Tag Archives: Science

Lessons Learned at the Regional Science Fair

A young scientist presents in the undergraduate poster session of the American Chemical Society spring meeting, 2007.  Image credit: Ellen Valkevich.
A young scientist presents in the undergraduate poster session of the American Chemical Society spring meeting, 2007. Image credit: Ellen Valkevich.

This spring I had the privilege of being a judge at my local science fair. As a high school student, I had participated in the science fair and it was a huge part of my science learning experience. Now that I am qualified to be a judge, it is time for me to give back while avoiding the trap of turning into the dreaded Reviewer #2.*

I scored quite a few different projects, primarily in Earth & Environmental Science. I was pleased to see the large number of students involved in the discipline, and the interest they showed in environmental monitoring and sustainability. However, I was surprised to see the number of projects which focused on pH, but without understanding of pH of rainwater or the influence of carbonates.

Limited or non-existent access to instrumentation was clearly a limiting factor in many of the projects. That observation leads to a question: what can be done to address the disparity in instrument access and to improve the quality of data being used in science fair projects? I believe the long-term answer to that question is to fund our schools and support the teachers and staff who work in them.

Another solution would be to have students use and analyze publicly available data. In many cases, this cut out some of the hands-on portion of making measurements, which detracts from the overall learning goals. Using publicly-available data also means that teachers would need to be more aware of good data resources and ideas for how to go about analyzing that data—each significantly increasing the work load and responsibilities of the teachers. For research projects, it is important to have a low student:teacher ratio, so that the students can have the support they need to succeed in their project. However, publicly available data allow students to do cutting-edge research with the same tools and data used by professional scientists.

Here are a few examples of low-budget, high-quality data projects that could be interesting:

  • Weather forecast accuracy. Make a daily record of the National Weather Service forecast (for each day forecast) for your area, as well as the almanac data from the closest instrumented NWS station (often an airport). How does forecast accuracy change over time? How accurate is a forecast 72 hours out?
  • Earth-Observing Satellite data. With a constellation of Earth-observing satellites including Aqua, Terra, Landsat (7 and 8), and formerly EO-1, there are mountains of data waiting to be analyzed. Students can look at crop health locally, at glacial changes, deforestation, volcanic activity, wildfires, and a host of other things. Data are freely available, GIS software is freely available, and the data analysis skills are quite relevant in today’s job market.
  • Buoy data. As I’ve mentioned here before, there are several fleets of marine buoys which take various oceanographic measurements, such as conductivity-temperature-depth profiles and current measurements. Oceanography isn’t my thing, but I’m sure there are enough papers that use these data that some project ideas could be found. These projects are likely to use GIS.

* Reviewer #2 is known for being overly critical, wanting a paper that isn’t particularly close to the paper that was submitted, having unreasonable or unattainable expectations, and generally being a jerk.

Argo Buoy Update

Argo buoy tracks from buoys deployed on the Heard Island Expedition.  Red dots indicate most recent position.  The first set of buoys were deployed between Cape Town, South Africa, and Heard Island (center).  The color scheme has been reused for the second set of buoys, deployed between Heard Island and Fremantle, Western Australia.  Image credit: Bill Mitchell (CC-BY).
Argo buoy tracks from buoys deployed on the Heard Island Expedition. Red dots indicate most recent position. The first set of buoys were deployed between Cape Town, South Africa, and Heard Island (center). The color scheme has been reused for the second set of buoys, deployed between Heard Island and Fremantle, Western Australia. Image credit: Bill Mitchell, using OpenStreetMap continents (CC-BY).

About ten months ago, the Heard Island Expedition team launched the first of our eleven Argo buoys into the Indian Ocean. The buoys are equipped with conductivity-temperature-depth (CTD) instruments, and spend most of their time drifting about 1 km beneath the ocean surface. Every ten days, they dive to 2 km, then record CTD data as they ascend to the surface. At the surface, they relay the data via satellite over the course of a day before returning to 1 km depth. With a large network of these buoys, scientists can gather important data on currents under the ocean, as well as changes in temperature and salinity profiles.

