Science Stories: Adventures in Bay-Delta Data

  • June 28, 2023

Blog by Rosemary Hartman, Data collated by Nick Rasmussen

Our last post gave you an introduction to the water weeds of the Delta. Invasive submerged aquatic vegetation is taking over the waterways, making it difficult for boaters, fish, water project operations, and scientific researchers (Khanna et al. 2019). As we described in the blog “Getting into the weeds”, they are hard to control too. But how do we collect data on aquatic weeds and what do those data look like?

How do we collect data?

There are two main types of data that we can work with (see IEP Technical Report 92 (PDF) for details). The first, is collected with areal photographs, and is known as ‘remote sensing’ (Figure 1). A specialized camera (sensor) is mounted on a platform (a drone, airplane, or satellite), and it can collect a regular-old photograph, or a hyperspectral photograph that records a lot of wavelengths of light that our eyes can’t see. Because different types of plants reflect different spectra of light (they are different colors) these photographs can be used to map location and extent of vegetation (Figure 2).

Diagram of remote sensing process showing an airplane flying over the water. Lines representing light go from the sun to the ground and are reflected to the sensor on the plane.
Figure 1. Diagram showing how remote sensing works. Light from the sun is reflected by objects on the ground. Different wavelengths of light are reflected differently. The sensor (camera) on the platform (airplane, satellite, drone, etc) registers the different wavelengths and stores them as an image file. Later, experts can classify the images based on which wavelengths of light were reflected.

The UC Davis Center for Spatial Technologies and Remote Sensing (CSTARS) has been mapping vegetation in the Delta using hyperspectral imagery collected with airplanes for most of the last 20 years (like the map in Figure 2). They create maps every year and share their data online via the Knowledge Network for Biocomplexity.

False-color map of Suisun Marsh showing different colors for different types of vegetation.
Figure 2. Hyperspectral image map of Suisun Marsh collected by CSTARS. Colors are used to represent different vegetation types (they aren't really that color).

Hyperspectral imagery is very good for identifying floating aquatic vegetation and terrestrial vegetation, but the water makes it hard to identify submersed vegetation. We can map where submersed vegetation is, but not what kind of vegetation is there. To look at community composition, we need to actually get out in the field and check on the weeds directly. To survey submersed weeds, researchers use a thatch rake (Figure 3) – an evil looking tool with sharp tines on one end and a long handle.

Image of a rake with a long pole and very jagged blades on the end.
Figure 3. A thatch rake being used to sample aquatic vegetation. Photo from the Department of Water Resources.

To measure weeds, researchers either lower the rake into the water and twist it around to pick up the weeds, or they drag the rake behind the boat. When they pull the rake in, they rank the coverage of weeds on the rake head and identify the weeds to species. Several different groups have been collecting these data over the past twenty years– the State Parks Division of Boating and Waterways (DBW), UC Davis (including CSTARS), SePRO Corporation, and the Department of Water Resources. Dr. Nick Rasmussen (DWR) recently integrated all these datasets into one data publication available on the Environmental Data Initiative. He developed the data set because he was helping to write a series of reports about the environmental impact of drought and drought-related management actions. Those reports required rounding up aquatic vegetation data quickly, but at the time, virtually none of it was readily available. He wanted to fix that.

It ended up being a fun challenge for Nick because he got to learn how to create a fully reproducible process for integrating the dataset – including some hard decisions about how best to combine data that was collected in very different ways. He did all the cleaning and formatting in R scripts that are made available in a public GitHub repository.

What do the data look like?

Well, because these data were collected by different programs for different purposes, there are pretty big differences in number of samples and distribution of samples over the years (Figure 4). So it’s a little difficult to detect trends.

Bar graph showing lots of samples taken from 2007-2010, no samples taken 2011-2013, and a lower number of samples taken 2014-2021. The Franks Tract Survey only occurred from 2014-2021.
Figure 4. Graph of number of submerged vegetation rake samples per year in the integrated vegetation dataset.

