Biophysical Lab Report free essay sample

There are many patterns that can be observed throughout our environment. In this experiment, the temperatures of organisms in a biophysical environment were analyzed to see if a pattern could be recognized that was related to the behavior of an ectotherm. An ectotherm is an organism that relies on the environment to regulate its body temperature. Organisms such as Pseudemys (turtles) and Lacertilia (lizards) are examples or ectotherms. After arriving at Maxcy Gregg Park and analyzing the temperatures of four microhabitats within two habitats with infrared thermometers, the temperatures were compared between the microhabitats. Then, one habitat was chosen to analyze the temperatures between temperatures of ectotherms using I-buttons that were placed inside Peeps. After the five Peeps? were placed in each microhabitat our predictions were that as the ectothermal organism increased in elevation, the temperature of the organism would decrease do to access to the wind and convection (Biology 301 Handout 2013 Thermal Enviroments). After gathering the temperatures from the I-buttons, over a twenty minute reading in each microhabitat the data was organized in Excel? to be placed into an ANOVA? calculation to calculate the null hypothesis. Once the p value was compared to alpha (. 05) it was determined to be significant because the p value was lower than alpha. Therefore it is clear that the body temperature of an ectotherm could be affected by the elevation of the organism and convection. For future experiments in this subject it would be more beneficial to test microhabitats that vary more in temperature to truly be able to analyze the factors of thermoregulation in microhabitats. Introduction: The thermoregulation of ectotherms such as reptiles and insects has increased in â€Å"concern about the impacts of global warming on biodiversity†¦ into direct impacts on living animals that remain simplistic† (Kearney, Shine, Porter, 1). Unlike mammals, ectotherms â€Å"have variable body temperatures. Because physiological rates are temperature sensitive, an ectotherm’s behavioral and ecological performance†¦ can be influenced by body temperature† (Huey, Kingsolver, p. 131-135). The habitats chosen in this lab were under a large oak tree and in a shaded area in the front of the park. The habitat under the oak tree was used to analyze the microhabitats within it, one at the ground of the tree and one on a branch of the tree, six feet above the ground. Through analyzing the microhabitats of Maxcy Gregg Park it was predicted that the elevation, related to wind availability and convection, is a pattern that could affect ectothermal organisms by decreasing the body temperature of the ectotherm as the organism increases in elevation. Materials and Methods: After the lab members arrived at Maxcy Gregg Park groups were formed to analyze the habitat of Maxcy Gregg Park (taking into account the water access, common vegetation, and five temperature readings) and recording the observations. Within one of the habitats the groups analyzed two microhabitats within the habitat. The microhabitats were found by analyzing the temperature differences within the habitat. The temperature differences of the two microhabitats were taken by using infrared thermometers. A group member held the thermometer very close to the microhabitat and pressed the button on top for five different temperature readings (in degrees Celsius), these temperatures were recorded. The groups then found another habitat within Maxcy Gregg Park and repeated the steps from the first two microhabitats to two more microhabitats within the new habitat. The groups then analyzed the recordings of the infrared temperatures within the four microhabitats. A hypothesis was constructed about the ectotherms ability to sustain in the microhabitats the group analyzed. Since two microhabitats were on the ground, with more coverage, and had a higher temperature than the two other microhabitats that were more exposed to conditions and were higher in elevation, the group concluded that if an ectotherm is higher in elevation and exposed to conditions such as wind the body temperature of that organism will be lower than an ectotherm on the ground. The group then chose one of the two habitats to further investigate its microhabitats. The habitat with the biggest temperature difference between the infrared readings of the two microhabitats was the one chosen by the group. The group used ten I-buttons, temperature-recording devices, and ten marshmallow Peeps to analyze the temperatures of ectothermal organisms within microhabitats. The small I-buttons were taken out of ice water and pressed into a hole that was made with then end of a pen cap into the bottom of the marshmallow Peeps. Then five were immediately placed into each microhabitat for twenty minutes. The time that the Peeps were placed into the microhabitats was immediately recorded. Twenty minutes later the stop time was recorded and the group took the I-buttons out of the Peeps, recorded the serial numbers of the I-buttons corresponding to the microhabitat they were placed in and then returned the materials to the lab TA and the Peeps were discarded. Back in the laboratory, the data from the I-buttons was collected and put into a spreadsheet by a TA and put of Blackboard. sc. edu for the lab groups to use to use the data. The groups gathered at a computer to put the data from Maxcy Gregg Park into Excel to eventually be able to run an ANOVA. First, two charts (Figures 1 and 2) were made, one for temperatures from microhabitat one and the next for microhabitat two (labeled Branch and Ground. ) The groups started by making a column in each of the two charts for time elapsed, then the temperatures for each minute (recorded from the I-buttons) were put into the next five columns (Biology 301 Handout, Graphing Populus Data pg 1-4). Then the average of each of the five I-button readings were put into the seven column of each of the two charts. To find the average in Excel they typed â€Å"=AVERAGE (highlight all cells to be averaged)† in the function box and selected the box where the answer should go. Then, dragged the blue marker in the corner of the box down the column to find all of the averages (Biology 301 Handout, Graphing Populus Data pg 1-4). The standard deviation of the five I-button readings was put into the eight column of each of the two charts. To find the standard deviation a group member typed â€Å"=STDEV(highlight all cells to be averaged)† in the function box and selected the box where the answer was needed. Then, dragged the blue marker in the corner of the box down the column to find all of the standard deviations (Biology 301 Handout, Graphing Populus Data pg 1-4). The confidence interval for each minute of the five I-buttons was then calculated and put into the ninth column of each of the two charts. To find the confidence interval, a group member typed â€Å"=CONFIDENCE(alpha (. 05, standard dev of the row you are calculating from, and the sample size (5))†in the function box and select the box where the answer was needed. Then, dragged the blue marker in the corner of the box down the column to find all of the confidence intervals (Biology 301 Handout, Graphing Populus Data pg 1-4). The final two columns of the to charts were the upper and lower confidence intervals for each microhabitat. The upper confidence intervals were calculated and put in column ten. A group member calculated the upper confidence interval in each chart by, finding the average (from the row of the same minute) for the microhabitat and adding it to the confidence interval, and repeated for every minute elapsed in both charts. The lower confidence interval was then calculated and put in column eleven of each of the two charts. This was done by subtracting the average for each row (minute elapsed) by the confidence interval for that particular microhabitat. This was repeated for each minute elapsed. Then a new chart was created in Excel (below the previous two, but on the same page) by copying and pasting the time elapsed column, then the confidence intervals, averages, and upper and lower confidence intervals for both microhabitats. Then the groups preformed an ANOVA in Excel by clicking in a blank cell and hitting â€Å"data† then â€Å"data analysis† from the Data menu. Single factor ANOVA was then selected, and a group member clicked the red arrow in the ANOVA menu to ‘input range. ’ Then by highlighting the entire data set needed for the ANOVA (the time elapsed, the average, the confidence interval, and upper and lower confidence intervals for each microhabitat) and clicking the red arrow to ‘output range’ and selecting a single blank cell to place the output. Then the group member selected â€Å"okay† and the p value was indicated in the ANOVA chart that was generated in Excel (Figure 4. ) After analyzing the p value given in the ANOVA to the alpha, the group found that the p value (. 305201) was less than alpha (. 05) and the null hypothesis was rejected. Then a line graph was generated in Excel to display the data from the I-buttons (Figure 5. ) By clicking â€Å"Design† and â€Å"Line graph† and selecting the type of graph the group wanted they were able to select the data from the charts that were already created to generate a line graph.