To obtain resources, find mates, and find shelter, animals must traverse an area. The total area that animals travel to fulfill their needs is called its home range. Animals often have familiarity with their home ranges to better forage and hide or escape from predators. An animal’s home range can be occupied by a single animal, its family, or multiple families. Home ranges of different animals often overlap, too, but hostility over home range areas does not occur; animals do not defend the boundaries of their home ranges. However, they do defend smaller areas within their home ranges called territories. Territories may be where an animal mates.

The size of an animal’s home range is influenced by the distribution of resources available. Ecologists propose a hypothesis called the resource dispersion hypothesis to explain the trend of home range area and resource distribution. In an area with sparse resources, the animal must travel farther to meet its resource needs. In an area with abundant resources, the animal does not have to travel as far. Thus, in poor environments, an animal’s home range would be larger. In rich environments, an animal’s home range would be smaller.

To study the movement of an animal and calculate its home range, scientists use radiotelemetry, a tracking technique in which a transmitter is attached to an animal, like on a collar. The scientist locates and tracks the organism by locating the transmitter’s unique frequency. While it shows the movements of an animal, this method may not show the entirety of an animal’s home range because the animal may not visit every part of its home range. Additionally, not all parts of the home range have equal importance, even if some aspects are a habitat requirement, such as a stream for fresh water.

Home range tracking gives insight into habitat requirements, the resources that limit population growth, foraging behavior, and social organization of the organism. In this lab, students tracked the home ranges of cats. If students use GPS data of various domestic cats, they will find that the cats have small home ranges. As pets, they are likely given plenty of food and water, and they are provided shelter. Some may be spayed or neutered, eliminating the ability to reproduce.

Students tracked domestic cats through the website movebank.org. On the website, students used the tracking data map link and searched the scientific name for cats, Felis catus. This pulled up a series of programs. For this lab, data was collected from the Pet Cats of Australia, New Zealand, and the United States. Fifteen cats were tracked from each country for a total of 45 cats. The cats were chosen at random from the list. For each, the student downloaded the cat’s data and opened it in Google Earth. They then drew a polygon around the range, getting all data points inside the polygon. From there, they right clicked the white space and copied the data. They pasted the data into earthpoint.org under the polygon area tab, measuring area in hectares.  The area was then recorded in Excel. This process was repeated for all 45 cats.

Then, students combined their data with two other students for a total of 135 cats. They found the average home ranges of the Australian cats, the American cats, and the cats from New Zealand. Finally, with this data, students constructed a bar graph to compare domestic cat home ranges across the three different countries.

In Australia, the average home range of domestic cats was 2.66 hectares. Cats in the United States had a larger average home range of 5.96 hectares. Lastly, the cats of New Zealand had an average home range of 3.19 hectares. These averages are compared in Figure 1.

Figure 1. Average Home Range Areas of Domestic Cats in Australia, the United States, and New Zealand.

All in all, the home ranges of the domestic cats were relatively small. As shown in Figure 1, Australia had the lowest average home range with 2.66 hectares. New Zealand was in the middle with 3.19 hectares. The United States had the highest average with 5.96 hectares. Most cats lived in a forest habitat or an urban habitat, though a few were seen on a beach. While tracking the cats, the smallest home ranges came from domestic cats in an urban setting. Presumably, they had more dangers in these urban areas due to high traffic and more humans. Cats likely want to avoid interaction with humans because humans are larger and loud. Larger home ranges were often associated with forests or rural areas. Cats probably had more space to roam and fewer human interaction. Additionally, as the cat traveled farther from home, they could have traveled farther from their water source and had to find more.

So, humans are a biotic factor influencing the home range of cats. Available birds and mice, or live prey, could also be a biotic factor influencing home range. Water and shelter are abiotic factors that have an effect on the home range of organisms, including these domesticated cats. Based on the data of Figure 1, it is likely that cats have a greater influence on biodiversity in the United States. Cats in Australia and New Zealand have shorter home ranges, a reflection of human influence. In accordance with the resource dispersion hypothesis, shorter home ranges usually mean that the environment is rich in resources. If a cat has a larger home range, they may have already exhausted prey resources in the area. Thus, they have lowered the species evenness and perhaps the species richness of the area.

As an urban developer, one would likely want to space out homes slightly more. This would allow more area for the cats to roam and made it safer for the cats. The more space they have, the less likely they are to run into roads or be scared by humans. It could be beneficial to the wildlife to still limit the spacing so the cats cannot exhaust all nearby organisms.

              One fundamental part of ecology is an ecosystem. An ecosystem refers to all the living, or biotic, things within an environment, like plants and fungi; it also considers all the nonliving, or abiotic things within the environment, like water and soil. In an ecosystem, energy flows as a system from prey to predator. This unidirectional transfer of energy through a predator’s consumption of prey is a food chain. Food chains are simple with only one organism eating another. However, in ecosystems, the connections between the multiple predators and their food sources are complex. Often, a predator has many prey. Thus, food webs are constructed to represent the interlocking nature of food chains.

              As previously stated, energy flows in one direction. In a food web, energy from the sun is converted in primary producers. Grass, crops, and flowers are all primary producers that photosynthesize to convert sun energy to chemical energy. Then, primary consumers eat the plants. These first-level consumers are herbivores like rabbits. Secondary consumers eat the primary consumers. For example, foxes sometimes eat rabbits. At the top of the food web are tertiary consumers, who eat the secondary consumers. These consumers are also called top predators, and they are typically wolves, snakes, or birds of prey. Birds of prey are hawks, eagles, owls, and other raptors.

              In this lab, students studied one particular bird of prey: barn owls (Tyto alba). Barn owls, like other raptors, have a unique digestive system that presses all the undigested bones, feathers, and such from the owl’s prey into a pellet which they regurgitate. These pellets can be dissected for bones and scored to see what organisms make up the owl’s diet. In a temperate area, one would expect barn owls to eat small mammals like moles. In a dry area, barn owls would likely eat lizards or insects. Students sorted into groups of two and dissected pellets. When students score the bones, they will likely find the owl eats more small mammals, suggesting they are from a temperate zone. This will be expected if the owls are from the area because Chattanooga is in a temperate zone.

