Having a large amount of genetic diversity in any population means that if there is an ecological disturbance the population may have a greater survival rate compared to one that had few alleles present. Looking at genetic diversity and the loss of it is an important concept in ecology because it can help ecologists better understand the overall health of an ecosystem based on its genetic diversity, such as number of species present. For example, if there was a fire in a forest and there were 50 species present compared to 5, the one with the greater species richness, which is the number of species present in an environment, would probably have the most survivors. That is because with more species present the chance of more being resistant to fire increases.

Different types of habitats have different averages of species present. A desert will have less species present than a forest, but the same ecological disturbances that occur in a forest may not necessarily occur in a desert. Human disturbance is a large problem in forest habitats. A process where continuous patches of forest habitat is being broken into edges and forest patches is called habitat fragmentation. The edges of forests are getting pushed back because of human activity and large continuous forests are getting smaller. As stated before, species richness can be a large factor of resistance to disturbance in ecosystem.

In today’s lab we are looking at tree diversity using the systematic sampling method of a transect, specifically a line transect. We are measuring samples from the edge of a forest trail and off the trail and into the underbrush to see the differences in number of tree species down the line. The transect line measures 165 feet long, and every 5 feet we are stretching a string along the line and counting the number of species that are touching that line. In order to choose which side of the line we are sampling from we flip a coin: heads for the right side, tails for the left.

We wanted to know if there would be a significant different number in species present from the beginning of the line transect to the end that was deeper into the underbrush. I guessed that the farther away from the path we moved the number of species we found would increase.

Picture from class handout

Our method of using the line transect is quick, but it has its drawbacks. We are limited to just counting the species that touch the line, so when scientists use this method they can often underestimate the number of species that are actually present. Another method that is used in sampling is a belt transect. A line is places similarly, but quadrates are used along the transect intervals instead of a string. This sampling method takes a little longer, but you will have a bit better estimate of number of species present. However, because of our short class time we used a line transect in lab. We walked a few minutes into the forest and laid down our transect line.

1.The area our groups measure was in Chattanooga, Tn at Blue Blazes Trail on Moccasin Bend. The area is a few miles from down town and is in a small peninsula that is surrounded by the Tennessee river. The area is mostly flat and forested, although there are small patches of grassy fields along the 1.5 mile trail. The trail is unpaved. From what I saw there were some evidence of disturbance. People pointed our sections of grass areas along the trail, which could potentially be a result of habitat fragmentation. There were large trees as well as smaller ones and it was very cold, in the 30s when we went. It is in November so it is in the months where less sunlight reaches compared to summer months, and the temperature is very cold. It is winter, and so the density of the underbrush is much less than it is in summertime, which can cause our results of tree diversity to perhaps be lower than it would be if we did the same experiment in the spring.

Graph based on group transect line data

2. Overall, we did not see a large difference in number of tree species deeper into the forest compared to what we counted at the edge of the trail. The pattern that is calculated when graphed on a scatterplot between the x and y axis is weak based on the R^2 value of 0.28473. If that value were close to 1, then we could say that our results showed a strong pattern of having a larger tree diversity farther into the forest. However, this is not the case. Our number of species counted only was between 0 and 4. But, we can see that 4, being our maximum number of species present, was only counted at the farthest edges of our transect line deepest into the forest. The pattern mathematically calculated shows a weak pattern on number of tree species, but we can see that we counted 1 more tree species farther into the woods, it is just not enough of a difference to be important to the data analysis.

Data based off regression analysis

3. We did not just graph the data on a scatterplot, but we also did data analysis on it. We performed a regression analysis using the line transect values for the X input, and then the number of species found as the Y. The p-value is what we were interested in. When I ran the regression analysis the p-value was found to be 0.0045. This value is below 0.05 so we can say that the null hypothesis can be rejected and that there is a correlation between our transect lines and number of species found. The relationship is not due just to chance alone.

4. According to an article titled “Effects of Local Tree Diversity on Herbivore Communities Diminish with Increasing Forest Fragmentation on the Landscape Scale”, habitat fragmentation not only alters tree diversity, but also affects feeding patterns of herbivores in forest communities. Fragmentation is a problem in ecosystems and, according to the paper, “has the potential to jeopardize ecosystem functioning and forest regeneration”. Based on my results, with increasing fragmentation, I would expect tree diversity to lower. With lower diversity forest regeneration would not be as successful and I would expect it to take more time. Follow up questions I would have for future studies would be to ask what kind of tree species are more resilient to environmental disturbance and change. Are there certain kinds of trees that are still prevalent even in fragmented environments?

Answers based on the article “Gene Flow Halted by Fragmented Forests” by Asian Science Newsroom

1. The river floodplains in Japan are important because they hold a great amount of resources and are ideal habitats for many organisms. They are also important in keeping water ways clear of toxins as well as erosion prevention. It is important for the conservations of river floodplains and prevention of fragmentation because of the importance they have in the overall health of the environment.

2. Gene flow is important for monitoring endangered species because it can provide insights on how the genetics of a species differs based on older and younger trees, such as Acer miyabei have changed over time. As habitat fragmentation occurs patterns of pollination can occur, which can be studied to understand gene flow patterns. Looking at landscape patterns, such as where the environment is fragmented and comparing genetic variation in those plants between those areas, gene flow patterns can be made.

Photo of Acer miyabei leaves

3. The study found that the smaller trees had less genetic variation because they are more isolated from pollinators because of the fragmentation in their habitats. They show “fewer variant forms of the gene”, which can cause them to be more prone to suffer in environmental stress. The trees genetics are changing because of separation between different populations. This research informs that these older plants are important because of their greater genetic diversity so it is important that they should be protected. The plants grow best in river floodplains, so conservation efforts should be made in forests along rivers and as well as the areas around the water.

References

“Gene Flow Halted By Fragmented Forests.” Asian Scientist Magazine. Science, Technology and Medical News Updates from Asia, 12 Mar. 2018,

Peter, Franziska, Dana G. Berens, and Nina Farwig. “Effects of Local Tree Diversity on Herbivore Communities Diminish with Increasing Forest Fragmentation on the Landscape Scale.” PLoS One, vol. 9, no. 4, 2014.

For this week’s lab we are talking about animals home ranges and how we can find them in cats. It is important to define home range, which is the physical movements an animal lives and moves in an periodic basis. An animal’s home range is where they can find mates, shelter and food. It is an area where they are familiar and move in regularly, and so they are able to forage and hunt efficiently as well as hide from predators. A home range differed from a territory, which is the area an animal defends. A home range is usually larger than an animal’s territory and it can overlap with others of the same species home ranges.

The size of an organism’s home range can change based on the amount of food is available. The trend for an animal’s home range based on resources is knows as the Resource dispersion hypothesis. An animals will travel farther to obtain food and other resources when it they are not as readily available. When resources are abundant an animal’s home range should be smaller because they do not have to travel as far to find what they need.

Photo of a cat with a tracking collar

To find one’s home range scientists can fit the organism with a tracker and periodically plot their location over a time period of months to a year or even longer. One technique that is used to track an animal’s location is called radiotelemetry. Organisms are fitted with a tracking device, such as a collar, and a scientist can use an antenna and use the frequencies of the collars or other devices to track and plot their locations over time. Habitat requirements are resources that are necessary for an individual to survive however, the amount of time an animal spends there may not actually be the most compared to other locations in their home range. It is important for ecologists to study an animals home ranges because it reveals patterns of behavior such as interactions between organisms of the same species, foraging patterns, and resources available that interfere with the number of organisms that habitat can hold.

The purpose of our lab was to use the Cat Tracker Citizen Science Project and find the area in hectares of domestic cat’s home ranges in the USA, Australia, and New Zealand. These domestic cats are fitted with a collar using radiotelemetry and we can use the data from the project and see the points in Google Earth Pro.

There are many mathematical ways to calculate an animal’s home range. For our lab, we connected the points in Google Earth and converted the area in between the points to hectares. We used data from 15 random domestic cats each from the USA, New Zealand, and Australia. Overall we collected data from 45 domestic cats and based our findings off those cats’ home ranges.

