Optimal search theory ( OFT ) is a model that helps predict how animals behave when looking for food. While obtaining food provides animals with energy, finding and catching food requires energy and time. The animal wants to get the most benefits (energy) at the lowest cost during feeding, so as to maximize its fitness. OFT helps predict the best strategies that can be used by animals to achieve this goal.
OFT is an ecological application of the optimality model. This theory assumes that the most economically beneficial feeding patterns will be selected for a species through natural selection. When using OFT to model feeding behavior, the organism is said to maximize the variable known as currency , like most meals per unit of time. In addition, the constraint environment is another variable to consider. Limitations are defined as factors that can limit the ability of forecasters to maximize currency. optimal decision rule , or the organism's best food search strategy, is defined as a decision that maximizes currency below environmental boundaries. Identifying the optimal decision rule is the main purpose of the OFT.
Video Optimal foraging theory
Building an optimal food search model
An optimal feeding model produces quantitative predictions about how animals maximize their fitness as they seek food. The model formation process involves identifying the exact currency, constraints, and decision rules for the explorer.
Currency is defined as an animal optimized unit. This is also the cost and benefit hypothesis imposed on the animal. For example, a particular explorer gets energy from food, but raises food search costs: the time and effort spent searching can be used instead of other efforts, such as finding a mate or protecting young. It's in the animal's best interest to maximize the benefits at the lowest cost. Thus, the currency in this situation can be defined as the acquisition of clean energy per unit time. However, for different explorers, the time it takes to digest food after meals can be a more significant cost than the time and energy spent on food searching. In this case, the currency can be defined as the acquisition of clean energy per gut turnaround time, not the net energy gain per unit time. In addition, benefits and costs may depend on the community of forecasters. For example, a shark living in a nest is most likely to find food in a way that will maximize the efficiency of its colony rather than itself. By identifying the currency, one can construct a hypothesis about which benefits and costs are important to the pedestrian in question.
Limitations are hypotheses about limits placed on animals. These limitations can be caused by environmental features or animal physiology and can limit their feeding efficiency. The time it takes for a cruise to travel from nest to a feeding site is an example of a constraint. The maximum number of food items that can be brought back to the nest site is another example of a constraint. There can also be cognitive constraints on animals, such as limits for learning and memory. The more obstacles one can recognize in a given system, the stronger the predictive power the model has.
Given the currency hypothesis and constraint, optimal decision rule is the model prediction of what the best animal feeding strategy should be. Possible examples of optimal decision-making rules are the optimal amount of food the animal must bring back to its nest or the optimal size of the food to be eaten by the animal. Figure 1 shows an example of how the optimal decision rule can be determined from the graphical model. The curve represents the energy gain per cost (E) to adopt the feeding strategy x. The energy gain per cost is the optimized currency. The constraints of the system determine the shape of this curve. The optimal decision rule (x *) is a strategy in which the currency, energy acquisition per cost, is the largest. The optimal feeding model can look very different and become very complex, depending on the nature of the currency and the number of constraints being considered. However, the general principles of currency, limitations and optimal decision rules remain the same for all models.
To test a model, one can compare predicted strategies with actual animal feeding behaviors. If the model matches well-observed data, then the currency and limiting hypotheses are supported. If the model does not match the data properly, then the possibility of a particular currency or limit has been misidentified.
Maps Optimal foraging theory
Different feeding and predator classes
The optimal food search theory extends widely to feeding systems throughout the animal kingdom. Under the OFT, any interesting organisms can be seen as predators looking for prey. There are different classes of predators that organisms fall into and each class has a different food searching and predation strategy.
- Real predators attack a large number of prey throughout their lives. They killed their prey immediately or immediately after the attack. They may eat all or only part of their prey. True predators include tigers, lions, whales, sharks, seed-eating birds, ants, and humans.
- Grazer only eat a portion of their prey. They endanger the prey, but rarely kill it. Golfers include antelope, cow, and mosquitoes.