Over time, ocean currents move the buoys. None of our eleven buoys are where they started, and some have moved far away from where they entered the ocean. We deployed two batches of buoys: six before reaching Heard Island from Cape Town, and five more on our voyage on to Fremantle/Perth.

I have obtained the latest position data (as of Jan 14, 2017) for the eleven buoys. Their tracks are shown in the figure at the top of this post. Tracks are colored by buoy, reusing the colors for the first and second batch. Some of the buoys have moved more than 1500 km as the albatross flies, with path lengths approaching 3000 km!

The CTD data are also interesting. For instance, here are the temperature/depth and salinity/depth profiles measured by buoy 5902454 (dark blue path on second leg of map above).

Temperature/depth profiles over time for buoy 5902454.  Image credit: Coriolis Data Centre.
Temperature/depth profiles over time for buoy 5902454. Image credit: Coriolis Data Centre.
Salinity/depth profiles over time for buoy 5902454.  Image credit: Coriolis Data Centre.
Salinity/depth profiles over time for buoy 5902454. Image credit: Coriolis Data Centre.

Around December 1, buoy 5902454 encountered a different water mass with colder, saltier water throughout much of the 2 km water column.

Generally for these buoys, the surface water temperatures reflect the seasonal variations (warmer in Austral summer, colder in winter), while the deep water shows less variation—but sometimes there are shifts between different water masses.

Interested in keeping up with the latest from these buoys, or checking out their CTD data? Use these links (buoy number, in order by our deployment date):
1901910
1901812
1901811
1901813
1901809
1901838

5902451
5902452
5902453
5902454
5902455

Correction An earlier version of this article referred in several places to deploying ten buoys. The correct number is eleven.

Heard Island’s Most Spectacular Outcrop

Head-on view of a block of Drygalski Formation (mixed volcanics and glacial sediments, here glacial sediments with volcanic clasts).  Image credit: Bill Mitchell (CC-BY).
Head-on view of a block of Drygalski Formation (mixed volcanics and glacial sediments, here glacial sediments with volcanic clasts). 53° 01.927′ S, 73° 23.704′ E. Image credit: Bill Mitchell (CC-BY).

Heard Island is home to a spectacular outcrop. It’s the coolest outcrop I’ve ever seen, besting the Bishop Tuff tablelands, the potholes along the St. Croix River at Taylor’s Falls, Zion Canyon, The Badlands, and various outcrops in Yellowstone and Grand Teton. Admittedly this outcrop doesn’t intrinsically have the scale of many of the others just mentioned—it’s a roughly car-sized block—but the power that went into creating it and the effect it created is truly amazing.

On its face (see above), it looks quite pedestrian: a block of lithified glacial till with clasts of vesicular basalt reaching up to grapefruit size. However, it’s important to consider it from a different perspective.

Side view of a block of Drygalski Formation.  From this view, it is much easier to see this is a ventifact (carved by the wind).  There is a pile of sand on leeward (left) side. Image credit: Bill Mitchell (CC-BY).
Side view of a block of Drygalski Formation. From this view, it is much easier to see this is a ventifact (carved by the wind). There is a pile of sand on leeward (left) side.
Image credit: Bill Mitchell (CC-BY).

When viewed from the side, a pile of sand in on the leeward (left, east) side of the block is evident. Additionally, the basaltic clasts of the rock face seem to be protecting the softer, tan-colored glacial matrix from the sand-blasting.

Here’s a close-up from an oblique angle:

Close-up, oblique view of the outcrop face.  Here the differential weathering (resistant grey clasts, weak tan matrix) is very apparent.  Spires of matrix are left to the leeward of the clasts, and are roughly horizontal. Image credit: Bill Mitchell (CC-BY).
Close-up, oblique view of the outcrop face. Here the differential weathering (resistant grey clasts, weak tan matrix) is very apparent. Spires of matrix are left to the leeward of the clasts, and are roughly horizontal.
Image credit: Bill Mitchell (CC-BY).

