Study Problem: After reading your notes and the articles/material below, using a STELLA like format, including graphs for varying parameters ( example: predator # vs. risk or time for foraging) to design your own model for foraging including the critical parameters that determine what type of strategy an animal should use.

Foraging Strategies

1. Hunger factor

MARINE BIOLOGY
Sea slug's shopping habits dictated by hunger, scientists report
Jim Barlow, Life Sciences Editor
(217) 333-5802; b-james3@uiuc.edu
4/1/2000CHAMPAIGN, Ill. -- Conventional wisdom says that if you shop for groceries on an empty stomach you'll spend more than necessary because of impulse buying fed by hunger pangs, while a full stomach makes you a pickier shopper. You're in good company: Sea slugs shop the same way.
When hungry, the slugs (Pleurobranchaea californica) may ravenously attack even dangerous prey. With a full stomach, however, they actually turn away from and avoid potential food, scientists report in the March 28 issue of the Proceedings of the National Academy of Sciences. Such avoidance behavior is important for marine snails, because any time spent eating puts them at risk for being prey themselves.
The research was designed to study the mechanisms of decision-making, said Rhanor Gillette, a professor of physiology and neuroscience at the University of Illinois. Foraging animals and shopping humans should make decisions that produce the greatest benefit at the least cost. In this case, Gillette's team asked if degrees of appetite affected the readiness of snails to attack or avoid available prey. Responses were measured by the concentrations of food chemicals at which they would bite or turn away.
"What we've found in studying this very simple sea slug, with a very simple body form and a very simple brain, is that its behavior is organized hedonically, much like ours," he said. "If an animal's internal state changes, its responses to food and pain stimuli change, too. It is as if they make decisions based on a sliding scale of pleasure and pain. This is surprising for a simple invertebrate. Previously such behavior was thought to be exclusive to higher vertebrates."
Hungry snails tempted with the betaine -- a chemical found in most marine invertebrates that stimulates predators -- were quicker to strike than less hungry snails. Higher concentrations of betaine eventually induced biting by the satiated snails, but in general the less hungry snails withdrew their heads, turned and moved away from the food source.
Hungry snails also were more likely to try to attack a noxious acidic stimulus, researchers found. However, satiated snails avoided the noxious stimulus, and even hungry snails with previous exposure were more likely to avoid it. "This could reflect the need of the starving sea slug to pay a higher cost for a meal, if it had to overcome the defenses of prey unwilling to be eaten," Gillette said.
(To see a snail learning to avoid noxious prey, go to http://www.life.uiuc.edu/slugcity/movies.html. Click on "One Trial Learning.")
"We may have been looking at a very fundamental structural organization that will be found in the behavior of most foraging animals," he said. "Animals tend to make wise decisions when they forage, and they do so whether or not they have lots of brain power."

2. Fear Factor

Optimal foraging: importance of assessing different levels of predation risk
(This is not a peer-reviewed article.).
 
 Abstract
 
Foraging animals are constantly exposed to various predators and they are forced to make trade-offs between the benefits of maximum feeding efficiency and the costs of predation. Different predators pose different levels of risk due differences in diet selection, developmental stage, and efficiency. I used grey squirrels (Sciurus carolinensis) as a model to examine whether prey animals are able to effectively assess different levels of predation risk. Being able to do so enable them to continue foraging in the presence of not-so-risky predators. The squirrels were able to distinguish between "threatening" approach and a "non-threatening" approach suggesting that, indeed, they are capable of making this assessments.
 