However, when we look at the relative abundance of different species that have shown up in the rake sample data (Figure 5), we can really see an expansion in Potamogeton richardsonii (Richardson’s pondweed, black bars in figure 5) and Najas guadalupensis (southern naiad, brown bars in figure 5) after 2014. It’s not clear whether these species, both native to California, were recent invaders in the Delta or whether early surveys didn’t know how to tell the difference between them and similar looking species such as Potamogeton crispus (curlyleaf pondweed, red bars in Figure 5).

Bar plot showing relatively consistent communities of vegetation over time, except for increases in Potamogeton richardsonii and Najas guadalupensis after 2014. The enlarged image shows the stacked bar plots for each species.
Figure5. Graph of relative abundance of each species across all rake sample surveys by year. No sampling occurred 2011-2013. Click for a version that shows separate plots for each species.

To make the story a little more complicated, these species aren’t found evenly throughout the Delta. Both Potamogeton richardsonii and Najas guadalupensis are found chiefly in Franks Tract (Figure 6) and not in any of the other regions of the Delta. SePRO and DBW conduct extensive sampling every year within Franks Tract (see Caudill et al. 2019 (PDF)), but not as high an intensity in other areas, so high abundance there throws off the Delta-wide data if it is not weighted by location.

Map with pie charts in each region showing relative abundance of vegetation across all years. Most pie charts show mostly Egeria and Ceratophyllum, but Franks Tract also has Potamogeton richardsonii and Najas guadalupensis. The enlarged image shows the stacked plots for each species.
Figure 6. Average relative abundance of different types of submerged vegetation by region of the Delta for the entire dataset. Click for a version that shows separate plots for each species.

We can also look at the hyperspectral maps to give us a record of total coverage of submerged weeds by year (Figure 7). We can see that total coverage of weeds really increased between 2008 and 2017, then remained about the same from 2017 to 2022.

Bar graph showing coverage of vegetation across all Delta waterways. Coverage is about 15% from 2007-2020, then jumps to 18-25% between 2017 and 2022.
Figure 7. Graph of percent of waterways in the Delta covered with submerged vegetation by year. Triangles indicate missing years.

However, it is often more interesting to look at a smaller area of the Delta and see how vegetation shifts from year to year (Figure 8, 9). For example, distribution of weeds in Franks Tract – a large, open-water area of the Delta – changed dramatically when a barrier was installed in West False River in 2015 and 2021-2022. The open-water area in the middle of the tract filled in during 2015, but the area on the eastern side of the tract started to clear out during 2021-2022 (see Hartman et al, 2022 (PDF) for more information).

Map showing the location of the Delta in the central valley of California and Franks Tract in the center of the Delta.
Figure 8. Map showing the location of Franks Tract - a large, open-water area with extensive vegetation and the site of many vegetation surveys by DBW and SePRO. Maps of vegetation in Franks Tract through the years are in Figure 9, below.

Maps of Franks Tract for 2004-2008 and 2014-2022 showing shifts in vegetation over time.
Figure 9. Hyperspectral image of Franks Tract showing installations of a barrier in West False River during 2015, 2021, and 2022, and resulting changes in distribution of submerged aquatic vegetation. Click to enlarge.

Between all these surveys, we’ve collected a lot of data on weeds, but we haven’t done an extensive analysis. There are lots more questions waiting to be asked! How does the distribution of weeds change with floods and droughts? Which species of weeds grow in shallow versus deep water? Are any species expanding in range? Download the dataset yourself and take a look!

Further reading

Categories: General, Underappreciated data
  • June 15, 2023

By Shruti Khanna and Rosemary Hartman

CSTARS field crew member sampling submerged aquatic vegetation using a thatching rake in the southern edge of Little Frank's Tract. Azolla and water primrose in the background, Egeria densa on the rake.
Figure 1. The hard-working staff of the UC Davis Center for Spatial Technologies and Remote Sensing survey water weeds using large thatch rakes. Image credit: UC Davis.

