Science Stories: Adventures in Bay-Delta Data

Rotifers Rock
  • October 11, 2022

By Rosemary Hartman

With help from Arthur Barros and all the zooplankton taxonomists of the Stockton CDFW lab.

Photos by Tricia Bippus (CDFW)

Zooplankton never get as much appreciation as fish (Hartman et al. 2021), but even among zooplankton there are clear favorites. Copepods and mysid shrimp have dozens of publications dedicated to them, but rotifers often get the short end of the stick. Most papers about “zooplankton” in the San Francisco estuary don’t even mention rotifers. However, the Environmental Monitoring Program works very hard monitoring microzooplankton (guys smaller than 150 microns) and the expert taxonomists at CDFW’s Stockton laboratory spend hours counting and identifying rotifers in those samples. Rotifers are an important link in the food chain connecting bacteria, phytoplankton, and particulate organic matter to fish. They are eaten by larger zooplankton and larval fish (Plabbmann et al. 1997, Burris et al. 2022).

What is a rotifer anyway?

Rotifers are one of the simplest multi-cellular animals on earth, sometimes called "wheel animals" because they have a ciliated structure on their head that looks a little like a wheel. They are tiny, usually only half a millimeter long, and they eat phytoplankton and bits of organic material floating in the water.

How are the samples collected? Well, it starts with the field crew going out to long-term monitoring stations throughout the Delta. The crew lowers a pump nearly to the bottom, then raises the pump up slowly, sucking in water and zooplankton as it goes. The water is then passed through a 43-micron mesh net until 75 L of water have been filtered. All the critters in the net are carefully preserved in formalin, with a little bit of pink dye added to make the critters stand out better. See Kayfetz et al. (2020) (PDF) and the Zooplankton EDI publication metadata (Barros 2021b) for more information.

Back in the laboratory, trained taxonomists subsample the critters and carefully identify and count them under a microscope. Rotifers are tricky to identify, so most are only identified to the genus level, or lumped into “other rotifers”. The rotifers we see most frequently are:

Synchaeta spp.

  • Swimming form: top-shaped with pointed foot and lateral auricles with bristles at the widest point, bristles around corona.
  • Contracted form: roundish to donut-shaped with corona, auricles and foot sucked in. Not much clear space, organs more prominent than in Asplanchna.

Microscopic photo of synchaeta in both swimming and contracted form.

Synchaeta bichornis

  • Pointed ‘foot’ at posterior end, two ‘horns’ at the anterior end.
  • Body usually curved into a shallow “C” shape.

Polyarthra spp.

  • Body squarish with feather-like appendages at the “corners”.
  • Appendages extend beyond length of the body.

Keratella spp.

  • 6 prominent ‘teeth’ or hooks on the anterior margin. Posterior end variable, with zero, one, or two spines.
  • Rigid lorica.

Microscopic image of Keratella (rotifer).

Trichocerca spp.

  • Mostly cylindrical, more or less curved, tapering at the anterior and posterior ends.
  • Toes asymmetrical: one prominently elongated, filament-like, often held up ventrally.

Microscopic image of Trichocerca (rotifer).

Asplanchna spp.

  • Like a clear bag with few organs inside, more clear space than Synchaeta.
  • No ‘foot’. Contracted form with corona sucked in at one end.

“Other rotifers”

  • Including Branchionus, Playais, colonial rotifers, Notholca, Filinia, and many more!

Microscopic image of Brachionus and an unidentified rotifer

So, what can we learn from the rotifer data?

Well, we can start by graphing the average rotifer catch at all stations since the zooplankton survey began (Figure 1). The first thing that jumps out at you is that the standard deviation is HUGE! Rotifers (like all zooplankton) are highly variable critters with big changes from station to station, month to month, and year to year. The next thing that probably jumps out at you is that abundances were a LOT higher prior to 1980. What could have driven that decline?

Area plot of rotifer catch per unit effort by year from 1975-2021. There is a drop in catch around 1980.
Figure 1. Average catch per unit effort (number of rotifers per thousand cubic meters) of all rotifers per sample (dark green area). Standard deviation of catch per year (light green area).

But that is the average catch for ALL the rotifers lumped together. It might be interesting to look at each taxon individually (Figure 2). Here we can see that all species declined after 1980, but the biggest drops were seen Keratella, Polyarthra, and Trichocerca. Synchaeta didn’t show quite as big a drop. We can also see that Synchaeta is usually the most common taxa, while Asplanchna is pretty rare. Lots of other researchers have noticed a big drop in copepods and chlorophyll after 1986 when the invasive clam Potamocorbula amurensis started to take over the area (Kimmerer et al. 1994, Kimmerer and Thompson 2014, Kimmerer and Lougee 2015), but no one has looked at the post-1980 rotifer crash!

Bar plot of rotifer catch per unit effort by year for each of the six major rotifer taxa.
Figure 2. Catch of major species of rotifers caught by EMP over time. You can see that the abundance of many species of rotifers declined sharply around 1980. You can also see that Synchaeta, Keratella, and Polyarthra were the most common species.

Since 1980, the biggest years for rotifers were 2017 and 2011, both of which were really wet years. Maybe rotifers like wetter years better? Let’s subset our data so we just have data from after 1980 and see how water year time affects rotifer catch (Figure 3). The pattern isn’t super clear – all taxa had high catches in 2017, but not all wet years had high catches, and some taxa (like Asplanchna) also had high catches during drier years. However, when we graph the average total rotifer catch versus the Sacramento Valley Index (a measure of water availability), we see a positive correlation between water flow and rotifer catch (Figure 4). Why might this be? Are they getting moved in from upstream? Or are they reproducing faster?

Bar plot showing rotifer catch per unit effort by year with bars labeled with different water year types.
Figure 3. Catch per unit effort of each rotifer taxa over time, with bars color-coded with water year type.
Scatter plot showing rotifer catch per unit effort versus the Sacramento Valley Water Year index with a positive correlation.
Figure 4. Plot of total rotifer catch per unit effort versus Sacramento Valley Water Year index with different shapes and colors indicating water year type. The line indicates a linear model showing an increase in rotifer abundance with increased flow.

Of course, there are lots of different ways to display the data. We can make area plots, bar plots, streamflow plots, pie charts, maps, or pie charts on top of maps (Figure 5)! Different types of graphs help you see the data in different ways and pull out different patterns.

Map of the estuary showing rotifer abundance in different regions with pie charts.
Figure 5. Map of mean rotifer CPUE from 2017, which was one of the biggest years for rotifers since the 1970s. Each pie chart represents one of EMP’s long-term monitoring stations, with the size of the pie chart corresponding to the total rotifer abundance. The South Delta and Suisun Marsh stations were especially high in rotifers, with more Synchaeta in the Marsh and more Polyarthra and other rotifers in the South Delta.

Are you interested in finding more patterns in the data?

You can visualize the data yourself on the ZoopSynth Shiny app (which also lets you download the data). However, before you dig in, be sure to read all of the metadata available on the Zooplankton EDI publication. You can also read some of the most recent Status and Trends reports published in the IEP newsletter for more ideas about useful patterns waiting for you to discover (Barros 2021a). Feel free to reach out if you have any questions or find any cool patterns! We love talking about zooplankton. Consider sharing your findings with the Zooplankton PWT too!

References and further reading

Categories: Underappreciated data

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