              In groups of two, students obtained a pellet from a barn owl as well as two wooden probes, forceps, a magnifying glass, and a bone chart. Gently, students broke the pellet with their fingers or forceps, careful not to break any fragile bones within the sample. Any bones or skulls found were extracted and compared to the bone chart. Students then guessed which organism the bone matched. The bone types and the prey were scored. They repeated this until the pellet had been completely dissected. In the end, the groups combined their data.

              Students discovered that all bones found belonged to small mammals of rodents, shrews, moles, and birds. No insects, amphibians, or reptiles were found in the pellets. In all, students saw 73 rodents, 53 shrews, 39 moles, and 14 birds. This data is represented in Figure 1. Additionally, they found 203 ribs, 104 vertebrates, 73 hind limbs, 63 fore limbs, 45 jaws, 45 pelvic bones, 26 scapula bones, and 25 skulls, as shown in Figure 2.

Figure 1. Proportion of prey types found within the owl pellets from class data set

Figure 2. Proportion of bone types found within the owl pellets from the class data set

A single student group found eleven rodents, fifteen shrew, and eleven moles, as shown in Figure 3.

Figure 3. Proportions of prey found in a single owl pellet

From the experiment, students found that the proposed hypothesis was accepted. As shown in Figure 1, the owls had eaten only small mammals. No insects, reptiles, or amphibians had shown in the pellet, suggesting that the owl is from a temperate zone. However, it is important to note that the data may be somewhat incomplete. Many bones found were rib bones and vertebrates (as shown in Figure 2), which were very small and hard to assign to the corresponding organism. To confirm, ecologists would likely want to track the specific owl to its habitat and see what prey are available.

One group specifically found 11 rodents, 15 shrews, and 11 moles, as shown in Figure 3. If owls produced one pellet a day, an owl’s entire 30-day diet would likely consist of 330 rodents, 450 shrews, and 330 moles. Farmers would likely want to keep a barn owl around because a single owl could take care of a lot of rats and mice that might interfere with their work. If a single owl ate 330 rodents a month, that would keep a farm relatively pest-free.

Lastly, ecologists must consider the entirety of the barn owl diet. In a temperate zone, there are often still insects for an owl to feed on, but no group found any insects. Likely, small mammals were either easier to find and catch for the owls, or it was a season in which the barn owl’s preferred insect prey was not available.  

Evolution, the change in genotypic frequencies of a population over time, \ is a fundamental theory in ecology. Charles Darwin was the first to propose a mechanism to it, and that mechanism was natural selection. In his voyage on the HMS Beagle, Darwin drew three conclusions. For one, variation exists within a species. Secondly, some of this variation is heritable. Lastly, because of this variation, some individuals are more likely to survive and reproduce than others. When an individual with a favorite heritable trait reproduces, that trait is passed on to its offspring. Those without it may not survive to reproduce as successfully. This difference in survival and reproduction from heritable traits is natural selection, the mechanism which Darwin proposed. Each individual has a fitness relative to one another; fitness is an individual’s ability to survive and produce successful offspring.

In this experiment, students focused on the peppered moth (Biston betularia​) population. This moth has two variants of color: a light-colored mottle form and a dark-colored melanistic form. This color trait is an expression of a single gene, and the melanistic allele is dominant over the mottle allele. At the beginning of the industrial revolution, mottle moths were more common, as they could blend in with the white trees. However, as time went on, pollution began to darken the trees. Those mottle moths were eaten off, but the melanistic moths were better camouflaged and survived.

Because the moth color trait is coded by a single gene, researchers may predict the genotypes of future generations using Hardy-Weinberg equations. The Hardy-Weinberg equilibrium is a null hypothesis that assumes no evolution is occurring in a population. If no evolution is occurring, genotypic frequencies will remain the same over time. If the genotypic frequencies of the peppered moth remain the same, no evolution is occurring, but if they are different over time, evolution is occurring.

Students aimed to investigate the survival rates and genotypic frequencies of mottle and melanistic peppered moths in three environments of varying pollution: low pollution, moderate pollution, and high pollution. If students simulate generations of peppered moths in the three environments, they will find that allele frequency of M (melanistic allele) will be lowest in the environment of low pollution, approximately even with the frequency of m (mottle allele) in the environment of moderate pollution, and highest in the environment of high pollution. The environment of low pollution is light, so melanistic moths will stand out and be eaten. In the environment of high pollution, it is mostly dark so they will blend in. Moderate pollution brings nearly equal amounts of dark and light spots, so the melanistic moth will blend in half the time and stand out the other half.

Students worked in groups of four to simulate generational change of peppered moths in differing environments. They obtained their three environments. The low pollution environment was white with three black areas. The moderate pollution environment was checkerboarded with light and dark areas. The high pollution environment was mostly dark with three spots of white. Students also had a bag of moths: black homozygotes (MM), black heterozygotes (Mm), and white homozygotes (mm). Starting with the low pollution environment, students distributed ten heterozygotes by shuffling them over the paper. Then, they simulated survival. Birds preyed on poorly-camouflaged individuals, so any black individual in a white area would be eaten and any white individual in a black area would be eaten. A moth was considered to be in an area if the position of its head was in that block.

After picking off the moths who were eaten, students scored the remaining genotypes and calculated the observed allele frequency of the survivors with the following formula: p + q =1, where p represents the allelic frequency of M and q represents the allelic frequency of m. Using this frequency, students calculated the expected genotypic frequencies for the next generation with the following formula: p² + 2pq + q² = 1, where p² represents the frequency of melanistic homozygotes, 2pq represents the frequency of melanistic heterozygotes, and q² represents the frequency of mottle homozygotes. Assuming the population would double in size from reproduction, students multiplied the expected genotypic frequencies by the doubled population size and, rounding to the nearest whole number, calculated the number of individuals of each genotype the next generation would begin with. They added the corresponding individuals to fulfill the number expected. Next, students simulated survival and reproduction again. They continued until four generations had been met. Then, they completed these steps for the moderate pollution environment and the high pollution environment. Students found that in the environment of low pollution, Mm individuals occurred at a frequency of 0.4, MM individuals occurred at a frequency of 0.33, and mm individuals occurred at a frequency of 0.35 in the last generation. In moderate pollution, Mm individuals occurred at 0.46 and mm individuals occurred at 0.65. In high pollution, 0.67 individuals were MM and 0.08 were Mm. These observed genotypic frequencies are shown in Figure 1

Figure 1. Observed Genotypic Frequencies of Fourth Generation Pepper Moths in Environments of Varying Pollution

Students also found that in the low environment, the first generation had a p of 0.5. The second generation had a p of 0.33. The third had a p of 0.25, and the fourth had a p of 0.33. In the moderate pollution environment, the first generation had a p of 0.5, a p of 0.57 in the second generation, a p of 0.64 in the third generation, and a p of 0.69 in the last generation. In the high pollution environment, p was 0.5 in the first generation, 0.59 in the second generation, 0.59 in the third generation, and 0.68 in the last generation. These allelic frequencies are shown in Figure 2.

