Science Stories | zooplankton

April 26, 2024

Untangling the estuary’s food web with data analysis and synthesis

Blog by Rosemary Hartman. Paper and analysis by Tanya L Rogers, Samuel M Bashevkin , Christina E Burdi, Denise D Colombano, Peter N Dudley, Brian Mahardja, Lara Mitchell, Sarah Perry, and Parsa Saffarinia.

Did you know that one female threadfin shad can lay 5,000 to 20,000 eggs at once?! The California Department of Fish and Wildlife’s Fall Midwater Trawl, which surveys the Sacramento-San Joaquin Delta and upper San Francisco Estuary, caught 1,551 threadfin shad in 2022. If half of those were female, they could have produced three to fifteen MILLION baby fish. But in 2023 the Fall Midwater Trawl only caught 1,922 threadfin shad. What happened to all the baby fish?

Apart from the number of parents, fish populations (and other populations) are usually controlled by a combination of three ecological processes: 1) Individuals cannot find enough food or nutrients to survive and reproduce (what’s known as a ‘bottom-up’ process), 2) they are eaten by predators (what’s known as a ‘top-down’ process), or 3) they encounter unfavorable environmental conditions such as high water temperatures that lower survival. It is frequently very hard to tell which process is dominating, but understanding whether lack of food or too many predators is controlling a population can be very helpful in figuring out how to recover populations (Figure 1).

Diagram of a food web pyramid showing that predators exert top-down pressure on their prey, whereas food exerts bottom-up effects on their predators.

Figure 1. Fish populations depend on the number of fish you start with (parents), amount of food available, number of predators, and suitable environmental conditions such as temperature and salinity. Diagram by Rosemary Hartman, DWR.

To try and figure out which processes might be controlling threadfin shad (and other estuarine fishes), a team of scientists recently applied structural equation models to a long-term dataset of physical variables, phytoplankton, zooplankton, clams, and fishes. The resulting paper “Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient” was recently published in the journal Ecology. It is a great example of how long-term monitoring data, data integration, cutting-edge statistics, and diverse teams can work together to provide new insights about our environment.

The team started by drawing out their hypothesized relationships between ecosystem components in a food-web diagram (Figure 2). They wanted to create a mathematical model that described the relationships between each component in their diagram, but could only do so if there were adequate data available. They compiled data from six different long-term monitoring programs and one model dataset for a combined package of forty years of data across the estuary. Unfortunately, some of the key variables in their conceptual model, such as specific types of phytoplankton, aquatic vegetation, and large, predatory fishes, haven’t been monitored as well as others, so couldn’t be used in the model, but what was available was impressive.

Diagram showing that predatory fish eat smaller fish, smaller fish eat zooplankton, zooplankton eat phytoplankton, and clams eat phytoplankton and zooplankton. Nutrients control amount of phytoplankton.

Figure 2. Conceptual model of the estuarine food web used as a basis for the NCEAS synthesis team's food web model. Diagram adapted by Rosemary Hartman, DWR, from Rogers et al, 2024.

They applied a modeling technique that allowed them to quantify each of the connections in their food web diagram to determine whether changes in one population were due to top-down effects of their predators, bottom-up effects of their food supply, or environmental variables like temperature and flow, and this was not an easy task! As Tanya Rogers, one of the team members and co-lead author on the final paper said, “It was sometimes hard to find the right balance between detail and interpretability”. Really complicated models have lots of detail, but can be hard to understand, but if the model is too simple it doesn’t describe reality. They eventually settled on a model that was somewhere in between. They found that fish and zooplankton trends were more driven by food supply in freshwater areas of the estuary, but the same populations were more driven by predation in the brackish water areas of the estuary. Abiotic drivers (temperature, turbidity, and flow) were frequently important in all regions and at all levels of the food web and had similar or greater effects than food supply or predation.

This was a great example of using an integrated dataset to address big questions about the ecology of the estuary, but the coolest part of it may have been the team that put it together. This project was part of a synthesis program sponsored by the Delta Science Program and led by the National Center for Ecological Analysis and Synthesis (NCEAS). The Delta Science Program recruited a diverse team of researchers from State and federal resource agencies and local universities with complementary areas of expertise. The team then participated in training workshops run by NCEAS on open science practices, data synthesis, reproducible workflows, data publications, and statistical techniques. Once the team had these new skills, the NCEAS trainers helped them assemble the integrated datasets and analyses used for the project. This workshop resulted in a really cool paper, but more importantly the team gained the skills to do more research like this in the future. Plus, they had fun getting to work with other early career researchers from different organizations who bring different perspectives.

