Archive for March, 2016


The effects of extreme pH are known to effect enzymatic function in organisms. Extremes on either end of the pH scale can denature enzyme proteins and impede function. The goal of this experiment was to test whether pH extremes, (of 2 on the acidic side and 10 on the alkaline side), would register a discernable effect on enzyme function during fermentation.

To understand how to measure enzyme function, it is first necessary to understand how fermentation works. Fermentation is an anaerobic process, in that oxygen is not necessary during the process. Following glycolysis, pyruvate remains to be metabolized, which in an aerobic environment happens through the citric acid cycle. Lacking oxygen, pyruvate can either be converted into alcohol using alcoholic fermentation or lactate through lactic acid fermentation. During alcoholic fermentation, NADH and ATP created during glycolysis is broken down to NAD+ and ADP and inorganic phosphate to be reused in glycolysis. Also released is ethanol and carbon dioxide. The output of alcohol and CO2 are the best methods for measuring fermentation productivity. (http://www.nature.com/scitable/topicpage/yeast-fermentation-and-the-making-of-beer-14372813).

There is a good deal of experimental evidence that suggests yeast productivity is optimized in a pH environment of about 5. Many of the studies focused on the medium acidity range of 5.5 to 4, and not environments of extreme acidity. Furthermore, most of the available research focused on ethanol output, likely for its usefulness in aiding brewers of alcoholic beverages. (https://www.researchgate.net/publication/233755737_Factors_affecting_ethanol_fermentation_using_Saccharomyces_cerevisiae_BY4742, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1087585/, http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2672.1988.tb04312.x/full).

For our experiment we are particularly interested in the output of carbon dioxide as a result of exposure to extreme pH. Typically, when concerning enzyme function, it would be reasonable to hypothesize that extremes in pH will lead to the denaturing of enzymatic proteins, reducing their ability to function and thus their output. However, since carbonic acid results from CO2 released in fermentation reacting with water, it is possible that the carbon acid would neutralize the basic solution, reduce the pH of the control and buffer the more acidic solution back toward neutrality. If this is the case, the extremes could function with relative normality, in relation to the neutral solution. (http://ion.chem.usu.edu/~sbialkow/Classes/3650/Carbonate/Carbonic%20Acid.html).



The experiment was conducted by making three 9ml solutions (9ml being constant). The independent variable being pH, the control group lacks a pH buffered solution, but contains distilled water (6ml), corn syrup – diluted to 50% (1ml) and a yeast suspension (2ml). The alkaline solution consists of no distilled water, corn syrup (1ml), yeast suspension (2ml) and a buffer solution with a pH of 10 (6ml). The acidic solution was made with no distilled water, corn syrup (1ml), yeast suspension (2ml) and a buffer solution with a pH of 2 (6ml).

The three solutions were added to test tubes labeled “control,” “tube 1” (pH 10), and “tube 2” (pH 2). The test tubes were sealed with a rubber stopper with tubing leading to a second test tube filled with water and placed upside down under water in a beaker. The starting air pocket in the test tube was marked with a wax crayon on each of the test tubes, and each tube was marked again at 5 minute intervals (another constant). The experiments were placed in a water bath of 45 degrees Celsius, to keep temperature constant during the experiment’s duration. Finally, following the experiment, each group was tested with a pH buffer strip, to see whether the solutions had changed pH over time.



6 – test tubes

3 – rubber stoppers with tubing

3 – beakers

5 – pipettes

1 – metric ruler

1 – wax pencil

3 – pH buffer strips

6 ml – distilled water

3 ml – corn syrup (1 ml for each group)

6 ml – yeast suspension (2ml for each group)

6 ml – pH 10 buffer solution

6 ml – pH 2 buffer solution



The results of our experiment did not entirely confirm our initial hypothesis. We intended to run our experiment for 30 minutes, but only made it to 10 minutes with 2 of the 3 groups. The control group performed best, with tube 2 (pH 2) achieving similar results, while tube 1 (pH 10) performed poorly. With the control group, the air had been displaced by 38mm after five minutes and 110mm after ten minutes. The 15 minute mark was not reached, as the size of the test tube was exhausted. Tube 1 (pH 10) reached only 8mm after five minutes and 10mm by ten minutes and remained at that level at fifteen minutes. Tube 2 (pH 2) reached 40mm by five minutes and 74mm by ten minutes, and had also exhausted the size of the test tube by the fifteen minute mark.

The starting pH for the control was 7 and had departed to 4 by the end of the experiment. The starting pH for tube 1 was 10 and had departed to 8. Finally the pH for tube 2 started at 2 and ended at 4. This shows that for the lower pH range our hypothesis was largely confirmed, but was only partially confirmed for the alkaline group.


