Well, we’re finally here: it’s the final post of the semester. We’ve had some good times and some bad times- i’m looking at you Excel. Besides my constant struggle with Excel, there have been a lot of important concepts discussed so far. By far one of the most important concepts to understand for studying Ecology is biodiversity. Biodiversity is the variety of life in a habitat or ecosystem. It’s important to understand how biodiversity works and how the loss of diversity in a ecosystem can make an impact. When out in the field, there are many ways for Ecologists to measure biodiversity. One of the easiest ways to sample diversity is by using a transect method. Transects are when there are samples taken from a straight line. In order to keep samples free from bias and to make sure it is a true random sample, you flip a coin to see what side of the transect you take your measurement from. There are two different kinds of transect methods: line and belt. In a line transect there is a piece of string extending from the transect, with every organism touching the string being counted. A belt transect quadrats are laid along the transect and the organisms inside the quadrat are counted. This week we went into the field to try our hands at the line transect method. The class was split up into groups of four. Each group was given a piece of string and a transect tape.

(Gif Credit: Giphy)

The setting of our field study was a hiking trail about ten minutes off the UTC campus. The trail we went to was in a fragmented habitat. One side of the trail was more natural- we predict it was the original forest before it was fragmented. Further down the trail you meet part of the Tennessee River and you get into more swampy territory. On the opposite side of the more natural entrance to the trail, it seemed to have different species of trees. This was most likely due to the fact that there is a golf course on the side. Between the trails is a parking lot and an empty field. Fragmentation like that can alter the abundance of species and the types of species present in an environment.

The weather was barely above freezing and I accidentally locked my snacks in the van, but nevertheless we went on the hiking trail with our transect tape and string in tow.  Each group walked the trail and decided how far to go into the trail to go before laying out the transect. My group walked just into the entrance and past a small creek. Using our 50 meter transect tape, one of us (me) held the end of the tape while another member of our group walked in a straight line through the trees with the other end of the tape. Once we reached the end of the fifty meters (165 feet) we began to calculate the tree diversity. With the string we were given and a coin, we started to measure. Every 1.5 meters (five feet) we flipped a coin to determine what side of the transect to sample (with heads being right and tails being left). Once we determined the side of the transect, we extended the string and counted the different tree species the line touched at each interval. These steps were repeated until we reached the end of the transect tape. Once our group collected our data, we were instructed to create a scatterplot to identify the pattern (if any) in the tree diversity as we got farther away from the path. My scatterplot is shown below:

As you can see in the graph, the number of species we found stayed mostly consistent as we got deeper into the trees and away from the trail. The patterns shows a weak effect of tree species as  At 165 meters we did find five different species and that was the highest number of different species at any of the areas we measured. Tree species were determined by leaf shape- broad or narrow. Because it is late fall, most of the trees have lost their leaves so we had to guess based on their shape. After running a Regression Analysis with the same data, we were left with a p-value of 0.00553. The p-value is lower than the alpha level of 0.05, which means the data is not statistically significant- or there is not a relationship between the number of trees present and how far you go down the line transect.

As discussed earlier in this post, a major threat to species diversity is habitat fragmentation. Understanding habitat fragmentation is also very important when studying forestry management, according to C.F.E Bacles, et al. Bacles also points how that it is crucial in understanding how genetic variation. This week’s science article takes a look at some of the impact fragmentation can have on gene variation and flow. Researchers from the University of Tsukuba highlight the conservation of river floodplain ecosystems as the greatest challenge of the 21st Century. This is because floodplain habitats provide habitat for wildlife, and act as erosion and flood control. Fragmentation of these habitats prevents gene flow among organisms. Fragmentation can also prohibit the pollination of plant species- such is the case with the Acer miyabei (an endangered maple native to Japan). The conclusion that the researchers came to regarding gene flow in A. miyabei. was that they are important for researching genetic diversity, therefore their habitats need to be preserved.

References:

Bacles, C. F. E., et al. “Genetic Effects of Chronic Habitat Fragmentation on Tree Species: The Case of Sorbus Aucuparia in a Deforested Scottish Landscape.” Molecular Ecology, vol. 13, no. 3, Blackwell Science Ltd, Mar. 2004, pp. 573–84, doi:10.1046/j.1365-294X.2004.02093.x.

“Gene Flow Halted By Fragmented Forests.” Asian Scientist Magazine | Science, Technology and Medical News Updates from Asia, 12 Mar. 2018, http://www.asianscientist.com/2018/03/in-the-lab/gene-flow-endangered-maple/.