Figure 2. Allelic Frequency of p (allele M) Through Time in Environments of Varying Pollution

Additionally, q occurred at 0.5, 0.67, 0.75, and 0.67 throughout generations in the low pollution environment. Then, q occurred at 0.5, 0.43, 0.36, and 0.31 throughout generations in the moderate pollution environment. Lastly, q occurred at 0.5, 0.41, 0.41, and 0.32 throughout generations in the high pollution environment. This is shown in Figure 3.

Figure 3. Allelic Frequency of q (allele m) Through Time in Environments of Varying Pollution

The expected genotypic frequencies of each genotype in the low pollution environment are shown in Figure 4, in the moderate pollution environment are shown in Figure 5, and in the high pollution environment are shown in Figure 6.

Figure 4. Expected Genotypic Frequencies Versus Time in the Low Pollution Environment

Figure 5. Expected Genotypic Frequencies Versus Time in the Moderate Pollution Environment

Figure 6. Expected Genotypic Frequencies Versus Time in the High Pollution Environment

Lastly, Table 1 compares the fitness of genotypes in the first and last generations of each environment. In all three, the first generation had the following fitness calculations: MM with 0, Mm with 1, and mm with 0. In the low pollution environment, MM had a fitness of 1, Mm 0, and mm 0.5 in the last generation. In the moderate pollution environment, MM had a fitness of 1, Mm 0.9, and mm 0.45 in the last generation. In the high pollution environment, MM had a fitness of 0.74, Mm 1, and mm 0.

Table 1. Fitness of Genotypes in the First and Last Generations of Each Environment.

Figure 1 shows that black individuals were more common in final generation of the area of low pollution, which contradicts the prediction that white individuals would be more common. This is possibly due to too many white individuals landing in black spots. The figure also shows that white individuals occurred slightly more than black individuals in the moderate environment, but only black individuals were left in the area of high pollution. However, this figure does not prove the population was evolving.

Figures 2 and 3 track the allele frequency of both M (p) and m (q) throughout the generations in each environment. In low pollution, p dipped and went back up while q went up and slightly down.  With these graphs, it can be seen that each population is seeing a change in allelic frequency over time. Thus, evolution is occurring in the populations of each environment. Figures 4, 5, and 6 also show changes in genotypic frequencies over time. Because allelic frequency is changing, so are genotypic frequencies. This is true for each environment.

Table 1 shows that in the low pollution environment, black individuals (MM) were actually more fit than white individuals (mm). This, again, may be due to the distribution of the moths along the paper. Typically, one would expect the white individuals to have higher fitness. In the moderate pollution environment, black individuals (both MM and Mm) were more fit than white individuals (mm). One would have expected each to survive equally as well, but such was not the case. In the high pollution environment, black individuals were fit (both MM and Mm) while white individuals (mm) had a relative fitness of 0. This fits with what is expected, likely because black individuals were better camouflaged in the environment with high pollution and got eaten less than white individuals throughout the generations. The black individuals survived to reproduce.

Overall, evolution was found to be occurring in the populations of pepper moths in all three environments. However, allelic frequencies only in the high pollution environment were as hypothesized. The differences in the other environments may be attributed to random chance of where individuals landed on the paper.

Diversity is a crucial part of the natural world. Primarily, it gives insight into the health of an ecosystem. Healthy ecosystems need high diversity in order to function well and provide its variety of services. Most know that biodiversity is different between different ecosystems; desert life is different from rainforest life. However, diversity can also be different within a single ecosystem. Species richness, for example, may change as one travels from the inside of a forest to the outside. Additionally, ecologists study habitat fragmentation, or the creation of new edges and patches from a continuous habitat. The causes of habitat fragmentation can be anthropogenic, like the creation of roads through a forest, but they can also be natural, like a river running through the center of a habitat. Habitat fragmentation can lead to a change in both the abundance and types of species present in the area.

Scientists often approach changes in diversity from the inside to the edge of a forest with a sampling method called transecting. A transect is a straight line that runs from one point to another. Along this line, sampling can occur in two ways: line transects, where the organisms touching the line are recorded, or belt transects, where quadrats are placed at random spots along the line and organisms within the quadrats are recorded. The line transect method is often faster, but belt transects offer a more complete view of the change in biodiversity.

In this exercise, students study tree diversity by transecting an urban forest from the edge to the interior. If students use a line transect to study the forest from edge to interior, they will find more species richness within the interior of the forest. Species within the interior are less exposed to humans, whose activity can negatively impact the habitat and thus the species.

The fall 2019 ecology lab students were unable to collect samples due to weather, but they used data collected by a previous class with a line transect method. Groups of that class obtained a 1.5-meter (5 foot) string. One student in the group took one end, while another student took the other end of the string. The first student remained at the edge of the forest while the second walked the string into the interior of the forest until the string went taunt. Then, a coin was flipped to determine which side of the line would be sampled. Students tracked the number of different tree species along this side of the line.

Once finished, the second student remained where they were while the first student on the edge of the forest walked to the interior until the string was taunt again. A coin was flipped, and students sampled the tree species along the side of the line. This process was repeated until students had taken samples from a total of 165 ft.  Back in lab, students analyzed the data by creating a scatter plot with species richness along the y-axis and distance from the trail edge along the x-axis.

Of the second supplied data set, species richness, or number of tree species, varied from zero to four. Students were unable to determine if any species were overly abundant from the given data due to only having the number of species found at the interval of the line. The regression line had an R² value of 0.1154. Pictured below is Figure 1, the scatter plot depicting the change in species richness from the forest edge to the forest interior.

