Results tagged “copepods”

A dynamical systems link between traits and ecosystems

Over the past few decades, ocean ecosystem modelers like myself have gone from a Nutrient-Phytoplankton-Zooplankton picture of the ocean that looks like this:
NPZ.jpg


...to one that looks more like this:

NNPPZZ.jpg

The motivation, I would say, has been that the simple paradigm misses important processes, like species life history, or important properties, like diversity and its consequences. (Not to mention the myriad of Z that do not consume P.) The more complex picture, although impressive looking, comes with baggage. Among others, there is also the problem of parameterizing these models. Lots of boxes and arrows means lots of rates and properties to be measured--increasing roughly with the square of the number of species (or state variables).

One approach to simplifying this mess is to organize the ocean according to certain traits. Trait-based perspectives are not new, and I think they might be able to clean up the NNNNPPPPPZZZZZZZZZZ spider web we're tinkering around with these days. For example, we can approximate the distribution of a trait across species with a curve. With some analysis, we can then look at the effect of the shape of this curve on the structure of the community. In the example below, a Gaussian distribution of growth rates (gamma) produces realistic rank-abundance curves in a zooplankton population. Different curves produce different community-level patterns.

Traits.jpg

This is an idealized analytical model. The real world is messier. Still, as long as we are modeling species by using collections of ecologically important traits, we can use the distributions of those traits to inform the model. 

As a messier example, I'll draw from a more complex copepod model. We included a number of traits, such as activation energies, development times, and diapause strategies (many copepods go into dormancy during certain stages). These traits have been painstakingly tabulated across many species by people who I assume have lots of coffee and live in cold, cloudy places. By drawing from these distributions in a sort of stochastic way, and plugging the model into different parts of the ocean, we get very different communities emerging at different places.

TraitModel.jpg


The next image shows preliminary output from a North Atlantic model. These are the results for the diapause trait. Basically, in northern latitudes, species that diapause make up the majority of the population. Closer to the equator, they don't fare well. On the right you can see an image of what the population looks like in terms of the size of the animals, and a distribution of the diapause trait as a function of life stage. In the north, there are large diapausing species with long development times. In the south, the opposite.

DiapauseMap.jpg

If you know the North Atlantic well, you'll recognize that this map is not perfect, and we are still a ways from describing the whole ecosystem this way. Still, by shifting the perspective away from the individual species, and towards properties of the community, we are able to make some more sense out of the NNNNNNPPPPPPPZZZZZZZZZZZZ spider web.

Nick Record, signing off


Hubbell SP (2001) The unified neutral theory of biodiversity and biogeography (MPB-32) (Vol. 32). Princeton University Press.

Record NR, Pershing AJ, Maps F (2013) Emergent copepod communities in an adaptive trait-structured model. Ecol Model

Record NR, Pershing AJ, Maps F (2013) The paradox of "the paradox of the plankton". ICES J. Mar. Sci. 

  

The Wonders of Copepods

SeascapeModeling is still waiting for copepod jokes to hit the mainstream.  In the meantime, here's a short video produced by UMaine featuring my views on why copepods are important.  The video was featured on NSF's news site last week.  The video was produced about a year ago and foreshadows some of the work going on in the lab, notably, Karen's work on copepods and carbon and Walt's work on bluefin tuna condition.  

Dr. Record

Nick successfully defended his Ph.D. yesterday, and as the photo below shows, he is now Dr. Record*.
DrRecord2.jpg
Not that kind of doctor.

The general assessment of Nick's committee was that his thesis was one of the best they've read.  It covered a wide range of topics, from computational methods, to copepod life history, to biodiversity theory.  The Kraken, though, seemed a bit skeptical, and had posed one of the harder questions:


I thought Nick handled the question well, but the Kraken seemed to have more he wanted to discuss.  Still, Kraken did decide to give Nick a bottle of something to help him celebrate.  Congrats Nick!

*assuming he turns in his thesis.

Growing Copepods

Editor's note: The LTER zooplankton team has generously allowed Karen some time and resources to do some of her own work.