For those of us who have cats that we let outside, we often wonder where they go when we can’t see them. I know I have personally imagined strapping a camera onto my cat and watching his adventures throughout the day. Scientists in the United States, Australia, and New Zealand have teamed up with civilians and their cats to do just that. Scientists reach out to citizens with house cats that go outside. The cats had a camera and GPS device strapped onto them for about a week at a time and were let outside as they normally would. The camera gave the scientists and pet owners a cat eye view of their pet’s domain and the GPS let them see exactly where the cats went. The regular area in which animals-such as cats- travel to find food, shelter, mates, etc. is referred to as a home range. The purpose of tracking the home range of domestic cats is for ecologists to be able to better understand where cats go when they go outside and to also study how they impact the ecosystem. Studies have show that domesticated cats have a negative impact on biodiversity in their environment, especially the levels of small mammals and birds. Ashley Gramza in “Understanding Public Perceptions of Risk Regarding Outdoor Pet Cats to Inform Conservation Action.” discusses some of the ways that cats can impact biodiversity. According to Gramza, behaviors of the owner with the cat and the cat’s general attitude will impact how they hunt and interact with the outside environment when they are left outside to roam. In the article it also supports the idea that the more pet owners and other citizens that are educated on cat behavior, the better we can help keep cats safe when they roam outside and put plans into place to conserve the wildlife.

(Gif Credit: Giphy)

This week in lab we used data compiled from the cats in New Zealand, Australia, and the United States to calculate the average home ranges per country. Using the data from movebank.org for Felis catus, we each picked 15 random cats per country to calculate home range. (Personally, I picked the cats based on how cute their names were but my fellow students all had their own criteria for cat selection.) Once we picked the cat we copied the data from movebank to Google Earth to be able to better see the home ranges of the cats and to be able to plot the area of the home range. Using tools on Google Earth to get the area, we copied the area into Earth Point to get the area in hectare. The data I found is shown below:

Many of us are familiar with owls. As children (if you were anything like me) we were fascinated by the creatures that could almost turn their heads completely around. Fans of Harry Potter probably imagined what it would be like to have an owl of your own to fetch their mail for them (No? Just me?). Owls aren’t just an important part of pop culture and animals often running wild in a child’s imagination, they are very important animals in the study of food webs. Food webs are interlocking systems of independent food chains- in simpler terms, food webs are a diagram of who-eats who. Ecologists study food webs in order to better understand how energy moves through an ecosystem and changes over time. The reason owls are particularly helpful to the study of food webs is because they will regurgitate pellets of the undigestible parts of their prey; hair, feathers, bones, teeth, etc. (Hedwig doesn’t look so cute now, does she?)

(Credit: Glacialpool via Tenor)

In this week’s lab- as you may have guessed by now- we analyzed owl pellets of owls from different regions. Each student was given one pellet, one probe, a magnifying glass, tweezers, a sheet with different prey species and the region of the United States they’re found in, and a sheet with the bones common prey found in owl pellets. Using the probe and tweezers we gently broke apart the pellets and separated the bones and other things we found inside the pellet. As we did so we were tasked with keep tallying which bones we found. After the pellet was completely dissected we used the bones we found to guess what type of prey was found in the pellet. My data is shown below:

As shown above you can see that I found two skulls, five jaw pieces, two scapulas, etc. The skulls that I found were mostly intact and with the jaw pieces, I took the educated guess that the animals my owl had eaten were both Voles. I was able to make this guess based on the size and shape of the teeth. According to the sheet with the different regions and species on it I concluded that if the animals were indeed voles, then the owl was most likely from the Northwestern region of the United States. Each student then put their data into the class data set. Overall the pellets analyzed in class had a majority rodents, and rib bones.

The animals listed on the legend of the  Prey chart, but are not show in the pie chart has 0 specimen found in the owl pellets for our class data.

Based on the graphs the owls fed the most on rodents and moles, shrews and birds were almost even in the amount of prey consumed. Insects, amphibians, and reptiles seem to be missing from the data. This leads me to believe that all of the owls sampled were from temperate regions. I was surprised that the latter was missing from the diet of the owls. I can also assume that my owl comes from a temperate and productive area. Ecologists have found that owls are indiscriminate predators- meaning that they change up their diets based on the environmental conditions. In temperate areas with high productivity they eat small mammals and in hot, dry, areas with low productivity they eat reptiles, insects, and amphibians. Based on this, I concluded my owl was from a temperate area because the animals were both small mammals. To confirm my guess further I would need the exact area in which my owl was from. 

Based on my data and the data above, if an owl were to form one pellet each day and my pellet containing two animals was normal the owl would eat around fourteen animals a week, four hundred-twenty animals a month, and around five thousand animals a year. Keeping that and my analysis in mind, owls would be a helpful animal to farmers because they would keep rodents and other pest species off of farm land and away from crops.

We were also tasked with comparing our data (2018) with last year’s data (2017). The graphs below show the 2017 data:

Like with the data from 2018, the 2017 data shows that there were mostly ribs found in the pellets.

With the prey type, again rodents were the most commonly found in the pellets. However, the ratio of shrew found in the pellets was higher and the bird and mole were smaller in 2017 than 2018. Insects, amphibians, and reptiles were also absent in 2017.