Below is an example of a cat’s home range area. The white part is the space we highlighted off Google Earth and converted into hectares.

How we calculated the area of the Home Range (USA cat Ziggy)

Data based on cat home range collection

Graph based on data based on home range cat collection

1. Based on my data the domestic cats from the US had the largest home range with and average of 6.24 hectares. Australia and New Zealand had smaller home ranges with 4.0 hectares and 4.4 hectares respectively. But is the data statistically significant? In order to find out I ran a one-way ANOVA on my data. If the p-value is under 0.05 that means that there is a statistically significant difference in my data.

Results of my one-way ANOVA

As you can see in the highlighted box above is the p-value I got for my ANOVA. It is greater than 0.05 and so the difference in the area of the cat’s home ranges between the 3 countries is NOT statistically different. Their average hectares they occupy for their home ranges is close among the 3 countries.

Cat named Anubis in Durham, North Carolina (Home Range: 2.3 hectares)

2. Based on my observation from Google Earth the cats that I studied had the most points plotted around one central area, most likely it was the house their owners occupied. Many lived in neighborhoods and some would have several points on or close to a few other houses on the street or close by. The cats may spend time at these other houses because the are familiar with the people who live there or there are other people who feed them. I also found that, even if cats lived in mostly urban areas with a low amount of greenery, the cats would travel to green areas close by. These points were usually not close to their central most popular location, but away from the other close points. I assume that the cats would go there to find more food/hunt or even mates because they could not get it near their owners homes. The cats that had home ranges along the oceans, such as many in Australia, they would venture out onto the beach but would not go close to the shoreline. Overall, if cats main location was in a urban area like a neighborhood, they would have a few outside points located in green areas.

Cat named Asher in a more rural area in Calvander, North Carolina (Home Range: 11 hectares)

3. Cats located in more urban environments I found, on average, their average home range was smaller than those in more rural environments. In more urban environments my assumption is that the cats there have more humans around to feed them and there is more human food available, which is easy prey because it does not move. In urban areas there is also more pets around, and so more food is laid out outside people’s homes, which the cat living in that area would know about and could travel to again and again and eat. I found that cats in a more rural area where there was more trees and forests, such as in a large part of New Zealand, will travel further and their home range area will be slightly larger. Above is an example of a cat in rural North Carolina. As you can see the cat traveled away from its house several times into the forested area. My assumption is maybe it is searching for more food other than what it’s owner leaves for it.

The other picture above showed the home range of a cat named Anubis, also located in North Carolina but in a more urban environment. This cat lives near a shopping area and we can see that its home range is much smaller (2.3 hectares) and it stays within the 2 block area usually. It avoids the area with the shopping center, presumably because of heavy traffic.

Table based of New Zealand Cat’s Home Ranges

On average I found that New Zealand is more rural with extremely dense vegetation. I am not familiar with the environment in New Zealand at all so I wondered how abundant small mammals were for the cats to eat as well as what kind of predators they have there. The average home range of New Zealand is slightly smaller than the USA, but their size of home range is relatively constant across all 15 cats. That is, their Standard Deviation is lowest. Most of the cats had about the same size in home range and each lived in mostly the same semi-rural type of environment surrounded by dark green.

4. Biotic factors that might influence a cat’s home range include trees and other greenery.  The cats can find additional prey here, such as small rodents, and these green areas can expand their home range because these green areas are not necessary right in the backyard of their owner’s house. Another biotic factor could include other animals. Cats and dogs are not known to get along, and so maybe the cats will avoid other houses with dogs. This will alter the size and shape of their home ranges.

Abiotic factors that influence home range area include cars and roadways. The cats may avoid busy roads because they are dangerous and loud. Cats may also avoid many open spaces because of the lack of places to run and hide when danger is present, such as open roads. Forested areas and homes also give cats protection from other abiotic factors such as wind and other weather.

Picture from realtor.com

5. I think that the cats whose home range data I collected do have the potential to impact the local biodiversity. Cats are known for hunting small rodents and birds. Areas with cats may see smaller populations of these animals around and these animals may learn to avoid the areas where they know a cat has its home range. The cats could negatively impact the biodiversity by hunting these animals, or they could benefit the environment by keeping their numbers down. The cats who are located in more rural environments have a greater chance of catching prey because of the abundance of trees and other plants.

6. Because cats do impact local wildlife biodiversity, if I was a urban developer interesting in designing a city that was safe for both cats as well as the local wildlife, there would be several things that would need to be accounted for. First, I found that even cats living in heavily populated areas will still travel to open fields or small forested  areas located nearby. Traveling further distance from their owner’s homes can be dangerous, especially if they lived in neighborhoods where people drive all the time. I would build homes with large yard area and shrubs so that the cats may have a lower probability of venturing out to larger green areas where they could harm the biodiversity there. Having a larger yard may keep the cats contained to a smaller area and keeps them out of danger because they have more area near their owner’s homes so they may not be compelled to cross dangerous streets. Each house in the neighborhood will have a yard, and the larger forested area would be located away from the neighborhood. A heavily trafficked road may be located on the outside of the neighborhood, separating the cats from a more urbanized city center. With the larger yards the cats have more room to roam for their additional food sources, and so they may not decide to venture off into forested areas as much and local biodiversity in such forests can be protected.

In order to have a greater understanding of the ecological role of cats, I fount the peer-reviewed article titled, “TNR and conservation on a University campus: a political ecological perspective.” Cats play an ecological role in ecosystems in the present and past. People domesticated cats to help in “pest control” centuries ago and today the cats are not used for this necessarily, but are kept as household pets without the intention of them hunting. Domestic cats can produce feral offspring and it is these cats that are causing the most issues in the environment in regards to their impact in native wildlife. However, domestic cats can be a problem as well. Both cats prey upon small animals, and conservation biologists and animal activists have had different ideas on how to deal with the problem the cats have in the community. The Trap-Neuter-Return program is used so that the feral cat population will not increase, yet this technique does not keep the animals from hunting. From my observations cats who had lived in urbanized areas had a smaller area of their home range, presumably because they had easier access to resources, though those resource were mostly from humans such as extra food set out by neighbors as well as trash and other food taken from other pets nearby. Feral cats in the city would have a smaller home range than those in rural areas. Free-roaming cats in rural areas would maybe have an even larger home range because they do not get fed at a single home regularly. They must hunt for the majority of their food. The article states that cats have a “low dependence relationship with humans”. Cats are not like other pets such as dogs socially because they are not very social animals. Because of this low dependence, I could see from the cat tracking that the cats can often wonder several hectares away from their owner’s home. We do not see this with many other pets. The article helped me understand some of the reasons why a cat will stray so far from their owner’s homes. Some of these reasons are behavioral, as cats are not necessary a social animal. Cats can hunt as a “way to maximize fitness”. That is, cats hunt because it is evolutionary engraved in their behavior as well. A cat’s home range can change depending on several environmental factors, and using radiotelemetry is one way scientists can find out these patterns in order to find the mating, feeding and other habits of different species.

References

Dombrosky, Jonathan, and Steve Wolverton. “TNR and Conservation on a University Campus: A Political Ecological Perspective.” PeerJ, 2014. ProQuest.

 

 

 

The flow of energy through a community can be arranged into food chains and food webs. Food chains show the flow of energy in one linear direction, and is considered to show a more basic understanding of energy flow in the environment compared to a food web. Food webs are more dynamic and are made up of multiple food chains put together. However they are both made up of the same components and are a graphical representation of who eats who in an environment based on tropic levels. In this lab we are more interested in Food Webs. The components that make up these charts are as followed:

Example of a Food Web from Class Handout

• Autotrophs- They are the photosynthetic organisms that supply the energy for consumers (trees, grasses, algae) (chemoautotrophs- microbes and archaea) The autotrophs (Primary Producers) make up the most important level. The more autotrophs there are the more energy there is coming into the system, which is how much energy is available for the next trophic level.
• Primary Consumers – Herbivores, omnivores (such as mice, voles, cattle).
• Secondary Consumers – carnivores, omnivores (snakes, lizards).
• Apex Preds/Tertiary Consumer – Carnivores (Lion, shark, polar bear, wolves) *humans

It is important for ecologists to study Food Webs because it can reveal to them how energy moves through an ecosystem and how it can change over time and space. Every piece in a community effects the other in some way or another, and understanding how energy moves is an important aspect of the overall health of an ecosystem.