Table 1

Test Tube # 0 min 5 min 10 min 15 min
Tube 1 0 38 110
Tube 2 0 8 10 10
Tube 3 0 40 74



Fig. 1

Yeast figure


Both high and low pH extremes tend to denature enzyme proteins, reducing their ability to function. Thus it would logically follow that our results should mirror this. However the internal chemistry of organisms often have mechanisms for buffering high and low pH to maintain equilibrium. This experiment exhibited both this capacity for buffering, as well as the limitations. As was seen in the alkaline experimental group in test tube 1, the solution of pH 10 buffered to 8 neutral, but failed to make it all the way before function ceased. This is still impressive because the final result was a solution with a pH 100 times less basic. In the case of the acidic experimental group in test tube 2, the pH buffered back to a pH of 4, or 100 times less acidic than the original solution. Both solutions were able to use carbonic acid to neutralize, but enzymatic function was considerably better in an acidic solution.

The original hypothesis stated that both solutions would be able to buffer enough toward neutral to function with relative normalcy. This assumed that yeast enzymes functioned optimally at a neutral pH, and that tolerable departure from neutral would be the same in both directions. Through research it was discovered the yeast function better in mildly acidic environments and through experimentation it was show that yeast toleration to acidity is much higher than its toleration of alkalinity. The original assumptions were not verified by this experiment, and future experiments would be useful to determine what degree of acidity is tolerable to yeast enzyme function.

This experiment, while able to offer valuable information feel short in some important regards. Mainly, the test tube size proved too small to appreciably handle the amount of CO2, and limited the length of time the experiment could be conducted. This also limited the results to only 2 data points instead of 6. More data would have given a higher degree of confidence in the results. That said, the experiment was not inconclusive and did offer useful information.


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The argument for more certainty in climate science strikes me as intentional self-delusion about how science works. Those who invented the argument seem to understand the uncertain nature of science (see Merchants of Doubt), but manipulate the argument for a culture increasingly driven by click-bait. (i.e. Science proves ___).

To start at the beginning, science is inherently uncertain. Because it is a process driven by skepticism–in which claims are verified by experiments that either confirm or deny the original suppositions–it is impossible to arrive at a conclusion with 100% certainty. There is this idea that laws are infallible and theories are “just theories,” but they both contain a degree of certainty and uncertainty. When a phenomenon is observed consistently enough times, its occurrence becomes a scientific law. Theories are explanations of why these phenomena occur the way they do. Both can be disproven by a negative experiment, but neither can be proven to the point where experimentation is no longer necessary.

Anthropogenic Climate Change is a theory that explains observed data on greenhouse gas concentrations, its relationship to fossil fuel burning, and climate data. The theory changes in particulars occasionally, but has remained relatively stable in its broader implication, despite countless experiments. We are at a point where Climate Change theory is about as widely accepted in the scientific community as the theory of how gravity works (a theory almost everyone accepts).

Furthermore, the positive implications of using renewable energy provide us with more good than just addressing climate change. The list includes energy independence, less air pollution, not subject to wild price fluctuations (since it is renewable), creates manufacturing jobs, etc.. There are some negatives. Wind farms kill migratory birds. Solar requires the mining of rare earth metals. Hydroelectric impacts fisheries. Nuclear energy has the potential for meltdown. Still, the picture looks much better than that presented by fossil fuels, which also degrades the environment, contributes to human health issues and leads to dependency on unstable and limited sources.

If you take both the scientific consensus of global warming, and the realities surrounding energy generation, it is a no brainer that we need to change our energy economy.

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A ways back I wrote a primer on woodlot mensuration to illustrate the value of woodlots as net carbon sinks. As I’ve learned more about forest ecology, I have gotten better at measuring and knowing which equations to use when. This post largely serves to update past posts on the subject.

The first thing one must do when attempting to conserve land is define what is being conserved. For the sake of this post, we’ll call it the wood lot behind my house:

Management Plan1

This NDVI map shows vegetative thickness as a categorized raster. The closer to 1 the higher the vegetative thickness. The woodlot is about 1 acre, and in forestry it is standard practice to take 1/10 of an area’s measurements, and extrapolate out for the rest of the area. Thus the circular plot amounts to 1/10 of an acre.

To establish the plot, I marked a center point and drew out a tape measure 37 feet out in all the cardinal directions. I took the diameter of all the trees within this established plot. The total diameter of all the measured trees was 100 inches, and the total height was 280 feet.