In the oblique view, the volcanic clasts making up the face of the outcrop are seen sheltering the matrix directly to the leeward from mechanical erosion. To tie all of these views together, I took a short video (embedded below).

This outcrop is located on the edge of a volcanic sand plain roughly 1.5×1.5 km. Strong westerly winds are present most of the time (9 m/s is average, measured at a site nearby).[1] In fact, the audio which accompanies the video is mostly wind noise, though there’s a little unintelligible chatter with my field partner, Carlos. Winds when the recording was made were “moderate” (for Heard Island) and from the west, exactly the kind of winds that shaped this outcrop. At the time of the recording, the winds weren’t strong enough to kick up much sand, nor were ice pellets falling from the sky, but on a gustier, stormier day, the face of this outcrop will take a beating.

Looking toward the ventifact outcrop from Windy City, Heard Island.  Although the outcrop itself is hidden behind the small reddish rise at center, this image illustrates the expanse of vegetation-free sand plain. Image credit: Bill Mitchell (CC-BY).
Looking toward the ventifact outcrop from Windy City, Heard Island. Although the outcrop itself is hidden behind the small reddish rise at center, this image illustrates the expanse of vegetation-free sand plain.
Image credit: Bill Mitchell (CC-BY).

In my travels and geo-adventures, I’ve seen differential weathering and ventifacts (outcrops shaped by wind), but never so strikingly combined as at this outcrop on Heard Island. That’s why I can confidently say it’s the coolest outcrop I’ve seen on Heard Island or anywhere else in the world.

[1] Thost, D., Allison, I. “The climate of Heard Island” in Heard Island: Southern Ocean Sentinel, ed by K. Green and E. Woehler. Surrey Beatty & Sons, Chipping Norton 2005, p. 52-68.

The Making of the Windy City Gigapan

Looking eastward at Windy City, with a person for scale. The gigapanned portion of the outcrop is at right, but two spires of similarly eroded rock outcrop further to the north of the photographed portion. The stake coming out from the outcrop is a marker for one of our temperature/light intensity sensors. Image credit: Carlos Nascimento
Looking eastward at Windy City, with a person for scale. The gigapanned portion of the outcrop is at right, but two spires of similarly eroded rock outcrop further to the north of the photographed portion. The stake coming out from the outcrop is a marker for one of our temperature/light intensity sensors.
Image credit: Carlos Nascimento

In my previous post, I discussed the gigapan of Windy City. However, the making of that gigapan was quite the adventure in field work.

After the Azorella Peninsula gigapan, the unit was packed up and taken back aboard the Braveheart for a trip to the southeast portion of the island. Rough north winds were expected, and with no protection afforded against those winds and swells from Atlas Cove, the ship had to move. Our expedition leader and two scientists not involved in the radio operations left camp and went to ride out the storm south of Stephenson Lagoon. At that time, it had become clear that I personally would not be able to go to Stephenson Lagoon—an area which was an extremely high priority for a gigapan image. I put fresh batteries into the gigapan mount, and sent it on its way. Sadly, in the almost four hours the team had on the shores of Stephenson Lagoon, they did not have an opportunity to take a gigapan. I’ll have to go back for that one!

Upon their return to camp, I knew since they had not attempted any gigapanning that there were fresh batteries in the unit. As the end of the expedition drew near, it was time to get the gigapan done at Windy City. Mid-morning, Carlos joined me for a trip to the outcrop (about 1.4 km each way). Although we didn’t have a bright sunny day, it was dry with a temperature around 5 °C. When we reached the outcrop and everything was set up, I turned on the gigapan mount. Nothing happened. With new batteries and a limited task, I hadn’t brought the whole kit with me. We headed back to camp, arriving in time for lunch.

