Hudson Data Jam Competition 2018 Written Report Form Team Information Project Title: Hurricane Sandy-What Happened and When School Name: Speyer Legacy School Name of Dataset(s): Hurricane Sandy and the Hudson River Level of Dataset(s): 2 Team Advisor’s Name(s): Mrs. Schwab Team Members’ Names (First and Last): Christopher Ortiz, Jonathan Manta, Alexander (Sasha) Goncharenko

1. Title Hurricane Sandy-What Happened and When ​ ​

Christopher Ortiz, Jonathan Manta, Alexander (Sasha) Goncharenko-7th grade- Speyer Legacy School

2. Introduction ​

We went out to Pier I for a Day in the Life of the Hudson River and collected data on tide and current among other things. Being outside all day, we experienced much of the tide cycle, from very low to nearly high and noted the incredible change in speed and direction of the current. But this was simply a calm, typical day on the river. We wanted to compare the height of the water on this day, with the raging waters of Superstorm Sandy that we all experienced in various parts of the city. One of our team members was driven from his home for three months after the storm as his home was damaged and the power was out in his neighborhood. Where did all of that water come from? How did it flood the interior of one of our homes, flood the subway and knock out power to parts of the city? Could it have been worse? Will we face these conditions again?

3. Dataset Description ​

We used three main data sets to do our study. We used tide height data from NOAA (Fanelli, ​ ​ 2018) as compiled for the Level 2 Hurricane Sandy and the Hudson River and then used that same NOAA data to reveal the predicted astronomical tide plus the storm surge from Sandy. This set of NOAA metadata was very precise and showed the motion of the storm in 12 hour segments, along with information as Sandy passed from the majority of the sensors and the water levels in that area as the hurricane came by, along with what records it broke for maximum height in certain areas. We used data from HRECOS to plot graphs of tide height around the time of A Day in the Life of the Hudson River. Note that HRECOS sensors in the lower

Hudson were damaged by Sandy storm surge. We used moon phase data from a moon phase calendar.

The independent variables for the graphs using data from NOAA and HRECOS are location, time and date. The dependent variable with all of these graphs is the height of the water. For the moon phase data, the independent variable is date and the independent variable is the phase of the moon.

4. Data Representations ​

Figure 1. This graph shows the change in water level at Pier 84 on October 12, 2017, which is the closest HRECOS recording station to Pier i where we spent A Day in the Life of the Hudson River. We were at the pier from approximately 9:30, when the tide was quite low approximately 2:30 when the tide was quite high.

Figure 2. This graph shows a week’s worth of tide height at Pier 84, revealing the variation in tide height level over one week.

Figure 3. This graph reveals tide height data from October 12, 2017 to October 31, 2017 revealing more of the monthly variation in water level.

Figure 4. This image shows the phases of the moon during the month of October 2017 ("Turning the Tide on Modeling Storm Surge", 2018)

Figure 5. This graph, created from the Cary Institute data jam dataset shows the tide height at the Battery from October 28, 2012 to November 3, 2012.

Figure 6. This graph shows the predicted astronomical tide in blue and the actual tide in green at the Battery from October 28, 2012 to November 3, 2012. ("National Oceanic and Atmospheric Administration", 2018)

Figure 7. This image shows the phases of the moon during the month of October 2012 ("Turning ​ the Tide on Modeling Storm Surge", 2018)

5. Data Trends or Comparisons ​

Figure 1 reveals typical tidal patterns on the Hudson River, without the addition of storm surge. The water level rises up for two high tides per day and drops to two low tides per day. Figures 2 and 3 show how this daily rise and fall of water levels varies in height across several days. This variation coincides with the phases of the moon recorded in figure 4. The lowest high tides and highest low tides coincide roughly with the first quarter moon. The highest high tides and lowest low tides coincide with the new moon. October 12 is interesting because it has close to the highest low tide, like during the first quarter, but also a high high tide.

Figure 5 shows a huge spike in water level at the Battery. The height jumped from around 9 feet to 14 in a very short amount of time. Figure 6 shows that the actual tide was roughly three times higher than the predicted tide. It also shows that at the time that water levels spiked, the predicted tide was high. The moon phase chart shows that the moon was full on October 29, 2012.

Some of the trends that our team noticed was the sudden stop in data on the HRECOS website. When researching for the components for our research, we noticed that the scanners malfunctioned, and didn’t produce the data collected. The scanners stopped on the day Hurricane Sandy hit, and started again few months later. another trend we saw was that the day leading up to the super storm were quite normal, until Sandy hit New York. We found it interesting that the water before the storm did not affect the storm’s water patterns. Also as we looked at previous storms, the water beforehand seemed to lead up to the actual hurricane. Sandy however was unique in that it came very suddenly with seemingly no warning at all from the waters.

6. Data Interpretation ​

After A Day in the Life of the Hudson River, we studied the phases of the moon and how they affect the tides. We learned that daily high tides occurred when the moon was directly overhead our location, its meridian, and 12 hours and 25 minutes later when it was located 180 degrees from our meridian. ("NOAA National Ocean Service Education: Tides and Water Levels", 2018)

When the moon is directly overhead, its gravitational force is the strongest because it is the closest. A tidal bulge is created simultaneously on the far side of the earth because of inertia. ("NOAA National Ocean Service Education: Tides and Water Levels", 2018)We then learned that the position of the moon relative to the earth and sun can magnify or diminish the daily tidal cycle. When the earth moon and sun are aligned in syzygy, during both full and new moons, the the gravitational forces of the sun and moon are pulling in the same direction, yielding the

highest high tides and lowest low tides of the month, called spring tides. When the moon is at a ninety degree angle to the sun, during the first and third quarters, the gravitational forces of the sun and moon are pulling at ninety degree angles yielding the lowest high tides and the highest low tides of the month, called neap tides. ("NOAA National Ocean Service Education: Tides and Water Levels", 2018)

The moon travels around the earth in an elliptical path, meaning that it is not a fixed distance from the earth. When the moon is at its closest point to the earth in its orbit around the earth it is at perigee. Since the moon is closer to the earth at perigee, its gravitational force is the strongest, magnifying the usual monthly tide cycle. When the moon is the farthest from the earth, at apogee, its gravitational force is less, diminishing the usual monthly tide cycle.

These factors describe baseline astronomical tides, but during a tropical storm, reduced air pressure can cause water levels to rise and wind can push water onto shore. This water, on top of the astronomical tide based on the position of the moon, is called storm surge ("Storm Surge Overview", 2018) The combined astronomical tide plus the storm surge produces the storm tide as shown by the green line in figure 6. The astronomical tide was already high because the moon was full and its meridian relative to New York City, so it was also high tide.

If the moon was in a different position than it was during Hurricane Sandy, the storm tide level would have varied greatly. The astronomical tide greatly affects the storm tide, even if the storm surge remains fixed. Using NOAA data and the AIR hurricane model, scientists modeled different storm surge elevation possibilities based on different landfall times for Hurricane Sandy. (2018) The storm tide would have been 8 inches higher if it struck during the higher morning high tide. If Sandy struck during a new moon spring tide, occurring roughly 2 weeks earlier or later, it would have been either 18 inches higher, at high tide, or 47 inches lower at low tide. If Sandy struck at low tide, either roughly six hours earlier or later than it struck, storm surge was projected to be 42 inches lower. If Sandy struck when the moon was at perigee, at high tide with a full moon, the storm surge was predicted to be 20 inches higher. The higher the storm surge, the farther the water penetrates inland. If the storm surge was 47 inches lower, Sandy would have had much less effect on New York City. Subways would probably not have flooded. Far fewer houses, if any, would have flooded. If the storm surge was 8 inches higher, or, far worse, 18-20 inches higher, far more damage would have occurred. We learned how important the timing of landfall is in determining the outcome of a storm because the moon’s position is continually changing, determining the baseline astronomical tide on which storm surge rests. We know that New York City will have more storms of Sandy’s intensity. We can only hope that they make landfall when the astronomical tide is as low as possible.

7. New Questions and Hypotheses ​

In the future, how often will we see storms like Hurricane Sandy? What correlations or irregularities will we see in those storms?

The “Perfect Storm” is a timing issue. How can we predict the impact of future storms on our homes based on relative position of the earth, sun and moon?

What can we do to prevent disasters similar to this one in the future? What ideas have been proposed and do they work? Will plans for resilience in Howard Beach be put into place? (2018) ​

8. Written Explanation of Creative Project ​

We decided to tell the story of one of the members on our team. He had a personal experience with the high water levels caused by Superstorm Sandy and we really wanted to bring that to life and to help understand the devastation caused by high storm tides.

9. Brief Reflection on Data Jam ​

We struggled to get the project off the ground but once we did it was much easier. Although we struggled getting our act together in the beginning, we really came together and created a project worthy of our 100% effort. We worked cohesively and efficiently, but took our time and made sure that all our research was charted and the sources were included. Once this contest is over, we will present for our school at an assembly. Furthermore, our wonderful teacher, Mrs. Schwab was our guide in all of this, showing us how to differentiate datasets and plot them, all the way to providing the sources for our research.

10. Reference List ​

(2018). [Ebook]. Retrieved from https://www.nature.org/media/newyork/urban-coastal-resilience.pdf ​

(2018). Retrieved from http://Hurricane Sandy-What Happened and When

Hope, M. (2018). Turning the Tide on Modeling Storm Surge. [online] Air-worldwide.com. Available at: http://www.air-worldwide.com/Print-Preview/28756/ [Accessed 17 May 2018]. ​ ​

Hudson.dl.stevens-tech.edu. (2018). HRECOS: Hudson River Environmental Conditions ​ Observing System. [online] Available at: http://hudson.dl.stevens-tech.edu/hrecos/d/index.shtml ​ ​ [Accessed 18 May 2018].

