In the wild, animals have various ways of getting nutrients in order to survive. The better they are at getting this energy the higher probability they have on surviving as well as a higher fitness. Fitness is a measure of an organism’s reproductive capability. For our lab this week we studied Optimal Foraging as it relates to an organism’s cumulative energy gain over several patches of food resources. Optimal forager’s are organisms who gain the most energy in the least amount of time compared to other animals while foraging. These animals net energy gain is maximized during the time it forages. Their energy spent while foraging is low compared to how much energy they are gaining during their foraging time.
- net energy gain = energy gained – energy spent while foraging
In our lab this week we performed a simulation where we acted as the foragers and measured our time finding “food” in different “patches/areas” that were set up. In a flat grassy area 9 buckets were set up, each spaced equally apart. Each bucket was filled with uncooked white rice and a various number of uncooked beans. The beans were our food source and our objective was to “maximize the net rate in which you find beans”. Each of us were timed while finding the beans in 3 patches each. Each time we picked up a bean and placed it in a cup we swirled it around 3 times to represent the time it would take an animal to eat prey. It was our choice when we decided to leave each patch once the resources began to be depleted. Our goal was to leave each patch once the same optimal energy intake rate was reached. The goal was not to find the most beans as we can, but to optimize our time to find the most beans in the time we are at each patch. This marginal value that we tried to reach is correlated to the Marginal Value Theorem. The theorem states:
- Areas with a higher density of prey is where forager should capture the most prey
- Foragers should spend more time in areas with higher prey densities because it will take more time to start depleting the resources
- Foragers should capture more prey in a shorter amount of time in high density patches than those which are sparsely populated with food resources
- Foragers should move on to other patches when their prey/unit time begins to drop below the average rate, or, when it begins to take a longer time to find each prey source.
When graphed we have to take into account the traveling time, or time it takes to reach each patch. At this time the energy gained is 0. Time is graphed on the x axis and energy gained is on the y axis. For each of our 3 patches we foraged in we did not stop the stop watch once. The start began when we started to walk toward the patch and then ended when we decided to leave the last patch. One important aspect of this lab is our Giving up time (GUT), which is calculated by subtracting the time we find out last bean by the time we decide to leave the patch. Hypothetically, this time should be similar across each patch.
Graph based on class experiment data
4. In our results it is important to find out of our results followed the rules in the Marginal Value theorem. The graph above supports the rule of the theorem that areas with a higher density of prey is where forager should capture the most prey. And our graphed data supports this as I found a larger number of beans in patches that had more beans available in that specific patch.
Graph based on class experiment data
Our data above also supports the Marginal Value theorem. I spent more time in patches of higher density of beans than I did of patches that did not have as many beans available.
Graph based on class experiment data
In regards to the 3rd rule of the Marginal Value Theorem that foragers should capture more prey in a shorter amount of time in high density patches my data also supports this. My capture rate was higher in patches that held a higher density of beans.
Graph based on class experiment data
The only part of the Marginal Value Theorem that my data does not support is that my giving up time (time we find out last bean – time we decide to leave the patch) is not constant in each patch. Instead, it took me longer to leave patches that held a lower density of beans than it did from high density patches. I kept searching for beans even when they became increasingly harder to find in the bucket of rice. I could have stayed in the bucket with a total of 80 beans in it for longer, instead I stopped at 30 and quickly went to another patch. I could have stayed and found a few more beans and perhaps my GUT time for that patch would be closer to the average of the other 2 instead of being somewhat of an outlier.
Based on our data I do not think that the behavior of humans in this experiment applies to the Marginal Value Theorem. Humans in this day and age do not move on from areas when they have taken resources enough that they begin to become scarce. Humans are continuing to deplete the environment of every resource available until it is not found there anymore, if only in trace amounts. Related to farming, we use plots of land over and over until the nutrients in the soil are almost completely used up, then we move on to another plot while the previous now is depleted.
Graph based on class experiment data
Based on my data the number of prey captured correlated to the level of density. I also found more beans in shorter amounts of time in areas with a higher density.
I would expect an animal to behave in a manner that follows the Marginal Value Theorem and maximizes optimal foraging in an environment that is abundant with resources. I would not expect animals preparing for hibernation to follow this pattern. Instead, I would expect animals preparing for hibernation to stay in the area past the optima and continue to search for food. In this way they would spent more time in each patch and their travel time would be the same, but would be over a greater time period.
Reflection Questions
An ecological study named “Risk perception by endangered European bison Bison bonasus is context (condition) dependent” reveals real life examples of optimal foraging and the Marginal Value Theorem. There are certain conditions in which the bison showed optimal foraging strategies as well as conditions where they veered from it. The Bison’s foraging strategies changed with predation by wolves as well as in winter when their resources were scarce. In summer they avoided areas with high human density however; in winter when their resources were not as abundant they risked getting closer in order to eat from haystacks. This study reveals that an animal’s optimal foraging strategies change depending on the conditions of the environment they are in. Predation, water and food availability can alter their GUT and capture rate as well as other variables.
Studying optimal foraging strategies can give us a greater understanding on how organism’s alter the environment with their foraging habits as well as patterns of migration. We could perhaps predict how much an organism could alter the environment in a certain period of time. Ecologists study this behavior in order to manage certain organisms. For example, placing haystacks in the bison’s environment can enable one to guide the bison herds to the location they want as to keep them away from causing problems with human populations. Optimal foraging strategies also can be a way to study natural selection by ecologists. Those animals who are not able to find food may face starvation and die because of their lack of foraging strategies Hayward, MW, et al).
In my life there are a few optimal foraging strategies that I can think of. I grew up across the street of a farm. The cows that were in the field would not stay in one spot grazing the whole day, they would move across the field throughout the day and then would return to the previous location after a few days. There would not be a mass number of cows where they depleted the resources available, but enough in the field where their grazing patterns could potentially be studied in real life following this lab outline.
4. The black bears in the Sierra Nevada are changing their feeding behavior because of the amount of food available is changing because of humans. Based on optimal foraging strategies, the food is available in a larger quantity in a much smaller area. Bears are able to spend more time in one location because there is a lot more food available in urban areas. And they do not spend as much time searching for food or traveling from food source to food source. Their optimal rate of “city bears” is maximized as they are able to consume a greater amount of calories in one day compared to “country bears”. It seems as if this is a good thing for the bears, as the article says that there is not evidence that the food source from fast food and other human garbage does not negatively impact the bears. However, the “city bears” are now in urban human territory and this has became a problem when the bears are sleeping under people’s decks and raiding their trash.
Picture of a bear taking trash outside a urban home
Bears can be dangerous and so it is not a good thing that they are residing in urban areas close to humans. A practical solution to keep bears away could be to keep dumpsters locked and not to leave trash piled up. Trash pickup could come around more than one time a week in order to stop bears from being attracted to the trash being left outside of people’s homes for days at a time.
References
Fountain, Henry. “Fast-Food Nation is Taking Its Toll on Black Bears, Too.” Nov, 25, 2003.
Hayward, MW., Ortmann, S., Kowalczyk, R. “Risk Perception by Endangered European Bison bonasus is Context (Condition) Dependen.” Landscape Ecology. DOI 10.1007/s10980-015-0232-2
UTC Ecology Lab Blog 