Several of the rechargeable batteries I had for the gigapan had been sitting on the charger and were ready to go. I tossed those into the battery holder, put it under my arm to keep warm, and headed out with Carlos once again. At the outcrop I set up the rig again. When everything was set to go, I removed the batteries from inside my jacket, and put them into their slot. I powered it on. The LCD display brightened, but displayed an error message: Button-pusher disconnected or plugged in backwards. Cycling the power on and off didn’t fix it. Everything was as it had been before when it worked. Once again, this was a problem I was unable to deal with at the outcrop.

Back in camp, Carlos looked online for a solution while I tried to see if anything was likely to have come disconnected, although our team had been very gentle with the unit. Nothing stood out. Eventually we found online that the error is commonly caused not by a disconnected or backwards button-pusher, but by a low voltage. That made a bit more sense. Out came the volt-meter, and two sets of six AA alkaline batteries were verified to be fresh. One set went into the battery holder, the other went into a storage case. Now that it was late in the afternoon, Carlos had to report for radio duty, but Adam was willing to come with me—I needed this gigapan before the light died, as there was no guarantee that I would have the weather conditions or time to get it later.

Adam and I hurried over to the outcrop, the light already beginning to fade. I set up quickly, got the batteries out from my jacket, and set up the gigapan.

Please, light, stay with us long enough to complete this shot. Please, batteries, keep up your voltage!

It was clear from the beginning that the shot would not be truly completed. Somewhere in the middle either the light would die or the batteries would. Eventually, both did at about the same time. We quickly put everything back into the packs and headed back for camp. It was getting dark, but we arrived just in time for dinner and the start of my shift at the radio desk.

Although it was too late to be of use, I asked on Twitter what some of the other cold-weather folks had done about their gigapans. By the end of my four-hour radio shift, I had responses from @rschott and @callanbentley. Evidently this is a common problem, which is fought by insulating the gigapan unit as well as possible, and using hand/toe-warmers to add a little heat.

I think it’s time to ask Gigapan to make some design adjustments to improve the cold-weather operation of the units. It wasn’t all that cold where I was gigapanning, yet I still couldn’t get 15 minutes of operation on fresh batteries at 3–5 °C.

Windy City Gigapan

Processing the Windy City gigapan.  Image credit: Bill Mitchell (CC-BY).
Processing the Windy City gigapan. Image credit: Bill Mitchell (CC-BY).

This is the third in a series of three posts about the gigapan images taken on Heard Island (1: Big Ben, 2: Azorella Peninsula), with more information about the Windy City gigapan.

Windy City is located about 200 meters south of Atlas Cove, in the northwest portion of Heard Island. It comes from a fin of Drygalski Formation rocks, which are a mix of glacial sediments and volcanics, and is mostly surrounded by sand and gravel plains.

Looking closely at the outcrop, there are a number of interesting things to observe. First, there are the striking roughly-horizontal marks, which are particularly evident toward the base of the outcrop. Second, the outcrop is made of massive, fine-grained jointed rocks with few vesicles. Third, there are quite a few fractures within the rock, with discolorations along many of the cracks.

All of these observations combine into a remarkable tale of how Windy City has been formed. The massive, fine-grained, and jointed appearance leads to the conclusion that we are looking at a volcanic outcrop, rather than glacial sediments. Fracturing and discoloration have been brought on by weathering from the very wet, near-freezing environment. Finally, the wind has been a huge factor! Sand, gravel, snow, and graupel (ice pellets) have all been blasted against the side of this outcrop, primarily from the west (at right). On Heard Island, a 9 m/s wind is typical, with maximum recorded gusts exceeding 50 m/s on three days during the 1948-1954 period.[1] The high winds sandblast the outcrop, leading to the horizontal striations.

Here are a few wider-angle shots for context, and with better light than I ended up with for the gigapan.