- Parasites , like grazers, eat only a portion of their prey (host), but rarely are all organisms. They spend all or most of their life cycles living on a single host. This intimate relationship is typical of tapeworms, liver worms, and plant parasites, such as potato disease.
- Parasitoids are especially typical of wasps (Hymenoptera messages), and some flies (order Diptera ). Eggs are placed in other arthropod larvae that hatch and consume the host from within, killing them. This unusual predator-host relationship is typical about 10% of all insects. Many viruses that attack single-celled organisms (such as bacteriophages) are also parasitoids; they reproduce inside a single host that is definitely killed by the association.
The optimization of these different feeding and predation strategies can be explained by the optimal food search theory. In each case, there are costs, benefits, and limitations that ultimately determine the optimal decision rule that predators should follow.
Optimal dietary model
One of the classic versions of the optimal food search theory is the optimal dietary model , also known as the prey model or contingency model. In this model, the predator finds different prey items and decides whether to eat what it has or find more favorable prey items. The model predicts that the gatherer should ignore the low profitability prey when more profitable items are present and abundant.
Profitability of prey goods depends on several ecological variables. E is the amount of energy (calories) given predator items to predators. Handling time ( h ) is the amount of time the predator takes to handle food, from when the predator finds the prey item until the time the prey item is eaten. Profitability of prey goods is then defined as E/h . In addition, search time ( S ) is the amount of time it takes for a predator to find a prey item and depends on the abundance of food and ease of finding it. In this model, currency is the energy intake per unit of time and constraints including actual values ​​ E , h , and S , also as fact that prey goods are found in sequence.
Preferred models between large and small prey
Using these variables, the optimal diet model can predict how predators choose between two types of prey: large prey 1 with energy values ​​ E 1 and handling time h 1 , and small prey 2 with energy value E 2 and handling time h 2 . To maximize the overall energy acquisition rate, a predator must consider the profitability of two types of prey. If it is assumed that large prey 1 is more advantageous than small prey 2 , then E 1 /h 1 & gt; E 2 /h 2 . So if the predator meets the prey 1 , it should always choose to eat it, because of its higher profitability. It should not have to bother looking for prey 2 . However, if an animal encounters a prey 2 , it must reject it to seek a more favorable prey 1 , except the time it takes to find prey 1 is too long and expensive for it to be wasted. Thus, animals should only eat prey 2 if E 2 /h 2 & gt; E 1 /(h 1 S 1 ) , where S 1 is the search time for the prey 1 . Therefore it is always advantageous to choose prey meal 1 , the preference of eating prey 1 is not dependent on the abundance of prey 2 . But since the length of S 1 (ie how difficult it is to find prey1) logically depends on prey density 1 , prey choice 2 is depending on the abundance of prey 1 .
General and specialist diet
Optimal dietary models also predict that different types of animals should adopt different diets based on variations in search time. This idea is an extension of the prey choice model discussed above. Equation, E 2 /h 2 & gt; E 1 /(h 1 S 1 ) , can be rearranged to provide: S 1 & gt; 2 )/E 2 ] - h 1 . This rearranged form provides a threshold for how long S 1 should be for animals to choose to eat both prey 1 and prey 2 . Animals that have S 1 ' that reach the threshold are defined as generalists. In nature, generalists include a variety of prey items in their diet. An example of a generalist is a mouse, which consumes a variety of seeds, grains, and nuts. In contrast, predators with relatively short S 1 ' s still better choose to eat only prey 1 . This type of animal is defined as a specialist and has a very exclusive food in nature. A specialist example is a koala, which consumes only eucalyptus leaves. In general, the different animals in the four predatory functional classes show strategies ranging from the continuum between generalists and specialists. Additionally, since the prey2 option to eat depends on the abundance of prey1 (as discussed earlier), if prey1 becomes so rare that S1 reaches the threshold, then the animal must switch from exclusive prey1 to eat both prey1 and prey2. In other words, if food in a specialist diet becomes so rare, a specialist can sometimes turn into a generalist.