If you’ve ever tried to take a boat across one of the large, flooded islands in the Delta, there is a good chance you’ve gotten your propeller snagged on water weeds. Submersed aquatic vegetation (which is the fancy, scientific term for ‘water weeds’) have been getting worse and worse in recent years, with invasive species taking over areas that were previously open water. Most of these weeds are introduced species from South America, but even some of our native species have been expanding rapidly. Weeds were the subject of several papers in the most recent State of Bay Delta Science, they were the subject of a recent paper synthesizing many years of herbicide data to look at weed control effectiveness at a landscape scale, and data on aquatic weeds in the Delta has been published in several different datasets. Classification maps are now available for all aquatic plants in the Delta for most of 2004-2022 (with 2023 in the works) and there is a new, integrated dataset made up of vegetation samples from four different programs. Together, these data and publications are increasing our understanding of where weeds are a problem and what to do about it.

The recent paper “Multi-year landscape-scale efficacy of fluridone treatment of invasive submerged aquatic vegetation in the Sacramento San Joaquin Delta”, published earlier this year in Biological Invasions, was an excellent example of how many datasets could be integrated and used to answer a major management question. Water weeds have been managed with herbicides by California State Parks Division of Boating and Waterways for years, but its always seemed like a Sisyphean affair – constant use of herbicides and mechanical removal while weeds just keep growing back (for more info, see recent special issue of the Journal of Aquatic Plant Management). This gave lead author Dr. Shruti Khanna the idea that someone should look at whether the treatments were working. So she enlisted the help of Dr. Jereme Gaeta – statistician extraordinaire, Dr. Edward Gross – an expert in hydrodynamic modeling, and Dr. Louise Conrad – who could provide both scientific and policy advice.

For Shruti, the best part was working with the other members of the team: “Honestly, what I enjoyed most was working with Jereme on this project. He brought methods of analysis to the table that I couldn't have dreamed of when I was doing my PhD. I just didn't have the know-how. So, it was a very complementary effort - my remote sensing and geospatial skills to extract spatial information and integrate it with diverse other datasets, his statistical skills to create a nuanced GAM model to explore the data, and Louise's expert knowledge on the treatment program, its history, and management perspective made the publication a lot stronger. It was Jereme's idea to add Ed's hydrodynamic modeling of water speed to our analysis. Incorporating speed gave a whole new perspective to our study and turned out to be critical to understanding why treatment is sometimes not effective."

They gathered data gathered in the field on when and where weeds were treated, annual maps of weed distribution collected with hyperspectral imagery, and predicted current speed based on hydrodynamic models. The final integrated dataset was made of five diverse datasets with field data tables, rasters, vectors, and model output. They then created a statistical model to see whether multiple years of herbicide treatment resulted in lower probability of weeds. They found that when high levels of herbicide were applied there was a lower probability of weeds being there, but only at low current speeds (See figure 2). If the water was moving quickly, all the herbicide would be washed away and there was no effect of treatment (See figure 3).

Graph showing how the probability of vegetation presence decreases with increasing current speed. However, effectiveness of fluridone treatment also decreases with increasing current speed. Figure 2. Graph of model results from Khanna et al. 2023 showing probability of vegetation presence versus current speed. At low current speeds, fluridone decreased the probability of vegetation being present. But at high current speeds, there was no effect of fluridone. Graph reproduced from Khanna et al. 2023, with permission of the authors.

In a complicated, tidal system like the Delta, it is extremely difficult to control weeds when the herbicides get washed away by the tides more often than not. New tools are currently under investigation by members of the Delta Region Areawide Aquatic Weed Project, including the Division of Boating and Waterways, US Department of Agriculture, UC Davis, and others to get a better handle on this sticky problem.