As always we were given an article to read and reflect on that relates to the lab topic of the week. This week the article was Spiders Eat Astronomical Numbers of Insects by Melanie Lehnert. According to the article, spiders eat between 400 and 800 million tons of insects annually. We mostly find spiders in grasslands, shrublands, and forests as well as crop land, urban areas, tundras and deserts. This is significant to their role in the food web because they can be found in a diverse number of areas so they could have a diverse diet and role in food webs. The amount spiders eat isn’t even among all the ecosystems they are found in. Around 95% of prey killed by spiders is done in the forests and grasslands. This would make sense because those are two areas that are more closely distributed and generally have greater biodiversity than urban and crop lands. If spiders were to disappear from these food webs one might expect that the amount of insects in the area would increase and a new top predator would emerge. With more insects there comes more herbivory (the consumption of plants or plant materials), which means plants would suffer. Research by Roman Bucher, et al., states that “Predators can indirectly enhance plant performance via herbivore suppression”. This means that predators- in our case spiders- help plant species thrive when they reduce the amount of insects that eat plants. With the evidence presented by Bucher and previous knowledge of science and ecology, I can safely predict that less spiders would mean more insects and a negative impact on the plant species in the food web.

Prior to completing this lab, I didn’t know how important spiders and owls were in regards to food webs and the study of food webs. With owls, because of their diets we can tell the productivity of a region simply based on what type of animals the owl eats. Spiders are very important to their respective food webs. Not only do they eat an insane amount of insects annually, they also serve an important role in plant health. I’ll definitely be sure to think twice before stepping on any spider I see in my house from now on.

References:

Bucher, Roman, et al. “Risk of Spider Predation Alters Food Web Structure and Reduces Local Herbivory in the Field.” Oecologia, vol. 178, no. 2, June 2015, pp. 571–77, doi:10.1007/s00442-015-3226-5.

Lehnert, Melanie. “Spiders Eat Astronomical Numbers of Insects.” Scienmag: Latest Science and Health News, 14 Mar. 2017, scienmag.com/spiders-eat-astronomical-numbers-of-insects/.

Natural selection is a mechanism by which individuals better adapted to their environment tend to survive and reproduce. Natural selection impacts variation in populations. In some cases, variation can be adaptive- which means it reflects the result natural selection has on a population. Some species, such as Oak trees, are very adaptable species and show much variation. Variation is compared in two ways: within-individual variation and between-individual variation. Within-individual variation states that individuals with the same phenotype will eventually show differences in survivorship and reproduction based on chance encounters. Between-individual selection states that individuals with different phenotypes will experience differences in reproduction and survivability. In leaves there are a couple of things than influence leaf size and shape. The first is that leaves must capture sunlight for photosynthesis while at the same time minimizing their heat intake. The second is they must take in carbon dioxide without losing too much water. Because of these reasons we might expect leaves from inner branches to be wider in order to maximize surface area to capture sunlight and you may expect leaves from the outer branches to be narrower to reduce sunlight exposure. This past week we looked at two different species of oak trees (red oak and white oak) to test if sunlight exposure impacted the size of the leaves. I hypothesized that leaves from the inner branches of oak trees would be smaller in size than the leaves of outer branches because inner branches receive less sunlight. My hypothesis was different than what you might expect from leaves because of my personal observations.

Each student was tasked with gathering ten leaves from the inner and ten leaves from the outer branches of either a red or white oak tree. In total in our lab we had six people (including myself) gather leaves from white oaks and twelve students gathered leaves from red oaks. In total we had around 360 leaves total in our class data set. Once we were in lab, each student was handed a handful of 1-cm grid paper to be able to calculate the surface area of their leaves. We laid one leaf at a time on the grid paper and traced the outline with a pencil and then removed the leaf to be able to count the grid squares. In order to count the leaf surface area we counted the squares inside the leaf only if they were fully inside the outline or more than 50% in the outline and we did not count the stem area. We repeated the steps with all twenty of our leaves and then added our information into the class data set.

(Photo credit: Pearson)

After we had calculated the surface areas for our leaves, we added the information into the class data set making sure to specify which species (red or white) we had as well as if the leaves were from the inner or outer branches. We kept this data separate in order to perform a student t-test. My data is shown below. Based on my data collected my hypothesis was proven true- the leaves from the inner branches of a white oak tree were overall smaller than the leaves from the outer branches.

Again, once everyone had collected their own data it was added to the class data set. With the class data set we were able to compare the average size of the leaves from both the inner and outer branches as well as perform a student t-test to compare the red and white oak leaves.

With the white oak tree, the leaves from the outer branches had on average a larger surface area than the leaves from the inner branches.

With the red oak trees, again the outer branches had an larger surface area average than the inner branches. There is smaller difference in overall surface area between inner and outer branches with the red oak. Overall the white oak leaves were larger than the red oak leaves. After performing the student t-test we had a value of 0.836. When t-tests you must look at the alpha (which is always 0.05). If p (the value you get from a t-test) is greater than 0.05 it means your data is significant. To put it into simpler terms and to relate it to our p value. Because our p value is 0.836, and that is greater than 0.05, it means that yes there is a significant difference in the size of inner leaves and outer leaves of oak trees. Below is a picture of my largest leaf from the outer branch (168 cm2) compared to my smallest leaf from the inner branch (17 cm2).

