Optimal Foraging Lab

Introduction:

Ecologists hypothesize that natural selection will favor foragers who maximize their fitness. The challenge is that the fitness of wild organisms is usually very difficult to measure directly. Often, fitness is often estimated from an easily measured variable, such as energy gain. Therefore, as scientists, we assume that individuals who gain the most energy, will have the greatest fitness. An optimal forager is an organism foraging for food who seeks to maximize their net energy gain, the difference between the energy gained and the energy spent while foraging.

The optimal forager must make the appropriate decision on whether or not a forage site is worth the time to search for food. Prey that are potentially consumed in some conditions may be ignored in other conditions. This could be due to the optimal forager encountering with more valuable types of prey or prey the forager may perceive to be more valuable. The density of prey numbers in a patch also can influence the rate and quality of prey that are consumed by the consumer.

In this experiment, our class acted as the foraging animals going to the patches in search for prey. In the groups, each member took his or her turn to be the forager and was recorded by the official timekeeper. Before the experiment, students were asked to make predictions of the optimal foraging model. This model describes the behavior and tendencies of a consumer to stay at a specific site to consume prey before the forager perceives that his time would be better spent searching for a new prey site at another patch.

 

The “prey” that we as foragers tried to consume consisted of beans hidden in containers of rice. There were 12 containers but for time purposes, we were only asked to approach three prey patches per each group member. The purpose of these buckets of rice and beans was to mimic natural settings as best as possible. In nature, prey are often distributes and found in patches of individuals. An example of this scenario are clusters of mussels, oak trees, and herds of wildebeests or other migrating animals.

The objective as the optimal forager in this experiment is to maximize the net rate at which you find beans in the rice containers, and not to just find as many beans as possible. Therefore, the student must decide the appropriate time to leave each patch. When foraging for the bean “prey” in the containers, students must use only one hand to search for the beans hidden in the rice. When the forager finds a bean in the patch, they must then place it in a bowl or cup and swirl it around 3 times. This behavior is done to mimic handling time, the time it would take you to eat the prey if you were in actuality a foraging animal.  The net energy gain is also impacted by the time it takes to travel from one patch to another, but we were advised not to run due to safety reasons. All patches are equidistant from each other.

The optimal rate shared by all patches is called the marginal value and the standard for testing this is the Marginal Value Theorem model.

This model allows us to make four predictions:

1. Foragers should capture more prey in patches of high prey density than in patches of low prey density.
2. Foragers should remain longer in patches of high prey density than they should in patches of low prey density.
3. Foragers should catch more prey per unit time in dense prey patches than in sparse patches.
4. Foragers should leave each patch when the intake rate for that patch has declined below the average rate for the environment as a whole. The intake rate at departure should thus be similar across all patches.

 

Graphs Made:

A) Number of beans found (y-axis) as a function of patch density (x-axis)
B) Time spent in patch (y-axis) versus patch density (x-axis)
C) Capture rate (y-axis) versus patch density (x-axis)
D) GUT (y-axis) versus patch density (x-axis)

 

Data Analysis Questions:

 

Which analyses tested which of the four specific predictions derived from the model? In which cases do the experimental data seem to support those predictions?

The Number of Beans Found (y-axis) as a Function of Patch Density (x-axis) graph was used to support the first prediction of the Marginal Value Theorem. This prediction states that, “Foragers should capture more prey in patches of high prey density than in patches of low prey density”. This prediction off of the theorem is mostly supported by my graph A (located above under “Graphs Made” section) that I have created. In this example of me as the optimal forager, I spent more time at patches with greater density than I did at patches of less density. I spent slightly more time at the patch of 48 prey than I should have and this can be evidenced by the graph. At the patches containing 48 and 80 beans, I collected 30 at each which may distort the visual representation of the data.

The Time Spent in Patch versus Patch Density graph was used to support the second prediction of the Marginal Value Theorem. This prediction states that, “Foragers should remain longer in patches of high prey density than in patches of low prey density.” As evidenced by my graph B (located above under “Graphs Made” section), this assumption is true. I spent more time as a forager at patches of higher density of “prey” than patches of lower prey density.

The Capture Rate versus Patch Density graph was used to rebuke the third prediction of the Marginal Value Theorem (in my case). This prediction states that, “Foragers should catch more prey per unit time in dense prey patches than in sparse patches.” As you can see from my graph C (located above under “Graphs Made” section), I surprisingly had lower capture rates at higher density patches of prey than lower density patches of prey. This information is unusual and could possibly due to a miscalculation or error in time-keeping.

The GUT versus Patch Density graph was used to test the fourth and final prediction of the Marginal Value Theorem. This prediction states that, “Foragers should leave each patch when the intake rate for that patch has declined below the average rate for the environment as a whole, and the intake rate at departure should thus be similar across all patches.” As shown by my graph D (located above under “Graphs Made” section), my data is inconclusive as I had a greater capture rate at my lowest patch density I visited. Also fair to include, the patch with the least amount of prey (20), is the only patch that I left without collecting 30 prey. This would be more consistent with the final prediction of the Marginal Value Theorem if more patches were visited.

Examine your own gain curves. Based on your curves, do you think you left each patch too early, too late, or at an optimal time?

In examination of the overall trends of the net energy gain curves I created from my time as an optimal forager, I believe I spent too much time at the patch with 20 prey individuals. In fairness to myself, this was the first patch I visited and I was the first forager of my group so I hadn’t yet developed a competitive strategy or had the benefit of observing other foragers first. As we learned after, more than 20 beans were actually found in the container that specified that it contained 20 beans in it. Surprisingly, I had my greatest capture rate at the patch I visited with the lowest density of prey in it. Because of these circumstances, I believe that my results for this patch may be invalid which could have affected the results from the other two patches I visited.