HRECOS: Hudson River Environmental Conditions Observing System. (2018). Retrieved from ​ http://hudson.dl.stevens-tech.edu/hrecos/d/index.shtml

National Oceanic and Atmospheric Administration. (2018). Retrieved from http://www.noaa.gov ​

Turning the Tide on Modeling Storm Surge. (2018). Retrieved from http://www.air-worldwide.com/Print-Preview/28756/

Your Bibliography: Choi, C. (2018). Hurricane Sandy-Level Floods Likely to Hit NYC More Often. Retrieved from https://www.livescience.com/56447-hurricane-floods-more-likely-climate-change.html

Fanelli, C. (2018). NOAA Water Level and Meteorological Data Report. Retrieved from https://tidesandcurrents.noaa.gov/publications/Hurricane_Sandy_2012_Water_Level_and_Meteo rological_Data_Report.pdf

11. Creative Project ​

What Could Have Been Worse?

Water is just another background to me. I live by the water and when the tide is high, the water flows out into the streets. John F. Kennedy airport is near my house, and water is similar to the airplanes. I hear airplanes everyday and at one point I ignored them and now I only hear them when people point them out. Water is no different, I used to recognize it all the time but now I do not, as if it were a fly on the wall. However, I did notice that whenever we had a high tide, it was usually a full moon. Now I understand what the moon does to the tides, but at the time I thought it was only a coincidence. Hurricane Sandy was a devastating moment in my life and in my family, but when completing this project, I learned why Sandy was so terrible and what role the position of the moon played in this superstorm. Hurricane Sandy hit Howard Beach on October 29, 2012, when I was seven years old. I live by Rockaway Beach on a peninsula and my house is situated by Jamaica Bay (Figure 7). The night before the storm hit, my family and I put a foot high wall of sandbags in front of our house, thinking that we could stop the water from reaching the house, based on what we heard, from some of the weather channels on the television. As the day progressed, night and its stars were visible. It was a full moon with the stars glistening upon my neighborhood, giving a deceptive

perception of becoming a beautiful night. The day before, the tide was high, but not as high as it was now. At around 5 pm, Sandy’s winds started to take down trees, and the water level began to climb. At 7 pm, I estimated that the water level was around three feet high because it reached our door handle (Figure 6). Obviously, the sandbags were useless. I watched the water from the balcony window, swallowing up our car and continuing to climb. First, the water flowed from the east. The water from the south came, over the beach, slowly climbing to the highest point of the park and then rushing down the slope into my neighborhood, like it was flowing down a ramp. It was like air, it just wanted to fill all the space. Then water from the west joined the water from the south in its race to our house. The flows from three directions surrounded our house with water. My family still debates just how high the water got. I think it was around five feet high, my dad thought that it was around seven feet high, my brother said nine feet, but my mom said eleven. The Queens Chronicle supports my mom.(2018) It said that it was over ten feet ​ ​ ​ ​ near our home (Figure 1). During the storm, my brother and I were in our rooms, on the second floor of our house, and on the second floor trying to fall asleep when the water started to seep into the house at around 8:00 PM. After a long time of trying to fall asleep and listening to trees and transformers shudder and fall crashing into the water, my brother and I were scared to death. What happened to the parrots that nest on the transformers? Could we be electrocuted at any second? My mom noticed that we were scared, and allowed us to stay in my parents bedroom as she comforted us with positive thoughts, telling us that everything was going to be fine and cuddling with us on the bed. She played games with us in which we would finish each other's sentences, taking our minds off the storm. As all the outside noise began to quiet down, we heard a different, but scarier noise. A stealthy intruder was climbing the stairs inside our house. By 10:00 PM most of our ground floor was filled up water. It was impossible to see how much though. My brother and I were crying as both of my parents tucked us again into bed, telling us that everything was going to be okay for a third time, but this time we finally did fall asleep. Fortunately, our intruder did not reach the second floor. God blessed us that day by saving us, making the storm weaker and diminishing the height of the storm surge. Some of our neighbors were forced to retreat to their flooded attics. Some neighbors died. Our first floor was totally destroyed . The next morning, my brother and I looked out the windows of our house and saw cars, trees, wires, and furniture all destroyed on the ground. Blood was smeared on one of the houses like in a horror movie. From our balcony window, we could see two blocks and they were all filled with debris (Figure 5). My parents finally woke up and we walked down the stairs. There is no way I could say in words what I felt like. To be honest, it felt like someone punched you extremely hard in your stomach and ripped your heart out of your body. The number of items and prized positions I lost, was more than 1,000. I distinctly remember before Sandy hit our house that we had a total of 300 CDS and VCR tapes, but when I walked down our stairs, I saw no complete CD or VCR. Then I saw my Curious George stuffed animal torn apart, I started to cry again (Figure 3). Not a single one of us were smiling that day. There was no way we could

clear and clean everything, so we pushed through some debris and made a path to our front door and, we opened the door. The door was stubborn, but after a few pushes, we opened it. Our Nissan Maxima was fully flooded, requiring replacement, creating ongoing financial stress for my parents. According to my parents, we lost our birth certificates, my dad’s college diploma, my parents marriage license, and my parents citizenship papers (Figure 4). Our house was not safe and my parents said that it was too dangerous for us to stay. We had no electricity, no water, and no way of heating the house. With winter coming, we stayed in a relative's home for 3 months until we could get back these three necessities. At that age I would of never thought that the moon affected Hurricane Sandy and could have made Sandy even worse. If Sandy had hit during the morning high tide, which was higher ​ than the evening high tide, flood waters might have made it to the second floor. That may not seem like a lot, but if there is already 55’ inches of water in your house and you add another 8” inches, then I would have had 63” inches of water plus more cars, trees, and transformers swimming around and possibly hitting my house, damaging it and possibly hurting my family and me. On the other hand, if Sandy had struck earlier or later in the day during low tide, the surge could have been an estimated 42 inches lower. In class we studied about Copernicus’ wild idea that the earth was spinning at 1,000 miles per hour. We can’t feel this spinning, but the water can. This difference between high and low tide, between 5:00 PM and 11:00 PM, between 42” higher or lower water in my house, almost makes me feel the spin too. My house would ​ ​ have had less water and less of a clean up, allowing my parents to spend less money. The water might not have even breached the sandbags protecting our home. In the absolute worst case circumstance, if the storm struck during a full moon during a spring tide at perigee, the storm surge could have been 20 inches higher than it was. This would have definitely filled the second floor of our house, perhaps even the attic. This storm tide could have put my family in greater peril, or, even worse, dead. Luckily, God blessed and saved our family and allowed us to live another day. Learning more about storm surge, I now know that it could have made my life worse. I am going to be watching the position of the moon, paying attention and possibly helping people and informing them about what the tide is going to look like at the end of the day. I know that more storm surge will be coming my way and I want to be able to better predict the impact on my home by knowing where the moon is. Our community was brought closer by this natural disaster.

Figure 1. The view from our porch in Howard Beach, Queens, New York, NY. October 22, ​ 2012.

Figure 2. The view of the front door of our house in Howard Beach, Queens, New York, NY. ​ October 23, 2012

Figure 3. Some of our possessions outside our house in Howard Beach, Queens, New York, NY. ​ October 23, 2012.

Figure 4. The view of the front of our house after some cleanup in Howard Beach, Queens, New ​ York, NY. October 23, 2012.

Figure 5. Other houses and debris in our neighborhood in Howard Beach, Queens, New York, ​ NY. October 23, 2012.

Figure 6. The height in which some of the water rose during the storm. Queens, NY, October 23, ​ ​ ​ 2012

Figure 7. This is Howard Beach, and you can tell from the picture that the peninsula is ​ thin and where I live, it was easy for water to surround all of the houses. I live on 98th ​ street on the right side. Howard Beach, Queens, New York, New York

Hudson River Data Jam Competition 2018 Written Report

1. Title

Twenty Three Years to a Week in the Wappinger Creek By Miles Pulitzer and Ruby Hentoff 7th grade Speyer Legacy School Data Set: Hydrology In the Wappinger Creek Level 2 Advisor: Ms. Schwab

2. Introduction: ​ Wappinger Creek is a 41.7 mile long creek in Dutchess County, New York. It is the longest creek in New York State. It flows north-south on the east side of the Hudson River. The creek’s water source comes from Thompson Pond, Pine Plains, then it heads towards the Hudson River in New Hamburg. On its way, Wappinger Creek passes Wappinger Falls and into Wappinger Lake, a manmade reservoir. As the creek goes even further, it begins to form sandbars, mudflats, and marshes. Wappinger Creek is also the perfect place for fishing. Every year, the creek is stocked with 12,000 brown trout, 2,000 rainbow trout, and many other fish. Those fish thrive in the creek, especially between April and June. How does the temperature in the Wappinger Creek affect the fish living in it? Maybe it is similar to humans; when the water gets hot, they get dehydrated. When the water evaporates because of the heat, the ratio of salt to water would change. There would be less water, so the salinity would go up. The fish could dry out because of that.