I believe scientists study within-individual variation because it can give insight as to what factors can impact variation and natural selection the most. Earlier I described within-individual variation as when individuals with the same phenotype will experience variation in reproduction and survivability due to chance encounters. According to Salaheddine Essaghi variation in leaf size can be impacted by water content in leaves and then can be used to calculate the leaf shrinkage in other plants. Farmers and crop scientists may want to know how much variation there is in leaf surface area because in certain climates, plants with certain leaf surface areas could produce a greater yield based on how much sunlight they can consume during daylight hours, and based on the amount of water lost as a result of photosynthesis.

As well as our lab activities this week, we were tasked with reading In ‘Science’: Wildflowers combat climate change with diversity, an article by Adrienne Berard. Josh Puzey, the co-author of a study of wildflowers in the Iron Mountain range in Oregon. In the study he poses a question: how does genetic variation persist in the face of natural selection? This question is answered in the study. Puzey found that phenotypes (physical traits) and the fitness of individuals are linked to the genotype of an individual. The researchers found this link because they collected seeds from 187 monkey flowers and analyzed the seeds and how they grew. They were able to detect the exact region in the gene sequence that control the flowering time and size. With this research, ecologists could use it to predict how ecological disturbances and variation can impact the fitness and flowering time of the wildflowers.

References:

Berard, Adrienne. “In ‘Science’: Wildflowers Combat Climate Change with Diversity.” William and Mary, http://www.wm.edu/news/stories/2018/in-science-wildflowers-combat-climate-change-with-diversity.php.

Essaghi, Salaheddine, et al. “Leaf Shrinkage: a Predictive Indicator of the Potential Variation of the Surface Area-to-Volume Ratio According to the Leaf Moisture Content.” SpringerPlus, vol. 5, no. 1, Springer International Publishing, Dec. 2016, pp. 1–12, doi:10.1186/s40064-016-2900-3.

Many people are probably familiar with the idea of foraging. Whether you see a squirrel rummaging around a park or a hungry college student scanning the local dining hall for something edible, most of us have witnessed it. An optimal forager is defined as an animal that maximizes its net energy gain while foraging. In the wild it is important than animals maximize their net energy gain because it is hypothesized that natural selection acts on animals based on how they maximize it. It is up to each individual forager as to how they forage. The forager could leave the patch when there are no more resources left, or they could stay for a short while and then move on to the next patch. In this week’s lab that’s exactly what we did- maximized our net energy gain while “foraging” on Chamberlain Field on campus. To forage on our college campus we had buckets filled with dried rice and beans. The rice represented patches where we would find prey and the beans represented the prey we were looking for. Each bucket had a different prey density (amount of beans per bucket).

In our lab we split into groups of three. Within each group we rotated three positions: forager, timer, and recorder . The forager was given a styrofoam cup and went to three random “patches” (aka buckets of rice and beans). Each patch was the same distance away from each other. At each patch the forager had a set of rules to follow: you could only use one hand to forage, do not run to the beans, when you are done at each patch mix the beans back into the rice for the next forager. When the forager found a bean, they put it into their styrofoam cup and swirled it around three times. While the forager was looking for the beans, the timer was keeping track of the time the forager arrived at the patch, at what time they found each bean, and when they left the patch. The timer didn’t stop until all three patches had been visited. The recorder wrote down the times that the timer called out when the forager arrived, left, and found beans. Each member of the group took turns being the forager, recorder, and timer.

After every member of the group had a turn, we headed back inside to work on graphing the data we collected. The first graph shown below:

shows the amount of beans found in relation to patch density. Two of the patches I visited when I was the forager had a patch density of forty beans in the patch, the third patch had a density of eighty beans. At the first patch I found a total of 14 beans out of 40. The second patch I found 25 out of 40. The last patch I found 50 out of 80.

The next graph depicts time spent in each patch.

Again, two of my three patches had the same density which is why there are two dots over the same space on the x-axis. At the first patch I spent 39 seconds, the second patch 63 seconds, and at the last patch I spent 103 seconds.

In the next graph, it shows the capture rate.

This show the percent of prey captured in relation to prey density. At the first patch I had a 35% capture rate. At the second patch I had a 38% capture rate. In the last patch I had a 49% capture rate.

In the next graph it shows the Giving Up Time (GUT) or the time in which it took me to leave each patch.

In the patches with a density fo 40, my GUT was the same. To maximize energy gain each forager is supposed to leave the patch when you’ve reached the same optimal energy intake as the other patches. In the first two patches the GUT matched the Marginal Value Theorem, which is the model associated with GUT analysis.

The final graph depicts the Cumulative Gain Curve for all three patches.

The Cumulative Gain Curve is used to show the rate of energy gain between all the patches visited. Patch 1 had a density of 40 and a capture rate of 35%, Patch 2 had a density of 40 and a capture rate of 38%, and Patch 3 had a density of 40 and a capture rate of 49%.

Animals may behave in a similar fashion to the humans in this foraging experiment when there is a lot of competition in the area. In our lab, we were timing ourselves but we had to share patches with other groups so there was a pressure to not take too long per patch. If there is a case of high competition, animals may not have as many resources available or adequate time to forager, so you may see similar data in that situation.