Figure 1. Scatterplot of the Difference in Species Richness from Forest Edge to Interior

As seen in Figure 1, the species richness at various points along the line transect do not seem to follow the trendline. The R² value of the plot is 0.1154. This is much less than one, which solidifies the understanding that the data does not match the trendline. Thus, there is not a clear regression or change in species richness from the edge to the interior of the forest. The proposed hypothesis that species richness could be greater in the interior of the forest is rejected. The very beginning of the line had the same biodiversity as around 65 ft. The majority of the line had only one tree species within it. This pattern seems to be random, and so distance from the edge of the forest has no apparent correlation to species richness.

Moving forward, it is important to consider how habitat fragmentation might affect species richness. For one, habitat fragmentation does not affect only plant species. Animal species likely will feel the effects as well. Take the example of a road built through an animal’s habitat. If the animal burrows on one side of the road, but its biggest food source is on the other side, the animal would have to cross the road to obtain food. If the animal travels, it would likely need to cross the road at one point. So, habitat fragmentation might separate an animal species from its home, its food source, or others in its population. Species richness would probably be greater in a fragmented habitat or larger area. In a smaller area, there would not be as much space for organisms. Less reproduction would occur because the habitat may not be able to support many organisms. In a large area, though, more species may coexist.

When one thinks of phenotypic variation, they often think of variation between individuals, but many fail to realize that phenotypes can vary within an individual as well. Typically, within-individual phenotypic variation is due to natural selection. A tree, for example, may have varied leaves. Leaves have two primary functions: capturing sunlight for photosynthesis and taking in carbon dioxide through stomata. The larger the surface area of a leaf, the more sunlight and carbon dioxide it can take in. However, it comes at a cost. The larger the surface area of a leaf, the greater the potential for transpiration, or water loss, the leaf has.

Ecologists describe two types of trees. Trees in the understory are often said to be mono-layered, having only one or a few layers of leaves. This is often an adaption to limited sunlight. Trees in the canopy are usually seen to be multi-layer trees; they have several layers of leaves. Being in the canopy, a multi-layer tree has plenty of access to intense sunlight, but phenotypes may vary between leaves on the outer branch and leaves on the inner branch as sunlight may be less available for the inner branch leaves.

This exercise aims to detect phenotypic variation of leaves within an oak tree (Quercus spp.). Specifically, students will analyze the difference in surface area between outer-branch leaves and inner-branch leaves. If students analyze phenotypic variation of leaf surface area within an oak tree, the leaves on the inner branch will be larger than those on the outer branch. Sunlight is not as intense or available on the inner branch, so the leaves have increased surface area to take in more sunlight.

Each lab student collected twenty leaves from an oak tree before the lab period; they collected ten leaves from the outer portion of the branch and ten leaves from the inner part of the same branch. In lab, students placed the leaves on 1cm grid paper and traced the outline of the leaves. Then, they counted the number of squares in each leaf tracing. Partial squares were counted if at least half the square was covered by the leaf. Students recorded this number, the surface area of the leaf, for each leaf, separating the outer leaves and inner leaves.

After collecting the data, students grouped together and carried out a T-test. A T-test is a statistical test that figures the significance of the difference between two variables. In this case, it figures if the difference in surface area between inner leaves and outer leaves is significant. Students used a two-tailed test to account for variance. Students also found the critical t-value by calculating the degrees of freedom (sample size minus 1) and using an alpha level of 0.05.

One student had a mean of 118.7 cm² surface area for the outer leaves and a mean of 171.6 cm² surface area for the inner leaves. The other four people in the student group found means of 142.3, 110.8, 104.7, and 118.9 cm² for leaves of the outer branch. The means for the inner branch were 165.3, 120.8, 145.4, and 142.3 cm². The calculated t-test value was 0.029289. The five students had collected a total of 98 leaves, giving them 97 degrees of freedom. The critical value at alpha level 0.05 was 1.9847.

Students charted the mean surface areas of the inner and outer leaves and compared the two in Figure 1 below.

Figure 1. Mean Surface Areas of Inner and Outer-branch Oak Leaves

The students found that the mean surface areas of the inner-branch leaves were higher than those of the outer-branch leaves. The comparison is clearly shown in Figure 1. The results of the T-test, however, indicate that these differences are not statistically significant. The calculated T-test value is much lower than the critical value, which means there is a greater than 5% chance the results are random. Thus, the hypothesis should be rejected despite the differences in surface area.

Had the results been statistically significant and the hypothesis been accepted, it would likely be found that the variance is due to natural selection. Leaves on the inner branch do not have as great of access to sunlight as those on the outer branch, so to increase the amount of sunlight they absorb, the leaves lively would have adapted by increasing their surface area. The greater the surface area, the more likely they can capture sunlight.

 Surface area is an important adaptation about which most farmers want to be educated because it gives insight about the fitness of a plant. Those that minimize surface area while maximizing sunlight capture and CO₂ intake are going to be the most fit; they will survive and reproduce more than individuals that inefficiently take in what they need.

Some ecologists study population dynamics, or the changes in a population over time in regard to abundance, distribution, and composition. Typically, the most influential factors that affect a population are birth rates, death rates, age structure, and migration in or out of the population. To better understand a population, ecologists may study it from life to death, taking note of the annual births and deaths. This pattern on survival and reproduction is called the population’s life history. Ecologists compile this data into a life table. This table can be used to predict how likely an organism in that population will live, die, or reproduce at a certain age.

From a life table, ecologists can also create a survivorship curve. This curve is a graphical representation of how likely an organism in the population will survive. There are three types of curves. A Type I curve shows a low juvenile mortality rate and a high mortality rate in later life. In other words, infants and young typically survive, but mortality is more likely in the organism’s later years. In a Type II curve, organisms die equally at each age interval, creating a straight, negative slope. This type of survivorship curve is typical of birds, which have few offspring and provide a lot of parental care. Lastly, a Type III curve shows high juvenile mortality. Those who do survive live long lives, usually. Trees may have this type of curve, and they have multiple offspring at once without providing care to the young.

For this lab exercise, students look at the births and deaths of humans by collecting data from the Confederate Cemetery. If students take data from the Confederate Cemetery, the survivorship curve created from the data will be a type I curve. Humans usually exhibit a type I survivorship curve because most humans survive to later in life. Low juvenile mortality and high late mortality is characteristic of that curve.