Windy City outcrop, viewed from the north.  The gigapan image covers from my right arm to roughly the center of this image.  Image credit: Carlos Nascimento
Windy City outcrop, viewed from the north. The gigapan image covers from my right arm to roughly the center of this image. Image credit: Carlos Nascimento

Looking eastward at Windy City, with a person for scale.  The gigapanned portion of the outcrop is at right, but two spires of similarly eroded rock outcrop further to the north of the photographed portion.  The stake coming out from the outcrop is a marker for one of our temperature/light intensity sensors. Image credit: Carlos Nascimento
Looking eastward at Windy City, with a person for scale. The gigapanned portion of the outcrop is at right, but two spires of similarly eroded rock outcrop further to the north of the photographed portion. The stake coming out from the outcrop is a marker for one of our temperature/light intensity sensors.
Image credit: Carlos Nascimento

I also managed a close-up shot of one of the pieces of float.

Float rock at Windy City.  The 1:1000 metric scale at right is effectively a mm scale.  Some olive/green crystals are visible, mostly 1-5 mm in their longest dimension, which are likely olivine (possibly clinopyroxene). Image credit: Bill Mitchell (CC-BY).
Float rock at Windy City. The 1:1000 metric scale at right is effectively a mm scale. Some olive/green crystals are visible, mostly 1-5 mm in their longest dimension, which are likely olivine (possibly clinopyroxene).
Image credit: Bill Mitchell (CC-BY).

[1] Thost, D., Allison, I. “The climate of Heard Island” in Heard Island: Southern Ocean Sentinel, ed by K. Green and E. Woehler. Surrey Beatty & Sons, Chipping Norton 2005, p. 52-68.

When Counting Gets Difficult, Part 2

Prion sp., March 22, 2016, seen just west of Heard Island.  Image credit: Bill Mitchell.
Prion sp., March 22, 2016, seen just west of Heard Island. Image credit: Bill Mitchell.

Earlier I posed a question: suppose a group of 40 birds are identified to genus level (prion sp.). Four photographs of random birds are identified to species level, all of one species that was expected to be in the minority (fulmar prion) and likely would be present in mixed flocks. How many birds of the 40 should be upgraded from genus-level ID to species-level ID?

Clearly there is a minimum of one fulmar prion present, because it was identified in the photographs. With four photographs and 40 birds, the chance of randomly catching the same bird all four times is quite small, so the number of fulmar prions is probably much higher than 1. At the same time, it would not be reasonable from a sample of our photographs to say all 40 were fulmar prions.

If we have four photographs of fulmar prions (A), what is the minimum number of non-fulmar prions (B) needed in a 40-prion flock to have a 95% chance of photographing at least one non-fulmar prion?

To answer this question, I used a Monte Carlo simulation, which I wrote in R. I generated 40-element combinations of A and B ranging from all A to all B. Then for each of those populations, I ran 100,000 trials, sampling 4 random birds from each population (with replacement). By tracking the proportion of trials for each population that had at least one B, it becomes possible to find the 95% confidence limit.

pop_size <- 40  # Set the population size
sample_n <- 4  # Set the number of samples (photographs)
n_trials <- 100000  # Set the number of trials for each population

x <- seq(0:pop_size)  # Create a vector of the numbers from 0 to pop_size (i.e. how many B in population)

sample_from_pop <- function(population, sample_size, n_trials){
	# Run Monte Carlo sampling, taking sample_size samples (with replacement)
                # from population (vector of TRUE/FALSE), repeating n_trials times
	# population: vector of TRUE/FALSE representing e.g. species A (TRUE) and B (FALSE)
	# sample_size: the number of members of the population to inspect
	# n_trials: the number of times to repeat the sampling
	my_count <- 0
	for(k in 1:n_trials){  # Repeat sampling n_trials times
		my_results <- sample(population, sample_size, replace=TRUE)  # Get the samples
		if(FALSE %in% my_results){  # Look for whether it had species B
			my_count <- my_count + 1  # Add one to the count if it did
		}
	}
	return(my_count/n_trials)  # Return the proportion of trials detecting species B
}

create_pop <- function(n,N){  # Make the populations
	return(append(rep(TRUE,N-n),rep(FALSE,n)))  # Populations have N-n repetitions of TRUE (sp. A), n reps of FALSE (sp. B)
}

mypops <- lapply(0:pop_size, create_pop, pop_size)  # Create populations for sampling

# Apply the sampling function to the populations, recording the proportion of trials sampling at least one of species B
my_percentages <- sapply(mypops, sample_from_pop, sample_size=sample_n, n_trials=n_trials)

My simulation results showed that with 22 or more birds of species B (non-fulmar prions), there was a >95% that they would be detected. In other words, from my photographic data, there is a 95% probability that the flock of 40 prions contained no fewer than 19 fulmar prions.

