How has the BP Oil Spill Impacted the Gulf’s Phytoplankton?

By Savanna Ronco

Mark Benfield has established his career in exploring what he calls the “most unexplored and least understood habitat on Earth” — the ocean.

Walking into his office in the Energy, Coast and Environment building on LSU Campus, I first noticed Benfield’s love of the water. From the fish art hanging around his computer to the 2002 “Eyes Under the Sea” clipping from The Advocate on the wall, Benfield’s passion was immediately clear.

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Benfield working in his office. He was even wearing a T-shirt with a fish graphic on the back!

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That’s Benfield in the picture! This article about an underwater imaging tool he created was on the front page of the Science & Technology section of The Advocate in 2002.

Benfield has been collecting phytoplankton (tiny plankton) data at various fixed facilities across the Gulf for years. After the BP Oil Spill in April 2010, he began to use the migration data to track how the phytoplankton reacted to the spill.

Benfield uses state-of-the-art underwater imaging systems, supported by sonars and net systems in order to map and count out the phytoplankton.

“It’s been four years now that I’ve been working on this,” Benfield said. “In the end, I hope to put together all of this data into a story to show what I think happened to these plankton during the spill.”

“You can see that towards the late afternoon, the phytoplankton are migrating up and then by the time it gets dark, they’ll come into the surface water,” Benfield explained. “The reason they do that at night is because there are less predators, so they can feed without the risk of being eaten. Before dawn, they migrate back down.”

Benfield measured from almost 30 different rigs, spread out across the Gulf from the epicenter of the spill. He divided the water into four categories: day, night, surface (200 m and up) and deep (300 to 600 m). He then averaged the echoes for each day.

Locations of fixed and MODU oil and facilities during 2010 used for analysis. Station locations (Lease block number): 0 (MC252#1 Macondo Well); 1 (MC519); 2 (VK956); 3 (MC696); 4 (MC777); 5 (MC778); 6 (MC503/MC547); 7 (MC876); 8 (MC993); 9 (MC809); 10 (MC807); 11 (MC984); 12 (MC540); 13 (MC582); 14 (MC711); 15 (GC613); 16 (GC654); 17 (GC248); 18 (GC158); 19 (GC904); 20 (GC680); 21 (WR206); 22 (WR469); 23 (WR544); 24 (GB783); 25 (GB426); 26 (GB425); and 27 (GB668), where MC=Mississippi Canyon, GC=Green Canyon, WR=Walker Ridge, GB=Garden Banks. Refer to Table I for station details. Shaded region indicates the estimated cumulative surface oil distribution (source: NOAA).

Locations of fixed and MODU oil and facilities during 2010 used for analysis. Station locations (Lease block number): 0 (MC252#1 Macondo Well); 1 (MC519); 2 (VK956); 3 (MC696); 4 (MC777); 5 (MC778); 6 (MC503/MC547); 7 (MC876); 8 (MC993); 9 (MC809); 10 (MC807); 11 (MC984); 12 (MC540); 13 (MC582); 14 (MC711); 15 (GC613); 16 (GC654); 17 (GC248); 18 (GC158); 19 (GC904); 20 (GC680); 21 (WR206); 22 (WR469); 23 (WR544); 24 (GB783); 25 (GB426); 26 (GB425); and 27 (GB668), where MC=Mississippi Canyon, GC=Green Canyon, WR=Walker Ridge, GB=Garden Banks. Refer to Table I for station details. Shaded region indicates the estimated cumulative surface oil distribution (source: NOAA).

On the day I shadowed Benfield, he was writing code in MATLAB (a numerical computation and visualization program) in order to create a map of the data he collected from April to September 2010. Both April and August had data pre- and post-spill.

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Benfield working in MATLAB.

Benfield quickly drew out his hypothesis for me, stating that he believes the greater the distance between the rigs, the higher the variation will be.

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Benfield’s quick sketch of what he believes his work in MATLAB will show. (X-axis = distance apart, Y-axis = variation)

“It’s sort of like watching paint dry, isn’t it?” Benfield joked, as I watched him code.

Honestly, though, the two hours I sat watching Benfield code seemed to fly by. As he wrote code for each month, he would set the parameters of length and width, then look at the map, then look at the variance. If it didn’t look correct, he’d go back and fix his error.

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Once he finishes all of the maps for the months of April-September, Benfield will compare his physical maps made with MATLAB to the hard data that his technicians have been collecting. If they match up, Benfield will have a great visual tool to show how the oil spill affected the phytoplankton in that area.

“It’s been four years now that I’ve been working on this,” Benfield said. “In the end, I hope to put together all of this data into a story to show what I think happened to these plankton during the spill.”

At the end of our shadow session, these are what Benfield's work on the maps looked like.

At the end of our shadow session, these are what Benfield’s work on the maps looked like.

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