INTRODUCTION
Every animal's ultimate goal is to eventually pass on their genes to future generations. To do this an animal needs energy not only for itself but for the reproduction of viable offspring. However, being incapable of synthesizing its own food an animal must consume plants or other animals as sources of energy. Since an animal has a chance of becoming a food item to others the threat of predation becomes a central concern. Failure to avoid predation virtually prevents an animal from reaching its ultimate goal (Ydenberg and Dill 1986). For this reason the threat of predation is incorporated in every aspect of an animal's behaviour (Lima and Dill 1990). Moreover, the threat of predation has been found to influence community structure (Werner and Anholt 1993) and has also been implicated in the evolution of physiological defense mechanisms such as cryptic and aposematic colouration, protective armour, and chemical defenses (Sih 1987 cited in Lima and Dill 1990).
Ideally an animal should take in the maximum amount of energy possible constrained only by its physiology (gut size, feeding anatomy, etc.) (Lima and Dill 1990). However, an animal is prevented from realizing this since maximizing energy intake and minimizing predation risk are usually mutually exclusive (Lima and Dill 1990). An animal cannot fully concentrate on feeding because it has to once in a while look around for predators (Krause and Godin 1996). Birds, for example, have to  lift their heads in order to scan their surroundings (Krause and Godin 1996). The time spent looking out for, avoiding, and moving away from predators equates to a reduced energy intake due to losses in feeding opportunities (Bell 1991). A trade-off between foraging (maximizing energy intake) and predator avoidance (minimizing predation risk) thus exists and a well-adapted animal should behave in a manner that effectively compromises the costs and benefits between the two conflicting demands (Sih 1980). Are animals able to do this? Many empirical studies done on birds (Elgar 1986), fishes (Cerri and Fraser 1983), mammals (Lima et al. 1985), and insects (Rothley et al 1997) show that animals are capable of altering their behaviour in order to maximize feeding efficiency when exposed to predation risk.
Dynamic optimal foraging theory examines the decisions an animal makes in the presence of predators (Krebs et al. 1983). In contrast to classical foraging theory it takes into account the motivational state (hunger level), time budget, and the consequences of the decision that the animal just made (Krebs and McCleery 1984). These possible decisions are incorporated into a mathematical model after which the results are then compared to the behaviour of the animal in real situations (Krebs et al. 1983). The theory predicts that decisions resulting in maximum foraging efficiency will be chosen (Krebs and McCleery 1984). Stating that an animal "chooses" the best decision does not imply in any way that it does this in a cognitive manner (Bell 1991). It is assumed that animals follow simple decision rules or rules of thumb to make decisions about their foraging problems (Krebs and McCleery 1984). As an example, zebra danio, a small tropical freshwater fish, swim away from approaching objects when its loom rate exceeds a certain threshold (Ydenberg and Dill 1986).
 
Ydenberg and Dill (1986) designed an optimal foraging model on the flight distance of a prey under high and low levels of predation risk. The model takes into account the costs and benefits of remaining at a foraging site (Figure 1). At a particular distance from a predator the cost of remaining (curve B) is directly proportional to the risk of capture. Therefore, as predator distance decreases the cost of remaining, as well as, the risk of capture increases. The cost of fleeing (line C) which translates to lost foraging opportunity increases with predator distance in a linear function. Since the prey is trying to minimize costs (maximize energetic gain) the model predicts that the prey should choose to flee when the cost of remaining is greater than the cost of fleeing. The point where line C and curve B intersects represents the optimal flight distance (D*). At this predator distance the prey gets the highest possible energetic gain. When C is increased the optimal flight distance decreases (D*CH < D*CL). Conversely, when B is increased the optimal flight distance also increases (D*BH > D*BL).  
 
Most studies of optimal foraging that I am aware of examine and compare the foraging behaviour of a prey animal only in the absence or presence of its predator. Few have actually examined how a prey animal behaves in a situation where it is exposed to different levels of predation risk. In reality, foraging animals are prey item for different types of predators which poses different levels of risk. Predators may vary in their dangerousness due to differences in diet selection (Ydenberg and Dill 1986), developmental stage, and efficiency (Fraser and Huntingford 1986). Since different types of predators do not pose equal levels of risk it is, therefore, maladaptive for a prey to flee as soon as it detects any predator. In this study I used grey squirrels (Sciurus carolinensis) to examine whether prey animals, in general, are able to assess the level of predation risk and adjust their behaviour accordingly in response to that level. Any prey animal can be used since they are are exposed to basically the same predatory situation (i.e. multiple predators posing different levels of predation risk) and differ only in the specific factors that are being traded off. The abundance and the availability of grey squirrels at the time of the study made them suitable.
 