Optimal foraging strategies are important because they help ecologists understand how organisms are selected against with natural selection. An example- besides college students in the dining hall- for how humans exhibit optimal foraging strategies would be in memories. According to Thomas Hills, “We found evidence for local structure (i.e., patches) in memory search and patch depletion preceding dynamic local-to-global transitions between patches”. Hills research shows that humans “forage” for memories with optimal memory recovery in a similar fashion that animals forage for food.

Fast food is becoming a problem for the weight of adults and children in the United States. According to Henry Fountain, it is now a problem for black bears as well. The bears that live near urban and residential areas are less active than bears in the wild. Instead of foraging for more natural food sources, they often forage through garbage. The lack of activity and their diet makes them heavier than bears in a more natural setting. I believe the bears have changed their foraging habits because it requires less energy for them to dig through nearby garbages than to venture out farther away from towns and cities to forage there. Some solutions to encourage bears to stop foraging through garbage and go back to a better, more natural diet, would be to make it harder for them to get into garbage cans and dumpsters and for land developers to work harder to make more green spaces and leave more land untouched so that animals can live in their natural habitat.

References:

Fountain, Henry. “Fast-Food Nation Is Taking Its Toll on Black Bears, Too.” The New York Times, The New York Times, 25 Nov. 2003, http://www.nytimes.com/2003/11/25/science/fast-food-nation-is-taking-its-toll-on-black-bears-too.html.

Hills, Thomas T., et al. “Optimal Foraging in Semantic Memory.” Psychological Review, vol. 119, no. 2, American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications, pp. 431–40, doi:10.1037/a0027373.

In last week’s lab we began our observations of Dallis grass (Paspalum dilatatum) dispersion in the local Confederate cemetery next to the UTC campus. Five groups of four used the quadrat method fifteen times each to contribute to a class data set of the dispersion patterns. Last week each group complied their own data and put it into a master data set of the lab. This week we took the class data set analyzed all five groups together to figure out the dispersion pattern of Dallis grass.

To start we opened the dreaded Excel to get to the master data set for the class. The data set included the group names, the number of quadrats sampled (15 in each group), the number of individuals found in each quadrat, and the year in which the data was observed. After we had the class set of data we did several tests which each group’s data. First we found the mean (average) number of individuals in the quadrats and after we had the mean calculated we preformed a Poisson Distribution for the class data set and our own group’s data set. A Poisson distribution is a discrete frequency distribution that gives the probability of a number of independent events occurring in a fixed time. This distribution used with our data helps us see the number of expected individuals per quadrat, assuming the individuals are in a random distribution pattern. If the mean equals the variance in the Poisson distribution, the pattern is random and the hypothesis is null. Based on this knowledge we can also figure out- if the hypothesis is not null- which dispersion pattern the population has. If a population is in a clumped pattern the mean will be less than the variance and means there are many quadrats with values far from the mean. If there is a uniform dispersion pattern the mean is greater than the variance and most quadrats have values close to the mean. 

With this in mind and the Poisson calculation for our group done we could produce a bar graph for our group’s expected and observed values. The observed value is the number of individuals in each quadrat our group physically counted and the expected is found by taking the Poisson value and multiplying it by the number of quadrats. My group in particular had several outliers within our own data and compared to the class data set. Outliers can be accounted for because of possible counting error or variance in ways of counting. While all groups used the quadrat method and only counted plants fully or more than 50% of the way inside the quadrat, it was up to each group to decide which individuals of Dallis grass counted in the sample. My group chose full sprouted individuals as well as patches of young Dallis grass that hadn’t fully sprouted- other groups may have counted differently.  As shown in the graphs below, the observed values are vastly different from the expected value. After running the Poisson distribution analysis and graphing our observed and expected values we then had to run a chi-square analysis to determine if we can reject our null hypothesis. The expected has a more even spread of data points while the observed is all over the place with an extreme outlier at 264 individuals in one quadrat.

After the calculations for the individual groups were done we moved on to perform the same calculations on the entire master class data set. As with our group’s data you see a wide variation between the expected and observed values.

As shown in the observed value graph above, most of the data falls between 0 and 55 individuals per quadrat. However there are several outliers of data ranging from around 64 to 264 individuals per quadrat. The mean of the class data was around 31 (31.32 unrounded) individuals per quadrat. The majority of the data is not clumped around the mean and there is a very long tail. Because of these reasons the data shows a clumped dispersion pattern.

My group’s data shows that our samples of Dallis grass showed a random dispersion pattern based on the fact that our data had most of the points away from our mean of 62 (rounded up from 61.7). The random sample is not something that would be entirely unexpected. While I had predicted last week that the Dallis grass in the cemetery would be in a clumped pattern (as the class data supports), Dallis grass is dispersed naturally by wind, humans, animals, and water. The means by which the grass is usually dispersed could account for a random dispersion. The environment in which the Dallis grass is in could impact the dispersion pattern as well. The cemetery we sampled is home to a number of very large trees and several monuments that block the sun in some spaces and compete for root space, which could also account for the clumped pattern the group data shows.