While here in Antarctica, I am trying to grow copepods.  Copepods are small crustaceans that are part of the zooplankton, a word for all animals whose movement in the sea is mainly due to the movement of their liquid surroundings.  Their sizes range from less than one millimeter to several.  They have complex life histories, involving both naupliar and copepodite stages, before reaching maturity.  Copepod growth rates are thought to be primarily controlled by food availability, while their development rates are likely linked more to temperature.  Therefore, under different temperature conditions, it is likely that copepods will mature at different sizes.  I would like to find out what the relationship is between copepod egg development and temperature; eggs are interesting in this respect because they do not require food from the environment outside of the egg.  

I began by collecting live copepods in a net, selecting out mature females, carefully placing them in glass petri dishes.  I placed trays of petri dishes into two incubators at two different temperatures (0 and approximately 4 degrees Celsius).  The first time I did this, the copepods lived for about four days and that was it; nothing happened.  I was a little discouraged.
1_Assorted copepods.jpg
Many copepods together under a microscope; there are a few different species here.  The red-colored bits are their antennae, which they use to sense their surroundings.

petri_dishes_comp.JPG
A tray of petri dishes sitting at the bottom of the 0 degree incubator. I had to keep them at the bottom of the incubator, or they would freeze: a lesson learned by mishap.

The second time I tried the experiment, I had better luck.  The copepods I selected laid eggs within a couple days in the warmer incubator and within a couple more days in the colder one!  The eggs have yet to hatch and may have stopped developing.  The copepods that laid eggs were a Calanus species, the ones with the red antennae, which I have yet to identify to a species level.  

Calanus_expt_comp.JPG
Calanus sp. used in my experiment
Editor's note: Notice the shiny sack of  oil filling out the copepod's carapace.  This is why everyone wants to eat Calanus.  

eggs_comp.JPG
Copepod eggs

There is incredible copepod diversity here; it is both exciting and a little overwhelming trying to learn the different species.
2_Candacia spp.jpg
A copepod of the genus Candacia, distinguishable by its frilly black legs.  When Candacia are floating around in a tub with lots of other zooplankton, all you can see is their legs because their bodies are transparent.
Editor's note: I think Candacia would be an excellent candidate for the next stuffed copepod.
3_Paraeuchaeta antarctica.jpg
A mature female Paraeuchaeta antarctica, with a spermatophore attached to her uromsome (tail).
Editor's note: Paraeuchaeta is a voracious predator.  Not quite in the same league as a honey badger, but close.
Paraeuchaeta antarctica seta on Pr5_comp.JPG
The setae on the posterial corners of a Paraeuchaeta antarctica: a feature that helps distinguish this copepod from other species.

I am still working on definitively identifying the Calanus species that I used in my experiment; they may be Calanus propinquus.  You can tell the difference between Calanus spp. and Calanoides spp. by a serrated upper, inner edge of the most rear swimming legs.  Try seeing that in a microscope on a moving ship!  It's a great challenge.

Hmmm...

(Mathematicians) (Oceanographers) sin θ ?

Something about a "sine wave"?

I'm sure there's a punchline there somewhere.

At any rate, I recently returned from a great workshop where a subset of mathematicians and a subset of oceanographers intersected in the same pool.  It took place at the Mathematical Biosciences Institute at Ohio State.  The mix of people and perspectives was great, and the atmosphere was one of learning and brainstorming.  There is certainly a need for more integration of these two fields.

The talks spanned a range of topics, ranging from mathy to oceany.  Many of the presentations were live-streamed, and can be downloaded here.  My talk, "Toward a Grand Unified Theory of Copepods" is posted here:
MBI.jpg
Before you click, be warned: it's nearly an hour long.  Make certain you have some time--you might not be able to tear yourself away.

-Nick Record, signing off.

The Sea is an Unknowable Beast

We can measure the speed of light to an astonishing level of accuracy.  Uncertainty is a millionth of one percent.  We can measure and count sub-atomic particles that weigh a billionth of a billionth of a billionth of a gram.  We can detect earth-like planets hundreds of trillions of miles away by measuring wobbles in stars.

Yet we can't measure egg mortality in the ocean.

This week I'm at the Ecosystem Studies of Sub-Arctic Seas conference.  I've attended this meeting, and other similar meetings, in the past.  It's a good chance to meet with scientists from all over the world, hear their ideas, learn from them, and (if you're like me) interact with them awkwardly.  It's fun and educational.