To find basal area for all the tree trunks the equation A = πr^2 is used. This amounted to 1584.92 in^2 or 10.85 ft^2.

To find stand volume the equation V=A*H, which gives us 532.62 ft^3.

Finally to find biomass we take the approximate density (in oak/hickory type forests this is about 40 lbs/ft^3 and plug that into the equation M=D*V  to get 21304.87 lbs or 10.65 tons. Of this about 14% is Carbon, so 1.49 tons.

These numbers are all for the test plot. By multiplying by 10 we see there are about 106.5 tons per acre in biomass and about 15 tons of carbon stored per acre.


These numbers are much more conservative than the original estimates, but result from a more intensive survey.


south stand data



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Hawk Migration on the Kittatiny Ridge and Climate Change


By Glenn Nelson



ABSTRACT: Much has been recorded about the phonological changes in the migration cycles of birds, as related to climate change. These studies typically focus on modeling for future changes in migration dates for a given range of species. However, there appears to be little focus on recording the change in numbers of birds seen in migration, as they relate to temperature and weather phenomena. This study hopes to find first, whether climate change is observable over time in a given location, and second, whether those changes appear to have a correlation with the total number of migrating hawks. Anecdotally, it has been noted that certain species will stay in their breeding ground as long as there is food source availability. Thus, we hypothesize that as temperature rises, fewer birds will get the impulse to migrate during the typical migration season, (or in warmer years at all), lowering the number of birds observed during the migration period. Initial results from a statistical analysis of NOAA weather data and migration data from Hawk Mountain suggest that there is a likely correlation between warming regional climate and lower than average hawk flights, particularly since the 1980s, which has been a period of increasingly rapid warming. There are a few possible explanations for this change. While this study hypothesizes fewer birds are migrating, it is also possible they are using thermals instead of the updrafts found along the mountain ridges. Whichever explanation accounts for the observed changes, it still represents a change in migration behavior, which could have ecological consequences.





Hawk migration has long been seen as a harbinger of seasonal changes, associated with the coming winter. However, less attention has been paid to how the phenology of hawk migration may change over time, on the various hawk watches in North America. While still politically debated, the scientific community has come to a consensus that the world is warming and that this effect is likely anthropogenic.[1] However, the warming effect is not universal, and climates are changing in a variety of ways. Thus it is important for this study to establish a trend in temperature within the given study years, as a primary goal, before trying to understand the relationship between temperature and hawk migration.

The usual volumes associated with hawk watching in the northeast, such as Pete Dunne’s Hawks In Flight and Jerry Ligouri’s Hawks From Every Angle, generally chart when to expect high flights of certain species.[2] (An example of such migration time tables can be seen in Figure 1.1).[3] Looking at these charts, the question arises as to whether such projections change and whether any changes are associated with climate change.


Fig: 1.1



There is a good deal of available research which has produced projections about the changing timetable for raptor migration, but the available research focuses more on the changing dates of a specie’s migration, as opposed to whether the total numbers changed as well. For instance, a 2012 study by Josh Van Buskirk, asserts that:

“The migratory period has become more extended, especially for short-distance migrants. Opposite responses during the two seasons had the effect of extending time spent to the north of the study area, by up to 30 days in some species since the early 1970s. These phenological shifts—potentially related to climate change— are causing dramatic changes in the annual cycle of North American raptors.”[4]

Another study, conducted in Europe by Mikael Jaffre, (et al), resulted in similar findings.


“We found that when the temperatures increased, birds delayed their mean passage date of autumn migration. Such delay, in addition to an earlier spring migration, suggests that a significant warming may induce an extension of the breeding-area residence time of migratory raptors, which may eventually lead to residency.”[5]


Yet, a change in total numbers should also be concerning for two reasons. First, hawk migration data is often used to estimate the health of a species population. This fact is highlighted in a forest service study by Kyle McCarty and Keith Bildstien. Their study points out that, “One particularly cost-effective method for monitoring populations of these birds is to sample regional and even continental populations at traditional migratory bottlenecks and concentration points.”[6] Second, even if the population remains healthy but the migration numbers change, it could signal a change in habits that may have detrimental effects to a species, or other ecological repercussions within the bird’s traditional range. For instance, if a species lingers too long in their breeding range, they may stress available resources in the breeding area, or be surprised by a rapid change in weather such as an unusually cold year. Furthermore, prey species may suffer from exponential population growth in areas that were in the hawks’ previous migration and southern range. Similar relationships have been noted in lower trophic levels, when keystone predators are removed from ecosystems.[7]

Inevitably, there are a number of factors that complicate the results. First, the period of acceptable data has been shortened to control for DDT. As was evidenced by Rachel Carson, the spraying of DDT had an effect on migratory bird populations, including raptors.[8] From 1934 to 1972, DDT spraying was allowed in the United States, (as well as in the migration range countries, where DDT still sometimes persists). DDT has since been illegalized in the United States, (1972).[9] It is clear that numbers jump drastically between the 1960s and the 1970s, as the DDT ban was enacted and conservation efforts for migratory raptors were put into place, (fig 1.2). Since there is no effective way to control for this effect, data prior to the illegalization of DDT was discarded.