The Confederate Cemetery is nestled at the corner of UTC’s campus, across from Holt Hall. It is full of hundreds of gravestones of various years and weathering. The class of 21 students divided the cemetery into sections. Each student sampled in a different section. In their respective sections, students recorded data from 25 gravestones. They noted the year of birth, the year of death, and the age of death of each person. In total, the class collected data from 525 individuals.

Once data was collected, the class pooled their data. In total, 521 individuals were counted. Either some students did not collect from 25 gravestones, or students miscounted during pooling. With this data, students created a life table with age intervals, the number of surviving organisms at the beginning of the interval, the number of dying organisms during the interval, and the age-specific mortality rate. Lastly, students created a survivorship curve.

In total, data from 521 individuals was collected. These individuals were of varying ages. From this, students constructed the life history table below in Table 1.

Table 1. The Life Table from Class Dataset of the Confederate Cemetery Population

Age Interval (years)	# Surviving at beginning of age interval (Ax)	# Dying during age interval (Dx)	Age-specific mortality Rate (per year)
0-0.99	521	13	0.0250
1-5.99	508	29	0.0114
6-10.99	479	9	0.00376
11-15.99	470	8	0.00340
16-20.99	462	21	0.00909
21-25.99	441	21	0.00952
26-30.99	420	15	0.00714
31-35.99	405	26	0.0128
36-40.99	379	16	0.00844
41-45.99	363	21	0.0116
46-50.99	342	16	0.00936
51-55.99	326	26	0.0160
56-60.99	300	27	0.0180
61-65.99	273	38	0.0278
66-70.99	235	65	0.0553
71-75.99	170	51	0.0600
76-80.99	119	47	0.0790
81-85.99	72	30	0.0833
86-90.99	42	25	0.119
91-95.99	17	13	0.153
96-100.99	4	1	0.05
101 & over	3	3	1

Additionally, students used the data to make a survivorship curve shown in Figure 1.

Figure 1. The Survivorship Curve of the Confederate Cemetery Population.

Overall, the results were as expected. Table 1 shows that the annual mortality rate is lower in the early years and increases later in life. The survivorship curve shown in figure 1 is roughly a Type I curve, as stated in the hypothesis. Most survived in the infant years but survivorship declined later in life. The highest mortality rates occurred at age 101 and over, which is to be predicted because humans do not survive into ages of 150 or 200 years. They will certainly die during the 101 years and over interval. Other than natural factors influencing mortality rate, some people buried in the cemetery had passed away from war. Additionally, students did not have data on migration in or out of the population, which would also be affecting the population.

When determining the spatial structure of a population, ecologists look at three properties: distribution, density, and dispersion. Distribution is how the population is spread out over a landscape, or a population’s range. Density refers to the number of individuals in a given unit of area or volume. Lastly, dispersion is the arrangement of individuals of the population within their area relative to one another. Dispersion hints at different abiotic and biotic factors that could be affecting the population.

              There are three main patterns of dispersion. The first, random dispersion, occurs when individuals arrange themselves independently of one another. This arrangement usually suggests that neither abiotic nor biotic factors are acting on the population’s dispersion pattern. The second, clumped dispersion, occurs when individuals arrange themselves in clumps or communities. Often, this is a result of social behavior, like mating, or the clumped availability of resources. Lastly, there is uniform dispersion, in which individuals are arranged evenly. This sometimes happens due to competition for territory, like in plants.

              Ecology students sampled dallisgrass (Paspalum dilatatum) in the Confederate Cemetery at the corner of UTC’s campus in the previous week. Now, they aim to determine the dispersion pattern of that plant in this area. In the wild, the null hypothesis is that all populations exhibit random dispersion. In this case, students have created an alternative hypothesis: when the data is applied to the Poisson distribution and chi-square analysis, it will be found that the dallisgrass species exhibits a uniform distribution due to competition for resources like root space.

              To test the alternative hypothesis, students used random sampling of the dallisgrass at the Confederate Cemetery. The cemetery is the site for hundreds of individual graves, so it is maintained, and UTC students sometime walk through it. Students began in their groups with four to five individuals. One member of the group generated a random number, and another walked that many steps in one direction. Another random number was generated, and the student walked that many steps in a different direction. A 1m² quadrat was placed at their feet, and the group counted the number of dallisgrass individuals within the quadrat. The group repeated this to sample a total of ten quadrats. With four groups in the class, this came out to be forty quadrats total for the entire class.

              Students then analyzed the data. To determine the number of plants per quadrat for a random dispersion pattern, they used the Poisson distribution and graphed a histogram with the results. Poisson distribution is as follows:

P(x)=(a^xe^-a)/ x!

In this formula, a represents the mean density of individuals per subplot and x is the number of individuals in the subplot. P(x), then, is the probability of finding a given number of individuals in any given subplot.

Students also created a histogram for the observed results to compare the two. If the histograms were found to be different, students determined if the observed results suggested clumping or uniform dispersion. To prove that the results were not random, students performed a Chi-square analysis test and compared the calculated p-value to the critical value at the alpha, or probability, level of 0.05. The Chi-square analysis formula is as follows:

X²=∑ [(Observed-Expected)² / Expected]

In one group, students analyzed 9 plots, as they had the outlier. They counted 37 individuals total, had an average of 4.33 individuals per plot, and found a maximum of eight individuals within a single plot. The expected and observed histograms are shown in Figures 1 and 2.

Figure 1. The group’s expected dispersion histogram based on Poisson’s test.

Figure 2. The group’s observed dispersion pattern as a histogram.

The group had a total sample size of nine plots (N). The calculated Chi-square value was 6.83. The critical chi-square value was 15.507, as the group had eight degrees of freedom and an alpha level of 0.05.

Students found that there was an outlier in their data, so they disregarded it in their results and analyzed data from 39 plots instead of 40. As a class, they counted a total of 191 individuals, had a mean of 4.9 individuals per plot, and found a maximum of 16 individuals within one plot. The expected and observed histograms are shown in Figures 3 and 4.

Figure 3. The expected class histogram based on Poisson’s test.

Figure 4. The observed histogram of the class.

The class had a total sample size of 39 quadrants (N), making for 38 degrees of freedom. The critical value was 53.384, and the calculated Chi-square value was 668.17. The alpha level for the critical value was 0.05.