Conceptual diagram showing how fluridone pellets sit on the bottom of the water. At low current speeds, water sloshes back and forth by the fluridone stays in place. At high current speeds, the fluridone washes away. Figure 3. Diagram of what is going on at herbicide treatment spots in the Delta. Fluridone comes in pellets which slowly release into the water column. When current speeds are low, water ‘sloshes’ back and forth a bit, but fluridone concentration remains high and weeds are killed. When current speeds are high, all the fluridone washes away before it can be effective. Image credit: Shruti Khanna, CDFW.


Categories: General
  • April 7, 2023

Blog by Rosemary Hartman, Data by Tiffany Brown, Sarah Perry, and Vivian Klotz. Photos from BSA submitted to DWR.

The base of the estuarine food web is phytoplankton – microscopic, floating, single-celled organisms drifting on the currents (“phyto” meaning “plant” and “plankton” meaning “drifter"). Most people know that trees produce oxygen, but phytoplankton put them to shame. Phytoplankton in rivers, lakes, and oceans worldwide produce an estimated 80% of the world’s supply of oxygen. Phytoplankton are also the base of the aquatic food web – providing food for zooplankton, other invertebrates, and fish. They are also the source of the omega-3 fatty acids that make seafood so good for you!

The Environmental Monitoring Program, a collaborative team of scientists and technicians from DWR, USBR, and CDFW, have been collecting phytoplankton samples to monitor the status and trends of phytoplankton in the San Francisco Estuary for the past forty years, and just recently made their data from 2008-2021 available online!

How are the data collected?

  • The monitoring program crew go out on DWR’s premier research vessel, the Sentinel, once per month and visit 24 fixed stations and 2-4 ‘floating’ stations across San Pablo Bay, Suisun Bay, and the Delta.
  • At each station, scientists collect a 60 mL water sample from 1 meter depth and stain it with Lugol’s iodine solution to make the phytoplankton easier to see.
  • The samples are shipped to a lab where highly-trained taxonomists identify and count the phytoplankton under high-powered microscopes.

What do the data look like?

For each sample, we see the count (number of cells) for each type of plankton as well as the size (biovolume) of these cells.

  • Each phytoplankter is identified to genus or species, but we often lump them into larger taxonomic groups to make it easier to see trends in the data. These groups are based on genetic and morphologic similarity, so they have similar shapes, pigments, motility, etc. Our understanding of the microbial tree of life is constantly evolving, so it is vital that we keep our entire data set up to date on the latest science as we categorize these groups.
  • The phytoplankton samples are collected at the same time as water quality, nutrients, and zooplankton samples, so you can put all the data together if you want to see the bigger picture.

What trends do we see?

  • There was a big change in average biovolume and relative abundance of cyanobacteria in 2014 when we switched contracting laboratories. Differences in methods made a big difference in the data, and we’re still trying to work out the consequences (See Figure 1 and Figure 2).
Bar plot showing annual average phytoplankton biovolume by taxonomic group.
Figure 1. Biovolume of each algal taxonomic group by year. Centric diatoms and pennate diatoms make up most of biovolume in every year, but differences in contractors in 2014 means we can't compare to earlier years. Click to enlarge.
Relative abundance of each algal group by year. 2012 and 2018 had a lot of centric diatoms, 2016-2018 had the most chrysophytes, and 2021 had particularly high percentage of centric diatoms.
Figure 2. Relative abundance of biovolume of each algal taxonomic group by year. Click to enlarge.
  • We always catch more critters in the spring and summer, when days are long and temperatures are warm (Figure 3, Figure 4).
Bar graph with average biovolume per month color-coded by algal group. The highest biovolume is Feb-April.
Figure 3. Average Biovolume of major algal taxonomic groups by month (data from 2014-2021 only, to control for changes in contractors). Click to enlarge.
Bar graph of organisms per mL by month, color-coded by algal group. All months are totally dominated by cyanobacteria (>80%).
Figure 4. Average concentration (organisms per mL) of major algal taxonomic groups by month (data from 2014-2021 only, to control for changes in contractors). Click to enlarge.
  • Cyanobacteria are very small in comparison to other phytoplankton, so even when we catch a lot of them we don’t get much biovolume. Looking at the graphs, Figure 3 shows the biovolume of each phytoplankton group in each sample while Figure 4 shows the number of organisms in each group in each sample. The cyanobacteria are hard to see in the biovolume graph, but they totally dominate the number-of-organisms graph!
  • Environmental factors frequently impact abundance of phytoplankton. For example, if we plot the abundance of each group versus net freshwater flow coming through the Delta, we find higher concentrations of most groups during months with higher flow (Figure 5).
Set of nine scatter plots showing relationship between algal concentration and Delta Outflow for each major algal group. All taxa have position relationships, but some are steeper than others.
Figure 5. Relationship between monthly mean concentration (organisms per mL) of each algal taxonomic group and net Delta outflow. Click to enlarge.