Fig: 1.2

Decade Averages

Decade 1=1950


There are also issues in the standardization of the data, since the period of the hawk watch varies from year to year, and from weather and other factors. From 1943-to1945 no hawk watch was conducted, due to World War 2. More telling results could be gleaned from comparing population numbers to the percent of migrating birds, but systematic data on that topic does not currently exist, and collecting it would present logistical challenges.

Other issues must also be raised as possible factors. I had been informed, and since found anecdotal accounts in both Jerry Liguori’s Hawks From Every Angle, as well as Don Heintzelman’s Guide to Hawk Watching in North America, as well as research from Tarra E. Gettig, that hawks have a tendency to fly in advance of and behind certain frontal systems.[10] Since the number of frontal systems affecting this area per year is difficult to accurately calculate, it is not a factor included in this study. However, it is something that should be looked at in the future, in order to glean the full picture of what weather processes are affecting migration.

There is also research to suggest that hawks may be using thermals if the temperature is high, or if wind is unavailable. When hawks use thermals, they soar much higher, and are thus harder to observe. This is what is posited in Michael J. Lanzone’s study “Flight responses by a migratory soaring raptor to changing meteorological conditions This offers an alternate explanation for changes in migration numbers.”[11]

Overall, this study hypothesizes that, first, climate change will be observable in the temperature data, and may be observable in the rainfall data. I expect that this will affect migration numbers negatively and residency numbers positively, though the effect is likely gradual, requiring a large data set, or decade means, to see the trend.



Fig: 2.1

Decade Averages



This study is designed firstly to establish whether climate change is occurring in the Lehigh Valley area of the Kittatiny Ridge. To this end, climate summary data for the study period was requested of and provided by NOAA, for the Allentown weather station, ______ miles East of Hawk Mountain. All data were collected for the period of the fall hawk watching season on Hawk Mountain, between August and December. This data was used to establish decade means and charted as the dependent variable, while the decade functioned as the independent variable. (fig 2.1) Temperatures were measured in degrees Fahrenheit, in order to be more relatable to a domestic audience. As can be seen in figure 2.2, the decade mean for temperature generally rose, excepting one anomaly. The temperature for the period of the 2000s was 53.6 degrees (f), about a degree above the period average.


Fig 2.2:

Average Yearly Temp


Yearly hawk totals from Hawk Mountain Sanctuary in Kempton, Pennsylvania were then plotted as the dependent variable, (fig. 2.3). This showed us both the average flight, as well as the trend over the study period.



Fig 2.3

Hawk Per Year Decade Average


Precipitation data was collected similarly, and used as a stand in for weather variability.

Fig 2.4

Average yearly rainfall


The data were then compared using standard deviation to show variability, and pearson’s correlation coefficient, using the following equations:



Standard Deviation:



Pearson’s Correlation Coefficient:





Finally, data were collected from the Winter Raptor Survey results from Pennsylvania Birds Magazine.[12] The data are only available during the 2000s, and are thus of limited applicability, but can be used to suggest a possible explanation for the observed phenomena.


  1. Results:


The following data tables represent the results from the statistical analysis at hawk mountain, (Tables 3.1 – 3.3) and (Figures 3.1-3.2)



Table 3.1

Decade Averages Temp Rainfall Number of Hawks BW SS RT
50 50.8 19.4 15535 8600.63 2858.55 2539.09
60 51.3 16.9 16162 9729.82 2187.18 2788.82
70 52.2 20.4 21513 11011.82 5535.55 3393
80 52 17.6 22042 8220.82 7190.09 4035.18
90 52.5 18.7 18631 6426.27 5316.36 3676.64
00 53.6 20.5 17971 7052.55 4397 3090.36
correlation 0.3788 0.1126 -0.4651 0.4459 0.4260
correlation from 1980s -0.8336 -0.8725 -0.4645 -0.9179 -0.9972
correlation from 1970s -0.852923146 -0.399197261


