The guiding question was how do different salinity concentrations affect daphnia ppt. survival? We hypothesized that if we are given five different salinity concentrations then we predict the low concentration of five and the high concentration of 35 ppt. will have the greatest effect on the daphnia not surviving. Then we used a statistical computing program named R to gather all of our data and formulate visuals for them. We then analyzed all of our data and observed various patterns and created our claim. Our claims were that if the daphnia are put into the salinity with a 35 ppt. concentration then the daphnia will not survive and our p-value output will be positive but very weak. We supported our hypothesis and we also rejected the null hypothesis.
Introduction:
This experiment allowed us to observe the effect of a natural stressor to the body of a daphnia species. The salinity within each concentration of water was an environmental change that put stress upon the daphnias’ body resulting in their decreased chance of survival. (Baillieul, 1998) The question that was asked was how do different salinity concentrations affect daphnia pp. survival? We know that daphnia are only found in small pools located along the Atlantic Coast due to their tolerance to salinity. The daphnia species are a part of the Arthropoda phylum and contain characteristics such as a body size up to 5mm, and a head shield. Daphnia can be found in lakes and small rock pools that contain a salinity concentration between 4 and 8 ppt. The reason why we hypothesized that the low and high concentrations used in this experiment would have an effect is because we know the daphnia could withstand a certain salinity. We also know that the amount of salinity present whether it may be considered low or high has an effect on the growth and reproduction of daphnia. (Ebert D., 2005)
Materials and Methods:
In order to complete our research, we first began by choosing three out of five salinity concentrations to work with. We chose zero ppt. as our control concentration, five ppt. as our low concentration, and thirty-five ppt. as our high concentration of salinity. We then distributed a total of five daphnia with a pipette, in each salinity concentration and proceeded to observe the specimens after twenty minutes. Our next task was to enter our data into an excel spreadsheet where we recorded the number of daphnia that survived within each different salinity. We saved that document as a textile to ensure that our data would be processed correctly in the software R studio. While using the R software we ran some codes and changed a few things within our data to get our graphs and results.
Results:
Here is a visual of the graphs and statistical results of our daphnia data. As I mentioned above we used the statistical programmer called R. R is a similar software to the MEGA program that is available for download on any computing database. R is simply a data analysis software that is used during scientific innovations and other industries.
• Side note for graph above it should read a, a, and b from left to right.
Discussion:
My group and I concluded that the daphnia specimen that were placed in a high salinity did not survive. We compared the number of specimens that survived in our control group to those that were place in the five and thirty-five concentration salinity solutions. To further our investigation, we had a class discussion similar to previous experiment before this. In order to completely accepted our hypothesized and be confident that the null hypothesis is void we had an argument based session which gave my group and I the approval to fully accept our hypothesis. Just as MEGA, the computing program R is a program used for statistical data ( Rfoundation , 2018). And in the real-world industries such as Mac makeup products need to know the statistics behind how much a product can affect a user. They need software like R to test products and see whether the effects are harmful to the human skin types.