For lab this week we looked at a top predator and its eating habits. Apex predators are important for research because what they eat reveals what components go where in a food web. Our top predator we looked at is the Barn Owl (Tyto alba). This top predator is the most widespread land bird species in the world and is found all across the United States. It has a very diverse diet. In more temperate areas it may eat mostly small mammals such as rodents and shrews and moles. In more hot and dry areas it may consume more lizards, insects and amphibians.

The purpose of the lab this week is to analyze a barn owl’s diet in order to find what it feeds on to understand its role in a food web. This data can also help us find a general area of the USA where it hunted.

In order to gather our data and then analyze it we analyzed the diet of the barn owl by dissecting owl pellets. These pellets are the things that the owl cannot digest. We collected our data on bone types and prey items individually and then collected it into a larger class data set.

Photos from the Owl Pellet Dissection

We each dissected our Owl Pellet and compared them to bone charts in order to first count how many of each type of bone we had in our pellet. Then based on what animal these bones came from we found what type of prey that owl ate. Based on what type of prey the owl consumed we could find a general area of the USA that the owl could potentially inhabit.

My data from the owl pellet I dissected

Above, is my results from the owl pellet I had. I found 3 skulls in mine and by looking at these along with the teeth in the jaw bone I was able to find what type of animal the owl ate. The smaller bones found also helped me identify if the bones I was looking at came from a bird, amphibian or a rodent. I concluded that they were from rodents such as 1 rat, 1 mouse, and possible 1 shrew.

Chart based off data from 2018 lab class

1. If an owl forms one pellet each day and that is the average I would expect, based on looking on my data that an owl would eat 3 animals a day based on the amount of skulls I found, 21 animals a week, 93 animals in a month and around 1,100 animals in a year.

2. Based on my data I think that farmers would like owls in their barns very much. The owls would eat the mice and other small mammals that could get into their hay and food for their animals. The droppings from mice and other small mammals could also cause contamination in the barn and, with the owl there, the farmers would not have to worry as much about these problems.

Handouts from Lab

Handout from Class

3. Based on my data I believe that my owl is from a temperate environment. The animal species I found led me to this conclusion. We learned from our handouts that owls in a temperate environment generally eat things like small mammals. If I had found insects, lizards or amphibians I would have concluded that my owl was most likely from a hot and dry area such as a desert. In my owl pellet I found a rat skull, 1 mouse, and possibly 1 shrew. The smaller bones I found also were similar to those from the rat skeleton bone sheet used to help us identify our findings. I also said that the owl most likely is from the Southeast United States based on the rat in its diet. Most of the rats in the US are found in the South. However, I cannot conclude this for sure, and so in order to form more concrete conclusion I would need to find out the exact species of mouse, rat and the other small rodent I found in the owl pellet. If I knew the species specifically I could narrow my conclusions even more and be more confident the owl came from where I believe it to be from.

Chart based off data from 2018 lab class

4. Based on the data and the graphs I made, our owls fed on rodent, shrews, moles, and birds the most. In fact, those were the only animals we found the owls had consumed. Rodents, moles, and shrews were the most found species respectively. These 3 small mammals have a lot in common in phenotype as well as where they live. They all are found burrowing in the ground and in grasses and forests. No insects, amphibians or reptiles were found at all, and so no data supports the idea that any of our owls are from a dry, arid environment. I was surprised that none of these species were found in the owls, specifically insects, as insects are found all over the US. Insects do not have cartilaginous bones like the other prey, so maybe insects are not likely to show up in the owl pellets? I expected someone would find these types of prey in their pellet, but they did not.

Graphs based on 2017 class data

5. Overall, the data collected from the 2017 class data and the 2018 class data conclude the same results. Both years rodents were found to be the most abundant prey types,  followed by shrews and moles being the other most popular. Hindlimbs and ribs were the most abundant bone types found as well. For the 2017 data sets, no insects, amphibians or reptiles were found at all, same as in 2018. Birds were found to be in the owl pellets both years, though not in as much abundance as the small mammal types.

1. Spiders are found all over the world and in many different environments. The following environments, according the article, “Spiders Eat Astronomical Numbers of Insects” are the top 3 that have the most number of spiders found: forests, grasslands, and shrublands. Because they are found all over the world, and eat more insects than any other animal species in the entire world, they are extremely important in the food chain by regulating insect numbers for the entire planet.

2. 95% of the world’s spiders prey kills annually are in grasslands. Forests and grasslands hold the most spiders because they are types of environments that are not usually disturbed much by humans. However, I would not expect spiders to flourish as well in urban areas or agricultural environments. Urban areas have a lot of man made buildings and pollution, which also drives out many insect species because of the lack of greenery. Agricultural fields are not a pleasant environment for spiders to live in either because of the use of pesticides, which are meant to kill many insects as well as weeds that are in the field, so the spiders would have a limited diet here. The ground is also frequently disturbed by plows, which would destroy spider’s webs.

3. If spiders were removed from our planet the world would be a very different place insect wise, as well many other populations of animals would be altered. We know by the article that spiders globally eat the highest amount of insects. However, spiders eat insects, but many other organisms eat spiders and they are a large part of their diet. If spiders were taken out of the global food webs then the insect populations would start to increase in great numbers. Birds and many other animals would have one less piece of their diet as spiders are important in many other diets. If spiders were suddenly gone from our planet insects would not have a consumer to keep them in check. Spiders are extremely important in food webs. According to an article titled “Predator hunting mode influences patterns of prey use from grazing and epigeic food webs”, spiders have the ability to alter trophic cascades due to their importance in food webs. This strength can alter throughout the seasons but there is no question that, although they are small and not very popular with humans, we need spiders in the world.

4. This lab has changed my view on spiders and owls by making me appreciate their role in food webs much more than I did before. I learned that they are both more important than the average person realized. Owls, because of their large diets on rodents as we found through dissecting owl pellets, they play an important role in keeping rodent numbers down in communities. Spiders are one of the most important predators of insects in the world, and eat hundreds of millions of pounds of them every year! Even the smallest of organisms play an important part in food webs.

References

Handouts from Lab- Owl Pellet Regional Inquiry Kit

“Spiders Eat Astronomical Numbers of Insects.” Scienmag: Latest Science and Health News, 14 Mar. 2017.

Wimp, Gina M., et al. “Predator Hunting Mode Influences Patterns of Prey use from Grazing and Epigeic Food Webs.” Oecologia, vol. 171, no. 2, 2013, pp. 505-15.

 

Have you ever looked up at a tree and all its leaves and wondered what their purpose was? Have you ever picked up a fallen leaf from the ground and looked at its ridges and veins and wondered what was going on inside? If you have you may have noticed that all the different leaves on a single tree are all different sizes, each is not exactly like the other, whether in size or color or another factor. The leaves of trees, as well as those of all other plants, serve many purposes and aid in the organism’s survival. Natural selection, because of the phenotypic differences we can see in the different leaves, is acting within the individual and adaptations may have taken place. The kind of adaptations I’m taking about is leaf size and how it relates to a tree’s photosynthetic rate as well as transpiration and heat gain or loss. Trees gain energy from photosynthesis, and so they need sunlight in order for this process to occur. However, photosynthesis (aka being in direct sunlight for long periods) can attract a lot of heat as well, which leads to water evaporation. Also, a trees leaves also need to take in Carbon Dioxide, and they do this by pores in their leaves called stomata. But, as said previously, having those pores open leaves them very susceptible to water loss. Therefore, trees need to gain adaptations in order to balance the amount of water they lose but still be able to gain energy from photosynthesis which means taking in CO2 and opening their stomata.