Let’s take a look at it graphically.

library(ggplot2)

mydata <- data.frame(my_percentages, 0:pop_size)  # Make a data.frame with the results and the # of species B
names(mydata) <- c("DetProb", "B")  # Rename the columns to something friendly and vaguely descriptive

p <- ggplot(mydata2, aes(x=B,y=DetProb)) + geom_point() # Create the basic ggplot2 scatterplot
p <- p + geom_hline(yintercept=0.95)  # Add a horizontal line at 95%
p <- p + theme_bw() + labs(x="# of species B (pop. 40)", y="Detection probability of B")  # Tidy up the presentation and labeling
print(p)  # Display it!
Results of the Monte Carlo simulation.  At left is all A, while at right is a population with all B.  The horizontal line is the 95% probability line.  Points above the line have a >95% chance of detecting species B.
Results of the Monte Carlo simulation. At left is all A, while at right is a population with all B. The horizontal line is the 95% probability line. Points above the line have a >95% chance of detecting species B.

With 22 or more non-fulmar prions, there’s a >95% chance one would be photographed. With 19 fulmar prions and 21 non-fulmar prions, there’s a >5% chance the non-fulmar prions would be missed. So our minimum number of fulmar prions is 19. I may have seen a flock of 40 fulmar prions, but there aren’t enough observations to say with statistical confidence that they were all fulmar prions.

When Counting is Difficult

A fulmar prion glides swiftly over the swell of the Southern Ocean.  Image credit: Bill Mitchell (CC-BY)
A fulmar prion glides swiftly over the swell of the Southern Ocean. Image credit: Bill Mitchell (CC-BY)

During the Heard Island Expedition, including the nearly three weeks at sea on the Southern Ocean, I made a few observations for a citizen science project: eBird. It’s a pretty simple system: identify and count all the birds you see in a small area and/or time period, then submit your list to a centralized database. That database is used for research, and keeps track of your life/year/county lists. With so few observations in the southern Indian Ocean in March and April (and no penguins on my life list before the expedition), I figured I would make a few counts.

On its face, identifying and counting birds is straightforward. Get a good look, maybe a photograph, and count (or estimate) the number present of that species.

It gets more difficult when you go outside your usual spot, particularly when the biome is much different. Although I have some familiarity with the Sibley Guide for North American birds, I’ve never payed very close attention to the seabird section, and have never birded at sea before. All the birds I expected to see on this expedition would be life birds, and that changes things a bit. I would have to observe very closely, and photograph where I could.

Before the expedition, I read up on the birds I would likely find on the island. In addition to four species of penguins, there were three species of albatross (wandering, black-browed, and light-mantled sooty) and two species of prions (Antarctic and fulmar). Albatrosses are large and the species near Heard are readily distinguished. Prions, however, can be quite difficult even with good observations. They’re not quite to the level of the Empidonax flycatchers, but close.

At sea, we usually had prions flying near the ship. I took pictures, knowing that I might be able to get help with ID if I needed it—and of course I needed it.

That’s where the problem started: I had a count where I had observed 40 prions flying around the ship, which I identified only to genus level. From my reading on Heard Island, I knew that breeding populations for prions on Heard Island were generally larger Antarctic prions than fulmar prions, with an estimate of a 10:1 margin. I had four clear pictures of individual birds, which my helpful eBird reviewer was able to get to an expert for further identification. All four were fulmar prions.

With 40 birds identified to genus level, and four photos of random birds identified to species level as a species expected to be a minor proportion, how many of the original 40 birds can I reasonably assign as fulmar prions?

I have an answer to this question, which I will post next week.