3. Dataset Description: The data from the Wappinger Creek was collected by Vicky Kelly of the Cary Institute. It was collected at the Cary Institute in Millbrook NY, on the Wappinger Creek. The data was collected every fifteen minutes, then was averaged daily. The temperature was taken with a thermometer. The height was measured with a fixed staff gauge. The data we analyzed includes discharge and stream temperature, from the Wappinger Creek Data Jam dataset (Caryinstitute.org, 2018). The air temperature data was retrieved from the Cary Institute Meteorology and Climate Monitoring Archive (Environmental Monitoring Program, 2018.) The ​ ​

independent variable in this project is time. The time remains continuous, and flows at a steady rate. The dependent variables are the discharge and the temperature. They are always changing, the data proves it. We also drew from data on average temperature requirements for trout and found that 12-20 degrees C is the optimal temperature range for growth and development. Above 22 degrees C growth ceases; temperatures above 25 degrees C can be lethal. ("Average temperature requirements for Rainbow and Brown trout", 2018)

4. Data Representation, Graphs:

5. Data Trend(s) or Comparison(s):

Since 1994, the average yearly water temperature in the Wappinger Creek has increased by around 2º celsius. It fluctuates from 0º C to over 17º C yearly. The overall shape of the graph has remained similar since 1994. The discharge has greatly changed. In 1994, it was at 4 cubic meters per second, but then drastically went down to just over 1 CMPS in 2001. It shot up to 3.5 in 2004, and fluctuated between 3 CMPS and 3.5 CMPS until it shot back up to about 4.75 CMPS in 2015. In the beginning of the week, on July 14, 2005, the discharge was at 13.5 CMPS. It followed the pattern of the weather, up and down, until the 16th, where the discharge went up to 17 CMPS and the temperature remained at 28º. The rest of the week, as the discharge went down, the temperature went up, and as the discharge went up, the temperature went down. Trout are stressed by temperatures over 20º C, so this week, the trout were extremely stressed.

6. Data Interpretation: The increase in water temperature occurred because of the air temperature. There is a correlation connecting the two, as the air temperature increased, so did the water temperature. If the air temperature decreased, so did the water temperature. Looking at the air and water temperature from July 14th to July 21st, 2005, you can see that the air temperature and water temperature rise and fall together. Not only does the water temperature correspond with the air temperature, but so does the discharge. In the same week (July 14th to July 21st, 2005) the discharge is very low. It ranges from .13 CMPS to .19 CMPS. As the temperature rose, the discharge declined, and as the temperature declined, the discharge rose. This happened from July 16th to July 21. The fact that the discharge is low, makes it easier for the water to heat up. With less water flowing on the streambed, the extreme heat can heat it up faster and hotter than it could have if the depth were higher. During the 14th to the 16th, the temperature rose and fell at the same time as the discharge. Which is most likely an outlier in this set, because during the next 6 days, the temperature and discharge rose and fell oppositely. These trends created a very stressful situation for the trout in Wappinger Creek during the week we zeroed in on. The dissolved oxygen in the creek went down with higher temperatures, and the low discharge compounded the problem because there was less water flowing over the gills of the fish. Fish do not feed much in this temperature range, making them very difficult to catch. The week of 7/15/2005-7/21/2005 had the highest stream temperature in the entire dataset, so we chose to set our story in this time period, because the fish would be at maximum stress. This week was preceded and followed by extremely high water temperatures as well.

7. New Questions, Hypothesis: As we were working, we had some questions. Our first question was: what can be done to cool off a stream? We knew right away that snow and cold weather would make it cooler, but we also thought maybe you could plant trees by the creek to create shade and make the temperature drop. We were curious, so we decided to do some research. We found out that trees are actually very useful to making a stream cooler by casting shadows onto the creek and making shade over the water. The United States Environmental Protection Agency, (Blog.epa.gov, 2018). Trees for ​ ​ Tribs, (Dec.ny.gov, 2018). We also had another big question - why can some fish tolerate higher ​ ​ temperatures than others? We thought that maybe it's because some fish require more dissolved oxygen than others, or maybe because some fish can survive with less dissolved oxygen than other fish can.

8. Explanation of Creative Project: After we collected all of the data and information we needed, we had to think of a creative project to put everything together. One of us is a very passionate writer, so we decided that a simple short story which included all the data we had with a creative twist to it would be a great idea. The story would not only say everything about the way the temperature of a stream affects the fish living in it, but it would also be an imaginative, fun, and amusing project. It's much more than a few thousand words - it's filled with passion, information, and intrigue.

9. Reflection on Data Jam Experience: In the beginning, we were overwhelmed with the amount of data we had. We weren’t sure what to do. As we met with our science teacher, and each other, we started to understand what to do. We decided to write a story about a girl who grows up by the Wappinger Creek. Writing the report and making the graphs was really challenging, but we were able overcome the hard parts by dividing the work up, and having each other do what we each felt the most confident about. For example, one of us knew more about the Wappinger Creek itself, and the other knew how to better deal with data. We think that our project turned out really successful, and are very confident with our finished product.

10. References, at least 2: ​ ​

Blog.epa.gov. (2018). Plant a Tree, Save a River!. [online] Available at: ​ ​ https://blog.epa.gov/blog/2011/01/plant-a-tree-save-a-river/ [Accessed 20 May 2018].

Caryinstitute.org. (2018). Wappinger Creek Data Jam Dataset. [online] Available at: ​ ​ http://www.caryinstitute.org/sites/default/files/public/downloads/page_2016/hydrology_wapping er_creek_daily_avgs.xls [Accessed 17 May 2018].

Environmental Monitoring Program. (2018). Meteorology Data. [online] Available at: ​ ​ https://cary-environmental-monitoring.squarespace.com/meteorology/ [Accessed 17 May 2018].

Dec.ny.gov. (2018). Hudson Estuary Trees for Tribs - NYS Dept. of Environmental ​ Conservation. [online] Available at: https://www.dec.ny.gov/lands/43668.html [Accessed 21 ​ May 2018].

Average temperature requirements for Rainbow and Brown trout. (2018). Retrieved from https://henrysfork.org/average-temperature-requirements-rainbow-and-brown-trout

Story:

When the Rainbows Disappeared Ruby Hentoff and Miles Pulitzer

I've always loved living in Dutchess County. It was my ideal home. A beautiful county in Poughkeepsie with nature and water all around me. I lived with my parents and two sisters in a large house right next to my favourite place: Wappinger Creek. Ever since I was a small child, I loved to go fishing. We went fishing nearly every day. Right next to our house was a long creek constantly stocked with fish. We’d grab our nets and flies and run down to the stream. My sisters, Andra and Adele, would fit into a long canoe with me as each of our parents grabbed a boat and sailed out into the creek. We’d sit for around five hours in our boats, holding the fishing rod as the line stuck into the water. We’d catch a fish every few minutes, and take turns throwing them back into the water, making sure to save a few for our delicious homemade supper. It was all perfect, until one day in the beginning of July, I cast the fishing line over the water, the fly securely attached. I sat there for hours and hours, waiting to feel a pull. But all I did was sit there, gripping the fishing pole. Soon, my arms started to drop. “Amber?” Adele asked, turning to me. “Are you alright?” “Yeah, I'm fine. I just... I just can't find any fish,” I sighed. “Here, let me have the pole.” She grabbed the fishing pole and swung it around. As she wound up the line, all she pulled out was a fly made of feathers and rubber. “Ugh,” she groaned, handing the pole back to me.

“Well, why don't we try again? I'm sure there are still ​some ​fish.” Andra suggested. Ten minutes. Nothing showed up. Thirty minutes. Still nothing. After an hour or so, we gave up. “Why is this happening?” Adele complained. “Why are there no fish?” “Yeah, there are usually tons of trout in the Wappinger Creek,” Andra agreed. “I'll do some research tonight,” I promised the two of them. They nodded.

That night, as our parents were making dinner, the three of us slipped on our boots and carefully walked down to the Wappinger Creek, Andra in the lead, holding a flashlight. I wanted find out why we couldn't find any fish today, and since we couldn't find too much information online, we decided to walk down to the creek itself. Just as we were about to start down the hill, I suddenly felt my ankle hit a large fallen branch; I couldn't help it - I instantly crashed to the ground and began rolling down the hill. “Amber!” Andra and Adele called worriedly, but there was nothing I could do. Without a second thought, I plunged face-first into the water. I shut my eyes and held my breath as I blindly threw my arms around, trying to make my way up. Suddenly, I felt two wet hands grab my arms. I smiled as my sisters slowly pulled me out of the water. “Jeez, it's ​hot​ in there!” I exclaimed, gasping for breath as Andra and Adele dragged me onto the shore. “It is?” Andra cocked her head. She dipped her hand into the water, swishing it around for a few seconds before taking it out. “It ​is ​hot!” she exclaimed. “Maybe that's why all the fish are gone,” Adele giggled.

Suddenly, I felt like a switch had flicked in my head. I gasped. “So ​that's ​why we didn't find any fish today! The water’s too hot for them!” “Amber, I was kidding,” she smiled. “No, actually, I think you're right. Let's go home and check it out.” The girls nodded, and we headed back up the hill. Just as we were about to walk into the house, I turned my head around for a few seconds. “I'll be back,” I whispered. I then followed my sisters into the house. I could only imagine the coarse, wet voice mumbling, “I know.”

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

“Mom?” I ran up behind the woman flipping eggs by the toaster. Surprised, my mother turned around. “What do you need, sweetie?” “Can Andra, Adele and I go down to Wappinger Creek this morning?” I asked with puppy eyes. Mom cocked her head. “I thought all the fish were gone.” “Yeah, but we want to figure out why.” She sighed and looked at the clock. “Sure, but be back by breakfast.” “Thanks!” I threw my arms around her and ran off.

“So,” Andra mused as we started walking down the hill, “Last night, we found out that trout don't like hot water.” “Rainbow trout especially,” Adele added. “Is that because it makes them too hot or something?” I asked confusedly.