Most exciting of all, the team made several recommendations for future research, such as exploring nutrient dynamics in more detail, testing changes to the dynamics in different salinity zones, exploring different time scales, and filling monitoring gaps such as large predatory fishes. With the team’s new skills and the available data, there are a lot more possibilities to explore.

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

Barros, A. 2021a. Zooplankton Trends in the Upper SFE, 1974-2018. IEP Newsletter 40:5-14.
Barros, A. E. 2021b. Interagency Ecological Program Zooplankton Study ver 7. Environmental Data Initiative.
Burris, Z. P. L., R. D. Baxter, and C. E. Burdi. 2022. Larval and juvenile Longfin Smelt diets as a function of fish size and prey density in the San Francisco Estuary. California Fish and Wildlife 108.
Hartman, R., S. M. Bashevkin, A. Barros, C. E. Burdi, C. Patel, and T. Sommer. 2021. Food for Thought: Connecting Zooplankton Science to Management in the San Francisco Estuary. San Francisco Estuary and Watershed Science 19:art1.
Kayfetz, K., S. M. Bashevkin, M. Thomas, R. Hartman, C. E. Burdi, A. Hennessy, T. Tempel, and A. Barros. 2020. Zooplankton Integrated Dataset Metadata Report (PDF). IEP Technical Report 93., California Department of Water Resources, Sacramento, California.
Kimmerer, W. J., E. Gartside, and J. Orsi. 1994. Predation by an introduced clam as the likely cause of substantial declines in zooplankton of San Francisco Bay (PDF). Marine Ecology Progress Series 113:81-93.
Kimmerer, W. J., and L. Lougee. 2015. Bivalve grazing causes substantial mortality to an estuarine copepod population. Journal of Experimental Marine Biology and Ecology 473:53-63.
Kimmerer, W. J., and J. K. Thompson. 2014. Phytoplankton growth balanced by clam and zooplankton grazing and net transport into the low-salinity zone of the San Francisco Estuary. Estuaries and Coasts:1-17.
Plabbmann, T., G. Maier, and H. B. Stich. 1997. Predation impacts of Cyclops vicinus on the rotifer community in Lake Constance in the Spring. Journal of Plankton Research 19:1069-1079.

Categories: Underappreciated data

Tagged: zooplankton rotifers underappreciated data flow

Zooplankton: Not just for [fish] breakfast anymore!

December 30, 2021

Bosmina logirostris. Photograph by CDFW staff.

Pseudodiaptomus forbesi. Photo by CDFW staff.

Tortanus preying on larval anchovies. Photo by CDFW staff.

Lots of Interagency Ecological Program (IEP) scientists research fish. Of the 22 surveys in IEP's Research Fleet, 17 are primarily focused on fish. But fish in the San Francisco Estuary are hard to catch these days. Over the past thirty years, Delta Smelt, Longfin Smelt, and even the notoriously hardy Striped Bass have declined precipitously (CDFW FMWT data). To figure out how to reverse these declines, we need an understanding of the “bottom-up” processes that exert control on these populations—we need to study fish food. Therefore, we need to increase our understanding of what pelagic fish eat: zooplankton.

Magnifying glass with cartoon images of several zooplankters

If you’ve spent any time around fish people, you’ve probably heard the word “zooplankton”, but you might not really know what it means. Zooplankton are small animals that live in open water and cannot actively swim against the current (“plankton” means “floating” in Greek). They include crustaceans (copepods, water fleas, larval crabs, etc.), jellyfish, rotifers, and larval fish. Most of them are hard to see without a microscope, so they are easy to overlook – but you’d miss them if they weren’t there because most of your favorite fish rely on zooplankton for food.