For our lab this week we observed and collected data on Oak tree leaves. Trees have several layers of leaves, they have an outer portion of leaves and an inner. The outer layer of leaves is thicker than the inner because the large amount of sunlight that reaches them. The inner layer of leaves is much thinner because not as much sunlight is reached, so it would make sense for the tree not to have as many here because it could be difficult for photosynthesis to occur when they were blocked from sunlight by the above layers of leaves.

In our lab this week we wanted to find out a little more. Our objectives included:

• Assess within individual variation in oak tree leaves
• Compare degree of within individual variation between species (red vs white oak).

Between individual variation can be adaptive. We compared the size of leaves of a tree’s outer and inner canopy in order to test our theories. Hypothesis: We wanted to know if there is a significant difference in surface area between them. Is there a difference in leaf size from the inner to the outer part of the tree? And what might this adaptation mean for the tree?

Difference in Oak tree Leaves

We each collected 20 leaves each (10 from the outer part of the canopy and 10 from the inner), either from a Red or White Oak. Next, we used graphed paper in 1 cm^2 cubes and measured the area of each leaf by counting the squares each leaf took up.

We put our data into a class data set, for Red Oak vs White Oak as well as inner and outer leaf area for each. Next, we needed to perform our statistical analysis in order to see what our data means.

Data from class data sheet 

Above is our class data simplified just a little bit. There were many more red oak leaves collected, as seen by n. The average leaf surface area for the red oak leaves (inner + outer) seems to be very close (93.96 for the inner and 74.82 for the outer).

The white oak tree leaves had a much greater difference in their average size of the inner and outer leaves, and there was less less collected by the class.

However, we cannot simply just look at this data table and made conclusions about our data. There is still an important test that must be conducted in order to see if there was indeed a significant different in leaf size based on the location of the leaf on the tree. It is called a t-test.

t-test from RED OAK LEAVES data

We were interested in the two-tail P-value to determine if the difference in leaf size based on its location on the tree is significantly different. The data above is from the Red Oak leaves. The p-value is 0.688, which is more than 0.05.

• There is NOT a significant difference in leaf size based on its location on the Red Oak trees we collected from. The average size of the inner and outer leaves were relatively close to one another and we can conclude that our value is not statistically significant.

t-test from White Oak Leaves data

Our leaves from the white oak trees, however, tell us a different story than the prior did. When performing our t-test our p-value as can be observed above is 6.38E-07, which is well below our table value of 0.05.

• YES! There is a significant difference in size of White Oak Leaves and in their location on the tree. Out results are statistically significant. There was a statistical difference between the inner and outer leaves.

Graphs based off class data sheet

The right bar graph, above, shows the differences in between the average leaf sized in our class data of our White Oak tree leaves. The outer leaves on the white oak tree have a much larger average leaf size of those in the inner part of the tree.

With-in-individual variation of trees is important for ecologists because it shows the process of natural selection. Difference in phenotypic variation is a mechanism for natural selection, and so studying and finding out what the benefits are for trees having different sized leaves, ecologists can better understand how they in turn alter the rest of the community. The leaves could be larger on the outer part of the tree because they receive more sunlight and are able to grow larger. The leaves in the inner part of the tree are the ones that do not receive as much sunlight and therefore are not able to grow as large.

Farmers and crop scientists would want be interested to know if leaf size positively correlates to crop yield. From our data we found that leaves from the outer part of the white oak tree were significantly larger than those from the inner part. Larger leaf size means the leaf has more surface are where photosynthesis can take place. A greater developed plant is correlated to healthier fruit yield, according to a study conducted in 2016 titles, “Enhancing crop yield by optimizing plant developmental features“.

From the study titles “Enhancing crop yield by optimizing plant developmental features”.

If larger leaves were correlated with a higher crop yield then it would be in the farmer’s best interest to have the healthiest plants in order to make the greatest profit. Crop scientists would also be able to benefit by studying the benefits of the adaptation and what it means for our world wide crop yield for the future.

According to the Article by Adrienne Berard genetic  variations persists in the wildflower population simple “because it helped them survive”. If this was explained to a general audience you could The traits that are best suited for the environment are the ones that are passed on to future generations. However, the environment is constantly changing, traits that were best suited one year may not be the best the next. It is kind of like fashion, certain looks will be in one year and then out the next. They addressed the question of variation in phenotype linked to genotype by first explaining Darwin, the person who brought about the theory of evolution. By explaining how the theory had its origins gives non-scientists the background in order to understand how the authors linked their findings to scientific fact.

The article asked the question of whether there would be enough variation to be able to withstand climate change and be able to quickly adapt to the changing conditions. This is an extremely valid point as our world is constantly changing, and human caused climate change can be unpredictable. The seed monkey flowers studied, they do believe will be able to adapt and survive. However, there are many other flora that may not be as well adapted, and who are an important source for bees. Their study was not able to include all the other factors and flowers in the area, but if many other flowers are harmed by climate change and are not able to adapt, their favorite pollinator (the bee!) could be harmed as well. It is impossible to study every aspect of the environment to know what will be harmed and what will survive with change in the environment.

References

Berard, Adrienne. “In ‘Science’: Wildflowers Combat Climate Change with Diversity.” William and Mary, 2 Aug. 2018.

Horn, H. S. 1971. Adaptive geometry of trees. Princeton, N.J.: Princeton University

Press.Mathan, Jyotirmaya, et al. “Enhancing Crop Yield by Optimizing Plant Developmental Features.” The Company of Biologists, vol. 143, no. 18, 2016, pp. 3283–3294., doi:10.1242/dev.134072.

In the wild, animals have various ways of getting nutrients in order to survive. The better they are at getting this energy the higher probability they have on surviving as well as a higher fitness. Fitness is a measure of an organism’s reproductive capability. For our lab this week we studied Optimal Foraging as it relates to an organism’s cumulative energy gain over several patches of food resources. Optimal forager’s are organisms who gain the most energy in the least amount of time compared to other animals while foraging. These animals net energy gain is maximized during the time it forages. Their energy spent while foraging is low compared to how much energy they are gaining during their foraging time.

• net energy gain = energy gained – energy spent while foraging

In our lab this week we performed a simulation where we acted as the foragers and measured our time finding “food” in different “patches/areas” that were set up. In a flat grassy area 9 buckets were set up, each spaced equally apart. Each bucket was filled with uncooked white rice and a various number of uncooked beans. The beans were our food source and our objective was to “maximize the net rate in which you find beans”. Each of us were timed while finding the beans in 3 patches each. Each time we picked up a bean and placed it in a cup we swirled it around 3 times to represent the time it would take an animal to eat prey. It was our choice when we decided to leave each patch once the resources began to be depleted. Our goal was to leave each patch once the same optimal energy intake rate was reached. The goal was not to find the most beans as we can, but to optimize our time to find the most beans in the time we are at each patch. This marginal value that we tried to reach is correlated to the Marginal Value Theorem. The theorem states:

• Areas with a higher density of prey is where forager should capture the most prey
• Foragers should spend more time in areas with higher prey densities because it will take more time to start depleting the resources
• Foragers should capture more prey in a shorter amount of time in high density patches than those which are sparsely populated with food resources
• Foragers should move on to other patches when their prey/unit time begins to drop below the average rate, or, when it begins to take a longer time to find each prey source.

When graphed we have to take into account the traveling time, or time it takes to reach each patch. At this time the energy gained is 0. Time is graphed on the x axis and energy gained is on the y axis. For each of our 3 patches we foraged in we did not stop the stop watch once. The start began when we started to walk toward the patch and then ended when we decided to leave the last patch. One important aspect of this lab is our Giving up time (GUT), which is calculated by subtracting the time we find out last bean by the time we decide to leave the patch. Hypothetically, this time should be similar across each patch.

Graph based on class experiment data

4. In our results it is important to find out of our results followed the rules in the Marginal Value theorem. The graph above supports the rule of the theorem that areas with a higher density of prey is where forager should capture the most prey. And our graphed data supports this as I found a larger number of beans in patches that had more beans available in that specific patch.

