rdigregorio's blog

By analyzing the speed of the reactions performed and how the differences in temperature impacted them, we were able to conclude that the higher the temperature at which the reaction is conducted, the faster the reaction time, and the lower the activation energy. The previously calculated rate of the reaction helped us calculate K, and after converting the temperature to kelvin, we were able to graph the -Ea/R to show the activation energy. The increase in temperature would explain the decrease in activation energy because the higher the temperature, the lower the magnitude of the bond energy. This means that the bonds are more likely to break faster because they are weaker than they would be at cooler temperatures. This experiment was successful in explaining the connection between the temperature that a reaction is run at and the amount of activation energy needed to start the reaction.

In experiment 3, we were asked to take different volumes of H₂O, ~0.5M H2C2O4 and  ~0.02M KMnO4. We ran three different experiments with three different volume combinations using the burette to mix the solutions. We then timed how long it took for the solution to change color depending on the volume of each solution. After doing each experiment with three trials, we were able to find the relationship between volume concentration by calculating the rate.

The specific aims of this experiment would be to determine how the depth of the seeds in the sand they are planted in effects the growth and development of the seedlings. To demonstrate this we will be planting the seeds from Silybum marianum at different depths in sand. Sand is the typical terrain these plants grow in. They will be buried at depths of 2, 4, 6, 8, and 10 centimeters respectively. There will be three seedlings planted at each depth so we can account for any outliers, and to make sure we get at least one to germinate. After being buried the seeds will be put into the morrill greenhouse so they can stay at a constant temperature. When the seeds finally sprout out of the sand we will measure their lengths and compare the differences between the different seeds depths. That way we will find out what the effect of depth is on the development of Silybum marianum seeds.  

The specific aims of this experiment is to determine how the depth of the seeds in the sand they are planted in effects the growth and development of the seedlings. To demonstrate this we will be planting the seeds from Silybum marianum at different depths in sand. They will be buried at depths of 2, 4, 6, 8, and 10 centimeters respectively. There will be three seedlings planted at each depth so we can account for any outliers. After being buried the seeds will be put into the morrill greenhouse so they stay warm. When the seeds finally sprout out of the sand we will measure their lengths and compare the differences between the different seeds depths. That way we will find out what the effect of depth is on the development of Silybum marianum seeds.  

To conduct a time budget analysis, the entire 48 minutes and 55 seconds of footage was scored using the seven behavioral categories obtained in part one. Three specific bouts of behavior were also chosen to be scored: a playful bout, a bucking bout, and a standing still bout. The playful bout would be scored every time the horse approached with head up, nipped at a foal, and trotted away in that sequence. The bucking bout was categorized by ears back, bucking, and running away. The standing still bout was scored when the foal would survey, stomp hoof at the ground, sniff, and then graze. In addition, modifiers were used to determine the context each behavior occurred in; 1 being the foal behavior when alone, 2 the foal behavior with the other foal, and 3 the foal behavior in all contexts. This determined the percentage of time the foals spent in each behavioral category.

Animal communication is the study of the methods of transferring information from a sender to a recipient in the context of certain environments. For years, researchers have studied communication amongst species to determine evolutionary relationships between animals. Horses in particular offer a unique perspective on animal communication methods due to the fact they are a species that has been domesticated for hundreds of years. Better understandings of horse behavior has been known to increase the overall care and health of the animal, while also improving our scope of knowledge for horse behaviors and how it is used to communicate.

By analyzing the speed of the reactions performed and how the differences in temperature impacted them, we were able to conclude that the higher the temperature at which the reaction is conducted, the faster the reaction time, and the lower the activation energy. The previously calculated rate of the reaction helped us calculate K, and after converting the temperature to kelvin, we were able to graph the -Ea/R to show the activation energy. The increase in temperature would explain the decrease in activation energy because the higher the temperature, the lower the magnitude of the bond energy. This means that the bonds are more likely to break faster because they are weaker than they would be at cooler temperatures. This experiment was successful in explaining the connection between the temperature that a reaction is run at and the amount of activation energy needed to start the reaction.

After adding the solutions we tried our best to keep the temperatures constant as we timed the reaction. Once we noticed the color of each solution turn to a burnt yellow/orange color we concluded that the reaction was complete and stopped the timer. This data collected was our reaction time for the first trial. To get more accurate results, we performed the experiment again for 10℃, 20℃, and 30℃, and got the average reaction time at each temperature. With this information, we were able to calculate the rate of the reactions, which lead to determining the equilibrium constants, K, and eventually the slope of our observations and the activation energy.

After modeling two reserves after the example C, I wanted to increase the heterozygosity while increasing the number of runs without a lost of alleles. The best way to do this was to increase the size of the individual populations. I went from four to three habitats to achieve this and was successful. As you can see in the table above, the heterozygosity went up from 0.18 to 0.23, and there was one run more than the Reserve C that did not lose an allele. Out of 20 runs, these are very impressive results. This makes Reserve 1 the “best” fit reserve for the population of ferrets and will promote the success of more and more generations. In comparison to reserves A and B, Reserve 1 is much more successful. It is clear that the best reserve uses parts of each example to achieve a better grouping of habitats. In Reserve A, the size of the population was a successful way to promote the movement of genes through a population, however it reached fixation many times. In Reserve B, the generation of ferrets was represented in 4 separate habitats. This was not very successful as the habitats heterozygosity was minimized and fixation was reached very fast with such small populations. However, the idea to promote different alleles by separating the generation gave inspiration for genetic drift in Reserve C. As explained before, this reserve gave inspiration to both custom reserves, but itself was not successful enough because the distribution of populations was still too small. To avoid sampling error from small populations, and to still achieve the flow of diversity throughout the populations, Reserve 1 was the best model to save the ferrets with successful genetic drift.

After modeling two reserves after the example C, I wanted to increase the heterozygosity, while increasing the number of runs without a lost of alleles. The best way to do that was to increase the size of the individual populations. I went from four to three habitats to achieve this and was successful in my goal. As you can see in the table above, the heterozygosity went up from 0.18 to 0.23, and there was one run more than the Reserve C that did not lose an allele. Out of 20 runs, this is very impressive. This makes Reserve 1 the “best” fit reserve for the population of ferrets and will promote the success of more and more generations. In comparison to reserves A and B, Reserve 1 is much more successful. It is clear that the best reserve uses parts of each example to achieve a better grouping of habitats. In Reserve A, the size of the population was a successful way to promote the movement of genes through a population, however it reached fixation many times. In Reserve B, the generation of ferrets was represented in 4 separate habitats. This was not very successful as the habitats heterozygosity was minimized and fixation was reached very fast with such small populations. However, the idea to promote different alleles by separating the generation gave inspiration for genetic drift in Reserve C. As explained before, this reserve gave inspiration to both custom reserves, but itself was not successful enough because the distribution of populations was still too small. To avoid sampling error from small populations, and to still achieve the flow of diversity throughout the populations, Reserve 1 was the best model to save the ferrets with successful genetic drift.