Learning about the North is especially interesting, as the Arctic appears to be a harbinger of what's to come with climate change.  Complex systems of ocean currents and ice are shifting into new states, and consequently, so are the ecosystems they support.  Marine scientists are intrepidly probing the depths to help us figure out just what is happening, and how to deal with it.

The problem is that the sea is an unknowable beast.

One of my favorite talks at this meeting was on copepod egg mortality.  The question boils down to: how many eggs survive from one day to the next?  After showing measurements made using a variety of different techniques, the take home message was: none of these techniques works.  Actually, I saw a few talks on the impracticability of measuring mortality in the ocean.  Nobody seems to be able to do it except in highly simplified or idealized cases.

It's amazing all that we've accomplished in science.  I marvel in the feats of humanity against the powers of nature, staving off death and disease, conquering flight and space travel, and discovering the imaginary numbers.  That does give me some hope that we might have the cognitive toolkit and the sheer determination to figure out the science of changing seas and their ecosystems--to buffer humanity against the oncoming climate shifts.  But I marvel too in what we have yet to accomplish.  We haven't been able to travel back in time; we haven't been able to build cities on Mars or crack the speed of light.  

And we can't count how many eggs die in the ocean.  One might say oceanography is harder than quantum physics.  At the least, it's an intriguingly mysterious enigma.

-Nick Record, signing off

Fishermen's Forum

Greetings!  If you've visited this site before, you're probably aware that our lab has spent the past three July/Augusts cruising up and down the scenic coast of Maine, visiting coastal towns, gawking at amazing sealife, and gathering information on the zooplankton community under the speckled starlight of the summer sky.  Truly the good life.

It's one thing to spend your summers enjoying the Gulf of Maine, bobbing up and down, ogling skeleton shrimp under a microscope.  At the end of the day, however, we need to have something to show for our work.  There are many reasons to improve our understanding of the zooplankton community.  One such reason is so that we have better information on the migration patterns of planktivorous whales.  One of our main objectives has been to describe the feeding habitat of right whales, whose diet consists primary of the copepod Calanus finmarchicus.

This past weekend our lab and the biological oceanography lab presented some of the results of our cruises from 2008, 2009, and 2010 at the Maine Fishermen's Forum.  The forum is teeming with energy and activity, from seafood sampling to trade shows.  Thus I wondered to myself, as I found my way down a long, lonely hallway and up a grated stairway that led to our remote presentation room, How many people here will be interested in a talk with "Calanus finmarchicus" in the title?

After all, our time slot was competing with scallop farming, shrimp fishing, and "The Food Guys".

Not only was our talk well attended by an assortment of fishermen, managers, scientists, reporters, and others, but the array of questions that we received showed an impressive amount of interest, knowledge, and understanding of copepods and their ecological importance.  I doubt that there are many venues where a group of geeky scientists could talk about Calanus finmarchicus to such an eclectic audience, and receive such an enthusiastic response.

Nick Record, signing off.

The Twilight Series, part 2: what are those creatures?

Week 3 since the cruise, and the pieces of the puzzle are beginning to come into focus.  Cameron's incubation experiments indicate a presence we've not sensed since last year--the presence of diapausing copepods deep in the abyss.  As I typed earlier, I'm getting a similar signal in the laser data: an anomalously large aggregation of particles at just the size and depth we would expect to find C. finmarchicus.  Here is another view:

Diapause3D.jpg


While it is possible that this deep aggregation of particles is some mysterious, and as yet undiscovered presence in the gulf, the evidence points to one plausibility: if it quacks like a copepod, it's probably a copepod.

I returned to my personal microcomputer to plot up quasi-silhouettes from the lasers, showing these particles.  Here are the preliminary results:

WilkiFins.jpg
A glance at this image is far from conclusive, and it remains to demonstrate that the blobules we see are actually diapausing copepods.  I conjecture that they are indeed that, and I am presently taking steps to convince myself that I'm correct.

Nick Record, signing off.



October cruise: Nearshore - offshore zooplankton gradient

Our biological oceanography lab has a biweekly zooplankton time-series study collected from the Darling Marine Center in Walpole, Maine.  The study samples the zooplankton at two stations: one well within the Damariscotta estuary, and the other a few miles out.  At the nearshore station, we see an estuarine community, with a diverse collection of copepods and other zooplankton.  At the offshore station, depending on the time of year, the community is dominated by the large copepod, Calanus finmarchicus.  

