**NOAA Teacher at Sea
**

**Allan Phipps**

**Aboard NOAA Ship**

*Oscar Dyson***July 23 – August 11, 2012**

**Mission: Alaskan Pollock Mid-water Acoustic Survey**

** Geographical Area: Bering Sea**

** Date: August 2, 2012**

**Location Data**

Latitude: 61°12’61” N

Longitude: 178°27’175″ W

Ship speed: 11.6 knots (13.3 mph)

**Weather Data from the Bridge**

Wind Speed: 11 knots (12.7 mph)

Wind Direction: 193°

Wave Height: 2-4 ft (0.6 – 1.2 m)

Surface Water Temperature: 8.3°C ( 47°F)

Air Temperature: 8.5°C (47.3°F)

Barometric Pressure: 999.98 millibars (0.99 atm)

**Science and Technology Log**

The easy answer is… try and determine how many fish are in the sea. That way, you can establish sustainable fishing limits. But there is a little more to the story…

Historically, all fisheries data were based on length. It is a lot easier to measure the length of a fish than to accurately determine its weight on a ship at sea. To accurately measure weight on a ship, you have to have special scales that account for the changes in weight due to the up and down motion of the ship. Similar to riding a roller coaster, at the crest of a wave (or top of a hill on a roller coaster), the fish would appear to weigh less as it experiences less gravitational force. At the trough of a wave (or bottom of a hill on a roller coaster), the fish would experience more gravitational force and appear to weigh more. Motion compensating scales are a more recent invention, so, historically, it was easier to just measure lengths.

For fisheries management purposes, however, you want to be able to determine the mass of each fish in your sample and inevitably the biomass of the entire fishery in order to decide on quotas to determine a sustainable fishing rate. So, you need to be able to use length data to estimate mass. Here is where science and math come to the rescue! By taking a random sample that is large enough to be statistically significant, and by using the actual length and weight data from that sample, you can create a model to represent the entire population. In doing so, you can use the model for estimating weights even if all you know is the lengths of the fish that you sample. Then you can extrapolate that data (using the analysis of your acoustic data – more on this later) to determine the entire size of the pollock biomass in the Bering Sea.

How do they do that? First, you analyze and plot the actual lengths vs. weights of your random sample and your result is a scatter-plot diagram that appears to be an exponential curve.

Then you create a linear model by log-transforming the data. This gives you a straight line.

Next, you back-transform the data into linear space (instead of log space) and you will have created a model for estimating weight of pollock if all you know are the lengths of the fish. This is close to a cubic expansion which makes sense because you are going from a one-dimensional measurement (length) to a 3-dimensional measurement (volume).

Scientists can now use this line to predict weights from all of their fish samples and then extrapolate to determine the entire biomass of Walleye pollock population in the Bering Sea (when combined with acoustic data… coming up in the next blog!) when the majority of the data collected is only fish lengths.

Another interesting question… How does length change with age? Fish get bigger as they get older, all the way until they die, which is different from mammals and birds. However, some individual fish grow faster than others, so the relationship between age and length gets a little complicated. How do you determine the age distribution of an entire population when all you are collecting are lengths?

Just like weight, you can determine the age from a subset of fish and apply your results to the rest. This works great with young fish that are one year old. The problem is… once you get beyond a one-year-old fish, using lengths alone to determine age becomes a little sketchy. Different fish may have had a better life than others (environmental/ecological effects) and had plenty to eat, great growing conditions, etc and be big for their age relative to the rest of the population. Some may have had less to eat and/or unfavorable conditions such as high parasite loads leading them to be smaller… There are also other things to consider such as genetics that affect length and growth rate of individuals. Here is where the collection of otoliths becomes important. By collecting the otoliths with the lengths, weights, and gender data, the scientists can look at the age distributions within the population. The graph below shows that if a pollock is 15 cm long, it is clearly a 1 year old fish. If a pollock is 30 cm long, it might be a 2 year old, a 3 year old, or a 4 year old fish, but about 90% of fish at this length will be 3 years old. If a fish is 55 cm long, it could be anywhere from 6 to 10+ years old!

Collection of otoliths is the only way to accurately determine the age of the fish in the random sample and be able to extrapolate that data to determine the estimated age of all the pollock in the fishery. Here is a photo comparing otolith size of Walleye pollock with their lengths.

If we wanted to find out exactly how old each of these fish were, we would need to break the otoliths in half to look at a cross section. Below is what a prepared otolith looks like (courtesy of Alaska Fisheries Science Center). You can try counting rings yourself at their interactive otolith activity found here.

All of these data go into a much more complicated model (including the acoustic-trawl survey walleye pollock population estimates) to accurately estimate the total size of the fishery and set the quotas for the pollock fishing industry so that the fishery is maintained in a sustainable manner.

Next blog, we will learn about how the various ways acoustic data fit into this equation to create the pollock fishery model!

**Personal Blog**

Ok, so here is a long overdue look at the NOAA Ship *Oscar Dyson* that I am calling home for three weeks. I was pleasantly surprised when I saw my state room. It is bigger than I thought it would be and came with its own bathroom. I was also pleasantly surprised to learn I would be sharing my state room with Kresimir Williams, one of the NOAA scientists and an old college friend of mine! Here is a picture of our room.

The room has a set of bunk beds. Thankfully, my bed is on the bottom. I do not know how I would have gotten in and out of bed in the rough seas we had over the last couple of days. If I do fall out of bed, at least I will not have far to fall. Last year, the ship rocked so hard in rough seas that one of the scientists fell head first out of the top bunk! The room also had two lockers that serve as closets, a desk and chair, and our immersion suits (the red gumby suits). The bathroom is small and the shower is tiny! Notice the handles on the wall. These are really handy when trying to shower in rough seas!

Next, we have the Galley or Mess Hall. This is where we have all of our meals prepared by Tim and Adam. Notice that all of the chairs have tennis balls on the legs and that each chair has a bungee cord securing it to the floor! There are also bungee cords over the plates and bowls. Everything has to be secured for rough seas.

The Mess Hall also has a salad bar, cereal bar, sandwich fixings, soup, snacks like cookies, and ice cream available 24 hours a day. No one on board is going hungry. The food has been excellent! We have had steaks, ribs, hamburgers and fish that Tim has grilled right out on deck. Here is a picture of my “surf and turf” with a double-baked potato.

Most of my work here on board (other than processing fish) has been in the acoustics lab, also known as “The Cave” since it has no windows. This is where the NOAA scientists are collecting acoustic data on the schools of fish and comparing the acoustic data with the biological samples we process in the fish lab.

I also spend some time up on the Bridge. From the Bridge, you can see 10 to 12+ nautical miles on a clear day. This morning, we saw a couple of humpback whales blowing (surfacing to breathe) about 1/4 mile off our starboard side! A couple of days ago (before the weather turned foul), we spotted an American trawler.

Today, we got close enough to see the Russian coastline! Here is a picture of a small tanker ship with the Russian coastline in the background!

Here are some pictures of the helm and some of the technology we have onboard to help navigate the ship.

I have also spent some time in the lounge. This is where you can go to watch movies, play darts (yea, right! on a ship in rough weather???), or just relax. The couch and chairs are so very comfy!

When you have 30 people on board and in close quarters, you better have a place to do laundry! Here is a picture of our very own laundromat.

All for now. Next time, I will share more about life at sea!