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I thought that the author did a good job introducing his subject and setting up his argument and opinion by laying out the main points that he wished to expand on. However, the first issue that caught my eye was that the figures did not match up with the in-text citations. All 3 figures are labeled as “Figure I” but one in text citation refers to “Figure Ia.” Secondly the author points to many different areas of evolutionary temperature adaptation that need to be studied more, but often concludes with a statement about how we do not have a way to look into certain aspects. He does propose some solutions to these issues, but he also points out one glaring problem. Most of the studies that Clarke references have done studies on aquatic organisms rather than terrestrial species. It does not seem that we can necessarily apply the findings of temperature adaptation in aquatic organisms to those terrestrial organisms.

Clarke then shifts his focus to the ecological constraints of temperature, energetics, and life history. The cost of living for marine organisms in low temperatures is significantly lower than those in warmer tropic temperatures. Based on this, Clarke argues that a temperature related latitudinal variation in resting metabolic rates will lead to higher ecological growth in organisms at lower temperatures. Despite this, there is little data that show a connection between energy flow and food-web structures. He fights for a larger concentration on the implications of macroecological variations in energetics of organisms on food-web dynamics. Linking physiology with macroecology is difficult due to the varied findings in studies. We lack an understanding for how thermal physiology and climate determine biogeography

As a Biology major I am required to take an introductory statistics course during my undergraduate career. Personally I took AP Statistics senior year of high school and was able to test out of introductory statistics, so have not taken statistics for three years. Although I do not remember everything from statistics, I do recall main concepts that I have been able to apply to biology courses. One main takeaway from statistics I have been able to apply daily is creating and interpreting graphs. In statistics I grew in my skills in building and studying axes and graphical components. We learned how to create line graphs, bar graph, histograms, box plots, and more. We also learned how to create data sets and lists. Using these graphs and data sets we were able to compare mathematical data, and find correlations between different factors. We also learned that “correlation does not imply causation” when comparing data. In my current ecology course, we interpret data sets and graphs everyday, and apply these graphs to patterns in population dynamics and interactions between species.

    Aside from conceptual skills, I have been able to apply mathematical skills including chi square analysis and standard deviation. Chi square test for independence is used to determine if there is a significant association between two variables in biology. We used this in my genetics course to accept or reject hypotheses regarding gene linkage. Standard deviation is used to tell how measurements for a group are spread out from the average. We used this concept in basic math courses and calculus, as well as in our biology lab to see how far our observational results differed from the expected values.

Thermodynamics is a complex topic. One way that equilibrium is noted is with the letter “k”. K is the equilibrium constant, this metric is used to show the concentration of products in relation to the concentration of reactants at equilibrium. If k=1 at equilibrium then the concentration of products equals that of the reactants. Equilibrium means that the forward and reverse reactions are occuring at the same rate, it does not necessarily mean that the concentration of the products and the concentration of reactants are equal to each other. When k is greater than 1 that means that the concentration of products is higher than the concentration of reactants at equilibrium. Additionally, this means that products are favored when at equilibrium, this favor is not referring to what direction the reaction will move rather just what side of the reaction has a higher concentration. In order to determine which way a reaction will move you will also need to consider the metric Q. This is a different topic that I will talk about later on however it is related because you need it to fully assess a thermodynamic reaction. When k is less than one the concentration of products is less than that of reactants at equilibrium, meaning that the reactants are favored at equilibrium. K is only used when discussing a reaction at equilibrium.

There is an outstanding question posed by scientists: Can any animal survive without sleep? The facts are, scientists have not been able to truly find a sleepless animal but are not convinced that sleep is necessary for survival. A scientist conducted the first sleep-deprivation experiment in animals (ethical? definitely not.) The scientist kept puppies continuously awake and found that they died after a few days of sleep deprivation. Similar studies were performed on rodents and cockroaches and they all had similarly fatal results. Scientists, however, do know that multiple species can get very short amounts of sleep and be okay. For example, some flies hardly sleep, some averaging 72 minutes of sleep per day.

In the textbook and lecture, dopamine is described as a neurotransmitter involved in movement, attention, motivation, learning, and reinforcement. This article related to the motivational salience effect of dopamine as not only was the emotional pleasure of music compared to levels of dopamine, but the motivation to listen to the pleasurable music again was also measured. There is a common misconception that dopamine is the neurotransmitter responsible for pleasure. In fact, dopamine is more involved in mechanisms that can lead to these pleasures, especially motivation, learning, and reinforcement.

Although levels of dopamine have an impact on motivation and musical pleasure, I am curious as to what exactly causes this pleasure. Do the sound waves of the music stimulate a specific signaling pathway that leads to these effects? I know some music sounds “sad” and some sounds “happy;” exactly what in the music leads to us biologically establishing these connotations, and how do they affect levels of dopamine?

For cockatiels, the minimum power output was 1.3W at a speed of 5 ms^-1. The maximum power output was 3.7W at a speed of 14 ms^-1. For doves, the minimum power output was 4.3W at a speed of 7 ms^-1. The maximum power output was 7.5W at a speed of 17 ms^-1. The mass-specific power output of the dove was higher than the cockatiel at all speeds except 14 ms^-1.  Wing-beat in cockatiels was minimum at 9 ms^-1. It was 7 to 13 ms^-1 in doves. These results did not support the idea that minimum wing-beat frequency corresponded to the birds’ minimum power speed. Instead, they showed a close relationship between work and power. When compared to the mechanical power curve of the magpie, cockatiels and doves had a much higher mass-specific pectoralis power output. Although the magpie has the morphology and pectoralis muscles to support faster flight, the shape of their wings limited maximum forward velocity. Having broad, rounded wings and a long tail resulted in a lower drag/thrust ration and increased drag. On the other hand, the cockatiel and dove have pointed wings and experienced less drag and a higher drag/thrust ratio which allowed them to fly at higher velocities.  Additionally, magpies have a unique flight style in which wing-beat gait, flight velocity and altitude was constantly changed. This style could have potentially been limiting on power output.  Overall, the results of cockatiels and doves mechanical power curves did not support the aerodynamic theory at low and high velocities. The pectoral power used at slow speeds was predicted to be too high, and the pectoral power used at fast speeds was predicted to be too low. 

In the experiment of TLC, unknown 3 moved 2.1 cm. After conducting the experiment, the retention factors were calculated using Rf=y/x, ‘y’ was the distance the spot traveled and ‘x’ was the distance the solvent traveled. The average for each compound’s Rf was calculated. Aspirin’s Rf was 0.2115, Acetaminophen Rf was 0.358, caffeine's Rf was 0.089, unknown 1 Rf were 0.426, 0.089, and 0.375 and unknown 3 Rf was 0.360. Based on the Rfs and spotting, unknown 1 contains acetaminophen and caffeine. Unknown 3 contains acetaminophen but no caffeine. As a result, unknown 1 is not pure because it contains multiple compounds although unknown 3 is pure. Potential errors could be caused by human error if the solvent did not rise high enough.