Fig. 3.1: X= Temperature, Y=Hawk Migration Numbers




Fig. 3.2: X=Rainfall, Y=Hawk Migration Numbers




Table: 3.2

Year 1950-1960 1960-1970 1970-1980 1980-1990 1990-2000 2000-2010
Average Temp. (F) 50.8 51.31 52.15 52 52.46 53.63
Average Rainfall (In) 19.44 16.9 20.41 17.62 18.71 20.53
Average Number of Hawks 15535 16162 21513 22042 18632 17971
Sharp-shinned 2859 2187 5536 7190 5316 4397
Broad-winged 8601 9730 11012 8221 6426 7053
Red-tailed 2539 2789 3393 4035 3677 3090
Standard Deviations
Temp. 1.14 1.36 1.69 1.44 1.49 1.53
Rainfall 3.75 2.87 4.28 4.81 4.37 4.92
Number of Hawks 3008 2170 8679 4202 4105 3606
Sharp-shinned 800.3763319 564.5103751 3155.206788 1997.933055 1146.100543 1057.040586
Broad-winged 2443.611642 1709.996188 6480.008084 3327.400451 3009.409214 2702.533158
Red-tailed 714.1127998 633.1852522 1010.681651 839.4813659 889.580606 861.2376295


Table 3.3

Year Winter hawks RT Temp Rainfall
2010 5915 2665 53.1 19.96
2009 5789 2275 53.3 20.13
2008 5634 2390 52.3 21.68
2007 4948 2218 55.2 19.68
2006 5359 2184 54.9 18.5
2005 4512 2610 55 23.22
2004 3959 2052 53.6 26.01
2003 2374 1182 54.4 29.64
2002 2539 1399 52.8 19.75
2001 2192 1141 55.1 10.55




Statistical data do not always tell their own story (or they tell multiple stories depending on how they are interpreted). As has been shown over the years by the climate change debate, data can be less or more convincing, depending on how it is analyzed. This study looks at total hawks per year, averaged over a decade. It can be said that this was done to improve R2 values, in order to strengthen certain conclusions. Certainly, when looked at on a closer scale, like taking yearly averages as individual anomalies and running a correlation, the R value is reduced. However, this was not our justification for looking at means.

The justification for using decade averages has to do with the high degree of variability in individual anomalies, concerning hawk migration data. As has arisen in the climate debate, you may have hot years and you may have cold years, but individual anomalies do not establish a larger trend. In the case of looking at long term trends, relationships can be obscured by scaling too close, and what may be only a small change from year to year presents itself as quite pronounced over time.

When looking at hawk data (fig 3.3) this is particularly pronounced. You have good years and bad years, and there don’t appear to be any particular reasons for the individual anomalies. However, the means help to smooth out the often pronounced outliers, (such as 1979, which had more than 40,000 hawks, in what is the single biggest year in Hawk Mountain’s history).

If we go back as far as 1930, and we look at the lowest outliers, there is certainly a linear improvement. However, the number of days on the lookout has also improved, as has the reliability of the count’s protocol. This is a problem with the Winter Raptor Survey data as well, since in recent years count hours have gone up, alongside the number of resident hawks.


Fig: 3.3



There are a number of problems with looking at the data in this kind of resolution, since there are changes in variables, through a number of periods during the study. The charted data showed lower than average flights through most of the DDT period. In the period immediately following, there appears to be a bump, followed by a gradual decline. The polynomial trend line does a much better job of expressing the shifting periods, than does the linear trend line. This helped to separate the data by three distinct periods. The DDT era, the post-DDT recovery, and a notable decline.

The first period was defined as 1946-1974, since this period was dominated by the effects of DDT, as a limiting factor to hawk populations. In this period we see far fewer than average hawk flights, at 15,458 birds per year. We also see below average precipitation and temperatures. Despite that fact, there is a warming trend present, even in these years. Between 1950 and 1970, the average decade temperature increased by nearly 2 degrees (f). In this period, the correlation coefficient for temperature was -0.25. This aligns with the hypothesis, that as temperature increases, fewer birds would migrate, though, during this period the effect is slight. Furthermore, the correlation coefficient for precipitation is .011, which is too weak to suggest any relationship.

The next period, 1974 to 1992, represents what would appear to be a recovery from DDT and a relative temperature stagnation. These two phenomena transpired to produce higher than average flights for the period at 22,933 per year, on average. The correlation between temperature and hawks for the period was

-0.068, which is too weak to suggest any relationship. What is more, the -0.14 coefficient for precipitation suggests that there is a weak inverse relationship between rainfall and hawk numbers.