Functional response curve
As mentioned earlier, the amount of time required to search for prey items depends on the density of the prey. The functional response curve shows the rate of prey capture as a function of food density and can be used in conjunction with optimal dietary theory to predict predatory feeding behavior. There are three different types of functional response curves.
For the Type I functional response curve, the prey capture rate increases linearly with food density. At low prey densities, the search time is long. Since the predator spends most of his time searching, he eats every prey he finds. With increasing prey density, predators can capture prey faster and faster. At some point, the rate of capture of the prey is very high, so the predator does not have to eat every prey it encounters. After this point, the predator should only select the prey item with the highest E/hour.
For the Type II functional response curve, the prey rate is negatively accelerated as it increases with food density. This is because it assumes that predators are limited by their capacity to process food. In other words, as food density increases, the handling time increases. At the start of the curve, the prey capture rate increases almost linearly with prey density and almost no handling time. With increasing prey density, predators spend less time searching for prey and more time to tackle prey. The rate of capture of prey increases less and less, until finally the plateau. The high number of prey is basically "wetting" predators.
The functional response curve Type III is the sigmoid curve. The prey capture rate increases initially with the density of the prey at an accelerated acceleration rate, but then at high density changes to a negative acceleration, similar to the Type II curve. At high prey density (the top of the curve), each new prey item is captured immediately. Predators can choose and not eat every item found. So, assuming that there are two types of prey with different profitability that both have a high abundance, the predator will select the item with a higher E/h . However, at low prey densities (lower portion of the curve) the rate of capture of prey increases faster than linearly. This means that when the predator feed and the higher prey type E/h becomes less abundant, the predator will begin to shift preference to the prey type with lower E/h , since type is relatively more. This phenomenon is known as switching prey.
Predator-predator interaction
Interestingly, predatory-prey coevolution often makes it unprofitable for predators to consume certain prey items, as many anti-predator defenses increase the handling time. Examples include porcupine hedgehogs, palatability and digestibility of poisoned arrow frogs, crypsis, and other predatory avoidance behaviors. In addition, since toxins may exist in many types of prey, predators include much variability in their diet to prevent a single toxin from reaching dangerous levels. Thus, it is possible that an approach focusing solely on energy intake may not fully explain animal feeding behavior in this situation.
Theorem of optimal marginal and feeding value
The marginal value theorem is a type of optimality model that is often applied for optimal feeding. This theorem is used to describe the situation in which the organisms seeking food in the patch must decide when it is economically advantageous to leave. While the animal is in the patch, it experiences a diminishing law of yield, where it becomes increasingly difficult and difficult to find prey over time. This may be because the prey is being exhausted, the prey begins to take evasive action and becomes more difficult to catch, or the predator begins to traverse his own way more as he searches. This reduced return law can be represented as the energy acquisition curve per time spent in the patch (Figure 3). The curve begins with a steep slope and gradually slows down because prey becomes harder to find. Another important cost to consider is the travel time between different patches and nesting locations. An animal loses time to feed as it travels and exhausts energy through its movement.
In this model, the optimized currency is usually the acquisition of clean energy per unit time. The obstacle is the travel time and the diminishing return curve shape. Graphically, the currency (net energy gain per unit time) is given by the slope of the diagonal line beginning at the beginning of the travel time and bypassing the decreasing return curve (Figure 3). To maximize currency, one wants the line with the largest slope that still touches the curve (tangent line). The place where this line touches the curve gives the optimal decision rule about the amount of time that animals must spend in the patch before leaving.