These two communities are characteristic of two different marine ecosystems.  The big copepods in the oceanic system provide essential prey for pelagic species ranging from herring to right whales.  The smaller, more diverse estuarine system can serve as a nursery for larval fish.  The seascape modeling lab is interested in the processes that maintain the boundary between the two types of system.

In order to characterize the nearshore-offshore gradient, we ran a cruise on Thursday, taking profiles with the LOPC at fixed intervals of roughly 1 km (see map).  We're still feeding out and reeling the LOPC cable by hand, until we get the data logger fixed.  This can be tiresome, but thanks to ongoing splicing efforts (including some last-minute work before leaving the dock), it's effective.  We have a nice transect showing the shift in size distribution from the nearshore out towards the offshore.

Additionally, it was nice to be on the water on a brisk October day.  We got an early start, catching the sunrise ferry from Peaks Island, and we saw some fair wildlife, which, hopefully, Pete will share some pictures of in a later entry.

DMC20091001.jpg
Sampling stations (X).

LOPCdeployment.jpg
Reeling in the LOPC and cable.

Cruise fauna: a whale's breakfast

Much of our sampling was directed toward characterizing the abundance and distribution of whale food.  This image shows a sample containing a few of the delicacies enjoyed by whales in the Gulf of Maine.  The large shrimp-looking animals are krill, enjoyed by minke, fin, and humpback whales, all of which we observed during our cruise (stay tuned for photos).  Among the smaller animals in the sample are copepods: the breakfast cereal of right whales.  If you've swum in Maine waters, you've probably swallowed many mouthfuls of them.

The favorite variety, Calanus finmarchicus (Finnmark copepods) occur in very high abundance in the deeper waters of the gulf.  We generally find them in waters deeper than 100 m.  This year, there seemed to be strangely low numbers of them in the waters south of the Penobscot, where we also saw a lot of bioluminescence.  Of course, we'll have to do the full analysis before we can be sure about these results.

Keep checking back for more wildlife photos from the cruise.

Krill1y.jpg
Copepods and krill from a net sample.

Maine Cruise: Day 2 AM

From Peter:
After a nice sleep on the boat we woke up to clouds and drizzel, ate a nice breakfast and got to work setting things up. Here's a picture of me setting up the Tucker trawl. We got it together much faster this year since our winch, big Bertha, is working. Also Nick says "things look promising for the LOPC",  (laser optical plankton counter) but our intern missed the bus in Portland and will be late.
Time for a quick trip to town to get sunscreen, butt connectors (electrical supplies, thank you), and some diesel for Bertha. This afternoon we'll head out and test the gear, perhaps get the first station in.
foto2.jpg

Note: the Tucker trawl is a net system that allows nets to be opened at specific depths.  This gives us some information about whether the copepods are more abundant at the surface, bottom, or in between.  The LOPC is an entirely electronic system that measures the size of plankton-sized particles that pass through it.  It gives us even more information on how deep the plankton are, but we can't tell one species from another, only the size of the particles.  Unlike the nets that have to be counted by hand on shore, the LOPC data is available in real time and can help guide the sampling.

Maine Cruise Blogging

The Ecosystem Modeling Lab has teamed up with the UM/GMRI modeling lab and the UM bio-oceanography lab to conduct a zooplankton survey of the Maine Coast.  The cruise is funded by the Maine Department of Marine Resources, and its goal is to map potential right whale feeding areas.  Pete and Nick will be sending regular updates, which I'll be posting here.  Here's the first post:

And so begins the voyage. We're here working on the winch at the unh dock.  The gear has been unloaded from the Gmri truck. And we have some time to kill before the R/V Stellwagen arrives.
foto1.jpg

Pete is taking the photo.  Nick & I are standing in the foreground.  Rebecca is in the background trying to figure out why the winch won't start.

Forecast update

Our copepod forecasts are now appearing in habitat assessment reports produced by the Provincetown Center for Coastal Studies.  The PCCS runs cruises approximately weekly to characterize the prey resource for right whales in Cape Cod Bay.  Our forecasts include their samples from the previous week, coupling them with physical data to project into the upcoming week.