The final period looked at, was between 1980 and 2014. While there is an overlap with the previous period, there seemed to be certain trends in the charts, which needed to be followed back to 1980. In this period, mean birds per year fell to 19,540, which is 225 below the average for the whole study period of 19,765. Furthermore, both average temperature and average rainfall were higher during this period. Both temperature and precipitation correlation coefficients increased their previous trends to -0.097 and -0.227 respectively. Both of these trends remain slight, but suggest a very gradual, possible relationship, where both temperature and rainfall adversely effect hawk flights.

Finally, the data was broken up into decade averages between 1970 and 2010. By zooming out on the data, certain trends did emerge as more apparent. First, through the 1980’s bird numbers are on an increasing path, which suggests that there has been a certain level of success in conservation efforts. However, the 1990s and 2000s show a marked decline in those averages. The averages remain higher than they had been in 1950, but suggest an alarming trend. Furthermore, by averaging, we can see a steady rise in temperatures, though there appears to be three segments. Temperatures rise into the 1970s, stagnation for a decade, and then rising rapidly (by 1.1 degree (f)) between the 1990s and 2000s. In that same period, average hawks per decade drop off by 340. While, if we look at the correlation coefficient for the averages of each decade since 1950 we get the slightly positive .0379, if we look at it just spanning the high point in the 1970s, up to 2010, we get a more alarming coefficient of -0.85. The precipitation averages also rise in this period, and amount to a coefficient with hawk numbers of -0.4. These numbers are suggestive of a strong relationship between high temperatures, high rainfall and fewer hawk flights. While the temperature relationship relates closely with the hypothesized results, the precipitation data seems somewhat less suggestive of a relationship between variable weather and hawk flights.

While over the study period the story appears to be a slight linear positive, a closer examination of recent trends is more alarming. By putting the data into three periods we can see the gradual changes, but by averaging the decades and examining the relationship more closely, we can see a more startling trend, which appears to establish a strong relationship between rising temperature and lower average hawks per decade. Overall, 72.25% of the variation in average fall hawk numbers by decade can be described by a negative linear relationship with temperature. Only about 16% of the variation can be described by a linear relationship with precipitation in inches.

Finally, in looking at the decade of data from the Winter Raptor Survey, we can see a that hawks observed per year is rising, though with negligible correlation to fall temperatures. While the data is incomplete, and can’t be fully analyzed into decade means, there are a few emergent trends worth noting. First, 2010 saw 5915 hawks observed, which is 1,593 birds more than the period average of 4,322. There is a P = -0.26, which is a bit below statistically significant and suggests that only about 6-7% of the variation in hawks can be described by the fall temperature anomaly. This result may be more significant if averaged out.



Fig: 3.4





In the process of conducting this study, a number of questions have arisen. First there has been the problem of accounting for the DDT period and the period immediately afterward, (which appears to exhibit a recovery). While this is a very important finding, it is not helpful to the study, because the exceptionally low number of hawks in the DDT period creates an artificially low floor, while the recovery due to conservation efforts presents an artificially high ceiling. In many ways, it shows that human effort has helped to solve a human problem, which is optimistic for future challenges. The idea that we can overcome our mistakes once they are identified is encouraging, but the problem of identifying the problem remains.

There were certain trends, which appeared from the data to be correlative, that emerged when looking at the data from the height of the recovery period (1970) into the present. First, temperatures, which had stagnated during the recovery period rose after 1980; at first gradually and then rapidly after the year 2000. Furthermore, there appeared to be greater disparity in the scatter in rainfall data in the current period. These trends are magnified when examined by decade.

The increase in climate change related measurements, and a significant drop in birds per year, appears to have a significant, if gradual relationship. This relationship may have significant ramifications ecologically. First, as the paper by ____ notes, migration data is one of the cheapest ways to monitor hawk populations. If migration is occurring less frequently, or differently, it will be more difficult to monitor populations. Birds that stay in their breeding range to the north longer would likely effect both their breeding habitat and their wintering habitat, by stressing prey populations in one and not limiting them enough in the other. Even if that is not the correct assumption, and hawks are just travelling at higher altitudes on thermals, as ___ suggests, this is still significant, as it impedes our ability to monitory the populations of migratory raptors. This study also has ramifications for phenology, as hawk migration is seen as a harbinger of the changing seasons.