There are lots more questions we could ask with this dataset. Are certain taxa more common in wet years or dry years? Do certain taxa occur more frequently in salty water or fresh water? How have abundances of certain taxa changed over time? With a dataset like this, the sky is the limit! If you see anything interesting in the data, we encourage you to join the Water Quality and Phytoplankton Project Work Team to share what you see!

What’s your favorite phytoplankton? These are some of the most common taxa in our samples:

  • Cyanobacteria - Bacteria that photosynthesize! Some can even fix nitrogen out of the atmosphere. Others can produce toxins harmful to fish and wildlife, but most are harmless.
    Anabaena - a cyanobacterium that looks like a string of beads with one large bead on it.
  • Centric Diatoms - Big critters that look like wagon wheels and have a case (also called a ‘test’) made of glass-like silica. Considered very tasty and nutritious for zooplankton.
    Diatom in the genus Stephanodiscus. It looks like a small cylinder.
  • Pennate Diatoms - Closely related to centric diatoms, these guys also have a silica shell and are highly nutritious. Unlike centric diatoms, they are shaped like canoes and frequently live on surfaces instead of being part of the plankton.
    Diatom Asterionella sp. It looks like rods connected at one end.
  • Green Algae - Green cells that can be single or colonial and also have some flagellated species. They are also the distant ancestors of land plants.
    Green algae in the genus Cosmarium. It is round with some symmetric blobs inside.
  • Cryptophytes - Single-celled algae with a pocket in one end with two flagella sticking out of it.
    Algae in the genus Cryptomonas. They look like ovals with bits of dirt coming out of one end.
  • Euglenoids - Single cells with a flagellum that are frequently heterotrophic – they can eat other cells or photosynthesize to produce energy.
    Phytoplankton in the genus Euglena. It looks like an oval that is pinched on either end.
  • Crysophytes - Also known as “golden algae”, these guys have two flagella and many are encased in a silica cyst.
    Algae in the genus Dinobryon. They look like blobs with a lot of tails hanging off them.
  • Dinoflagellates - These single-celled algae have two flagella, one that circles their “waist” and one streaming off the side. They are more common in marine waters than freshwater, and can cause “red tides” which are harmful to fish.
    Dinoflagellate in the genus Peridinium. It is roundish with some grooves in it.

Further Reading

Categories: Underappreciated data
  • January 27, 2023

By Rosemary Hartman

Delta Smelt

Small fish with large eye, small pectoral fin, and adipose fin.
Picture of Delta Smelt, photo by Rene Reyes, US Bureau of Reclamation


Small fish with short pectoral fin, adipose fin, and upright dorsal fin. Very similar to the Delta Smelt.
Picture of a Wakasagi, photo by Rene Reyes, US Bureau of Reclamation

You’ve probably heard of Delta Smelt (Hypomesus transpacificus), and you may have heard of their cousin, the Longfin Smelt (Spirinchus thaleichthys), but there is a third osmerid in the estuary. The Wakasagi (Hypomesus nipponensis), also known as Japanese Smelt, is in the same genus as Delta Smelt, and was once thought to be the same species. It is native to Japan, but was introduced to reservoirs in California by the California Department of Fish and Game in the 1950s, and now it is established throughout the watershed, including the Delta.