Fortunately, the IEP Zooplankton Project Work Team has been tackling the problem head-on. The group got started when Louise Conrad and Rosemary Hartman were both collecting zooplankton samples near the same restoration site. They thought “We’d be able to say a lot more about the restoration site if we combined our data sets!” But with samples collected using different gear and identified by different taxonomists, it proved more difficult than they originally thought. They needed a team of experts to help them figure out how to deal with the differences in their data. So the Zooplankton Synthesis Team was born! The original team included Karen Kayfetz, Madison Thomas, April Hennessy, Christina Burdi, Sam Bashevkin, Trishelle Tempel, and Arthur Barros, but soon grew as more people heard about the discussions they were having.

The team started by identifying the major zooplankton datasets that IEP collects and dealing with tricky data integration questions:

Can you integrate data sets when the critters were collected with different mesh sizes?
What do you do when one data set identifies the organisms to genus and another one identifies down to species?
What if these levels of identification change over time?
Does preservation method impact the dataset?

diagram of three data sets being put into a machine and turning into one data set

To integrate data sets, the team standardized variable names, standardized taxon names, and summarized taxa based on their lowest common level of resolution.

While working through these sticky questions, they compiled what they learned about the individual zooplankton surveys into a technical report (PDF) describing each survey and how they are similar and different. They published a data package integrating five different surveys into a single dataset and Sam put together a fantastic web application that allows users to filter and download the data with a click of a button.

The team had put together the data, but there was more work to do. They realized they needed to do more if they wanted people to use their data. Lots of data on zooplankton get collected, but few research articles are published about zooplankton, and zooplankton data are rarely used to inform management decisions. To get the broader scientific community excited about zooplankton in the estuary, the ZoopSynth team worked with the Delta Science Program to host a Zooplankton Ecology Symposium with zooplankton researchers from across the estuary and across the country (you can watch the Symposium recording on YouTube.). From this symposium they learned a few important lessons to help increase communication and visibility of zooplankton data and research:

Managers and scientists should work together to develop clear goals and objectives for management actions. Is there a threshold of zooplankton biomass or abundance to achieve? Or is the goal simply higher biomass of certain taxa? This will make it easier to design a study that provides management-relevant results.
Scientists should understand the management goals and keep the end goal in mind. If the end goal is fish food, study taxa that are most common in fish diets. If the primary interest is contaminant effects, focus on sensitive species.
We need to start using new tools like automated imagery and DNA along with traditional microscopy to collect better data faster.
We need to maximize the accessibility of zooplankton data to scientists and managers. Scientists should share data in publicly available places in easy-to-read formats. Similarly, managers should share lessons learned from management actions widely, and use them for adaptive management. Both scientists and managers should be encouraged to ask questions of each other to ensure both understand the best uses for zooplankton data.

These lessons, (and more!) are summarized in a recent essay published in San Francisco Estuary and Watershed Sciences. If that’s too much reading, the team also produced some fact sheets summarizing the major take-home messages of the essay and the symposium:

The team has expanded into an official IEP Project Work Team that meets monthly to discuss new zooplankton research ideas, share analyses, look at cool pictures of bugs, and talk about trends. If you’re interested in joining, contact Sam at Sam.Bashevkin@Deltacouncil.ca.gov

diagram of organism giving presentation

Kayfetz K, Bashevkin SM, Thomas M, Hartman R, Burdi CE, Hennessy A, Tempel T, Barros A. 2020. Zooplankton Integrated Dataset Metadata Report (PDF). IEP Technical Report 93. Sacramento, California: California Department of Water Resources.
Bashevkin S.M., R. Hartman, M. Thomas, A. Barros, C. Burdi, A. Hennessy, T. Tempel, and K. Kayfetz. 2020. Interagency Ecological Program: Zooplankton abundance in the Upper San Francisco Estuary from 1972-2018, an integration of 5 long-term monitoring programs ver 1. Environmental Data Initiative.
Hartman R, Bashevkin SM, Barros A, Burdi CE, Patel C, Sommer T. 2021. Food for Thought: Connecting Zooplankton Science to Management in the San Francisco Estuary. San Francisco Estuary and Watershed Science. 19(3):art1.
Kimmerer W, Ignoffo TR, Bemowski B, Moderan J, Holmes A, Bergamaschi B. 2018. Zooplankton Dynamics in the Cache Slough Complex of the Upper San Francisco Estuary. San Francisco Estuary and Watershed Science. 16(3).