Andra rolled her eyes. “Amber, we explained this to you, like, twenty times last night.” “Oh, right!” I giggled. “And that's because when it's warmer, the dissolved oxygen starts to move faster through the water-” “Which turns it into water vapor-” “And the dissolved oxygen breaks through the surface of the water, and becomes atmospheric oxygen.” “However, the fish need dissolved oxygen to breathe.” “And just like humans, if they don't have oxygen-” “It sad to think of all those poor little rainbow trout drowning,” I sighed and took my thermometer out of my pocket. “Okay, let's see what happens.” The second I stuck my thermometer in the water, the red liquid flew up to twenty-three degrees Celsius. I gasped. “Twenty-three degrees! Small wonder all the fish are gone.” “So are we supposed to just sit and wait for the water to get cooler?” Adele complained. “Is there anything that ​we ​can do?” “Hey guys, check this out!” Andra suddenly exclaimed, staring at her thermometer a few yards away. “Over here, it's only nineteen degrees Celsius.” “Maybe it's because of the tree,” Adele suggested, pointing up. Andra and I followed her finger. Hanging over Andra was a huge willow tree, casting a wide, long shadow onto the creek. Another switch flicked inside my head. “There ​are ​ways to cool down the creek!” I grinned. The two girls turned to me hopefully. “I mean, not something ​we​ can do, but plants like trees cast shadows over the water, which make it cooler. There are ton of ways.” “But we can't plant trees everywhere,” Adele pointed out.

“True, but at least we know why there are no fish. It's more than eighty degrees outside!” “So does that mean we’re gonna stop fishing?” Andra groaned. “Wait a minute,” Adele said, grabbing Andra’s wrist. She looked up at her. “What's the matter?” Without saying a word, Andra kicked off her sandals and carefully stepped into the water. She looked down, her eyebrows furrowed, as she swished her feet around the bottom of the creek. Suddenly, she looked up at us and grinned. “Not all the fish are gone!” She leaped out of the water, wiping her soggy feet on the grass before she put her shoes back on. “Deep into the water, there are some colder spots, made by the groundwater in other lakes. That's where the fish find holes and live there. I can feel it with my own feet!” “So technically,” I started, “when the water gets cold, there are two possibilities.” “One is that the fish find cooler places at the bottom of the stream, since the water on the dirt is much colder than water from the surface.” “Another is that they don't have enough dissolved oxygen to live.” “So I guess they won't show up? I mean, we can't go fishing now, but soon we’ll be able to try again.” I sighed. I could again hear the voice of Wappinger Creek, “Soon, my friend. Very soon.”

“Over here it was seventeen degrees, and over here it's eighteen,” Adele said, pointing at the stream. We had come down to the stream every day since the fish disappeared, and now we were researching what happened to all the fish in Wappinger Creek and why.

“Oh my god!” I suddenly shrieked. Both girls looked at me. I turned to Adele. “Remember that tree you were talking about?” “What about it?” she said slowly. “Well, we can plant some ourselves!” I grinned. “Then the creek will have more shade, and it will get cooler.” “But Amber, how are we going to plant trees?” Andra asked skeptically. “And how will we know ​where ​to plant the trees?” “Everywhere!” “But trees don't grow in a day.” “But they ​do ​grow in a few months!”

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I was awoken by the pounding sound of feet stomping on my mattress. “Wake up, wake up, wake up!” Adele shouted in my ear as my sisters jumped up and down on my bed. Andra grabbed the shade on my window and threw it up to reveal a long valley blanketed in snow. Snowflakes poured from the pale white sky, and I could barely see five feet away from me. “What the-” I sprang up. “It hasn't snowed all month!” “I know!” Adele giggled. “Cool, right?” “The trees!” Andra suddenly gasped. We had planted trees that were meant to cast shade onto the Wappinger Creek, and were nervous that the insane amount of snow in December would repeat in February. “We have to see if they're still here!” I leaped out of bed, grabbed my jacket, and ran outside.

Beautiful, tall trees shot up to the sky, bright orange and red leaves circling around them as contorted transparent icecles hung from their branches. Coarse, dark wood surrounded the branches as snow slid down the trunk and onto the chalky ground. My eyes turned to Wappinger Creek. I ran over and dug the thermometer out of my pocket. The second I slipped it into the river, it flew down to 8 degrees. “Oh my god!” I shouted to my sisters. They ran over “Look, it's only eight degrees. The fish must love this.” Adele turned to me with a silly grin on her face. “So why don't we see?”

SIX HOURS LATER LATER…

“Are you ready to go, girls?” Dad asked, carrying the equipment in a sack draped over his shoulder. It had finally stopped snowing after a few hours and was beginning to dry up. After Andra measured the temperature and came back beaming, her arms waving, we got the boats out right away. “Yep!” I said excitedly. I shivered with excitement as I fastened the bait to the hook and cast it out into the water. I sat there for an hour, waiting for something to come up, but nothing happened. I sighed. “Well it was worth a shot.” My sisters nodded sadly. Just as I was about to put away the fishing pole, I felt a pull. Not just a little pull, but a strong yank. “Something’s coming!” I shrieked. Their faces lit up. I kept winding the string in, my heart beating faster by the second. “Somebody help me!” I shouted nervously. Adele and Andra put their hands over mine as we held on tight. After two minutes, the hook flew out of the water. Hanging

from the hook was a two-foot long rainbow trout, flipping and flopping, hanging precariously. The three of us squealed in excitement as we filled up a large bucket and set the fish inside. “We did it, we did it, we did it!” I beamed. Mom and Dad turned around, also smiling. “Well, are we going to keep him?” I looked at my sisters. We locked eyes and all nodded in agreement. It was the least that we could do to let the fish go. I smiled down at Wappinger Creek as I threw the grateful fish back into the water. “Thanks for coming back,” I said out loud. “You're welcome,” it said.

Stella Nakada (7th Grade) 5.21.18 Grace Keyt (7th Grade) Advisor: Kimberly Schwab Speyer Legacy School

Hudson River Data Jam Historic Pollution and Human Effects on the River

1. Title: A Series of Unfishunate Effects

Authors: Stella Nakada and Grace Keyt

Grade Level: 7

School: Speyer Legacy School

Dataset: Historic Pollution and Human Effects on the River

Dataset Level: 2

2. Introduction: Data is an important part of the scientific world we live in. Because scientists took the trouble to measure the levels of pollution in the Hudson River, we are able to discover historical mistakes and events that unravel the details of what happened to the river. In particular, the dataset that we studied contained four different measures of water pollution: total suspended solids (TSS), biological oxygen demand (BOD), total nitrogen (TN) and total phosphorus (TP). The data covered the period from 1900 to 1999, and was split into two regions: the Upper Hudson River and the Lower Hudson River. Before we studied the data, we came up with some questions. Has the Hudson River become more or less polluted over time? What has been the cause of the pollution? What actions have we taken over time that have reduced pollution? Where should we focus next to continue to reduce pollution? After we studied the data we came up with some answers to these questions, but it gave rise to another important question: why have we been unable to reduce TN and TP over the past 40-50 years, when we have made huge progress in reducing BOD and TSS?

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3. Dataset Description: Historic Pollution and Human Effects focuses on four variables that track pollution in the Hudson River, and one variable that tracks the population in the Hudson River area. The four pollution variables are: total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), and the basic oxygen demand (BOD). All of the variables were reported in metric tons per day (mt/day). The data was split into two sections: Lower and Mid-Hudson, and Upper Hudson. The map on the left shows that the split between Upper and Lower/Mid is around Albany. One of the good things about the data is that it includes a long history, all the way back to 1900. One of the problems with the data is that there aren’t that many data points, only one data point per decade. Each pollution variable measures pollution in a different way. TSS simply measures the amount of solids that are suspended in water. This is a pretty obvious way to measure pollution. Total suspended solids is a measurement taken from a sample of river water that documents the dry weight of the substances found in the river sample. TSS is a measurement taken using a filter and a water sample. Nitrogen and phosphorus occur normally in nature, but when there is too much of these in river water, it can cause pollution. The nitrogen and phosphorus data was collected through water sampling. Biological oxygen demand measures how much oxygen bacteria in the water are using up. This rises when there is too much nitrogen and phosphorus in the water. So in a way, BOD is related to nitrogen and phosphorus. Biological oxygen demand is collected using the Winkler method, which adds an acid compound that indicates the levels of dissolved oxygen. These variables work together to form a series of data that shows the detrimental effects humans have on the river. This data was compiled by Brosnan, Stoddard, and Hetling for their paper titled “Hudson River Sewage Inputs and Impacts.” There was involvement from several other parties including the Environmental Protection Agency, NY State agencies, and private sewer companies. The data itself was collected along the Hudson RIver at various launching points and by various collectors. These variables paint an accurate and clear picture of pollution over the span of 100 years. These specific variables were most likely studied because of their correlation to the ecosystem as well as human population. This dataset presented only one independent variable: the decade. The independent variables consisted of TSS, BOD, TP, TN, and population. These dependent variables make up nearly 80% of all of the data, which presented an interesting dilemma because we had so few independent variables. Figure 1-Visual description of Upper Hudson and Lower-Mid Hudson

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4. Data Representations

KEY TO GRAPHS: BOD- Biological Oxygen Demand ​ TN- Total Nitrogen ​ TP- Total Phosphorus ​ TSS- Total Suspended Solids ​

GRAPH 1:LOWER AND MID HUDSON DATA

Figure 2-Chart 1 (Cary Institute of Ecosystem Studies, 2018) ​

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GRAPH 2: UPPER HUDSON DATA

Figure 3-chart 3 (Cary Institute of Ecosystem Studies, 2018) ​

5. Data Trends: We graphed the four pollution variables as lines, and graphed the population as the blue area in graphs 1 and 2. The first thing we noticed was that TSS and BOD dropped a lot since the 1970s. We also noticed that the Upper Hudson TSS and BOD went up a lot from the 1920s to the 1970s, but the Lower and Mid-Hudson levels stayed about the same during that time. We also noticed that TN and TP didn’t go up nearly as much as TSS and BOD, but they also haven’t come down much in the past 40 years.