Graph based on class experiment data

Our data above also supports the Marginal Value theorem. I spent more time in patches of higher density of beans than I did of patches that did not have as many beans available.

Graph based on class experiment data

In regards to the 3rd rule of the Marginal Value Theorem that foragers should capture more prey in a shorter amount of time in high density patches my data also supports this. My capture rate was higher in patches that held a higher density of beans.

Graph based on class experiment data

The only part of the Marginal Value Theorem that my data does not support is that my giving up time (time we find out last bean – time we decide to leave the patch) is not constant in each patch. Instead, it took me longer to leave patches that held a lower density of beans than it did from high density patches. I kept searching for beans even when they became increasingly harder to find in the bucket of rice. I could have stayed in the bucket with a total of 80 beans in it for longer, instead I stopped at 30 and quickly went to another patch. I could have stayed and found a few more beans and perhaps my GUT time for that patch would be closer to the average of the other 2 instead of being somewhat of an outlier.

Based on our data I do not think that the behavior of humans in this experiment applies to the Marginal Value Theorem. Humans in this day and age do not move on from areas when they have taken resources enough that they begin to become scarce. Humans are continuing to deplete the environment of every resource available until it is not found there anymore, if only in trace amounts. Related to farming, we use plots of land over and over until the nutrients in the soil are almost completely used up, then we move on to another plot while the previous now is depleted.

Graph based on class experiment data

Based on my data the number of prey captured correlated to the level of density. I also found more beans in shorter amounts of time in areas with a higher density.

I would expect an animal to behave in a manner that follows the Marginal Value Theorem and maximizes optimal foraging in an environment that is abundant with resources. I would not expect animals preparing for hibernation to follow this pattern. Instead, I would expect animals preparing for hibernation to stay in the area past the optima and continue to search for food. In this way they would spent more time in each patch and their travel time would be the same, but would be over a greater time period.

Reflection Questions

An ecological study named “Risk perception by endangered European bison Bison bonasus is context (condition) dependent” reveals real life examples of optimal foraging and the Marginal Value Theorem. There are certain conditions in which the bison showed optimal foraging strategies as well as conditions where they veered from it. The Bison’s foraging strategies changed with predation by wolves as well as in winter when their resources were scarce. In summer they avoided areas with high human density however; in winter when their resources were not as abundant they risked getting closer in order to eat from haystacks. This study reveals that an animal’s optimal foraging strategies change depending on the conditions of the environment they are in. Predation, water and food availability can alter their GUT and capture rate as well as other variables.

Studying optimal foraging strategies can give us a greater understanding on how organism’s alter the environment with their foraging habits as well as patterns of migration. We could perhaps predict how much an organism could alter the environment in a certain period of time. Ecologists study this behavior in order to manage certain organisms. For example, placing haystacks in the bison’s environment can enable one to guide the bison herds to the location they want as to keep them away from causing problems with human populations. Optimal foraging strategies also can be a way to study natural selection by ecologists. Those animals who are not able to find food may face starvation and die because of their lack of foraging strategies Hayward, MW, et al).

In my life there are a few optimal foraging strategies that I can think of. I grew up across the street of a farm. The cows that were in the field would not stay in one spot grazing the whole day, they would move across the field throughout the day and then would return to the previous location after a few days. There would not be a mass number of cows where they depleted the resources available, but enough in the field where their grazing patterns could potentially be studied in real life following this lab outline.

4. The black bears in the Sierra Nevada are changing their feeding behavior because of the amount of food available is changing because of humans. Based on optimal foraging strategies, the food is available in a larger quantity in a much smaller area. Bears are able to spend more time in one location because there is a lot more food available in urban areas. And they do not spend as much time searching for food or traveling from food source to food source. Their optimal rate of “city bears” is maximized as they are able to consume a greater amount of calories in one day compared to “country bears”. It seems as if this is a good thing for the bears, as the article says that there is not evidence that the food source from fast food and other human garbage does not negatively impact the bears. However, the “city bears” are now in urban human territory and this has became a problem when the bears are sleeping under people’s decks and raiding their trash.

Picture of a bear taking trash outside a urban home

Bears can be dangerous and so it is not a good thing that they are residing in urban areas close to humans. A practical solution to keep bears away could be to keep dumpsters locked and not to leave trash piled up. Trash pickup could come around more than one time a week in order to stop bears from being attracted to the trash being left outside of people’s homes for days at a time.

References

Fountain, Henry. “Fast-Food Nation is Taking Its Toll on Black Bears, Too.” Nov, 25, 2003.

Hayward, MW., Ortmann, S., Kowalczyk, R. “Risk Perception by Endangered European Bison bonasus is Context (Condition) Dependen.” Landscape Ecology. DOI 10.1007/s10980-015-0232-2

 

1. Last week during lab we collected our group data on Paspalum dilatatum (aka Dallisgrass) by counting the individual specimens per 1m^2 quadrats in the Confederate Cemetery using the quadrant sampling method. Each group measured 15 quadrats each. Next, every group’s data was put into a large class data set. This week the purpose of lab was to find out what kind of spatial distribution pattern the Dallisgrass has in the local site of the cemetery and to find out if our results are significant. In order to calculate our results we used the Poisson formula as well as the chi-square test. The Poisson formula for our data works with the chi-square test because it gives us our expected value, which we need in order to perform the chi-square test.

Data of Poisson formula for SASSGRASS group

Above is our Poisson distribution for a portion of our data. The # of individuals found goes from 0-264 rows because the highest number of individuals we found in one quadrant was 264. The average number of individuals we found per quadrat was calculated to be 63.71. The Poisson formula was calculated not by hand, but by the formula in Excel.

Statistical Analysis based on group SASSGRASS data collected

Next, the Poisson value was used to find the expected value by multiplying the Poisson value by the number of quadrats we studied. The observed value is the actual number of individuals we found, which ranged from 8-264, but the number of individuals was tallied. For example, if only one of our quadrats has 8 individuals, we would put 1 in the observed value.

Using the Observed and Expected values we were able to calculate x for each (0-264) and then the sum of the (Observed-Expected)^2/Expected was taken to calculate the Chi-square value.

• (Poisson Value) x (# of quadrats=15) = Expected Value
• (Observed-Expected)^2/Expected
• chi- square

Graph based on SASSGRASS group data analysis

The purpose of using the Poisson formula is to see how many individuals one would expect to see if in fact the Dallisgrass is in a Random distribution. In a random distribution the mean equals the variance and it would be our null hypothesis. The purpose of the chi-square test is to see if our Observed results varied from the null hypothesis, and to find out if our data is in a uniform or clumped distribution.

Graph based on SASSGRASS group statistical analysis

Our observed values give us what kind of distribution we found our number of Dallisgrass to be in the cemetery. Our results do vary from the null hypothesis of a random spatial distribution because of our mean and variance values. We have to decide whether our results are now either in a clumped or uniform distribution.

• Group findings conclude that  SASSGRASS is in a Clumped Dispersion pattern in the Confederate Cemetery.

How do we know this? Well, in a uniform dispersion the mean is LESS than the variance. our mean is 63.71 and our variance is 264-8= 256! The graph above you can see that the histogram the values are not very spread out. The numbers we found in each quadrat varied greatly from one another.

Lastly, we can check our chi-square value in order to conclude that our hypothesis is statistically significant and that our results vary from the null hypothesis of a random distribution. And when looking at the chi-square chart our p-value is below 0.05 when we use the Degree of Freedom value of 264.

• Our results are statistically significant and SASSGRASS Dallisgrass is in a Uniform Distribution in the cemetery based on our group analysis.

Next, we can compare our data to that of the 2018 class data table in order to see if our results differ.

Data based of 2018 class statistical analysis

When all the 2018 class groups put all their data together we ended up counting 75 quadrats total. As one can see the average number of Dallisgrass found per quadrat is lower than the average SASSGRASS found. However, our overall results were still the same.