Contrary to popular belief grey squirrels do not hibernate and their primary activity during midwinter is to mate and breed (Gourmell 1987). Mating chases usually occur with many males following a female as she moves around during the day (National Audubon Society 1996). A litter of two or three young is borne in the spring and a second litter in late summer (National Audubon Society 1996).
 
Since reproduction (mating and giving birth) occurs in midwinter there is tremendous pressure to accumulate energy and hide enough food supply during the fall season when food is abundant. However, various types of predators prevent them from accumulating all the food (i.e. energy) they could possibly get from their environment. Different predators have different levels of risk therefore in order to behave adaptively grey squirrels must be able to effectively assess the level of risk of a certain potential predator and pattern their behaviour according to that level. Instead of unnecessarily wasting lots of energy (locomotion plus lost foraging time due to hiding) by hiding every time they spot a predator they can continue to forage, albeit cautiously, in the presence of a not-so-harmful predator.
 
To simulate a risky and a not-so-risky predator I approached a group of foraging squirrels in a "threatening" and a "non-threatening" manner. When approached in a "threatening" manner I predict that squirrels will flee earlier compared when the squirrels are approached in a "non-threatening" manner. After a few exposures to both manner the squirrels should realize that the approaches do not really pose a threat to them and therefore the time to flee becomes progressively longer.

 
DISCUSSION
 
The results of this study show that the grey squirrels were able to effectively assess the level of risk they are exposed to. The amount of time that the squirrels started running away during the "threatening" approach was smaller than during the "non-threatening" approach suggesting that the squirrels were able to perceive this approach as more risky, and hence supported my first prediction. They stayed longer during the "non-threatening" approach which allowed them to eat more sunflower seeds and presumably accumulated more energy. When approached in a "threatening" manner they judged that the risk of predation outweighed the benefits of staying further and decided to run away. The progressively increasing amount of time indicates that the squirrels realized that both approaches do not pose any real threat to them. Since both approaches actually have the same level of risk (in both occasions I just went right pass through them) the amount of time intersected and leveled off at about 40 seconds. This amount of time probably correlates to the optimal flight distance of the squirrels.
 
Now that I have shown that prey animals are able to assess the level of predation risk the next question is how do they actually assess these risks? The first step in assessing a prey animal's level of predation risk is to correctly identify a potential predator since different predators and even non-predators may be similar in appearance (Lima and Dill 1990). In a study on shoaling minnows Magurran and Girling (1986) showed that upon seeing a potential predator the minnows approach and inspect it. They presented the minnows with four models of a pike predator differing in shape and marking. The least realistic models were inspected more frequently suggesting that minnows found these models ambiguous since the models moved like a pike but did not have the distinctive markings.
 
Magurran and Girling (1986) also found that the most realistic pike model elicited a higher skittering (fleeing) response compared to the other models. This suggests that the minnows treated this model as posing the highest threat. Interestingly, in agreement with the point of my study the minnows after inspecting the less realistic models resumed foraging. Upon observing that these models did not pose any real threat to them they continued with their foraging. Although there is a cost in inspecting a potential predator (risk of capture plus momentary stoppage of feeding) it is still less costly than automatically swimming away and hiding. The perceived potential predator might just be harmless and there is no point in fleeing. The approaching and inspecting behaviour of minnows and other fishes is suggested to be analogous to that of the mobbing behaviour of birds (Magurran and Girling 1986).
 
A more extreme form of assessing the level of predation risk is exhibited by the California ground squirrels. These squirrels are known to not only approach but also harass and sometimes attack their Pacific rattlesnake predator (Hennessy and Owings 1988). It is common to see a squirrel kicking sand at a snake coiled near a burrow entrance (Coss and Owings 1989). The squirrels do these in order to provoke a rattlesnake and thus inducing it to rattle (Rowe and Owings 1996). By inducing the snake to rattle the squirrels can get information about the size and temperature of the snake (Rowe and Owings 1996). Larger snakes pose a greater risk to squirrels because they contain more venom and thus give more lethal bites (Hennessy and Owings 1988). Larger snakes also strike with more speed and can cover greater distances thereby making the squirrels' evasive leaps ineffective (Rowe and Owings 1996). The body temperature of the rattlesnake is also a big factor in the level of predation risk since warmer snakes give more accurate strikes and are less hesitant in initiating a strike (Rowe and Owings 1996).
 