While squirrels, raccoons, and household pets might be the culprits behind some dispersion of Dallis grass across UTC, other species of animals in various parts of the world also have a hand in plant dispersion. For example, in India, rodents impact how tropical trees are dispersed. Nandini Velho, et al., states that, ” Biotic dispersal may be vital for species that suffer density-dependent mortality factors under parent trees.” Meaning that animal interference can be very important to the survival of plants that are in dense populations. African savanna elephants are another example of animals who help with plant dispersion. The elephants have been recorded to carry the seeds of fruit anywhere from twenty to up to sixty-five kilometers away from the parent tree. The large range of the elephants in Africa could make a random dispersion pattern since there is such a large gap between their usual range.

References:

Stokstad, Erik. “’This Is Amazing!’ African Elephants May Transport Seeds Farther than Any Other Land Animal.” Science | AAAS, 8 Dec. 2017, http://www.sciencemag.org/news/2017/04/amazing-african-elephants-may-transport-seeds-farther-any-other-land-animal.

Velho, Nandini, et al. “Rodent Seed Predation: Effects on Seed Survival, Recruitment, Abundance, and Dispersion of Bird-Dispersed Tropical Trees.” Oecologia, vol. 169, no. 4, Springer-Verlag, pp. 995–1004, doi:10.1007/s00442-012-2252-9.

Understanding how organisms in a population disperse themselves is a crucial aspect of population ecology. Dispersion is defined as how individuals in a population are arranged in space. Dispersion, along with density and distribution, helps determine spatial structure. Many different factors can affect dispersion patterns in populations, some of them include: predation, sunlight, weather, competition, Within dispersion there are three typical patterns we see. Those patterns are described as clumped, uniform, and random. Clumped patterns are caused when resources are limited or there are herds or mating patterns within a population. Uniform patterns can come from an abundance of resources or even root spacing in plants. Random patterns are rare in nature because they form when biotic (living) and abiotic (nonliving) factors in an environment do not effect the dispersion of organisms. Dispersion patterns in nature aren’t set in stone, they can change as populations do or environmental conditions do.

This past week in ecology lab we looked at plant dispersion on our local campus with Dallisgrass, Paspalum dilatatum. (aka the tall grass that looks like it has ants on it) Dallisgrass is common in the mid-Atlantic and Southeastern regions of the United States, according to Matthew T. Elmore in the article, ‘Seasonal Application Timings Affect Dallisgrass (Paspalum Dilatatum) Control in Tall Fescue”.  It is a grass that does well in warm weather; it sprouts in March and stays until around the first frost of the next winter. It is a highly adapted grass and does well in areas with high salinity which is why you can often find it sprouted near or between concrete. Many consider Dallisgrass to be It has been unseasonably warm and rainy in Chattanooga for the last few weeks so there are large patches of this grass found all throughout the city and on our campus.

(A picture of Dallisgrass by Dr. Paul Baumann)

Our class was split into groups of four for our trip out into the field. The field for us was the local Confederate Cemetery (I always like to tell freshman and non-STEM majors that if you see students running around the cemetery… it’s probably an ecology/environmental science major!). To determine the individual frequency in the field we used the Quadrat Method. The quadrat method uses randomly selected quadrats or plots of an area to count individuals in that area. We used 1 square meter squares as our quadrat. To be sure the plots we laid the quadrat on were truly random, we used a random number table. Each group designated someone to be the pacer and that person pointed to random numbers on the table to guide us to our next plot. Once our pacer had walked to the next plot we laid out quadrat down and our group counted each individual Dallisgrass plant in the quadrat. We repeated this step fifteen times. The table below shows the data my group collected from our fifteen quadrats.

To interpret our data we calculated the density of the population. To do this we divided the number of individuals of the species in each quadrat by the number of quadrats times the quadrat. So we took the total number of species- in our case 926 individuals- by fifteen times 1m2. This gave us an average of 61.7 individuals per unit area. The density of the Dallisgrass in the cemetery we found could be attributed to several factors. As I mentioned in a previous paragraph, Dallisgrass does well in warmer weather. For the last few weeks the local temperature has been around the 80s in Fahrenheit, so that could account for the large amount of grass we found. Another thing that could account for the high amounts of the grass are the large rainstorm we got. We had around an entire week of rain following Hurricane Florence which gave many plants in the area a much needed drink. The rain plus the warm temperatures could account for the density of the Dallisgrass. Based on our data and observations I believe the grass forms a clumped distribution pattern. I believe this is due to the fact that it is in a cemetery so there are things in the ground impacting root spacing, as well as the fact that there are many large trees in the cemetery and would compete for resources and space with the smaller grass.

For the next part of this lab we will compile the data from each group in our lab. From there we will produce a chi-square analysis to further calculate the density and hopefully to get a more accurate reading of the density and dispersion of the Dallisgrass in the area.

References:

Elmore, Matthew T., et al. “Seasonal Application Timings Affect Dallisgrass (Paspalum Dilatatum) Control in Tall Fescue.” Weed Technology, vol. 27, no. 3, 2013, pp. 557-564. ProQuest, https://proxy.lib.utc.edu/login?url=https://search.proquest.com/docview/1441937897?accountid=14767.