According to the group dataset, the species is found to have a random dispersion. Though the observed histogram, Figure 2, differs from the expected, Figure 1, the null hypothesis was accepted because the calculated P-value fell below the critical value. This means that there was a probability greater than 5% that the results were due to chance, leading to acceptance of the null hypothesis that the species had random dispersion. In such a case, no abiotic or biotic factors would play into the population’s dispersion.

However, the opposite can be said for the class dataset. The observed histogram, Figure 4, differs visually from the expected, Figure 3. Figure 4 holds the appearance of a clumped dispersion histogram. Additionally, the calculated P-value was well above the critical value, which means the null hypothesis was rejected. Thus, it can be concluded that the dallisgrass exhibits a clumped dispersion pattern, rejecting the alternate hypothesis that the population is uniform in dispersion. This is likely due to the clumping of resources, like sunlight.

It is important to keep two things in mind when considering the results. For one, environmental factors may have impacted the results. Students may have failed to count some individuals of dallisgrass due to lack of familiarity with the species. Additionally, it hadn’t rained for days at the time of collection, so the grasses had wilted, and the blades curled in. This may have caused students to misidentify the individuals. Also, it must be noted that the sampling sizes between the group dataset and class dataset were extremely different. Sampling nine plots does not allow a whole picture of the entire community. Sampling thirty-nine plots, on the other hand, provides a greater scope of the community. Thus, the class data analysis is likely to be more accurate for both expected and observed results of the sampling.

To define the special structure of a population, scientists look at three properties: distribution, density, and dispersion. Distribution refers to a population’s range over a geographical landscape. Furthermore, density reflects the number of individuals per unit of area or volume. Lastly, dispersion is defined by how members of a population arrange themselves in their area. Each property tells something different about the environment, like availability of suitable habitat or resource availability. Dispersion tells about the abiotic and biotic factors present and how they influence the population. Some of these factors include wind, moisture, sunlight, competition, and predation.

The influence of abiotic and biotic factors causes three dispersion patters. With clumped dispersion, individuals form tight aggregations, or clumps. Clumping may be due to a few different events. Some populations clump for social interaction, like fishes swimming in a school to optimize hunting and keep better protection from predators. Some populations may just be responding to the clumping of resources, or they may be influenced by their own reproductive cycle and not have the ability to move far from the parent. In uniform dispersion, the individuals of a population are spaced evenly. Sometimes this happens in plants, who compete for root space; they may have a minimal distance from one another to better survive in their environment. Lastly, random dispersion occurs when individuals of a population are spread out independently of one another. This usually happens when none of those abiotic or biotic factors are influencing the population, which makes random dispersion patterns rare in nature.

Random dispersion patterns are considered to be the norm for populations, making it a null model. When scientists test the dispersion pattern of a population, they are testing to see if it deviates from the null hypothesis and fits an alternative hypothesis. In this lab, students are testing the dispersion pattern of dallisgrass (Paspalum dilatatum) in the Confederate Cemetery by using the Poisson distribution formula and chi-square analysis. Just this week, students collect data. If we perform random sampling of the Confederate Cemetery, we will find that dallisgrass exhibits uniform dispersion because it competes with other grasses and plants in the area.

In order to test the hypothesis, student groups divided into their four groups and traveled to the Confederate Cemetery. The cemetery is nestled on the north corner of the University of Tennessee at Chattanooga campus, directly across from the biological and environmental sciences building. It is regularly maintained, and some students walk through it as a shortcut to class. At the cemetery, the student groups obtained a 1m² quadrat. One group member generated a random number, and another walked that many steps in one direction. A second random number was generated, and the person with the quadrat walked that many steps in another direction and placed it at their feet. They then counted the number of dallisgrass individuals within the quadrat. Students repeated these steps nine times for a total of ten sampled quadrats.

Our group found four individuals in quadrat one, two individuals in quadrat two, three individuals in quadrat three, one individual in quadrat four, and two individuals again in quadrat five. In quadrat six, the numbers jumped to a total of fifty-four individuals found. In quadrat seven, five individuals were found, and in quadrat eight, eight individuals were found. Lastly, in quadrats nine and ten, seven individuals were recorded in each. These results are summarized below in Table 1. Overall, the group found an average of nine individuals per quadrat.

Table 1. Number of Individuals of Dallisgrass Found in 1m² plots.

Quadrat	Number of Individuals
1	4
2	2
3	3
4	1
5	2
6	54
7	5
8	8
9	7
10	7

Table 1 shows an odd pattern of dispersion. Most plots, like one through five, contained only a few individuals. Plots eight through ten had a few more individuals, but then the sixth quadrat stands out with fifty-four dallisgrass individuals. Because it is so different from the other numbers, we regard it as an outlier, and the new average of individuals found is 4.33. Disregarding our outlier, I’d say that this pattern is likely a clumped pattern that results from the clumping of resources. This strays from the proposed hypothesis. Perhaps the plants are competing for something other than root space. Sunlight, for example, may be a “clumped” resource because of the shading of trees. Some areas will have greater sunlight than others, so more dallisgrass would grow there. The class dataset suggests the same dispersion pattern, clumped. In order to evaluate if this interpretation is correct, we will perform statistical analysis next week with the Poisson distribution test and follow it with chi-square analysis.

              All in all, it is important to consider sample size when conducting field surveys and analyzing data. Each individual group only looked at ten quadrats, making a total of forty for the class set. That is only a small fraction of the size of the Confederate Cemetery. Similarly, the patterns seen may change with the spatial scale. What may seem clumped on a small scale may look more like uniform dispersion on a larger scale. Thus, we must keep that in mind when interpreting the data.

An optimal forager is an organism that maximizes their net gain of energy while foraging. Energy gain is a way that scientists can measure the fitness of an organism. As one would expect, the greater the net energy gain, the greater the fitness. When animals are in the wild, prey are distributed among patches of different densities. In an area of low prey density, a forager will have a low feeding rate. In an area of high prey density, a forager will have a high feeding rate. Organisms expend energy traveling to a new patch, and organisms must decide when it is time to leave a patch. Scientists can use a cumulative gain curve to show the rate of energy gain in one patch. To determine the maximum net gain of energy, they take the tangent of the line. If the animal leaves before or after that time point, they are not obtaining the maximum net energy. If scientists want to observe the maximum net energy of the whole environment, they use the same method and find the maximum for each patch.

