in the social condition than in the control condition. Although these changes are incompatible with overall rate maximization (gain rate decreased by about 24% by self-imposed changes), if the self-generated gain functions were used the MVT was a good predictor of patch exploitation under all conditions. We discuss the complexities of nesting optimal foraging models in more comprehensive theoretical accounts of behaviour integrating functional and mechanistic perspectives.     

Optimal foraging predicts the ecology but not the evolution of host specialization in bacteriophages

We explore the ability of optimal foraging theory to explain the observation among marine bacteriophages that host range appears to be negatively correlated with host abundance in the local marine environment. We modified Charnov's classic diet composition model to describe the ecological dynamics of the related generalist and specialist bacteriophages phiX174 and G4, and confirmed that specialist phages are ecologically favored only at high host densities. Our modified model accurately predicted the ecological dynamics of phage populations in laboratory microcosms, but had only limited success predicting evolutionary dynamics. We monitored evolution of attachment rate, the phenotype that governs diet breadth, in phage populations adapting to both low and high host density microcosms. Although generalist phiX174 populations evolved even broader diets at low host density, they did not show a tendency to evolve the predicted specialist foraging strategy at high host density. Similarly, specialist G4 populations were unable to evolve the predicted generalist foraging strategy at low host density. These results demonstrate that optimal foraging models developed to explain the behaviorally determined diets of predators may have only limited success predicting the genetically determined diets of bacteriophage, and that optimal foraging probably plays a smaller role than genetic constraints in the evolution of host specialization in bacteriophages.