Last week we looked at the urban environment. (If you missed Ant Picnic pt.1, you can catch up here) First we took a look at a large-scale study in New York City where researchers set baits out to see how urban environments impacted the diet of ants. On my college campus, my Ecology lab recreated the experiment with a much smaller scale and time-frame. Five different teams set all six baits (cookie, oil, amino acid, salt, sugar, water) out at three different sites around the UTC campus. After the baits were left out for about an hour, the five teams then collected their baits and the ants to be counted. This past lab each team counted the ants found on each of their bait samples and added it to a class data set. This experiment has been preformed on the UTC campus for three years now- 2018 being the third year. Because of that, not only were we able to compare class data between groups, but we were able to compare the data from students from previous years.

From the class data shown from the years of 2016 to 2018, on average the ants preferred the cookies, sugar, and oil baits to the others. The year 2016 was very similar to that of 2018 in the amount of ants on certain baits compared to others. 2017 differs from both 2016 and 2018. One thing that may account for this change in between 2016 and 2018 is the fact that Chattanooga, TN (the location of our campus and study) was in a major drought. Due to the hotter temperatures and drier environment, the ants may have had a shift in nutritional needs. The ants being able to adapt their diet to their environment may be an advantage to them. Whether the change is brought on by drought, temperature, or an urban landscape, being able to change and adapt their diet to whatever environmental factors would make them more competitive among organisms and ensure that their population would survive what others could not. The ability of ants to adapt easily may be a reason you often see them in urban areas. After running an ANOVA analysis in excel with the data from my class, I was left with a P value of 0.056. This means that there is very little statistical difference between groups of ants and their preferences.

In the graph above we see a visual of the temperatures of the studies between 2016 to 2018 the ants were most active between 25 and3 degrees Celcius. We do not see as much ant activity in the higher temperatures. There are a large number represented by 0. This is because for most groups who participated in the bait study, at least one site had very few ants or no ants at a bait site. When we did the experiment, we were instructed to pick two mostly green spaces (such as the cemetery next to our lab building or the green space near the campus library) and then two mostly pavement surfaces (such as parking lot or a side walk). For my group one of our pavement sites (or an impervious surface site) was a sidewalk near a trash can and the other right next to a parking garage on campus. The sidewalk area had 45% impervious surface and the parking garage had 84% impervious surface. For my group the parking garage site had 0 ants on all six of our baits. On the other hand, the sidewalk area had the most ants of any of our other sample sites- even the grassy areas we put our bait out in. My group’s data differs from the class norm, and that is illustrated in the graph below.

Based on the results of my class data and the data from years past, I have a few more questions that could possibly prompt further research. First I wonder how the ants would react to the opposite of what happened in 2017- a wet spell as opposed to a dry spell. Because the data from 2016 and 2018 were more similar than that of 2017, it seems that heat or drought have an effect on what nutrients ants need. Another question is what temperature has to specifically do with feeding patterns. How would ants react to unusual cold patterns versus unusual heat patterns? Lastly I wonder how the preference ants had for the different baits in our study would differ from other organisms preferences- such as that of a rat or squirrel or bird. As discussed in Ant Picnic Pt.1, the vast urbanization of our planet is a relatively new phenomena and we have much to learn from it. Marina Alberti, et al. stated “Our central paradigm for urban ecology is that cities are emergent phenomena of local-scale, dynamic interactions among socioeconomic and biophysical forces. These complex interactions give rise to a distinctive ecology and to distinctive ecological forcing functions,” in Integrating Humans into Ecology: Opportunities and Challenges for Studying Urban Ecosystems. This quote is another that states how imperative it is to understand these new and complex systems. A better understanding of these systems may help answer my questions.

This past week marks the second week in lab we have dealt with statistical analysis. Statistics are a huge part of Ecology so it is crucial for ecology students (and also the professionals) to have an understanding of statistical analysis and also be able to visualize and create visuals for your data. Personally, as I have continued to learn these skills I have found that being able to actually visualize my data points in the form of a graph helps me better understand the trends and significance of my data. By understanding the data I am able to understand more fully exactly how environmental factors can influence an organism. The main challenge I have faced a science student learning how to analyze and visualize my data is this beast called Excel… Excel is unforgiving and very confusing. As I mentioned earlier learning how to analyze and visualize data is crucial for any scientist, however Excel takes pleasure in making that as difficult as possible. My advice to any freshman, student, teacher, seasoned scientist, whomever, would be to beware Excel at all costs! … I’m just kidding… Okay, maybe I’m not but that’s okay. More serious advice I have to anyone new to the scientific field would be: to check, double check, and then triple check your data sets, make sure you truly understand your data and what each variable means in relation to the thing you’re studying, and if all else fails go to the library and seek help with Excel.

(Photo credit: imgflip.com)

References:

Alberti, et al. “Integrating Humans into Ecology: Opportunities and Challenges for Studying Urban Ecosystems | BioScience | Oxford Academic.” OUP Academic, Oxford University Press, 1 Dec. 2003, academic.oup.com/bioscience/article/53/12/1169/301939.