Example of an optimal food search model for animals
Optimal oystercatcher preservation
Oystercatcher shell feed provides an example of how the optimal diet model can be utilized. Oystercatcher looking for food on the shell and opened it with their bill. Constraints on these birds are characteristic of different shell sizes. While large shells provide more energy than small shells, large shells are more difficult to open due to their thicker shells. This means that while large shells have a higher energy content ( E ), they also have a longer handling time ( h ). The profitability of each shell is calculated as E/h . Oystercatcher must decide the size of the shell that will provide enough nutrition to exceed the cost and energy required to open it. In their study, Meire and Ervynck tried to model this decision with a relative profitability graph of different sized shells. They come with a bell-shaped indentation, indicating that medium-sized shells are the most profitable. However, they observed that if the oystercatcher rejects too many small shells, the time needed to find suitable shells is greatly increased. This observation shifts the bell-curve to the right (Fig. 4). However, while this model estimates that oystercatcher should prefer shells 50-55 mm, the observed data indicate that oystercatcher actually prefer shells 30-45 mm. Meire and Ervynk then realized the shell size preference depends not only on the profitability of the prey, but also on the density of the prey. Once this is taken into account, they find a good deal between model predictions and observed data.
Optimal foraging in starlings
The European Starling's feeding behavior, Sturnus vulgaris, provides an example of how the marginal value theorem is used to model optimal feeding. Starlings leave their nests and travel to food patches to look for larval leather tufts to bring back to their children. Starlings must determine the number of optimal prey items to be taken back in one trip (ie optimal load size). While the green of starlings in the patches, they experience diminishing returns: starlings are only able to hold so many leather tufts in their bills, so the larval pickup speed decreases with the number of larvae already present in the bill.. Thus, the constraint is the diminishing return curve form and the travel time (the time it takes to make a round trip from the hive to the patch and back). In addition, the currency is hypothesized as the acquisition of clean energy per unit time. Using these currencies and constraints, optimum loads can be predicted by drawing a tangent to the diminishing yield curve, as discussed earlier (Figure 3).
Kacelnik et al. wants to determine whether this species is indeed optimizing the net energy gain per unit time as a hypothesis. They designed an experiment in which starlings were trained to collect caterpillars from artificial feeders at different distances from the nest. The researchers artificially produce a fixed curve of reduced yields for birds by dropping mealworms at longer and longer intervals in a row. The birds continue to collect the mealworms as they are presented, until they reach the "optimal load" and fly home. As Figure 5 shows, if the starlings maximize the net energy gain per unit time, shorter travel times predict small optimum loads and longer travel times will predict larger optimum loads. In accordance with this prediction, Kacelnik found that the longer the distance between the nest and the artificial feeder, the larger the load size. In addition, the quantitatively observed load sizes are closely related to model predictions. Other models based on different currencies, such as energy gained per energy spent (ie energy efficiency), fail to accurately predict the size of the load observed. Thus, Kacelnik concluded that starlings maximize clean energy per unit time. This conclusion is not disputed in subsequent trials.
Optimal foraging in bees
Workers' bees provide another example of the use of the marginal value theorem in modeling optimal feeding behavior. Bees looking for food from flower to flower collect nectar to be brought back to the nest. While this situation is similar to what happens to starlings, both obstacles and currencies are actually different for bees.
Interestingly, bees do not experience a decrease in yield due to nectar thinning or other characteristics of the flower itself. In fact, the total amount of nectar consumed increases linearly with the time spent in the patch. However, the weight of the nectar adds a significant cost to the bee flight between the flowers and the journey back to the nest. Wolf and Schmid-Hempel showed, by experimentally placing varied weights on the backs of bees, that the cost of heavy nectar is so great that it shortens the life of the bees. The shorter the age of the worker bee, the less time it takes to contribute to the colony. Thus, there is a decreasing yield curve for the net result of the energy received by the hive when the bees collect more nectar during a trip.
The heavy nectar cost also affects the currency used by bees. Unlike the starlings in the previous example, the bees maximize energy efficiency (energy gained per energy spent) rather than the net energy acquisition rate (net energy gained per time). This is because the optimum load predicted by maximizing the net rate of energy gain is too heavy for bees and shortening their lifespan, lowering their overall productivity for the nest, as described earlier. By maximizing energy efficiency, bees can avoid spending too much energy per trip and can live long enough to maximize their lifetime productivity for their nests. In a different paper, Schmid-Hempel showed that the observed relationship between load size and flight time correlated well with predictions based on maximizing energy efficiency, but correlated very poorly with predictions based on maximizing net energy rates.