Graph based on 2018 class data analysis

Above is our expected values graphed for the entire 2018 class’s data on Dallisgrass. The range and the mean are the same and so, again, this is our null hypothesis of a random distribution.

Graph based on 2018 class statistical analysis Observed Values

Above is our observed values graphed. As one can see the observed values also differ from the expected values similar to the SASSGRASS group. For the 2018 class our variance was also much more than the mean. This means that the class data collected also reveals that our observed values differ from the null hypothesis and that we have a clumped distribution of Dallisgrass in the Confederate Cemetery.

Our chi-square value can also be looked up on the chart. Our degrees of freedom for the class is also 264. And so our p-value compared to the chart is below 0.05 and our class data results are also statistically significant. We can reject our null hypothesis.

• 2018 class distribution of Dallisgrass is in a Clumped Distribution as well.

2. So what could be some of the reasons the Dallisgrass is in a clumped dispersion in the cemetery?

In order to provide logical explanations as to why the structure of the environment caused the Dallisgrass to grow in a clumped dispersion we can look at the characteristics of clumped dispersion as well as the species Paspalum dilatatum in general.

In a clumped dispersion the organisms live close to each other with a high density and areas in between populations are not highly populated. A clumped dispersion can signify that resources the species need are located in one location. Clumped dispersion patterns can also provide some organisms with protection from predators (“strength in numbers”). For the clumped dispersion pattern of Dallisgrass, based on a study titled “Heterogeneous Distribution of Weedy Paspalum Species and Edaphic Variables in Turfgrass”, the scientists performed an experiment similar to our class’s, but they compared 2 different plant species and also measured other biotic and abiotic variables in the environment. They found that Dallisgrass was most abundant in “moderately compacted soil” compared to loose and powdery soil. The Dallisgrass was also found in less density in soil that had a low water content. Reasons the Dallisgrass showed a clumped dispersion pattern in the cemetery is that certain areas could have a higher water content than others. Soil nutrient levels could vary across the cemetery depending on other plant species in the area. Sunlight and the timeframe of mowing could also be a factor on the density of Dalisgrass. The cemetery has areas full of sunlight as well as areas under tall trees that are in the shade. From my visual observations it seemed as if the density of Dallisgrass was more abundant in areas of high sunlight. Dallisgrass grows best in the cemetery in areas of high sunlight and high moisture content in soil.

3. Explain how the population pattern the study may differ compared to Dallisgrass.

Compared to our experiment, the article “‘This is Amazing!’ African Elephants May Transport Seeds Farther Than any Other Land Animal” the focus in more on how seeds are dispersed more so than what kind of spatial patterns the seeds have in the vast savanna environment. If the seeds are transported by the elephants hundreds of kilometers away I would expect that the seed dispersal pattern would be similar to that of the elephants who are transporting the seeds. The patterns that the elephants travel would correlate to where the seeds fall and sprout. Elephants move in herds except for the males, as in the article they may break away from the groups to find mates. The seeds in the savanna are also moved more by natural organisms who live in the environment such as elephants, birds, and monkeys as mentioned in the article. Dallisgrass here in our cemetery is often moved by humans by mowing as well as water. There is not much water or rainfall in the African savanna and so the seed dispersal by elephants is important. Elephants are nomadic and follow water resources, and also often live in clumped dispersion patterns. They are also in the African savanna where many plants struggle to survive. However, elephants are not stationary like Dallisgrass and so it can be more difficult to calculate dispersion patterns because they are constantly on the move and their dispersion patterns may be more sensitive depending on the resources available. But compared to the our results on the clumped dispersion pattern of the Dallisgrass, the African savannah is a much larger environment that that of the cemetery, and there are many different species that carry the seeds long distances. I think that the seed dispersal pattern in the savanna is random based on the different species who live there that transfer the seeds. The animals that are spreading the seeds are moving constantly and the seeds are able to spread randomly because of the diverse distances and spatial distributions of the animals themselves.

References

Henry, Gerald M., Michael G. Burton, and Fred H. Yelverton. “Heterogeneous Distribution of Weedy Paspalum Species and Edaphic Variables in Turfgrass.” HortScience, vol. 44, no. 2, 2009, pp. 447-451. ProQuest.

Stokstad, Erik. “‘This is Amazing!’ African Elephants May Transport Seeds Farther Than any Other Land Animal”. 10 April, 2017.

Different species have various population distributions across their geographical range. For example, a species of deer may have a geographic range in all parts of the United States, but the way the organisms are spread out among their ecosystem can vary across different populations. The way that the individuals in the populations are arranged is called the population dispersion. There are 3 properties that determine a population’s spatial structure, or how they are arranged in a given space:

• Dispersion
• Distribution
• Density

All of these properties can vary among habitats. It is important to ask yourself: What causes the variations of spatial patterns among different populations of the same species? Well, causes of different spacial patterns include food availability, social interactions amongst individuals, geographical barriers, predation, and many other factors.

These factors cause the 3 main kinds of dispersion patterns we learned about in lab:

• Clumped
• Random
• Uniform Distribution

Photo from Khan Academy

In a Uniform Distribution, the individuals are spaced evenly among the environment with equal distance between them. Causes of this pattern could be aggressiveness between individuals or a depletion of resources.

In a Random Distribution the individuals are spaced without a pattern and the individuals can occur with the same chance anywhere in the geographic space. Caused of  this pattern could mean there is not any negative interactions between different individuals and there is not one certain location where a recourse is located.

In a Clumped Dispersion  pattern the individuals are grouped together in “clumps” within the geographic area. They are in groups and this could be caused by predation because there is safety in numbers, or because resources are located in one specific space within the area.

Above: Dallisgrass

This week our lab class looked at patterns of density and dispersion among the grass Paspalum dilatatum, aka Dallisgrass in a green area at our school. Dallisgrass is a perennial that thrives in warmer weather. It can flourish in both wet and dry areas as well as areas with high salinity.

For our data collection we split into groups of 4 and measured 15 quadrats in the Confederate Cemetery using a square made from connected poles that made a range of about 1 m^2. We chose the locations of where to lay down each quadrat by a random number table. Our pacer randomly pointed to a number on a paper and took that many steps straight. Next, another number was chosen and that many steps to the right were taken. For each quadrat the number of individual Dallisgrass was counted using the Quadrat Plant Sampling Method. This is called measuring the abundance, or density, which is the number of individual plants per unit area. For each of our 15 quadrats our number of Dallisgrass varied greatly in number.

Data from Ecology Lab data collection

In order to find the overall Density of our data collected you must find the sum of number of total individuals found, which is 926 across all 15 quadrats. Next, you divide that number by number of quadtrats (15) x Unit area (1 m^2).

• 1. The Density I found is 61.73 individual Dallisgrass per unit area.

Why is there such differences of Dallissgrass per each quadrat? Well, we know that Dallisgrass grows in a variety of warm environments. The temperature here in TN right now is very warm, even at night. Differences in the amount of Dallisgrass between each quadrant can be explained by the large area we spread our quadrats in. The cemetery where we counted the grass is a large area that is filled with small hills and a variety of short and tall trees. Some areas of the cemetery where we laid our quadrat were in the sun away from any tree cover. Some areas were directly under large tree species. The field is also mowed from time to time so there were areas in the cemetery that had been mowed recently while others have not. There were also areas where one can look on the ground and see it is a well traveled path by people who visit the cemetery, there are other locations in the cemetery where there is not a lot of foot traffic. Because of the slight mounds in the cemetery different locations where we collected our data may absorb more or less precipitation than others. One location we looked at had an extremely high density of clovers and we counted very little Dallissgrass. Perhaps the Dallisgrass was outcompeted by the clover population in that specific location of the cemetery?

2. Due to our data, it looks like Dallissgrass has a relatively random distribution pattern throughout the cemetery, though a few ares have much higher numbers. The density was  61.73 individuals per quadrat, which was average number found in each quadrat. Our highest amount found was 264 individuals and the lowest was 8 but for the other quadrats in between the numbers did not vary too much. However, a random distribution pattern is considered to be the null and actual populations rarely have random distributions in nature. Our data may seem like it has a random distribution, but our data was taken from a large area, and so it may not be the case because of the distance between our quadrats. And so, further tests must be made to see how our data varies from the random distribution, it does.