As our modern society expands more and more into Urban environments, there becomes a greater need for scientists- specifically ecologists- to understand how increased urbanization impacts other organisms. According to research, over 50% of the world population lives in an urban environment. The larger the amount of urban area- categorized by an area with high human population density and large amounts of buildings, roads, and other impermeable surfaces- the more surrounding species have to adapt to the changed environment. For some species, adaptation means moving into our urban landscapes and facing a change of diet. There is still little known about how exactly urban areas impact the amount or quality of food available. Many people are familiar with ants invading your picnic or birds pressuring you for a bite of your sandwich. One afternoon on the UTC campus I had a run in with one of our notorious squirrels where one jumped into a trashcan that I had thrown away a leftover piece of a granola bar. We may be able to see larger animals such as rats or squirrels interact with  our waste more often, but according to the New York Times millipedes, mites, spiders, ants and other bugs commonly compete with larger organisms.

(GIF Credit: Imgur)

In the summer of 2013 researchers in New York did a study to see what types of food the urban animals preferred. To start, the researchers got a variety of junk foods to use as bait: potato chips, cookies, and hot dogs. They then carefully weighed the different baits and placed them around their test sites in a container that kept larger animals like squirrels and rats out of them, but allowed insects to go in freely. After 24 hours the researchers returned to collect their bait samples. They found that the cookies and potato chips were consumed more than the hot dogs. For my Ecology lab we did a much smaller-scaled version of this study. We had six different food samples as bait : an oil, an amino acid, salt, sugar, water, and cookies. The reason these materials were chosen as our bait samples is because protein (amino acids), fats, and sugar are essential nutrients in wild ant populations. Depending on the time of day, season, location, etc. ants need these nutrients in different amounts. Like the researchers in New York City, placed our baits in different areas. Ours was much smaller of an area- the UTC campus to be exact. Instead of cages we used cotton balls for all of the materials except for the cookies, the cookies were placed in a small tube that the ants could get into easily but our pesky UTC squirrels could not get in. We left our traps for around an hour, instead of 24 hours, and then collected our samples and the ants. The differences in our studies could explain why certain samples of food were untouched or still left over when the New York study found most of the food was gone from their sites.

The goal of our study was to track the changes in ant food preferences across urban landscapes. This past week we only collected data from our individual groups of 4-5 students. In this upcoming week we will be combining class-wide data to see how each group’s compares to the other.

In general, cities may influence competition between species by limiting the amount of natural resources available to different species and make them compete for the scraps of food humans dispose of. It is commonly believed that these Urban areas have less biodiversity, and the food competition could be a factor of that. Still, some recent studies show that urban areas help increase biodiversity. Ants seem to be one of the most well adapted species in urban environments. I think that rats and squirrels are also well adapted to cities. Organisms that would not be well adapted most likely are larger, more niche organisms. The larger the organism the more food for energy is required, but in urban environments space and food can be limited and would limit larger organisms diets. Us as humans could help improve ecosystem functions in cities by having more green spaces and making a higher effort to pick up trash and reduce our waste. As Jari Niemela said, “In the face of these changes an important set of questions relates to how to plan and manage urban green spaces for the benefit of both urban dwellers and biodiversity.” By “changes” Niemela means that the world is in a period of unprecedented change in technology and the environment and it is important for us to begin to put more time and resources into studying how these changes effect ecology and how we can help them with urban green spaces.

(Photo Credit: Lauren Nichols/YourWildlife.org}

To get more insight on urban ecology I looked at another ecology blog and their studies of urban ecology. Diane Pataki of Frontiers in Ecology and Evolution wrote an article of the Grand Challenges in Urban Ecology, in this study she says that one of the major challenges of urban ecology is developing “the science of the built environment”. Meaning that we have to develop how we study urban environments because they are still relatively new and understudied. I found this article interesting because it highlights what challenges are ahead for urban ecologists and what progress they’ve made so far. Jeremy Lundholm, also of Frontiers, speaks more of green infrastructure. Green infrastructure is traditionally the natural ecosystems that surround and are in urban areas. Lundholm says that the definition has changed to the man-made green spaces in urban areas and how they mimic natural functions. Based on these two readings I feel as though it is important to take away that even in urban environments it is imperative that we make sure to include green spaces in our new environment so that other species can occupy those spaces.

References:

Lundholm, Jeremy T. “The Ecology and Evolution of Constructed Ecosystems as Green Infrastructure.” Frontiers, Frontiers, 25 Aug. 2015, http://www.frontiersin.org/articles/10.3389/fevo.2015.00106/full.

Niemela , Jari. “Ecology of Urban Green Spaces: The Way Forward in Answering Major Research Questions.” NeuroImage, Academic Press, 5 Mar. 2014, http://www.sciencedirect.com/science/article/pii/S0169204614000413.

Pataki, Diane E. “Grand Challenges in Urban Ecology.” Frontiers, Frontiers, 26 May 2015, http://www.frontiersin.org/articles/10.3389/fevo.2015.00057/full.