But what does this have to do with plankton?
Digital instruments are changing the way we view the ocean as well. While nets are still the most common plankton sampling device, other instruments are starting to catch on. In our lab, we use the laser optical plankton counter, or LOPC, which I've written about before. Instead of hauling up a net and counting every critter by eye, we lower this instrument into the ocean, it scans the nearby water with a laser, and records what it sees. Very futuristic.
The advantage to this technology is that we can now collect large amounts of detailed data at a much faster rate, and sometimes in rougher weather conditions. Also, we don't have to mess with chemicals and look through a microscope for long hours to identify each critter one at a time.
Still, as we march relentlessly toward a dystopian future ruled by hyper-intelligent robots, it's important to bear in mind the value of a human--in this case, a taxonomist human. To illustrate the point, I've invented a game called "Where's Sheldon? The plankton-or-detritus game." When we lower the LOPC into the water, it records every particle that is sees. Some of those particles are planktonic, and others are not. It can often be difficult to distinguish the two.
Can you tell the difference? I did a lab test, and passed these items through the LOPC:
Can you identify Sheldon the copepod? Click on the figure for the answer.
Some of the items are easy to identify, like the coin and the paper clip. Others are trickier. Also, these items are roughly 10 times larger (at least) than the plankton that we're interested in. Now imagine not just trying to pick out the plankton, but trying to identify the species. That means that the plankton-or-detritus game that we play in the lab is much more difficult than the version that you just played.
To me, this is an important reminder of the value of expert humans. It's also a reminder of the value of collecting samples of actual animals that can be identified by eye. Digital technology, so far at least, is at best a good compliment to conventional methods.
On the other hand, in order to get around this problem, scientists are now using machine-learning algorithms. Essentially, this means that we program computers to be able to think, and they are definitely getting smarter and smarter all the time. Still, I think it'll be quite some time before we have robot oceanographers.