To observe and describe behavior of consumers, scientists use an optimal foraging model. In this case, the Marginal Value Theorem is used. The model allows scientists to predict four situations. For one, foragers should capture more prey in patches with high prey density. They should remain here longer, too, and they should catch more prey in a timely manner. Finally, once the intake rate begins to decline in comparison to the environment, the forager should leave the patch. In other words, giving up time (GUT) should occur more quickly in areas of low prey density.

In this experiment, students aimed to use an optimal foraging model and act as foragers themselves. They foraged for beans in three patches of varying density. Students will spend more time scavenging in the patch with high density than that of average or poor density, where they will find more prey. This hypothesis is based on the predictions from the Marginal Value Theorem.

At Chamberlain Pavilion, three containers of rice and beans were placed at approximately two yards apart. Container one was a patch of low density with only five beans in the patch. Container two was a medium density patch with fifteen beans in it. The third container had the highest density with thirty beans. This set-up was repeated so there were four rows of the three containers. Students did not know the densities of the containers until the activity ended.

Each student foraged for two minutes. When time began, they traveled to a patch and foraged for beans with one hand. When they found a bean, they placed it in a bowl and swirled it around three times to mimic handling time. When they felt ready, the student left the patch and traveled to a new one to forage. They continued foraging until their two minutes were up. While the student foraged, a different student noted the time of arrival at each patch, the time of departure from each patch, the time at which each bean was found, and the ending time.

Students took this data to analyze. They recorded the patch density, or the total number of beans in each patch, and the beans they found in each patch. They recorded the seconds they spent in each patch. They also calculated capture rate in units of beans per second by dividing the total beans found in a given patch by the total time spend in the patch. Lastly, they calculated giving up time (GUT) for each patch by calculating the difference in the seconds between the last bean capture for a patch and the departure from that patch.

              I captured four beans in 17 seconds at patch one. This gave a capture rate of 0.24 beans per second. The giving up time was two seconds. In the second patch, I found nine beans in 36 seconds. The calculated capture rate was 0.25 beans per second, and the giving up time was one second. Lastly, I found 17 beans in 58 seconds in the last patch for a rate of 0.29 beans per second. The final giving up time was five seconds.

Figure 1. The forager’s cumulative gain curve for each patch

The forager’s prey captures are plotted with time to create a cumulative gain curve.

Figure 2. Beans found in each patch in comparison to patch density

The blue column shows the number of beans found in each patch. The orange column shows the total number of beans in each patch. In patch one, 4 beans were found. The patch had 5 beans total. Patch two had a density of 15 beans with 9 found. Patch 3 had a density of 30, and 17 beans were found.

Figure 3. Time, in seconds, spent foraging in each patch

The forager spent 17 seconds in the lowest density patch, patch one (dark blue). The forager spent 36 seconds in the medium-density patch, patch two (silver). Finally, the forager spent 58 seconds in the high-density patch, patch three (light blue)

Figure 4. Capture rate in beans per second of each patch

The forager captured 0.24 beans per second in the lowest density patch, patch one (blue). Beans were captured at a rate of 0.25 beans per second in the medium-density patch, patch two (orange). Lastly, the forager captured 0.29 beans per second in the high-density patch, patch three (silver).

Figure 5. The giving up time in seconds at each patch

The forager had a giving up time of one second in the low-density patch, patch one (yellow). The GUT for the medium-density patch, patch two (orange), was two seconds. The forager had a GUT of 5 seconds in the high-density patch, patch three (green).

Discussion

The given figures analyze different predictions of the optimal foraging model. The first prediction of foragers capturing more prey in high-density patches is represented in Figure 2. Figure 2 shows that the organism captured more beans in the high-density patch, capturing seventeen beans in patch three. The organism only caught nine beans in the medium-density patch, and four beans in the low-density patch. This is consistent with the model. Figure 3 shows the results of testing the second prediction from the model. According to the model, foragers should spend more time in patches of high density. The results are consistent with this prediction as the forager spent the most time, 58 seconds, in the high-density patch. The forager spent 36 seconds in the patch of medium density and only 17 seconds in the patch with the lowest density.

Figure 4 aligns with the third prediction of the model; foragers should capture more prey in a timely manner in the high-density patch. In this experiment, the forager captured 0.29 beans per second in the high-density patch, which is higher than the rates of the medium and low-density patches, 0.25 and 0.25 respectively. The final prediction of the model is shown by Figure 5. GUT is supposed to occur more quickly in patches of low density. The results from the experiment deviate slightly from the experiment. The forager did exhibit the longest giving up time in the high-density patch with 5 seconds. However, the forager had a GUT of two seconds in the first patch and one second in the second patch. The first patch was of lower density than the second, but the GUT was a second longer.

With Figure 1, it can be seen that the foraging does exhibit rather optimal timing. The slopes of the lines are relatively straight for all plots. So, while foraging, the organism was consistently capturing beans and spent more time capturing more prey in the high-density patch. This supports my hypothesis that foragers would capture more prey and spend more time in the patch of the highest prey density. However, I don’t think that the optimal foraging theory is accurate to apply to humans. This reflects the GUT in Figure 5. In the first patch, I had a GUT of only one second. That was because I had watched others and figured out that it was low density. I found that the second patch was in the middle, but I didn’t know how many beans were in it.

I would expect foragers to fulfill the predictions of the optimal foraging theory in a situation where they aren’t being hunted themselves. Every moment spent out hunting for an organism may mean they’re being hunted as well. So, in an ecosystem with a complex feeding web, however, I don’t think these predictions would hold up quite as well.

In the previous two experiments, students set out to measure the biodiversity of two communities in Chattanooga: Signal Point Park and the Confederate Cemetery. This week, students aimed to analyze the data of the whole class that was collected during this time and compare the biodiversity of the two communities. To do this, students used the Shannon Diversity Index to calculate the observed biodiversity value (H’), the maximum diversity value possible (H_max), the total species richness (S), and species evenness (J). When compared to Signal Point Park, the cemetery will have a greater Shannon Diversity Index value and thus greater biodiversity due to the greater human impact on the Signal Point Park community. 