Here are a couple of our forecasts, with comparison to the actual data collected around the same time.

This plot shows a forecast for April 11th, for total copepodid zooplankton in the bay.
SEASCAPEapr11.png

This plot shows the distribution based on data collected on April 10th.
PCCSapr10.png
The higher concentration in the southern part of the bay matches fairly well, though our prediction put this patch further south than where it was observed.  Our forecast also predicted two strong patches near the tip of the cape, which didn't appear in the samples.  Note that the color bars are not quite the same in the two images.

This plot shows our forecast for April 15, for all copepodid zooplankton.
SEASCAPEapr15.jpg
Below is the distribution from the survey on April 14.
PCCSapr14.jpg
The spatial pattern of abundance matched well, with a low concentration in the northern part of Cape Cod Bay, and a higher concentration to the south.  As in the plots above, note that the color bars are not quite the same in the two images.

In both the forecasts and the sampled data, regions of zooplankton abundance were dominated by Calanus finmarchicus at this time of year, marking a shift from earlier in the year, when C.fin. was low, and Pseudocalanus spp. and Centropages spp. were higher.

Forecast: right whale arrival date in the Great South Channel

Our lab et al. published a series of papers in the latest issue of Marine Ecology Progress Series in which we explored linkages between copepod abundance and the migration patters of right whales.  Better knowledge of where and when right whales might show up can help prevent ship strikes and gear entanglements.  The full articles can be found here: 1 2 3.

One of our results was a strong correlation between the computed abundance of Calanus finmarchicus and the arrival date of right whales in the Great South Channel critical habitat.  Researchers have known for awhile that right whales use this habitat every year, but the factors that influence the timing of that usage are harder to pin down.  (Details on our computations, like how we calculate arrival date and C.fin. abundance, can be found in the papers.)

This correlation may have use as a forecasting tool.  The correlation spans the years 1998-2006.  By computing the C.fin. abundance for ensuing years, we can use a linear fit to produce a forecast for the arrival date in the Great South Channel (see figure).  Our prediction this year is for an early arrival date--right around now, in fact.  We also predicted an early arrival for 2007, and a late arrival for 2008.


ArrivalDate20090317.gif
Figure.  Top: correlation between computed C.fin. abundance and right
whale arrival day in the Great South Channel (R^2=0.7, p=0.01).  Red dots
show predicted values for 2009, with the most current prediction indicated
by text.  Bottom: our predictions for the 2009 arrival date.  As the year
progresses, we assimilate more data, and our prediction changes (see point
2 below).  The abrupt drop in late February is due to a modification in our
calculation (see point 4 below).



Caveats

There are a few caveats to this forecast.  I'll outline them here.

1) A linear regression is a simplification of the dynamics at play, and there is variability about the line.  Therefore, even though we give a specific arrival date, our forecasts should be taken as approximate.  It's better to think of them as "early", "average", or "late", rather than as occurring on a specific date.

2) Our models rely on satellite data, which is updated as the year progresses.  Therefore, our forecast changes as the year marches on (bottom plot in figure). It's similar to how the weather forecast gets better as next week gets closer.  This limits us somewhat, but our previous work has shown that satellite data from January and February generally provide enough data to get a significant correlation.

3) We check our forecasts against a whale arrival date that is calculated from survey data.  That is, real people looking for whales from boats and planes.  It takes a long time for that information to be processed and passed to us, so we haven't yet been able to check our 2007 and 2008 forecasts.  So, unlike the weather forecaster, we don't have the advantage of knowing what "today's weather" is.  Even though our 2009 forecasts tell us that right whales are arriving in the Great South Channel right around now, or possibly have arrived already, we may not be able to check that for awhile.

4) The nature of satellite data changes with technology.  For example, resolution has improved.  We've developed a new interpolation method that helps the satellite data to be consistent over many years.  The down side is that we had to re-run our experiment with all of the satellite data in this new format.  The good news is that the correlations persisted, though altered a little.