While I can see that there may be concerns that the decade averages could be construed as an alarmist manipulation of statistics, I believe that it was necessary to view the data on such a scale, in order to establish long-term trends. While the overall trends show a growing number of migrating hawks since the 1930s, which do not correlate with climate change, (when plotted in a linear regression model)—the polynomial projection suggests a different relationship; depending on what period you are focused on. Averaging decades and discarding data that presented too many variables provided an amplified and simplified window for comparison. I was able to observe how higher and lower numbers, in each category, were skewing the mean in different periods. Furthermore, the patterns that appeared gradual, in a year over year analysis, emerged in a more pronounced manner. The years where DDT likely effected hawk populations were particularly apparent, as were the recovery years. The recovery years appear to be aided by a stagnation in temperature, as well. However, as the world economy began growing again in the 1980s, and greenhouse gas emissions rose precipitously though the 1990s (citation needed), many of the gains made by conservation efforts in the 1970s have been lost. The relationship between higher temperatures and lower decade averages of hawk numbers, since 1970, is thus an alarming one.

When we look at the data we see this process, but it occurs gradually, as indicated by the polynomial line on figure ______. There are waves of both progress and setbacks, that cannot be shown by a simple linear projection, even in the decade means. It is important to isolate the current period, one which is nearly a degree Fahrenheit warmer than the study average, and which has risen at a faster rate, from periods where other factors were likely dominant. Even when we look at the yearly averages, (instead of the decade averages) the correlation exists, but it is not as drastic, because we see it spread out over time. The decade averages isolate each period, and make a vague picture clear.

I think it is important to acknowledge how gradual this process has been, and that the low rate of change certainly effects the correlation coefficient. That said, the data set does tell a story. For instance, there have been 21 years in which high temperatures correlated with low flights, 11 of them occurring in the period of rapid warming after 1980. Higher than average precipitation correlates with lower hawk flights for 23 of the study years, but only 8 since 1980. Meanwhile, there has been an increasing frequency of higher than average temperatures since 1980, with 24 higher than average years, including every year since 2001. This compares with 13 before 1980. In that time, there has only been one year significantly below average. In the same period, there have been 14 years with higher rainfall than average. The frequency appears to have accelerated since the year 2000, with half those years occurring in that period. The number of lower than average rainfall years since 1980 have also increased from 5 in the previous 46 year period to 5 in just 34 years, with 2 since the year 2000. This is suggestive of more extreme and variable weather. There have been 15 years with lower than average hawk flights in this period, 10 of them since the year 2000. These numbers are why I think it is important to focus heavily on this period.

When we look at the R value between temperature and hawk migration numbers between the decades of 1970-2000, the value is a statistically significant -0.8529. When that is couple with a 0.73 R2 in a linear regression model, with a slope of -2434.419, it is clear that there is a strong relationship with temperature, that cannot be ignored. While there can be a number of explanations for this trend, all of the explanations have to acknowledge this relationship. The probability exists that 72.25% of the variation in hawk migration numbers can be described by the hypothesis of y= 2434.4x + 148029. In other words, there is a high probability that temperature is affecting hawk migration. This cannot be said with more certainty, because there are too many variables, and hawk migration itself is extremely variable.

We still have a net positive picture, as even the decade averages show. The decade of the 2000s has better hawk flights than that of the 1950s, when DDT was an active problem. If we plot the data in a linear manner, we have an upward trend. However, since the 1980s that trend has reversed, and that reversal has accelerated in the 2000s, in correlation with temperature and to a lesser degree rainfall.

I believe that this evidence is suggestive, then, of a change in migration that is likely due to climate change. The process is gradual, and it would be expected to be, but it appears to be occurring. While there are certain ways that this study could be strengthened, I think it shows that this understudied area would benefit from a greater intensity of study.


Appendix 1: Hawk Migration Data from Hawk Mountain Sanctuary and Weather Data from NOAA.