Signal Point Park is a community nestled on Signal Mountain in Chattanooga, Tennessee. The area sampled by students was a field adjacent to the parking lot, and a walking path ran through it to connect the lot to hiking trails and the mountain overlook. The Confederate Cemetery, on the other hand, is closer to the campus of the University of Tennessee at Chattanooga, nestled just across from Holt Hall. It is a cemetery, so it is maintained and visited. The occasional student may walk through the community to get to class, too. 

Students used the same methods to sample biodiversity from both communities. Students broke into four groups and obtained a 1m² quadrat. They generated a random number and walked forward that many steps. With a second random number, they walked to the right and placed the quadrat at their feet. Students recorded a description of each species found within the quadrat, and they recorded the number of individuals within each species. They repeated this until they sampled a total of ten areas. With four student groups sampling ten plots each, that came to a total of 40 areas sampled for each community. Back in the lab, students added up the number of individuals found within all their quadrants.

In order to calculate the Shannon Diversity Index values of the communities, students used the following equations:

P=  (Number of individuals of one species)/(total number of individuals)

H’ = ∑ [-p (ln p)]

S= total number of species

H_max= lnS

J= H’/H_max

From these equations, students calculated that Community A (Signal Point Park) had an H’ value of 2.54 and an H_max value of 3.61. It had 37 total species and a species evenness of 0.70. These values are listed in Table 1. In Community B (Confederate Cemetery), the H’ value was determined to be 2.48 and the H_max value was 3.58. It had 36 total species and a species evenness of 0.69. The determination of these values is listed in Table 2.

Table 1. Species Abundance and Shannon Diversity Index Values of Signal Point Park

Species	Abundance	Proportion	-p(ln p)
Granddaddy Long Leg Spider	1	0.00070	0.0051
Jumping Spider	2	0.0014	0.0091
Beetle	1	0.00070	0.0051
Moss	14	0.0097	0.045
Black Ants	56	0.039	0.13
Red Ants	3	0.0021	0.013
Small Red Insects	2	0.0014	0.0091
Lichen	67	0.047	0.14
Sawtoothed plant	45	0.031	0.11
Clover	83	0.058	0.16
Pine	2	0.0014	0.0091
Red Bud	6	0.0047	0.023
Wild Onion	47	0.033	0.11
Poison Oak	1	0.00070	0.0051
Heart-shaped Leaf Grass	14	0.0097	0.045
Wild Strawberry	51	0.035	0.12
Small, Woody Shrub	18	0.013	0.055
Pointed Shrub	1	0.00070	0.0051
Broad-leaf plant	99	0.069	0.18
Dandelion	17	0.012	0.052
Tear-shaped Plant	12	0.0083	0.040
Maple	1	0.00070	0.0051
Violet	20	0.014	0.059
Yellow Violet Vine	31	0.022	0.083
Flowering Vine	18	0.013	0.055
Thin Grass	304	0.21	0.33
Broad Grass	401	0.28	0.36
Short, fuzzy plant	16	0.011	0.050
Stemmed Grass	10	0.0070	0.035
Bunny Grass	7	0.0049	0.026
3 squiggly leafed plant	1	0.00070	00.051
Oval leaf sapling	3	0.0021	0.013
Branched leaf plant	11	0.0076	0.037
Prickly grass	16	0.011	0.050
Bumpy grass	41	0.029	0.101
Broad diamond leaf plant	8	0.0056	0.029
Lobed grass	8	0.0056	0.029
Total Individuals	1438	H’	2.54
Total Number of Species	37
J	0.70
Hmax	3.61

Table 2. Species Abundance and Shannon Diversity Index Values of the Confederate Cemetery

Species	Abundance	Proportion	-p(ln p)
Lichen	96	0.034	0.12
Wild Strawberry	7	0.0025	0.015
Pillbug	7	0.0025	0.015
Red bug	1	0.00036	0.0028
Leaf Bug	2	0.00071	0.0052
Weavel	1	0.00036	0.0028
Snails	5	0.0018	0.011
Spider	2	0.0071	0.0052
Fruit Fly	1	0.00036	0.0028
Caterpillar	1	0.00036	0.0028
Black Ants	476	0.17	0.30
Red Ants	1	0.00036	0.0028
Clover	144	0.051	0.15
Moss	10	0.0036	0.020
Poison Oak	4	0.0014	0.0093
Vines	12	0.0043	0.023
Two-leaf Vine	55	0.020	0.077
Ivy	5	0.0018	0.011
Lily pad leaves	141	0.050	0.15
Thin bladed grass	763	0.27	0.35
Broad bladed grass	192	0.068	0.18
Violets	80	0.029	0.10
Three leaf sapling	36	0.013	0.056
Broad sapling	23	0.0082	0.039
Sawtooth leaves	87	0.031	0.11
Woody stem plant	138	0.049	0.15
Long grass	121	0.043	0.14
Small leafy branch	15	0.0053	0.028
Flower shaped leaves	2	0.00071	0.0052
Fuzzy leaf	3	0.0011	0.0073
Oval leaf	3	0.0011	0.0073
Rounded leaf	16	0.0057	0.029
Ruffled leaf	42	0.015	0.063
Heart-shaped grass	281	0.10	0.23
Lobed grass	24	0.0086	0.041
Stemmed grass	10	0.0036	0.020
Total Individuals	2807	H’	2.48
Total Number of Species	36
J	0.69
Hmax	3.58

The relative abundances of each species in both communities is graphed as a rank abundance curve in Figure 1.

Figure 1. The Rank Abundance Curves of Signal Point Park and the Confederate Cemetery

The blue line plots the rank abundance of species in Signal Point Park. The orange line plots the rank abundance of species in the Confederate Cemetery.

The Signal Point Park community has a higher H’ value. Thus, it has higher biodiversity than the Confederate Cemetery. Both communities have a relatively moderate diversity when compared to their H_max values. My hypothesis is disproved by the data. Signal Point Park has the greater biodiversity when calculated with the class data set. When calculated with only my group’s data, however, this was not the case. The Confederate Cemetery was found to have an H’ value of 1.92, which is higher than the H’ value of 1.87 calculated for Signal Point Park. The differences in biodiversity between the two communities is minimal, and it is likely that human impact is roughly equal in both areas. Additionally, there could have been an error in recording data from either community. One group may have had a different name for the same species, causing the total species richness to be counted higher than it actually is. Lastly, when comparing the two communities in Figure 1, students can see that both communities are dominated by a few species. Overall, though, they have highly similar species evenness