Paspalum dilatatum is considered a invasive in certain countries and a pest weed in many areas. The plant is native to South American and not the USA, but it is found in dozens of countries because it has been introduced. One reason it has been able to thrive is that it has characteristics that help it outcompete other native species. This is why it could have a random distribution in the cemetery, because it has the characteristics that enable it to become one of the dominant grass species in our geographic area (Invasive Species Compendium). Climate change could also be a cause of why the grass is able to thrive in geographic areas other than where it is native. If the earth is gradually warming by a few degrees then the warm weather plant is able to inhabit many more locations (Balmer). A study with P. dilatatum showed that is can show severe water stress however, when compared to other plant species. This means with extremely dry weather the plant can show smaller densities. Perhaps certain locations within the cemetery did not receive much precipitation and therefore had much smaller densities of the grass (Napier, JD., et al).

Distributions of Paspalum dilatatum on earth from CABI.org

Data based off Lab Group Data Collection

The spreadsheet above is our Data Analysis based off our data collected. We need to find out if the grasses we counted are distributed in non-randomly or not. The Poisson series is used to calculate the expected number of individuals counted per quadrat. Next, a chi-square value was calculated. The chi-square value I calculated is 1.18, If the data distribution is different from the null the distribution could be clumped. There were Dallisgrass found in very quadrat, which looks random, but if the Chi-square value is significant, then the data deviates from random distribution.

References

Balmer, Jennifer. Plants Have Unexpected Response to Climate Change. 8 Aug. 2014.

Invasive Species Compendium: Paspalum Dilatatum (Dallisgrass). CABI.org, CAB International, 2018

Napier, Joseph D., Erin A. Mordecai, and Robert W. Heckman. “The Role of Drought- and Disturbance-Mediated Competition in Shaping Community Responses to Varied Environments.” Oecologia, vol. 181, no. 2, 2016, pp. 621-632. ProQuest.

Peacock, Charles. Dallisgrass. Turffiles, NC State Extension News.

Population Size, Density, & Dispersal. Khan Academy, 2016.

 

This week’s blog is on the subject of thermoregulation and how different organisms retain their body temperature in different environments. However, different organisms thermoregulate in various ways, and that is a concept that was tested in our lab this week.

In order to understand what we tested in our experiment and the results we concluded, one must know a background on thermoregulation first. An organisms body temperature is crucial to their overall performance. There is a range of temperatures that an organism can live in that optimize their performance. There are different metabolic states that an organism can be categorized in, and in lab we studied both endothermic and exothermic systems. An endothermic organism maintains a relatively constant body temperature relative to the external temperature. They have higher metabolic rates because of this, and are generally larger animals with greater insulation. For example, endotherms are usually have some type of fur or hair and a large percent of bodyfat. They are able to give off excess heat mainly through their extremeties. Mammals are an example of endotherms.

In contrast, ectotherms rely on the external environment for their body temperatures. They are usually smaller in body size in order to cool down or heat up faster than mammals with greater surface area. They have a much higher heat exchange with the environment than endotherms, or regulators. They can conduct heat more rapidly because they have very little to no insulation on their bodies. Their skin can be very thin and lacking fur as an insulator so heat exchange is high. Their metabolic rates are lower as they do not have to work as hard to regulate their body temperatures. A lizard in the desert is an example of an ectotherm and may bask in the sun in order to raise its body temperature.

Now, for our experiment we wanted to see endothermic and ectothermic processes occur in a lab setting in order to see temperature curves for thermoregulatory processes. We tested how environmental factors effected an animal’s heating and cooling rates. My partner and I chose coat color as our testable variable. We wanted to know how an organism’s body color, such as light or dark effected body temperature heating and cooling curves. We made our animals out of tin foil and cotton balls and used a heat lamp as our heat source. The purpose of the experiment was to be able to see visually what our organisms heating and cooling curve would look like when graphed. So, we began at room temperature and measured how long it took for our animal’s temperature to become constant. Then, we turned off our heat lamps and measured how long it took for our animal to return to room temperature. Our results were what we expected, which I will explain in much greater detail below…

  Graph based off results of lab experiment

Before we tested our animas first we practiced our technique with a foil cube. The thermometer was inserted into the cube, the heat lamb turned on, and then we recorded the temperature of the cube in 30 second intervals. It took around 5 minutes for the temperature to flatline, and that was when we turned off the heat lamp and the cube, over the next several minutes returned to room temperature. Our results from Part A can be seen on the scatter plot above.

Cube being heated in Part A of experiment

Then in Part B of the experiment came the fun part: testing our animals we made our of tin foil.

1. We selected to study color because we wanted to see how an organism’s coat color helps it survive or hinders it in certain environments. It is important to scientists because it shows that there is a “cost” to thermoregulation.  We created one animal out of just tin foil and the other with the addition of cotton balls on its external surface. However, the design of our animals could also be used to hypothesize the heating and cooling rates of endotherms vs ectotherms, but we already know how their graphs are supposed to look like so we thought that that would be too easy.

I gained knowledge of color factors from a scientific article titled “Color Change for Thermoregulation versus Camouflage in Free-Ranging Lizards”. The lizards in question were changing color and the scientists wanted to know why. Their body color was changing, however, their results still correlated to our experiment as the lizards were changing color to be able to be more efficient in thermoregulating as well as camouflage. From this article and the data from our experiment we found that color of a ectothermic organism can alter their thermoregulatory rates while basking in the sun.

2. My partner and I designed the study to answer the question by creating one animal made just from tin foil and the other with an external layer of cotton balls. The cotton balls act as an insulator as well as a heat reflector. We thought that the lighter color would have a smaller temperature curve as it would take much longer to heat up and cool down and the temperature from starting point to where it became constant would be smaller than that of the darker individual. A challenge we found was positioning of the thermometer as it was important to have it placed as similarly as possible for both animals in order to get the most conclusive results. We placed each animal about an inch from the heat source, which is a mimic of the suns rays. We started at room temperature and then took temperature reading (in Celsius) every 30 seconds. Once the temperature became constant we turned off the heat lamp and took the reading in the same intervals until the temperature was back to room temperature.

 

 

 

 

 

 

 

 

 

3. In our experiment the animal that most closely represents ours in terms of thermoregulation could be a turtle, which is an ectothermic organism. Turtles have thin leathery skin and bask in the sun for warmth. They have relatively small body size which is one characteristic of an ectotherm. The plain tin foil animal could represent a dark colored turtle basking in the sun. In contrast, the lighter color animal with the cotton balls covering it could represent a very light colored turtle, perhaps an albino. We expected the tin foil turtle to heat up faster and with a greater temperature range because dark color attracts the sun’s rays. Also, the plain tin foil turtle could also be seen as an ectothermic organism, and this variable also works for our temperature curves.

Hypothesis: Color of an ectothermic animal is a variable that alters how fast or slow their  body temperature will warm up and cool down while basking in the sun.

Graph based off data from lab experiment

Graph based on data from lab experiment

For the experiment, we took measurements on temperature over time. A scatterplot is best used to analyze the data because it will show us if we were correct that we thought there was a correlation between color and heating and cooling rates. A scatterplot shows the temperature curve between our two animals and how the size of the curves varies.

4. Color variation, even in the same species of organism, can alter their thermoregulatory rates. The experiment also revealed that an animal’s temperature variation can be investigated further when looking at metabolic rates. We can see how our variable of color, in dark vs light colored ectothermic turtles, the white turtles took longer to heat up and cool down and had a smaller curve than the darker colored turtles. The darker turtles heated and cooled much faster and has a greater variation in starting temperature and where their temperatures became constant. As one can see from the graphs above, the darker model has a temperature variation from 25 to 37 and the lighter model was from 25 to 35.

Data from lab experiment. Graph compares the heating curve of the different turtle models. As one can see the darker colored turtle had a large heating curve and reached a greater temperature variation than the lighter colored turtle.