Aside from using direct cues some prey animals used indirect cues in assessing the level of predation risk. Many predator and prey interactions occur at places where there are thick brushes or trees where cues can be imprecise or unavailable (Rowe and Owings 1996). Furthermore, a particular type of predator may pose a higher risk compared to other predators and detecting them away from visual range is necessary. Badgers pose a higher risk to hedgehogs compared to other predators because they are able to attack a hedgehog's unprotected underbelly (except for their underbelly hedgehogs are covered by sharp spines) (Doncaster 1993). Foraging badgers mark their territory by "squatting" and in the process leave a mixture of faecal matter and subcaudal and anal secretions (Ward et al. 1997). Hedgehogs are able to detect the odour given off by this mixture and judge the level of predation risk based upon its smell (Ward et al. 1997). Fresh odour indicates that a predator could be nearby while a stale odour signifies an infrequently used foraging area (Ward et al. 1997). Hedgehogs respond to the presence of badgers by shifting to other foraging areas and avoiding those areas tainted by the odour of badger faecal matter (Doncaster 1993).
 
In conclusion, the presence of various types of predators force foraging animals to balance and make trade-offs between the benefits of continuous foraging against, the costs of predation. A foraging animal's natural reaction to the presence of a predator is to run away and hide. However, different predators do not pose the same amount of danger and in order to behave in an adaptive manner prey animals must be able to effectively assess the level of predation risk. Not only must they be able to assess the level of risk but must also be able to alter their foraging behaviour in proportion to the amount of risk involved. The inability to do so results in very high energetic costs (locomotion and lost foraging time) since they automatically run away at the mere sight of any potential predator. Direct, as well as, indirect cues can be used for assessing these risks.

Foraging and time
Animals acquire resources in countless ways. Temporal perception is useful in many of them. Speciation mechanisms are undoubtedly related to competitive exclusion in competition for resources. A possible force of sympatric speciation is via resource partitioning in time. In this case, animals forage at different times of day but still eat the same foods, as is observed in several species of tern, lizards, crustaceans, and gastropods (Schoener, 1970; Schoener, 1974). This presumably reduces competition while simultaneously economizing daily time use. Whether or not these behaviors are learned is unestablished.
A basic assumption of optimal foraging theory is that animals recognize something about resource distribution. This recognition can be more or less behaviorally plastic depending on the cognitive faculties of the animal. If resource distribution is relatively stable over time, a species may evolve a patch departure schedule that is based on generations of trial and error without regard for the present environmental conditions. At the other extreme, an animal with an event timer could measure the rate of food intake at different patches or with different foods and compare them so as to optimize its schedule in the future. It could also measure the time between patches and incorporate this into its overall strategy. The marginal value theorem assumes that animals know resource distribution, transit and handling times even before they begin foraging (Charnov, 1976; Valone and Brown, 1989). This provides a useful null model against which to compare animal behaviors.
Yet, we know most animals require patch assessment before they can make optimal foraging decisions (Valone and Brown, 1989). Constraints on forager memory and resource changes over time force the reinvestigation of patches (Belisle and Cresswell, 1997). These costs to the perceptive forager are not well quantified.
Animals do use temporal aspects of resource distribution to make decisions about patch departure. Among central place foragers, there is a positive correlation between distance traveled to the foraging site and the patch residence time (Kacelnik, 1984).
Studies of risk sensitive foraging have exposed a sensitivity to the variance of resource acquisition even when the mean is unchanged (Real and Caraco, 1986). For example, honeybees prefer stable rewards to unstable rewards, regardless of the mean. This requires an event timer. The adaptive explanation for stable versus unstable preferences is based on minimizing the risk of starvation. I present a graphical argument for this in Figure 6. When animals are experiencing a positive energy budget they should prefer stable resources, as this minimizes the probability of starvation. When experiencing a negative energy budget animals should be risk prone because unstable resource distributions represent the highest probability of recovering (Stephens, 1980).
The data on animal preferences does not entirely corroborate this argument (Ha et al., 1990; Bateson and Kacelnik, 1998). Explanations for this phenomenon are based on cognitive constraints related to time perception and memory. Animal's may discount time in different ways depending on past experience or genetic predisposition, or they may average rate intake over different intervals. Animals also have certain constraints on their abilities to discriminate event times, as typified by Weber's Law (Bateson and Kacelnik, 1998). I suggested earlier that animals may also suffer from distorted perceptions of time based on intake rate. Mathematical models incorporating intake rate effects will help to explain how this perceptual bias affects observed foraging behaviors.
A kind of first impression among animals, called side bias, sometimes confounds psychophysical results (Ha et al., 1990). Side bias seems to refer to some unknown force controlling the animal's behavior. Experimenters typically make an effort to remove these animals from the analyses. Nonetheless, every animal may experience this kind of bias with variable time reinforcement schedules. Large initial rewards could lead to particularly strong cognitive bias. A series of large rewards might also instill a memory of a rare event that keeps the animal coming back. Exactly how the temporal sequence of events establishes memory biases is still an open question.
Another temporal factor in foraging is the effect of time horizons (Krebs and Kacelnik, 1984). Time horizons undoubtedly affect the behaviors of animals that are able to anticipate the ends of foraging bouts. Late in the day an animal may choose to continue foraging in a poor patch because it doesn't have enough time to get to a better one. A mechanism to avoid this problem involves organizing a series of patches in time and visiting them so as to maximize resource gain over the duration. Traplining fits this criteria.
Traplining is a behavior seen in bats and number of birds and frugivorous primates. It involves following a prespecified path during the daily foraging bout (Bell, 1991). Time horizons undoubtedly affect traplining schedules, but, once scheduled, traplining provides a short term answer to the time horizon problem. A similar behavior pattern is cropping. Cropping involves visiting locations at intervals that allow for resource replenishment. Cody (1971) observed various species of finches cropping seeds in the Mohave desert at the base of a mountain range. These birds moved their foraging sites to different distances from the mountain each day, scheduling visitation rates to match replenishment rates. Insect eating shore birds also appear to crop along the shore. The ant Veromessor pergandei makes radial changes in its foraging pattern outside the nest of between 14 and 17 degrees each day (Bell, 1991). In some manifestations of cropping, an event timer could help an animal know when to return to a foraging site.