First Forecast w/ Assimilation

As Pete just described, specifying the initial conditions for a model is difficult. In forecasting, whether weather or copepods, a major problem is estimating the initial conditions from the data. Ideally, you would send a few thousand undergrads to the locations of your model grid and have them simultaneously sample whatever you're trying to model. You could then use this data to initialize your model. In the real world, where undergrads are expensive and unreliable, we have to make do with samples collected from only a few locations. We need a way to estimate the state of the system from this sparse array of data.

Data assimilation is a grab-bag of techniques for merging observations and models, and state estimation is the most common application. The idea is to find an initial condition, such that, when the model is started from this condition, it comes close to hitting the observation points. There are lots of procedures that can do this, with cool names like "4D-Var" or "representers", and each has its advantages, disadvantages, and acolytes. For our system, I'm using the ensemble Kalman filter (EnKF), mainly because it is an ensemble method, and I like ensemble methods. At the moment, I'm using a 10 day update cycle. This means that every sample collected between day j and j+10 is used to adjust the model state at day j.

I made three runs of the system for Massachusetts Bay. The first is a control run. I started from the climatological (long-term average) initial conditions and let 'er rip. The second used the EnKF to assimilate the PCCS cruise on January 13, 2009, producing a new initial condition for January 11 (remember the 10 day update cycle). The third assimilated the cruise from January 30, producing a new initial conditions for January 21. Here's how the three runs compare on January 30 for Pseudocalanus:

No assimilation First cruise Second cruise
Fig_s2_d001_t5.png Fig_s2_d011_t5.png Fig_s2_d021_t5.png
Model estimates of Pseudocalanus on January 30

Assimilating the first cruise increased the concentrations on 1/13 a little bit. This led to increased concentrations on 1/30. Assimilating the second cruised increased the values even more. I continued to run all three versions until today (March 3):

No assimilation First cruise Second cruise
Fig_s2_d001_t9.png Fig_s2_d011_t9.png Fig_s2_d021_t9.png
Model estimates of Pseudocalanus on March 3

The model predicts that all three should decline (this is the model dynamics--Pseudocalanus usually declines this time of year), but the large values in model three allow some persistence.

In case you get the idea that assimilation always increases the values, here are the plots for Centropages on 1/30:

No assimilation First cruise Second cruise
Fig_s3_d001_t5.png Fig_s3_d011_t5.png Fig_s3_d021_t5.png
Model estimates of Centropages on January 30

The first cruise encountered some very high Centropages concentrations, but the levels had declined by the second cruise.

First unofficial forecast

Since there have been delays in getting out our first Cape Cod Bay forecasts (i.e. still waiting for satellite data and flow fields), I decided to attempt a crude forecast using the limited information that we do have.  You'll see that the forecast isn't too bad, but there is quite a bit of room for improvement.  As we incorporate more data (see below), and more advanced methods (e.g. ensemble Kalman filter), we should expect to see marked improvement in the accuracy of our forecasts.

Disclaimer: As you read on, bear in mind that these are not finalized results.  This description is to provide insights into the forecasting process.

First, the data that we have:
- flow fields from previous years
- satellite data from previous years
- zooplankton samples for 2009 from the Provincetown Center for Coastal Studies (PCCS)

What's missing:
- flow fields from 2009 (coming soon)
- satellite data from 2009 (coming soon)

As the weeks roll by, we'll also be getting zooplankton updates from the PCCS, as well as updated flow and satellite data.  The missing data, however, is critical to a good forecast, so this exercise should be taken with a grain of salt.

Now for the forecast:

There's not a whole lot we can do about the missing 2009 data.  Our computation requires sea surface temperature and chlorophyll values from satellites and flow fields, so I've used representative values from previous years.  I then tuned the output to a collection of zooplankton sample data, and scaled the values to correspond to what the PCCS has been seeing in the water this year to give us our forecast (Fig. 1).

RoughForecast.gif
Figure 1 Forecast zooplankton abundance (ind. m-3) at selected dates.

An important note: for simplicity's sake, I'm just using the Pseudocalanus parameterization to forecast all zooplankton, omitting for now the Calanus and Centropages groups.  The forecast will lose validity, therefore, as the assemblage changes later in the winter.

PCCS.gifIf we compare the forecast to the maps from the PCCS through the end of January (Fig. 2), there are a couple of points to make.  The first part of the temporal signal appears to come through, with populations rising to high levels by late January.  Whether or not the decline toward Feb. 21 is observed in the net samples remains to be seen.  The second point is that the spatial pattern is incorrect.  That is, the high concentration that appears in the forecast should be located more toward the southeastern part of the bay.  This is likely due to the fact that the 2009 flow field is missing from the calculation.