From learning about thermoregulation, I know that endotherms have adaptations that aid in keeping their body temperatures constant. One characteristic that most endotherms have is the existence of body hair and a higher fat content. Evidence of this is seen in the article “Huddling for survival: monkeys with more social partners can winter better”. The monkeys being studied huddle together during colder weather in order to stay warm. If I were to explain this scientific explanation to a child or retirees I would make an analogy. For example, The the monkeys huddled together act as an insulator and they are sharing body heat similar to what survivalist say to do when humans are in a dangerous colder environment: get close together. They are creating a greater surface area which takes temperature a greater time to fluctuate.

Maintaining a constant body temperature takes a lot of energy. In warmer conditions it does not take as much energy to maintain body temperature because the difference between the external temperature and internal does not have a very large difference. It is true endotherms do not truly rely on their external environment for body temperature, but their performance is heightened only under certain temperatures. So, if the temperature becomes too cold the animal has to expend more energy in order to thrive. For the monkeys in the article titles “Monkeys eat fats and carbs to keep warm”, the scientists conclude that the monkeys are eating more fats and carbs in the winter compared to different parts of the year. They must consume more in colder temperatures to maintain a healthy metabolism. An analogy to use when explaining this concept to students and older people is to compare the monkey’s body temperature to a car’s air conditioner. In winter, you must have the heat very high in order to stay warm. When you have the heat running on full blast for long periods it will cost a lot more gas to fuel the car and keep the heat running constantly all the time. Having others be able to understand such an important concept is a goal for the scientific community to communicate, and hopefully my blog will be able to convey a greater understanding.

References

Graphs based off classroom lab data

Smith, Kathleen R., et al. “Color Change for Thermoregulation Versus Camouflage in Free-Ranging Lizards.” American Naturalist, vol. 188, no. 6, 2016, pp. 668. ProQuest,

University of Lincoln. “Huddling for survival: monkeys with more social partners can winter better.” ScienceDaily. ScienceDaily, 30 May 2018.

University of Sydney. “Monkeys eat fats and carbs to keep warm: Golden snub-nosed monkeys adjust nutrient intake in winter.” ScienceDaily. ScienceDaily, 8 June 2018.

Last week in our Ecology Laboratory our topic was on Urban Ecology. This branch of ecology is relatively new and there is a lot to explore about the subject and how urban environments, such as city centers, act as an ecosystem as part of a mostly man made system. We explored how an organism’s diet is differed in a more urbanized environment compared to that of a green space. For the experiment, we looked at ants, which we collected across various places on campus in order to see which food type they preferred: cookie, sugar, salt, amino acid, oil, or water. This data collection was to see what the effects of urbanization have on ant diet preference. We also calculated the % impervious surface in order to get an idea of the extent of Urbanization in a small scale of the ants environment. For my prediction, before the experiment took place, I assumed that the cookie or the sugar would be the most desired food source, let’s see if I was correct in my assumptions below…

Our group’s data as well as the rest of the classes is uploaded into a wider spreadsheet of ant data collected throughout the past several years collected by students. The time the baits were set and picked up was taken into account as well as Latitude and Longitude and a general summary of the location. One of the most important factors was whether the baits were set in a green space, such as the grass, or an imperious surface, such as the side walk. The % impervious was calculated by steps of impervious surface of a 25 step pace in 4 directions making an x shape. The impervious surface is divided by the total X 100 to give a % value. The 6 types of bait were set on cards at each site and were set for relatively one hour each and then the baits, cards and the ants on them were collected in plastic bags. This was repeated at 4 different sites. Next, each individual and was counted from each of the baits at all of the locations. Here is a portion of the class data below.

Portion of Class Data

Categorical data in the experiment was the type of bait set and the continuous data that was measured included % impervious surface, temperature, and number of ants at each bait set.

Next, with so much data in front of us, it is easier to set up a smaller data set in order to be able to “see” the data distributions more clearly.

Photo of data distribution of UTC Ant Sampling Lab

Above is a chart that gives a summary of the class main data table. The standard deviation shows the variation from the mean of the number of ants. The average gives a hint at which bait was most preferred over all of the sampling locations. For the class data set, one can see that the cookie bait has the highest average of any bait set, which hints that the cookie was the most popular bait set out. The cookie held the greatest number of ants in the experiment.

Bar graph showing data distribution of UTC ant sampling lab

Using a bar graph for the data above, shows the average number of ants per bait. This visual representation of the data shows that my hypothesis was correct, and the cookie was the most popular food source that the ants chose. However, I was expecting for the sugar to be the second most popular, however, that ended up being oil instead.

We’re not quite done with graphs quite yet. Next, I looked at the data using a single factor ANOVA in order if there was a statistically significant difference between the averages of the baits. Following the rules of the ANOVA, none of the data between groups effects the other because all of the sites were set at different latitudinal and longitudinal locations across campus. None of the data was taken from the exact same location.

Below is the results of my one-way ANOVA. One of the most important aspects of the ANOVA is the P-value. A value of below 0.05 means that the results are significant. A higher P-value means random sampling error has taken place. In order to reject the null hypothesis that means there is not a difference between groups and the P-value is above 0.05. However, it is seen from the data tables that the number of ants collected at different sites varies with the various baits. Based on the results there is a difference between the groups measured, and so the data is significant.

Based on data collected from class data tables

Next, another visual representation used to show our data is the use of a scatterplot. The scatter plots show correlations between the x and y axis.

Data from UTC ant collection spreadsheet

Both the scatterplots show important information about the experiment. For example, the scatterplot showing temperature and number of ants one can see that the ants are more abundant in certain temperatures, such as between 29 and 35 degrees Celcius.

The second scatterplot shows the % impervious surface shows a positive correlation. The scatterplot shows that around half green space and half impervious surface seems to be the environment the ants are found in the greatest abundance.

During the 2016 class results there was a drought going on. In the graph I found evidence  that the data reflects the environmental conditions. During 2016 there was a simnifically lower number of ants found at each of the bait types. The highest number of ants found in one site was 103 while the highest number this year in 2018 found in one single site was 780, which is a very large difference. Also, the ants seemed to prefer the sugar bait during 2016 rather than the cookie bait, which was the most popular in 2018.

These preferences to different diets can be the cause of variance environmental factors such as weather patterns as well as food availability in their environments. An adaptive advantage to change ones diet in a new environment is to be able to survive there and gain new sources of nutrients that would otherwise not be in the diet.

Based on my results I would additionally be interested in studying weather patterns when the samples were collected. How much rainfall actually fell during the year or even month the data was collected versus the year before.

As a student of science this experiment has taught me that different ecological processes can change an organisms diet. For example, the drought in 2016 caused a significant change in ant number and bait preference. A limitation I faced when interpreting the data is simply my lack of knowledge on ants in general as well as the time the baits were set out. Not every site was collected exactly before or after the hour was up from when we set them. This can cause some bias in the results as some ants may not have reached the bait even though they could have had the potential during the hour time frame.  My advise to an upcoming science major is that being meticulous is important in order to get sufficient results that try to not have bias.

A study conducted in Malaysia performed an experiment similar to the one we performed during class. In they experiment they also studied in an urban environment and left their baits out for 60 minutes as well. For their results many of the ants were attracted to the sugar liquid bait as well as a gel bait. Though the experiment was similar to the one we conducted in class, different results were concluded. The cookie in our experiment was the most desired bait and oil was second. The cookie however, does have a great amount of sugar but also carbohydrates as well. Differences in bait preference could be the ant species in the given area prefer different food sources, as our data came from Chattanooga and the data from the study comes from a location in urban Malaysia (Lee, C. Y.). Also, in 2016 during the drought we saw that ant diet preference shifted. The difference in results between locations and years could be due to environmental factors such as amount of precipitation as well as elevation of the area.

References

Graphs based on class data samples

Lee, C. Y. 2008. Sucrose Bait Base Preference of Selected Urban Pest Ants (Hymenoptera: Formicidae) Proceedings of the Sixth Annual Conference of Urban Pests.