The GUT versus patch density plot is especially important, because it relates directly to the Marginal Value Theorem. Do the data for your class suggest that this theorem applies to the behavior of humans in this experiment? Explicitly justify the rationale for your answer.

The GUT versus Patch Density graph was used to test the fourth and final prediction of the Marginal Value Theorem. This prediction states that, “Foragers should leave each patch when the intake rate for that patch has declined below the average rate for the environment as a whole, and the intake rate at departure should thus be similar across all patches.” The data collected from the class experiment does not support this prediction of the theorem. As shown by my graph D (located above under “Graphs Made” section), my data is inconclusive as I had a greater capture rate at my lowest patch density I visited. Also worth mentioning is that the patch with the least amount of prey (20), is the only patch that I left without collecting 30 prey. But as stated in the previous reflection question, there was actually more than 20 prey in this container. This inconsistency likely threw off my data. This would be more consistent with the final prediction of the Marginal Value Theorem if more patches were visited.

Describe a natural situation where you might expect animals to behave this way? When might you expect animals to behave otherwise?

For the purpose of this question, I would like to answer using a study on the behavioral ecology of the Gila Woodpecker. Nest Defense and Central Place Foraging: A Model and Experiment (1981) determined that for a parent Gila woodpecker to best feed their young and achieve the optimal delivery rate maximization, they must, “Stay closer to
its nest, forage for shorter times per patch,

and deliver smaller loads than predicated.” The woodpecker is dealt with a trade-off: they must travel to patches to find food for their offspring, but cannot leave their young unattended for too long or their nest could be attacked by predators in search of a meal. The woodpecker must make a conscious decision on the optimal time spent at each patch and the distance to each patch while also considering the vulnerability of the nest. This is why I believe this natural situation very accurately represents the optimal foraging strategies we have been discussing in class and conducting experiments on.

 

 

Reflection Questions:

 

Why should someone care about optimal foraging strategies?

From many years of data and countless studies, ecologists can say with relative certainty that natural selection favors foragers who maximize their fitness.
The fitness of wild organisms is difficult to measure so when ecological studies are conducted, fitness is estimated from an easily measured variable such as energy gain. Understanding optimal foraging strategies for various organisms helps us to determine how an organism achieves its maximum energy gain. The assumption is that individuals who gain the most energy have the greatest fitness. This is possibly why ecologists devote studies to analyze the strategies of foraging to determine what factors affect fitness and the extent as to the importance of each factor to overall fitness.

Why do ecologists study this behavior?

As I mentioned previously, the optimal foraging strategies taken by an organism indicate the fitness of the organism. An individual who gains the most energy will very likely have a high fitness. This behavior is important as ecologists determine how certain behaviors inhibit fitness in organisms.

Are there examples in your life that relate to optimal foraging strategies?

An example in my life that can be loosely related to the strategies an optimal forager takes to maximize their energy is time management. Every human on Earth is given 24 hours every day to perform whatever work or duties that may have. Ultimately, you are in charge of how you send your own time so this discretionary behavior is much like the behavior of a foraging animal when they decide how much time to spend at each patch looking for prey.

How might humans exhibit optimal foraging behavior?

Early humans exhibited many optimal foraging behaviors in a hunter and gatherer society. Each individual had to determine how much time and energy was spent while foraging for food at their discretion in order to survive. In the more modern era, humans may exhibit optimal foraging behaviors when rationing out time for responsibilities and budgeting the time needed to consume food for survival around our daily schedules.

References:

Cancalosi, John. “Gila Woodpecker Melanerpes uropygialis Arizona at nest in Saguaro cactus Sonoran Desert” Alamy.com, Version BTHWRC, Alamy Stock Photos, April 2004, https://www.alamy.com/stock-photo-gila-woodpecker-melanerpes-uropygialis-arizona-at-nest-in-saguaro-32355552.html?pv=1&stamp=2&imageid=8150958C-1E21-4623-9D03-648CAFBBA19F&p=34327&n=0&orientation=0&pn=1&searchtype=0&IsFromSearch=1&srch=foo%3dbar%26st%3d0%26pn%3d1%26ps%3d100%26sortby%3d2%26resultview%3dsortbyPopular%26npgs%3d0%26qt%3dgila%2520woodpecker%2520nest%26qt_raw%3dgila%2520woodpecker%2520nest%26lic%3d3%26mr%3d0%26pr%3d0%26ot%3d0%26creative%3d%26ag%3d0%26hc%3d0%26pc%3d%26blackwhite%3d%26cutout%3d%26tbar%3d1%26et%3d0x000000000000000000000%26vp%3d0%26loc%3d0%26imgt%3d0%26dtfr%3d%26dtto%3d%26size%3d0xFF%26archive%3d1%26groupid%3d%26pseudoid%3d%26a%3d%26cdid%3d%26cdsrt%3d%26name%3d%26qn%3d%26apalib%3d%26apalic%3d%26lightbox%3d%26gname%3d%26gtype%3d%26xstx%3d0%26simid%3d%26saveQry%3d%26editorial%3d1%26nu%3d%26t%3d%26edoptin%3d%26customgeoip%3d%26cap%3d1%26cbstore%3d1%26vd%3d0%26lb%3d%26fi%3d2%26edrf%3d%26ispremium%3d1%26flip%3d0

Charnov, Eric. “Marginal Value Theorem Photo.” people.eku.edu , Eastern Kentucky University, http://people.eku.edu/ritchisong/behavecolnotes2.htm .

Martindale, S. 1981. Nest defense and central place foraging: a model and experiment. Behav. Ecol. Sociobiol. 10:85-89.