But what does this brother of the Delta Smelt do? Is there sibling rivalry? A group of IEP scientists was curious, so they decided to look at all of our existing data to see when, where, how big, and how many Wakasagi are in the Delta and how their environmental tolerances and diet compares to Delta Smelt. A paper about their analysis recently came out in the Journal San Francisco Estuary and Watershed Sciences.

In order to compare Delta Smelt and Wakasagi, they looked at all the data from thirty different fish datasets from San Francisco Bay, Suisun Marsh/Suisun Bay, the Delta, and the watershed (see map below). This resulted in a dataset with over 250,000 individual Wakasagi! They also looked at data from special studies of Wakasagi and Delta Smelt growth and diets in the Yolo Bypass.

Map of the San Francisco Estuary showing hundreds of sampling points in the San Francisco Bay and Delta with scattered points upstream.
Maps of the San Francisco Bay-Delta Estuary. (A) Four long-term CDFW monitoring surveys and region assignments used for the comparative Delta Smelt and Wakasagi analysis. (B) Sampling locations for a subset of additional surveys used to assess Wakasagi catch, as well as Yolo Bypass surveys used to assess life-history traits including growth, phenology, and diet. Map reproduced from Davis et al, 2022, with permission.

They found some similarities between delta smelt and Wakasagi – both fish really like hanging out in the Sacramento Deep Water Ship Channel and both like eating calanoid copepods (a particularly tasty variety of zooplankton). They spawn at about the same time, but Wakasagi are usually a little earlier (though this varies from year to year), and Wakasagi usually grow a little faster. They are similar enough that they sometimes interbreed and produce hybrid offspring.

Wakasagi aren’t the same as Delta Smelt though. Wakasagi aren’t actually very common in the Delta, instead finding their homes further upstream in reservoirs (they especially seem to like the Feather River, the screw trap there catches tens to hundreds of thousands of Wakasagi per year!). In the Delta they are mostly in the northern region, which might be just them washing in from upstream. Though they were mostly found in freshwater reaches of the Delta, Wakasagi can actually tolerate a wider range of salinity and temperatures than Delta Smelt, but they seem to prefer cooler temperatures.

So, are Wakasagi competing with Delta Smelt for limited food resources? Maybe a bit, but while they play a similar ecological role when they do overlap, they don’t overlap spatially very often, and both Delta Smelt and Wakasagi are rare in the Delta. However, they overlap enough that areas that are good for Wakasagi are probably good for Delta Smelt too. Delta Smelt are becoming more and more endangered, so we can use Wakasagi as indicators of good Delta Smelt conditions and as substitutes for smelt in some laboratory experiments.

Major similarities and differences between Delta Smelt and Wakasagi
Delta Smelt Wakasagi Comparison
Annual life span Annual life span Checkbox that indicates the items are similar
Spawn later Spawn earlier Two arrows pointing in opposite directions that indicate the items are different
Eat calanoid copepods Eat calanoid copepods Checkbox that indicates the two items are similar
Grow slower Grow faster Two arrows pointing in opposite directions which indicates the items are different
Narrower tolerances Wider tolerances Two arrows pointing in opposite directions which indicate the items are different
Endangered More common Two arrows pointing in opposite directions which indicate the items are different
Native Non native Two arrows pointing in opposite directions which indicate the items are different
Mostly semi-anadromous Mostly freshwater Two arrows pointing in opposite directions which indicate the items are different
Small and silver Small and silver Checkbox which indicate the items are similar
Loves the North Delta Loves the North Delta Checkbox which indicates the items are similar
Smells like cucumber Smells like fish Two arrows pointing in opposite directions which indicates the items are different


Further reading

Categories: General
  • 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