This is a good jumping off point, but more than that, it illustrates the importance of the missing data layers.  Stay tuned for a more refined forecast in the next week or so.







Figure 2 Measured zooplankton abundance (ind. m-3) from PCCS surveys.

Super-surface Planktivores

I'm not an ornithologist, but I played one for three months in Costa Rica (interesting bird research). Though I've moved from terra to aqua, I maintain an interest in happenings where the z-axis is positive, and in particular where it is small. Marine Ecology Progress Series published a theme section titled "Seabirds as indicators of marine ecosystems" (Volume 352).  In this blog entry, I'll going to chat about one of the articles from that section.

In Hot oceanography: planktivorous seabirds reaveal ecosystem responses to warming of the Bering Sea, Springer et al. looked at the diets of least auklets (Aethia pusilla) in the Pribilof Islands. Like some whales, these birds eat copepods! Upwards of 80% of their diet may be composed of Neocalanus spp. and Calanus marshallae. The former is a deep water species, and the latter is more often found in shelf waters. The authors examined the diet of birds on two of the Pribilof Islands - one in "shallow" shelf water, and the other closer to the "deep" basin water. The relative proportion of Neocalanus spp. to C.marshallae found in the birds is assumed to be representative of the availability of each copepod in the vicinity of each island. 

YanNetTow.jpgOne of the really neat things about this study is the methodology: that one can let the birds do the sampling. In the absence of an "indicator" such as the lesser auklet, we humans would need to hop on a boat and sample the water column with a net or with acoustic instrument(s). Finding indicator organisms is, in effect, biological remote sensing. Another aspect of this study that makes it interesting is it's location - tiny mountainous islands at high latitude. Look forward to a future post containing a list of super-surface planktivores.

Mass Bay Forecasts--Coming Soon!

 The pervasive cold and darkness that characterizes winters in New England means that primary productivity in the Gulf of Maine shuts down during this time.  However, shallow waters along the coast limit how far below the surface the phytoplankton can be mixed.  For this reason, the spring bloom in the Gulf of Maine starts along the coast and in the south, so biology in Mass Bay tends to lead the rest of the Gulf.  If you're an animal, like a right whale, that can swim large distances, Mass Bay is likely your first stop in your annual tour of the Gulf. Autobuoys.jpg

From the point of view of a right whale, the downside to Mass Bay is that it is surrounded by Massachusetts.  This means that the Bay is one of the more industrial stretches of water in the world.  Large ships bring cargo to and from Boston.  Other ships are supporting the construction of liquified natural gas terminals in the Bay.  Smaller fishing boats move through the area, taking advantage of the same productivity that draws the right whales to the Bay.  Oh, and all those people in Massachusetts, they produce a lot of sewage, and a lot of it ends up in the Bay (after suitable processing, we hope).  The upside of all this activity, is that there is a lot of science going on.  Our colleagues at the Provincetown Center for Coastal Studies regularly collect zooplankton samples, partly to provide information relevant to right whales, and partly to monitor the impact of the sewage outfall.  Our colleagues at Cornell's Lab of Ornithology have installed a series of "autobuoys" that can detect the presence of right whales in the shipping lanes and near the LNG sites (see the image).  All this data creates the perfect environment to try some right whale forecasting.  If we can't do it in Mass Bay, we probably can't do it!

Here's what we plan to do
  1. Run SEASCAPE to estimate the abundance of the whales' favorite copepods: Calanus finmarchicus, Pseudocalanus, and Centropages typicus.
  2. Use the ensemble Kalman filter to assimilate PCCS's weekly zooplankton survey into our model.  This will give an improved estimate of the whale's prey-field and should improve the accuracy of the model over the following week.
  3. Use the model output, plus satellite data and other variables to estimate right whale habitat in the Bay.
  4. Use an assimilation procedure to refine our whale habitat estimates using the Cornell acoustic detections.
I have implemented the Kalman filter procedure and am currently testing it with previous years.  Hopefully, I'll have this working next week and we can start making some forecasts.
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