6. Data Interpretation: It made sense to us that in general when the population went up, so did the total nitrogen and phosphorus. There are five causes of increased nitrogen and phosphorus in the water: 1) agriculture, 2) stormwater, 3) wastewater, 4) fossil fuels, and 5) household (fertilizer, detergents). (Dec.ny.gov, 2018) Except for stormwater, all of these things increase when there ​ ​ are more people. We looked into the rainfall and precipitation to see if the runoff of fertilizer had

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anything to do with the rises and falls in the dataset.(Weather Underground, 2018) To our surprise ​ ​ the correlation wasn’t obvious, there were some years where there was a lot of precipitation and others where there wasn’t. So when in 1970 there was a lot of people in Upper Manhattan and yet some of the lowest numbers of nitrogen and phosphorus and an abundance of rainfall, we researched reasons for the outlier. After analyzing the data, our group came to the conclusion that the population was in direct correlation with the pollutants that entered the river. However, despite knowing the correlation of the data, we were curious about the spike of pollutants in decades like the 70s, and massive decline of pollutants in decades like the 80s. We started from the beginning-- 1900-1920 shows a slight increase in population and mirrored data in terms of pollutants. Our research uncovered several important things that influenced the amount of pollution in the Hudson. The first that as recently as 1986, we were still dumping untreated sewage into the Hudson. The chart to the left shows that the North River water treatment plant only started in 1986(Clearwater.org, 2018). Before this Manhattan ​ ​ was dumping 150 million gallons of raw sewage into the Hudson every day. The chart also shows that water treatment plants in other parts of New York city started appearing in the 1940s and 1950s. This explains why the Lower and Mid-Hudson graph shows the increase in TSS and BOD stopping during this period. We didn’t find this same chart for the Upper Hudson, but we think that the Upper Hudson must not have had as many water treatment plants developed during that time. Next, we found out that in 1952 companies started putting phosphates in laundry detergents because it helped get clothes cleaner. It wasn’t until 1970 that people figured out that phosphates were bad for the environment, so they stopped using them. This explains why there is an increase in TP during this time (in both Upper and Lower Hudson). (Hetling et al., 2003) ​ Third, we discovered that in 1972 the government passed the Clean Water Act (Clearwater.org, 2018), which ​ ​ made companies reduce the amount of nitrogen and phosphorus they were putting into the water. In 1983, the Upper Hudson was declared a Superfund site, which meant that the government was even more interested in reducing the pollution there. This explains why we saw such a big drop in TSS and BOD from 1970 to 1999. (Hetling et al., 2003) ​

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7. Hypotheses and Questions: After researching Historic Pollution and Human Effects on the River, I came across several questions that resonated with me. As shown on the graph for Upper Hudson Data, In the decades after the Clean Water Act was passed, the levels of TSS and BOD dropped with a dramatically steep trend line. This drop is far steeper than the decline in total nitrogen and total phosphorus. I want to know why the levels of phosphorus and nitrogen stayed relatively steady while the BOD and TSS dropped by almost 70 metric tons between the decades of 1970 and 1999. Similarly, in the Lower and Mid-Hudson graph, the nitrogen and phosphorus levels never fluctuate wildly. Why are these two variables steady? What makes them impervious to the fluctuation in the population? The variables of this dataset have proven to be the most telling piece of the puzzle, which leads me to my new hypothesis. I think that household sources of nitrogen and phosphorus make up a bigger part of TN and TP than wastewater and fossil fuels. (Hetling et al., 2003) This is why even though we have lowered TSS and BOD a lot through better ​ wastewater treatment and reduced pollution from companies, TN and TP are still where they were in the 1960s. I think that solving this mystery could help us reduce TN and TP in the future.

8. Creative Project: After researching past data jam projects, we were inspired by the movie-themed projects and decided to create a spin-off for our own project. We settled on A Series of Unfortunate Events because of our goal to do something that people could understand and connect to. We acknowledge the importance of waste in the Hudson but we felt that unless we, in addition to underlying the dangers to fish, including eutrophication (Black, 1911), outline the harms that ​ ​ could come to humans, people wouldn’t care about the problem. We focused our creative project on the data from 1900 as well instead of trying to cram in all the data. Grace wanted to do A Series of Unfortunate Events and Stella wanted to add the unique spin of “Tim Burton Style” so that is what the project ended up being. We recognized how doing something other than a production with people would help us stand out and offer a new experience for the both of us. We have never done claymation before and looked forward to embarking on the challenge. However, with our time running short we realized that it would probably be best to do something that is less of a new experience. So we decided to animate our project and use voiceovers. We think that our project is different and creative and that it gets the message across to the dangerous of human effects on the hudson river in and accessible way.

9. Reflection: Reflecting on this process, our group encountered some difficulty with our data because of the limited data points. The fact that the data was plotted in terms of decades left us with very ambiguous dates and little to work with in terms of specific dates. Despite this complication the project was an exciting, one-of-a-kind journey that sparked many questions and discoveries. We was especially excited by the creative component of the project, and this was the highlight of our data jam experience. I hope that the future data sets will leave less to the imagination, or rather, research, and give more specific data.

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10. References: Hetling, L., Stoddard, A., Brosnan, T., Hammerman, D. and Norris, T. (2003). Effect of Water Quality Management Efforts on Wastewater Loadings During the Past Century. Water Environment ​ Research, 75(1), pp.30-38. ​

Clearwater.org. (2018). Fact Sheet 8 - Hudson River PCB Pollution Timeline. [online] Available at: ​ ​ http://www.clearwater.org/news/timeline.html [Accessed 21 May 2018].

The Discharge of Unpurified Sewage Into the Hudson River Near Yonkers Author(s): W. M. Black Source: Professional Memoirs, Corps of Engineers, United States Army, and Engineer Department at Large, Vol. 3, No. 11 (July-September, 1911), pp. 396-412 Published by: Society of American Military Engineers Stable URL: http://www.jstor.org/stable/44535119 Accessed: 21-05-2018 14:19 UTC

Dec.ny.gov. (2018). How is the Hudson Doing? - NYS Dept. of Environmental Conservation. ​ ​ [online] Available at: https://www.dec.ny.gov/lands/77105.html [Accessed 21 May 2018].

Weather Underground. (2018). Weather Forecast & Reports - Long Range & Local. [online] ​ ​ Available at: https://www.wunderground.com/ [Accessed 21 May 2018].

Data from Brosnan, T., A. Stoddard, and L. J. Hetling. 2006. Data published in "Hudson River Sewage Inputs and Impacts: Past and Present" in J. S. Levinton and J. R. Waldman (Eds.) The Hudson River Estuary; New York: Cambridge Press.

11. Link to creative project

LINK: https://www.wevideo.com/view/1153833866 ​

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8 1. Project Title:The Fish United Salinities Team Members: Abby Berman (7th Grade) and Eliza Pritchard (7th Grade) School:Speyer Legacy School Dataset: Level 2 Fish Populations Snapshot Day Dataset Level: 2 ______

2. INTRODUCTION: ​ Our Dataset was Level 2 Fish population (Snapshot Day) and we added Level 1 Salt Levels in the Hudson River (Snapshot Day).("Level 2: Fish Populations (Snapshot ​ Day)", 2018) and ("Level 1: Salt Levels in the Hudson River (Snapshot Day)", 2018) Both of these sets use data from A Day in the Life of the Hudson River, a citizen's science project that takes place one day in October during which students from elementary school through college collect data on various aspects of the Hudson River. One of the the subjects is fish population, and from 2004 through 2013 fish populations have been documented. Our data set has 3 main aspects; one the fish, two the location, and three the salinity in each location. We asked ourselves the question: which fish live in which salinities and why? Our project consists of a filmed puppet show, multiple colorful pie charts, and this report.

3. DATA SET DESCRIPTION: The Level 2 Fish population (Snapshot Day) data series ("Level 2: Fish Populations (Snapshot Day)", 2018) explicitly focuses on 8 different kinds of fish; Atlantic Silversides, Blue Crabs, Ctenophora (Comb Jellies), Striped Bass, Banded Killifish, Pumpkinseeds, Spottail Shiners, and Sunfish(collected and recorded by A Day in the Life of the Hudson River participants.) The data set contains six locations from where fish were caught and salinities were measured along the Hudson River; Beczak (HRM 18), Piermont (HRM 25), Beacon (HRM 61), Norrie Point (HRM 84.5), Ulster Landing (96.5), and Green Island (153). From 2004 to 2013 the data was collected yearly from the same 6 towns mentioned above. Variability in this data is very interesting, because data was taken by many students over a long period of time. This means that each year people came in with different skill sets and different tools for ​ doing so (seines, crab traps, rod and reel, minnow traps etc.) related to catching fish. ​ ("Level 2: Fish Populations (Snapshot Day)", 2018)This would affect the data because people skilled or unskilled in fishing are likely to catch different numbers of fish. Salinity levels were measured by the same students collecting fish data using refractometers, hydrometers and Quantab strips in Level 1 Salt Levels in the Hudson River (Snapshot Day). ("Level 1: Salt Levels in the Hudson River (Snapshot Day)", 2018) ​ 4. GRAPHS:

5. DATA TRENDS OR COMPARISONS: We have five different pie charts, each of which represents a different range of salinities that fish can live in. The charts show the names of the fish and the percentage of them that were caught in which salinity in the years 2004 through 2013. Out of the eight species there are two that lived in salty and brackish water (Atlantic Silversides and Ctenophora), three that lived in freshwater (Pumpkinseeds, Spottail Shiner, and Sunfish), and three fish that can live in all salinities (Banded Killifish, Striped Bass, and Blue Crabs). We found that there were a large number of Spottail Shiners in the freshwater and a large amount of Atlantic Silverside in the salty to brackish waters. In addition the number of Atlantic Silversides throughout the whole river outnumbered any other fish. Throughout the river the most common fish to least common fish were Atlantic Silverside, Ctenophora, Spottail Shiner, Blue Crab, Banded Killifish, Striped Bass, and finally Pumpkinseed. Although Striped Bass and Blue Crab are not right at the top of the list they appear in all 5 graphs indicating that they can tolerate the greatest range in salinity.