Year Temp Percipitation BW SS RT # of Hawks
1934 50.9 22.83 3 1703 5426 7874
1935 50.3 20.01 3873 4168 3214 14681
1936 50.7 15.45 6990 4406 3162 16083
1937 50.8 14.85 4343 4791 4932 15446
1938 52.1 18.16 10754 3105 2228 17007
1939 51.4 10.84 5736 8620 6208 22488
1940 49.5 17.45 3159 2406 4725 11228
1941 53.3 11.68 5170 3908 4698 15424
1942 50.5 25.71 4362 3200 2378 11014
1943 49.8 17.06
1944 50.6 14.41
1945 50.2 20.88
1946 52.8 14.62 2886 2382 2306 8729
1947 52.3 13.12 6664 1726 1680 11366
1948 52.3 16.07 15026 1650 2343 20483
1949 52.5 17.02 9579 2963 2749 17092
1950 50.8 16.19 5305 2667 3674 13366
1951 50.8 22.5 10997 3008 2307 17890
1952 51.5 26.45 12603 3566 2754 20737
1953 53 16.87 7247 2791 2051 13542
1954 51.6 20.44 5956 3183 2070 12606
1955 51.1 24.19 9542 4709 3764 19867
1956 51.8 16.7 8734 2048 1525 13469
1957 52.4 15.11 8935 2662 2730 15858
1958 49.9 15.58 8880 1752 2951 15128
1959 53.8 19.99 5301 2825 1904 11585
1960 50.6 19.83 11107 2233 2200 16832
1961 53.5 12.5 8642 1723 2566 14716
1962 49.1 20.59 8254 2181 2772 14651
1963 50.9 18.4 9791 1518 3402 15900
1964 51.7 11.62 10180 1259 2626 15202
1965 51.7 15.12 9235 3103 3297 17371
1966 51.4 18.73 10110 2883 2126 16582
1967 49.8 16.37 8000 2330 1854 13604
1968 52.5 16.38 14041 2253 3765 21789
1969 50.2 17.85 8515 2670 3566 16176
1970 53 18.6 9153 1906 2503 14960
1971 53.8 24.17 5603 2135 1781 10536
1972 50.8 22.24 8131 2233 3463 15285
1973 54 20.17 6404 3347 3098 14448
1974 51.3 24.03 9146 4477 3658 18519
1975 53.1 22.75 10390 5354 2880 20121
1976 48.1 18.48 8461 5376 3694 18941
1977 51.7 25.78 13009 10612 3504 29123
1978 51.8 19.18 29519 6826 2852 40576
1979 53.1 19.1 11173 10306 4175 27639
1980 53 10.01 10141 8319 5715 26495
1981 50.3 11.95 8660 9464 3939 24890
1982 52.8 16.54 7163 4541 5025 18742
1983 52.4 22.53 6922 6517 3954 19681
1984 54.3 14.27 13619 3796 3157 22343
1985 52.3 23.6 3415 5766 2895 13931
1986 50.9 19.46 13996 9239 3305 29200
1987 51.2 24.07 8409 6776 4215 22366
1988 51.2 14.1 5944 6714 4687 20034
1989 49.8 16.5 7504 9832 3710 24700
1990 53.8 20.78 4656 8127 3785 20084
1991 53.3 14.41 5858 5678 2970 17219
1992 50.6 17.68 10661 4629 3288 21125
1993 52 26.26 3592 5449 3744 15829
1994 53.7 17.34 3513 4934 4433 15713
1995 51.3 16.68 10077 6217 4854 24363
1996 52.2 24.85 1809 4468 2734 11589
1997 51.4 15.81 5519 4218 2402 15533
1998 54.6 12.6 9935 5835 4331 24238
1999 54 22.75 8634 4416 4999 22491
2000 50.2 16.68 6435 4509 2903 16767
2001 55.1 10.55 3843 4817 3741 16137
2002 52.8 19.75 12228 3211 3499 22212
2003 54.4 29.64 6134 3651 3385 16474
2004 53.6 26.01 6387 2958 2847 15027
2005 55 23.22 5273 4545 4551 18346
2006 54.9 18.5 11804 5480 3898 24940
2007 55.2 19.68 7836 5099 2426 19495
2008 52.3 21.68 4289 3358 1807 12205
2009 53.3 20.13 6640 4299 1762 15590
2010 53.1 19.96 6709 6440 3175 20492
2011 55.7 40.07 13323 4447 1697 22902
2012 54.2 18.97 8394 5222 2876 20078
2013 52.4 21.41 6430 3772 2030 15271
2014 53.8 12.7 6369 4772 2266 17415



[1] UN climate report.

[2] Ligouri: pg 11, Heintzleman: pg 79-80.

[3] Hawk Mountain

[4] Buskirk: pg 1. http://www.zora.uzh.ch/70159/1/auk%252E2012%252E12061.pdf

[5] Jaffre, et al: pg 1.

[6] McCarty and Biddlestein: pg 718. http://www.fs.fed.us/psw/publications/documents/psw_gtr191/psw_gtr191_0718-0725_mccarty.pdf

[7] environmental sustainability

[8] (citation from Silent Spring).

[9] Citation needed

[10] Liguori: pg 10, Heintzleman: pg 77-84, Gettig (http://www.gammathetaupsilon.org/the-geographical-bulletin/2010s/volume53-2/article2.pdf).

[11] http://rsbl.royalsocietypublishing.org/content/8/5/710

[12] PA Ornithological Society

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