Eat optimally in Centrarchid Fish
The prey of prey by two centrarchids (white crappie and bluegill) has been presented as a model combining optimal feeding strategies by Manatunge & amp; Asaeda. The visual field of foraging fish represented by reactive distance is analyzed in detail to estimate the number of prey encounters per search bout. The predicted reactive distance is compared with the experimental data. The energetic costs associated with fish-foraging behavior are calculated based on the sequence of events occurring for each prey consumed. Comparison of the relative abundance of prey species and the size category in the stomach to the lake environment shows that both white crappie and bluegill (long & lt; 100 mm) strongly prefer prey utilizing energy optimization strategies. In most cases, exclusive fish selected large Daphnia ignore the type of evasive prey (Cyclops, Diaptomids) and small cladocera. This selectivity is the result of fish actively avoiding prey with high avoidance abilities even though they appear to have high energetic content and have translated this into optimal selectivity through the success rate of capture. Consideration of energy and visual systems, apart from the ability of forecasters to capture prey, is the main determinant of prey selectivity for large sizes of bluegill and white crappie still at the planktivora stage.
Critics and restrictions on the theory of optimal feeding
Although much of the research, as cited in the example above, provides quantitative support for the optimal food search theory and demonstrates its usefulness, the model has received criticism about its validity and its limitations.
First, the optimal food search theory depends on the assumption that natural selection will optimize the feeding strategy of the organism. However, natural selection is not a very strong force that results in a perfect design, but a passive selection process for genetically based traits that enhance the reproductive success of the organism. Given that genetics involves interactions between loci, recombination, and other complexities, there is no guarantee that natural selection can optimize certain behavioral parameters.
In addition, the OFT also assumes that feeding behavior can be freely shaped by natural selection, since this behavior is independent of other activities of the organism. However, given that the organism is an integrated system, not a mechanical aggregate part, this is not always the case. For example, the need to avoid predators can limit collectors to feed less than optimal rates. Thus, the feeding behavior of an organism may not be optimized because OFT will predict, as they are not independent of other behaviors.
Another limitation of OFT is that it has no precision in practice. Theoretically, the optimal feeding model provides specific researchers, quantitative predictions about optimum predatory decision rules based on the currency hypothesis and system constraints. However, in reality, it is difficult to define basic concepts such as the type of prey, the level of encounter, or even patches as the explorers see them. Thus, while OFT variables can appear consistently theoretically, in practice, they can be arbitrary and difficult to measure.
Furthermore, although the OFT's premise is to maximize the fitness of an organism, many studies only show a correlation between observed and predicted observational behavior and stop testing whether animal behavior actually improves reproductive fitness. It is possible that in certain cases, there is no correlation between the results of feeding and reproductive success at all. Regardless of this possibility, many studies using OFT remain incomplete and fail to address and test the main point of theory.
One of OFT's most important criticisms is that it may not really be testable. This problem arises when there is a difference between model prediction and actual observation. It is difficult to say whether the model is fundamentally wrong or whether a particular variable has been identified or abandoned inaccurately. Since it is possible to add endless modifications to the model, the optimality model may never be rejected. This created the problem of the researchers who shaped their models to fit their observations, rather than testing their hypotheses about animal feeding behavior.
References
Further reading
- The Optimal Theory of Foraging by Barry Sinervo (1997), Course: "Behavioral Ecology 2013", Department of Ecology and Evolutionary Biology, UCSC - This section, from the Course at UCSC, considers OFT and "Adaptational Hypotheses" and error, instinct '). along with additional subjects like "Prey Size", "Patch Residence Time", "Patch and Competitor Quality", "Search Strategy", "Aversive Behavior Risk" and feeding practices subject to "Food Limitations". See also: up one Level for Main Section of the Course, where downloadable PDFs are available (because Images on the Page seem to be broken at this time). The PDF for Link above is a 26 page long (with Images).
Source of the article : Wikipedia
0 Comments