6. DATA INTERPRETATION OR EXPLANATION: Our results confirmed that some fish travel up and down river, and others stay in one place. The data shows us that salty, fresh, or brackish water fish live in the range of salinities that they were found in. We are able to tell in which salinity each fish lives in during the month of October, whether it be because they live in a fixed salinity year round or because of breeding, feeding, seeking a desirable temperature or other reasons for migration. We interpolated 2008 and 2009 salinity data onto a base map of the Hudson River enabling us to see clearly how salinity level locations could vary from year to (2008 Hudson River Estuary Salinity Data for 10/7/08, 2018) and (2009 Hudson ​ ​ ​ River Estuary Salinity Data 10/8/09, 2018). Beacon was a place that year by year the ​ ​ ​ salinity could vary, so one year you would find fresh water fish in Beacon and another year saltwater fish. This shows that the location on land does not matter, what matters to the fish is what the salinity level is. We now know that each species caught needs to maintain a similar salinity level in their blood, even though they live in water with a different salinity than their blood. Each species uses a different strategy for doing so ​ which determines where they can live. (Wurts, 2018)

7. HYPOTHESE: We had many questions related to our datasets. Why do some fish move throughout the river over the course of the year? What causes this migration? We asked ourselves these questions, along with many more. A question we came across multiple times was, what was the cause of the lack of fish caught in Green Island over the years? We realized that it was most likely a combination the temperature, time of year, and lack of equipment. We also had questions relating to why there were so many Atlantic silverside caught over the years in Piermont? We thought that because Piermont has a lower salinity, the Atlantic Silversides might travel upriver to spawn at that time of year. This would cause large numbers to be caught.

8. WRITTEN EXPLANATION OF CREATIVE PROJECT: We presented our dataset as a stick puppet show, and each of the puppets used, is one of the fish in our dataset. The conflict within the story was that all of the fish need to meet at the “Fish United Salinities”, but all of the fish live in different salinities. We hope that through this play, the viewer gains a deeper understanding of how each fish has a specific environment that they have to live in. We chose a stick puppet show because we found that the fish needed to interact, to best convey the data.

9. REFLECTION: Our Data Jam process was much harder than expected, and through the Data Jam we learned how hard it is to work with raw data. The hardest part was coming up with a plot that conveyed the data set accurately. The enjoyable part of the Data Jam was the initial analyzing of the data. If one thing could change about the Data Jam it would be to have a check in with a judge half way through to get input on the project. We think that these projects submitted for the Data Jam could be used in a curriculum to teach other students about the river.

10. REFERENCE LIST:

Level 2: Fish Populations (Snapshot Day). (2018). Retrieved from http://www.caryinstitute.org/students/hudson-data-jam-competition/data-jam-data-sets/level-2-fish-populat ions-snapshot-day

Level 1: Salt Levels in the Hudson River (Snapshot Day). (2018). Retrieved from http://www.caryinstitute.org/students/hudson-data-jam-competition/data-jam-data-sets/level-1-salt-levels-s napshot-day

Cary Institute of Ecosystem Studies. (2018). Retrieved from http://www.caryinstitute.org ​ Hudson River Environmental Conditions Observing System. (2018). Retrieved from http://www.hrecos.org ​

Wurts, W. (2018). Why can some fish live in freshwater, some in salt water, and some in both? [pdf]. ​ ​

2008 Hudson River Estuary Salinity Data for 10/7/08. (2018). [pdf]. ​

2009 Hudson River Estuary Salinity Data 10/8/09. (2018). [pdf]. ​

Image references used for stick puppets

(2018). [Image]. Retrieved from https://www.google.com/search?rlz=1CADEAC_enUS773US773&biw=1366&bih=621&tbs=sur%3Afc&tb m=isch&sa=1&ei=wbD9WpCBFYXl5gKv95zgCA&q=river%20sunfish%20painting&oq=river%20sunfish% 20painting&gs_l=img.3...18720.22447.0.22520.13.12.1.0.0.0.173.832.9j2.11.0....0...1c.1.64.img..1.2.160.. .0j0i30k1.0.2Gnk7eFF_yw&safe=active&ssui=on#imgrc=Lo345EmbxsZYNM: ​ (2018). [Image]. Retrieved from https://www.google.com/search?q=pumpkin%20seed%20fish&rlz=1CADEAC_enUS773US773&source=l nms&tbm=isch&sa=X&ved=0ahUKEwiS48G8mI3bAhWszlkKHYw9BeIQ_AUICigB&biw=1366&bih=621&s afe=active&ssui=on#imgrc=-nSeSvubMYGvJM: ​ (2018). [Image]. Retrieved from https://www.google.com/search?q=striped%20bass&rlz=1CADEAC_enUS773US773&source=lnms&tbm= isch&sa=X&ved=0ahUKEwjvnsTcmI3bAhWurFkKHY8lAnwQ_AUICigB&biw=1366&bih=621&safe=active &ssui=on#imgrc=KTMLKGC0VxkUtM: ​ (2018). [Image]. Retrieved from https://www.google.com/search?q=spottail%20shiner&rlz=1CADEAC_enUS773US773&source=lnms&tb m=isch&sa=X&ved=0ahUKEwj7nMP0mI3bAhWyt1kKHW6jDrsQ_AUICigB&biw=1366&bih=621&safe=act ive&ssui=on#imgrc=ooHvQkvJUl1VVM: ​ (2018). [Image]. Retrieved from https://www.google.com/search?q=ctenophora&rlz=1CADEAC_enUS773US773&tbm=isch&source=lnt&t bs=sur%3Afc&sa=X&ved=0ahUKEwix79yimY3bAhVszlkKHebJCMgQpwUIHg&biw=1366&bih=621&dpr= 1&safe=active&ssui=on#imgrc=Fb4msoxiYALebM: ​ (2018). [Image]. Retrieved from https://www.google.com/search?q=banded%20killifish&rlz=1CADEAC_enUS773US773&tbm=isch&sourc e=lnt&tbs=sur%3Afc&sa=X&ved=0ahUKEwj28-XYmY3bAhXJjVkKHSudDPAQpwUIHg&biw=1366&bih=6 21&dpr=1&safe=active&ssui=on#imgrc=RtDy4-R2zrKf9M: ​ (2018). [Image]. Retrieved from https://www.google.com/search?q=blue%20crab&rlz=1CADEAC_enUS773US773&source=lnms&tbm=isc h&sa=X&ved=0ahUKEwiKi8znmY3bAhWBuVkKHaRMAdAQ_AUICigB&biw=1366&bih=621&safe=active &ssui=on#imgrc=FIec3njnjHV9yM: ​ (2018). [Image]. Retrieved from https://www.google.com/search?rlz=1CADEAC_enUS773US773&biw=1366&bih=621&tbm=isch&sa=1&e i=h7L9WqqzN5CW5wKs75-ADQ&q=atlantic%20silverside&oq=atlantic%20sliver&gs_l=img.3.0.0i10i24k1. 74999.80344.0.81393.21.16.0.3.3.0.84.1002.15.15.0....0...1c.1.64.img..5.16.915.0..0j0i67k1j0i5i30k1j0i30 k1j0i8i30k1j0i24k1.0.Jq4UNKv9A-o&safe=active&ssui=on#imgrc=EFdpobeFhL1LzM: ​

11. VIMEO LINK TO CREATIVE PROJECT https://vimeo.com/271100059/f824cff9a8

Dylan Gerstenhaber Hudson Data Jam Competition 2018 Written Report Form Team Information Project Title: Hurricane Sandy School Name: Speyer Legacy School Name of Dataset(s): Hurricane Sandy and the Hudson River Level of Dataset(s): 2 Team Advisor’s Name(s): Kimberly Schwab Team Members’ Names (First and Last): Dylan Gerstenhaber ​

1.Title-The Effect of the river on Hurricane Sandy

Dylan Gerstenhaber 7th grade Speyer Legacy School

2. Introduction ​ The topic I will be explaining data sets about is Hurricane Sandy. Hurricane Sandy was one of the deadliest and most destructive hurricanes of all time. It happened in 2012 during the atlantic hurricane season. My scientific question is how did Hurricane Sandy affect the Hudson River? My data set shows where the water level rose and how detrimental the storm was compared to other places that suffered from the hurricane.

3. Dataset ​ This data set shows the water level in different areas in the storm. Thermometers were put around the hudson river to track the rise in the

Dylan Gerstenhaber water level. We use storm surge and tide heights from HRECOS. The storm hit on October 29th, 2012 but the tide/water level was at its highest one day after the storm hit. The water level was slowly rising, but on one day it skyrocketed to its peak. The moon also had a large effect on Hurricane Sandy. The full moon of October made the waves significantly bigger, causing a lot of damage.