History and genetics
BOTTOM LINE
Information obtained from studies with hand-raised white- tailed deer is scientifically valid for wild deer.
Summary
A study was conducted to compare the foraging behavior of hand-raised and wild white-tailed deer. We found that hand-raised deer
are the foraging equivalents of wild deer. Plant species selection and foraging efficiency of deer are largely innate and are not greatly influenced by learning.
Introduction
Recent work on sheep and goats has indicated that social learning plays a significant role in the development of dietary preferences of
domestic herbivores, and furthermore efficiency in harvesting browse in- creases with experience. This infor- mation is of particular concern to wildlife biologists since much of our more detailed information on the foraging behavior by deer comes from studies using hand-raised animals.
Tame animals are invaluable in the detailed study of foraging behavior because of the difficulties involved in approaching and maintaining contact
with wild deer. But hand-raised animals lack the opportunity to learn by imitating their mothers and usually have restricted access to natural forages. If learning is an integral part of foraging behavior of deer, then studies in which hand-raised animals
were used are of little practical value in wildlife management.
Experiment
In order to test the effects of learning on diet selection and foraging efficiency on white-tailed deer we conducted two sets of experiments.
1. Identical arrays of 6 browse plants were presented to bottle- raised fawns weaned on to either natural browse or pelleted ration, or to hand-raised adult deer.
2. The same arrays were presented to tame adult deer or placed near a corn-feeder where the re- sponses of individual wild deer could be recorded.
Details of plant species prefer- ences, bite size and biting rate were recorded. All tests were replicated five times to test for evidence of learning within the experiment.
Results
Previous experience of eating browse had no effect on plant species selection or the efficiency of food harvesting by fawns. There was no evidence of
learning during the trials and the diet selected by the fawns was very similar to that selected by the adult deer.
Plant species selection by the tame adult deer was comparable to that of the wild deer and they were equally efficient in harvesting browse.
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