Data representation(s):

4. Data trends ​ This next graph shows the water level/depth in different places. The water level was the highest in Port of Albany and the lowest in the battery water level but all of them increased on the date after the storm. This is all because of the storm surge. The water level and depth was continuously changing in different areas due to the storm surge. The ​hurricane's storm ​

Dylan Gerstenhaber surge​ — the pulse of seawater pushed ashore by Sandy's winds and low atmospheric pressure is the result of all the floods and high tides letting water onto the streets. The storm surge of hurricane sandy was even more dangerous than the hurricanes winds and terrible rains.

This next picture shows some of the damage storm surge has dealt.

6. Data interpretation ​

Dylan Gerstenhaber The reason the graphs I have provided look the way they do is all because of the moon phase. When the moon is in its full moon stage, there is high tide.The gravitational pull of the Moon and the Sun pulls the water in ​ the oceans upwards making the oceans bulge, which creates high tide in the areas of Earth facing the Moon and on the opposite side. So where did all this water come from? With the moon in its full moon phase, high tides on Earth will rise about 20 percent higher than normal. The full moon will add more power to the already intense storm surge of Hurricane Sandy, which is already expected to reach heights of 6 to 11 feet in parts of Long Island Sound and New York. The higher tides during a full moon occur because at this time the sun, Earth and moon line up with our planet in the center. The cosmic arrangement allows the gravitational pull of the sun and moon to reinforce one another, creating stronger tides on Earth. This is exactly what happened on hurricane sandy when the moon was in its full phase.

Dylan Gerstenhaber A​ ​storm surge s is a rise in sea level that occurs during tropical cyclones, intense storms also known as typhoons or hurricanes. The storms produce strong winds that push the water into shore, which can lead to flooding. This makes storm surges very dangerous for coastal regions. ... They form over warm, tropical oceans. Picture yourself blowing on the top of a cup of water. The water will start to pile up on the opposite side of the cup. Now picture this happening over hundreds of square miles over the ocean. That is what storm surge is.The storm.storm surge of hurricane sandy was definitely the most dangerous and detrimental factor of the storm. The storm surge stretched along coastal regions. What also made the tide so bad was that the moon in its full stage combined with storm surge is a huge danger.

7. ​New hypothesis and question(s) My question is about climate change and considering whether it really caused hurricane sandy. I do not believe it did. My hypothesis is that climate change had nothing to do with the hurricane and it was all ingredients of the storm. Even though climate change slowly intensifies hurricanes in the future, but trends in the frequency of storms are less certain and I believe the number of storms will decrease. (Samenow, 2018)

8. Creative project: My story- ​ ​

My creative project is the story of my Hurricane Sandy experience. 9. ​ Reflection on data jam:

Dylan Gerstenhaber When I started researching about hurricane sandy, multiple questions popped into my head. For example, was it just a coincidence that the moon was in its full phase right when the storm hit making it way more dangerous? Or, why was the storm so much more devastating a whole day after it hit the east coast? This could possibly be because the water pulled back not letting the tides reach the sea wall but there are so many possibilities of why this happened.The reason I chose this project is because I personally wanted to learn as well as teach people about what happened. I chose to format my project in this way because I felt it would be easiest to explain data through visuals and words. I also feel describing my story and perspective of the hurricane can help you have a better understanding of why the hurricane was so bad. Throughout my experience of data jam, I had a lot of fun learning. Learning about why the hurricane was so bad was very interesting to me as well as learning the ground knowledge about storm surge and the affect the moon can have on a storm. The most challenging part of data jam for me was thoroughly explaining data sets/graphs. If there was one thing I could change about data jam, it would be to allow participants to perform actual experiments at the contest.

10. Reference list: ​

Samenow, J. (2018). Climate change did not cause Superstorm Sandy, but probably intensified its effects. Retrieved from https://www.washingtonpost.com/blogs/capital-weather-gang/post/the-whole-truth-about-superstorm-sand y-and-climate-change/2012/11/15/d3b7ceea-29e4-11e2-bab2-eda299503684_blog.html?utm_term=.31db e3df7e1c

Lemonick, M. (2018). Sandy’s Storm Surge Explained and Why It Matters. Retrieved from http://www.climatecentral.org/news/hurricane-sandys-storm-surge-explained-and-why-it-matters-15182

11. My Story ​ Hurricane sandy formed and started making its way towards Atlantic City. We originally thought sandy was a category 2 storm but then it weakened and became a category 1 storm. Once this happened, it started coming towards the U.S coast and tension spread.Hurricane sandy was overall a

Dylan Gerstenhaber terrible storm. On november 1st, 149 people died. A huge portion of those deaths were in New York, and Haiti. Streets were flooded, streets and power lines were knocked down, and the city’s famed boardwalk was completely destroyed. This was only in Haiti.Along the Jersey shore, ​ people were left stranded in their homes and waited for rescue teams in boats to rescue them. More than 80 homes were destroyed in one fire in Queens. Several other fires were started throughout the New York metro area. There were also reports of sea creatures getting on the streets because the tide was so high. Seawater surged over Lower Manhattan's seawalls and highways and into low-lying streets. The water inundated tunnels, subway stations and the electrical system that powers Wall Street and sent hospital patients and tourists scrambling for safety.The storm ​ damaged or destroyed at least 650,000 homes, and 8 million customers lost power. Even before hitting land, Sandy was the largest tropical storm in the Atlantic, reaching 900 miles in diameter. 71.5 billion dollars were spent all on economic damages because the storm was so huge. Hurricane sandy left 50 million people at risk.

I live on the water down in tribeca and school for me was canceled during the hurricane. This gave me the chance to go out to the water and see the tide. The water was rising above the sea wall and I even got soaked. I stayed out of the water for about an hour just looking at the tides getting higher and higher. At one point, down the river farther into Battery Park, and eel got onto the street. This was because the tide was so high. Later in the day I went back home and the power was already out. Every few minutes the power would turn off and on continuously. The elevator was not working, and the whole basement of our building where the gym is was completely flooded. The table on our roof split in half and all the chairs were broken into pieces. Luckily nothing flew off the roof but there was still severe damage. Hudson Data Jam Competition 2018 Written Report Form Team Information Project Title: Hudson River Fish School Name: Speyer Legacy School Name of Dataset: Fish Populations Level of Dataset: 2 Team Advisor’s Name: Ms. Schwab Team Members’ Names :Eugene Yoo and Michael Korvyakov

1. Title Hudson River Fish. By Eugene Yoo (7) and Michael Korvyakov (7) from the

Speyer Legacy School

2. Introduction

One day, our science teacher, Ms. Schwab, said, “Let's go down to the river and see what we can catch.” We walked three blocks down to the river, past hundreds of people. It was amazing how we switched from a busy city to a calm river. For a whole day, my peers and I tried to catch marine animals with a seine net, fish traps, and crab traps. We caught small shrimp, comb-jellies, small minnows, and mussels. Like many of the other students, I was captivated by the river, but when we caught a huge male blue crab, I was set on finding out more about the life of the Hudson River. When we started working on this data jam, we were very surprised at what we found. For example, a female blue crab can lay anywhere from 600,000 to 8 million eggs. We also noticed from the scatter plots how much the Atlantic Silverside numbers changed. From this project, we analyzed the fish populations from the graphs provided and concluded that either the salt in the water significantly changed or the temperature and climate changed radically.

3. Dataset Description

A day in the Life of the Hudson River is an annual event held along the Hudson River by ​ different schools. The data was collected by different students and teachers every October between 2004 and 2013 and compiled by the New York State Department of Environmental

Conservation. ("Level 2: Fish Populations (Snapshot Day)", 2018) The dataset locations were ​ ​ Beczak, Piermont, Beacon, Norrie Point, Ulster Landing, and Green Island. The fish were caught by seine nets, fishing rods, and crab and minnow traps. These independent variables like time, environment, and location are clearly are important as they affect the fish. If for example the temperature was the same, then the graphs would clearly be steadier. For our data jam project, we focused on the data collected for eight different fish: Atlantic Silverside, Blue Crab, Ctenophora, Sunfish, Striped Bass, Spottail Shiner, Banded Killifish and Pumpkinseed. These schools collected a lot of information not only relevant to us, but also important to scientists, conservationists, and more. This data can be used to protect fish, humans, and much more.

4. Data Representation

5. Data Trends and Comparisons

We have three different types of graphs to represent the data that we have collected. For example, the eight scatter plots indicate how each fish species has changed in size over the years. The pie chart shows the percentage of each fish species in correlation to each other. And the bar graph shows how many of each fish in total were caught.

The scatter plots show interesting information. The number of pumpkinseed was the biggest fluctuator, jumping from 0 in one year to thirty the next, and then back to 0. Sunfish had the biggest jump of about 250 from one year to the next. The banded killifish had the most consistent graph, and the blue crab, silverside, and striped bass clearly proved to be the most constant fluctuating graphs. According to the pie chart, the Atlantic silverside was the most commonly found fish at 34.6%. The hardest fish to find was the pumpkinseed, at a mere 2.3%.

6. Data Interpretation

Many of the numbers of fish over the years are volatile, and we came up with some reasons for why they fluctuate so much. First of all, location was a major variable in the data. We noticed that some of the locations higher up the river have less fish. For example, the minimal amount of pumpkinseed can be explained by this fish’s necessity for brackish water, and there were not many sites with this particular water. This is probably because the smaller the river mile, the saltier it is since it is closer to the Atlantic.

Second of all, temperature and climate were probably other important variables. For example, many of the fish like warmer waters, so when it is colder on the top of the river, as it sometimes is in October, they all swim towards the seabed. Furthermore, if it is a bright day, the striped bass will hide deeper in the Hudson River because it hates light. Also, there were not many sunfish that were caught because they only come up to shore when they lay eggs, and it was not egg-laying season during the time that the students tried to catch them.

On the other hand, Atlantic silverside and spottail shiner were found in copious amounts because they tend to stay near the shore and are small and easy to catch. These two species fluctuated significantly because of predators and tides, and that is why some years many or none would be caught.

7. New Questions and Hypotheses

Throughout our project, we noticed the sudden spikes and decreases in the numbers of fish. We kept asking questions about why this happened. One of those questions was, “Is there an answer for why Ctenophora spiked in 2013?” Perhaps they move with the current, and there was an abundance of them in one location. Why did the Atlantic silversides change the most from one year to the next? That was also probably because they were not in the same location as where the data collection sites were. The number of banded killifish increased and decreased constantly. Perhaps this could be explained by its migration patterns.

8. Explanation of Creative Project ​

For our creative project, we created a video about each fish. We want to give the audience some insight about how unique and important fish are and that they are not merely food in the ecosystem but, just like us, have interesting lives. We felt like a fun video would be the best way to get facts and some analysis out effectively. We hope that people will be intrigued about how these fish live and want to find out more. 9. Reflection on Data Jam Experience ​

The Data Jam was both easy and hard. What we found the most challenging was finding a good story and a good way to share it. At the same time, this was the part that motivated us the most to keep learning about the fish. We found out how each fish lived, their qualities, and their populations. We also found it hard to explain why the graphs did not have steady data points. The easiest part was writing the report and starting off. We knew what to do, how to do it, and how to plan it. Other than learning about fish, we found a love for science and the life in the Hudson River. Since the day we caught the animals out on the Hudson, I have been amazed at how much happens without us knowing. If I could change the Data Jam Project, I would make it so that the report did not have to follow a certain format but that we could make it ourselves. Lastly, I do believe that there is a way to share our projects with an outside audience. We could put our projects on the website and encourage schools to watch them. Throughout the Data Jam, I would say that what we learned the most was how interesting these fish are and that there is more to them than one might think.

10. References

Level 2: Fish Populations (Snapshot Day). (2018). Retrieved from http://www.caryinstitute.org/students/hudson-data-jam-competition/data-jam-data-sets/level-2-fish-populat ions-snapshot-day

Fish of the Hudson River Estuary - NYS Dept. of Environmental Conservation. (2018). Retrieved from https://www.dec.ny.gov/lands/74069.html

Fish. (2018). Retrieved from ​https://en.wikipedia.org/wiki/Fish

Atlantic Silverside – Tybee Island Marine Center. (2018). Retrieved from https://www.tybeemarinescience.org/naturalist/atlantic-silverside/

Blue Crab – Tybee Island Marine Center. (2018). Retrieved from https://www.tybeemarinescience.org/naturalist/blue-crab/

Florida Pompano – Tybee Island Marine Center. (2018). Retrieved from https://www.tybeemarinescience.org/naturalist/florida-pompano/

Hudson River. (2018). Retrieved from ​https://en.wikipedia.org/wiki/Hudson_River

11. Creative Project Link https://www.youtube.com/watch?v=8KXPTyjoU4g

​WRITTEN REPORT

Team Information Project Title: Glass Eels School Name: Speyer Legacy School Name of Dataset(s): Glass Eels in Hudson River Tributaries( Eel Project) Level of Dataset(s): 2 Team Advisor’s Name(s): Ms.Schwab Team Members’ Names (First and Last): Maya Doron-Repa, Jade LeDoux, and Marshury Malla

1.Title - The Adventures of the Glass Eels

Names- Jade LeDoux Maya Doron-Repa Marshury Malla Grade- 7th grade

School name- Speyer Legacy School

2. Introduction

The topic we chose to do was the Level 2 Eels in the Hudson River. Glass eels are baby American eels. We were given a data set of the number of eels caught per day. We then went down the path and found eel prices per pound per year. We were really interested in why there were such a big volatility in the prices every year. We explored that topic and now we are here to answer it

3. Dataset Description

The dataset is about the glass eel migration, specifically in the Hudson River.("Level 2: Glass ​ Eels in Hudson River Tributaries", 2018) The independent variables for this data set are the year ​ and the location at which glass eels were captured in fyke nets by volunteers, then counted, weighed and released. The dependent variable is the number of glass eels caught in each location each year The data contains information of when and where glass eels were captured in fyke nets by volunteers, then held briefly to be counted and weighed before returning them to their stream. The data source is from New York State of Department of Environmental Conservation (NYSDEC). A variety of people collected this information such as students, teachers, and community members with the help of the staff. This data was collected in 15 or 16 places along the river which were Richmond Creek, Bronx River, Beczak, Yonkers, Minisceongo Creek, Furnace Brook, Indian Brook, Quassaick Creek, Hunters Brook, Fall Kill, Crum Elbow, Black Creek, Indian Kill, Saw Kill, Hannacroix Creek, and Blind Brook. ("Level 2: Glass Eels in ​ Hudson River Tributaries", 2018)

4. Data Representations

5. Data Trends or Comparisons The data trends and comparisons vary a fair amount. In 2012 to 2013, we noticed that the average amount of glass eels caught per day went from 442.4 down to 400.7. In 2014 to 2015 it went from 381.9 down to 591.6. In 2016 it went all the way up to 1065.2. In 2017, it went down to 451.5. In the beginning, the amount of eels decreased, and then in 2016 it increased, then in 2017 it decreased again.

6. Data Interpretation

We saw that in the first years (2012-2014) the amount of glass eels caught was low, but then suddenly it rose in large amounts. We think that this could be because more recently, the technology for catching eels could have improved, as we are progressing rapidly. In addition, we think this could also be because skills of the varying skills of the fyke net users could have improved, the weather on the sampling days could have made more eels want to come, and the number of volunteers on those days could have differed. We are concerned that the high prices in the market for glass eels could affect the Hudson River population, even though it is not legal to

catch them in New York State. (2018) Native Americans in Long Island argue that treaties guarantee their rights to fish the glass eels and the threat of poaching exists because eels are work so much money.

7. New Questions and Hypothesis The most likely explanation for the decreasing harvests is that there were decreasing populations of eels across the time periods. This assumes that similar and comparable methods were used to measure the harvests across the different time periods, and that there wasn’t, for example, simply a decrease in fishing that would lead to the lower harvest numbers. Why might the eel population be decreasing over time? Some reasons could be a loss of habitat, water pollution, climate change, and the construction of dams, which server as barriers to the eels.

One explanation for the increase in eels caught could be a change in the techniques used—perhaps the researchers got better at catching eels! But assuming they used similar methods and equipment etc, then the data suggest that the glass eel population may be rising in the Hudson River over the last 5 years. One possible reason is the addition of “eel ladders”, which help the eels travel over dams toward their destinations. For example, many researchers began to install some eel ladders in 2011.

8. Written Explanation of Creative Project (PLAY OR SCRIPT) Our play is about two eel catchers, one who is named Martha, and the other one who is a little insane. Each year they try to catch eels at Quissack Creek and once they do Martha sells the eel at a sushi market. This repeats for about 5 to 6 times until one time Martha is curious about why the eel prices per pound have changed. Then the info eel comes in and helps to answer the question. We have put some humor into it so we hope you enjoy!

9. Brief Reflection on Data Jam

Overall, we enjoyed this project and we were glad to have an opportunity to participate in it. Not only was it fun, but we ended up learning a lot about glass eels and eel prices. We have also learned about the sea route of eels and how they go through the Sargasso Sea. We were really excited to find out that we were participating, and throughout the entire time working on this it was all laughs, fun, and joy. Thank you!

10. Reference List

***Level 2: Glass Eels in Hudson River Tributaries (Eel Project). (2018). Retrieved from http://www.caryinstitute.org/students/hudson-data-jam-competition/data-jam-datasets/level-2-glass-eels-huds on-river-tributaries

Level 2: Glass Eels in Hudson River Tributaries. (2018). Retrieved from http://www.caryinstitute.org/sites/default/files/public/downloads/page_2016/level_2_eels_in_hudson_river_me tadata_012218.pdf

(2018). Retrieved from https://www.nytimes.com/2018/02/01/nyregion/hamptons-shinnecock-indians-eels.html?hpw&rref=nyregion &action=click&pgtype=Homepage&module=well-region®ion=bottom-well&WT.nav=bottom-well

http://bangordailynews.com/2018/03/27/business/fisheries/price-offered-for-maines-baby-eels-hits-record-high/ https://wtop.com/national/2018/03/fishermen-of-baby-eels-expect-high-price-as-stocks-dry-up/ ​ https://www.seafoodsource.com/news/supply-trade/poor-glass-eel-harvest-in-japan-leading-to-higher-prices https://news.nationalgeographic.com/2015/10/151027-american-eel-migration-animal-behavior-oceans-science/ https://www.bostonglobe.com/magazine/2017/07/05/selling-for-high-pound-baby-eels-have-changed-fortunes-for-m aine-fishermen-and-brought-trouble/lON96WKku1Db5AqDI6HkfM/story.html

https://news.nationalgeographic.com/2015/10/151027-american-eel-migration-animal-behavior-oceans-science/ (http://www.dec.ny.gov/docs/remediation_hudson_pdf/082415eelreport.pdf, p. 25).

11. YouTube Link: https://www.wevideo.com/view/1153817728 ​ ​ ​