Science Stories: Adventures in Bay-Delta Data

  • 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.

Further reading

Categories: General
  • November 22, 2023
Adult Chinook Salmon with a fin tag being released by a scientist into a river. Photo from CDFW.

By Peter Nelson

The Challenge

Spring-run Chinook salmon (“spring-run”) are listed as threatened under both the California Endangered Species Act and the Federal Endangered Species Act. Like most salmon, these fish are anadromous: The adults, having grown and matured in the ocean, return to their natal stream to spawn, and the juveniles, after rearing in freshwater, eventually migrate downstream to the ocean (see Figure 1). During that downstream migration, juvenile salmon are exposed to a gauntlet of threats, including warm water temperatures, predators of all sorts, and “taking the wrong turn” through water diversions and getting lost on their way to the ocean. Managing or reducing the risk posed by water diversions is a responsibility of the Department of Water Resources, and to do that water managers need to know the number and timing of those outmigrating juvenile spring-run as they enter the Delta. Coming up with an accurate prediction of this—what’s termed a Juvenile Production Estimate (PDF) or JPE—is not simple. This is the first of a two part piece about our efforts to develop a JPE, both what’s been accomplished and what’s planned, as well as a timeline.

Diagram of spring-run salmon life cycle showing adults migrating upstream into the mountains where they hang out in cold water pools below dams all summer before spawning. Juveniles then travel through the delta to the ocean to mature. - link opens in new window

Figure 1. Spring-run chinook have a complex life cycle. The adults migrate upstream in January through March, but instead of spawning right away like most salmon they hold in coldwater pools all summer and spawn in the fall. Diagram by Rosemary Hartman, Department of Water Resources. Click to enlarge.

The Approach

We know when to expect adult spring-run to return to their natal streams to spawn based on past experience: Humans, beginning with the indigenous peoples of the West Coast, have been observing these runs for generations, and we might reasonably expect that the numbers of returning adult salmon are a decent predictor of the juvenile fish those returning salmon will eventually produce. Observations by multiple teams of biologists of adult salmon throughout the Central Valley allow us to predict the likely numbers of juvenile spring-run expected to migrate downstream and enter the Delta on the way to the Pacific Ocean each year. “Hold on a minute,” you might say, “What about the water in those streams? If the creeks are low and the water is warm, surely those baby salmon won’t do as well as they might when conditions are good.” You’d be right! The number of reproducing salmon—the parents—isn’t a perfect predictor of the number of offspring: There are many environmental factors that affect juvenile production, but, based on past studies of salmon ecology, we can include factors like flow in our analysis of the likely number of juveniles that will be produced by the annual return of adult salmon (for example, see Michel 2019; Singer et al. 2020).

These estimates, however, are just that—we can’t know exactly how the varying amount of water will affect the survival of juvenile salmon as they grow and migrate, but we should get reasonably close, and we have another source of information to improve our estimates, the number of outmigrating juveniles that we observe directly as they swim towards the Delta: The streams where spring-run spawn regularly have rotary screw traps (Video) (RSTs, Figure 2) on them. These devices divert migrating juveniles into a holding pen where biologists count and measure them each day before releasing them back into the stream to continue their journey to the sea. Data from these RSTs give us another check on our estimates based on spawner production, and are themselves an alternative means for estimating spring -run juvenile production.

A rotary screw trap with a conical trap and surrounding deck deployed in a narrow channel with trees growing on the bank.

Figure 2. A rotary screw trap floating in the Yolo Bypass Toe Drain with its cone out of the water (not sampling). Photo courtesy of the Department of Water resources.

One last point: In order for water managers to use these predictions for how many (and when) spring-run are expected to reach the Delta, these estimates need to happen each year before spring-run are expected to enter the Delta when water managers need to make decisions about their operations. This is especially tricky for estimates that rely on that RST data because it only takes a few weeks for juvenile salmon to travel from the RSTs to the Delta. This means that the process of counting adult salmon and (especially) juvenile salmon in the RSTs, entering those data into a shared database, and crunching the numbers to produce a JPE must be fast, efficient and accurate.

Gathering Information

This is a collaborative, interagency effort, which we began by holding a broad-based, public workshop in September 2020 with the Department of Fish and Wildlife (see Nelson et al. 2022 for details) and writing a science plan (PDF) with our agency partners to determine what monitoring data were needed to develop a spring-run JPE. Estimating an annual spring-run JPE is complicated by (1) the broad geographic and geologic range of Central Valley streams that support spring-run, (2) the challenge of developing a holistic, coordinated multi-agency monitoring framework for generating quantitative estimates of juvenile spring-run across their range, (3) the variable life history displayed across the spring-run streams, and (4) the difficulty of distinguishing juvenile spring-run from other run types (fall run, late-fall run, and winter run) found in the same streams (we will talk more about distinguishing salmon run type in our next blog post).


Most of the monitoring in spring-run streams is conducted by the staff of several governmental agencies (e.g., Deer Creek), gathering data on the numbers and timing of returning adults and of migrating juveniles, and tracking the changes in these metrics from year-to-year, but monitoring historically was designed to focus on local management needs, employed multiple methods and focused on different life stages across the watershed. Some work has been done to integrate data on number of returning adults (CDFW's GrandTab dataset, which produced the graph of returning adults, Figure 3 below). However, a spring-run JPE will require more a coordinated approach with the means of combining data from more than 40 monitoring programs from eight regions, several governmental agencies, and nearly two dozen data stewards and managers, using diverse methods and having large discrepancies in monitoring histories. These are significant challenges, but they can be met as long as we’re aware of the limitations (see below).

Bar graph of returning adult spring-run chinook salmon in the JPE tributaries. Total escapement varies from over 20,000 to less than 1,000, with Butte Creek having the highest returns. - link opens in new window

Figure 3. Total escapement (number of returning adults) by tributary for 2000-2022. Click for an enlarged version broken out by tributary.

In addition to gathering data on the number and timing of returning adults and departing juveniles, we’ll also need data on year-to-year salmon spawning success and on the survival of those outmigrating juveniles as they move from higher elevation habitats through lower, slower and warmer tributaries, and as they migrate down the mainstem of the Sacramento River to finally reach the Delta (streams with major spring-run spawning are shown in Figure 4).

Environmental conditions too are crucial: Preeminent are the quantity of water in the system and water temperature; we know that these have strong effects on salmon survivorship and behavior. The number and location of predators also vary from year to year and can affect the number of juvenile spring-run reaching the Delta.

Map of the Sacramento Valley watershed highlighting Clear Creek, Butte Creek, Battle Creek, Deer Creek, and the Feather River, where spring-run spawn. - link opens in new window

Figure 4. Map of the Sacramento River watershed highlighting the rivers and streams where data is being collected for the spring-run JPE. Some spring-run also spawn in the San Joaquin watershed, but they have not been added to the spring-run JPE dataset yet. Click to enlarge.

Data Management

You may have heard the expression, “garbage in, garbage out”? Wherever the phrase originated, it certainly applies to ecology! Quality data and metadata (how, when, where, and by whom the data are collected) are critical to an accurate spring-run JPE and its application to salmon conservation and water management. DWR led the formation of a team to design a data management system. This team conducted extensive outreach to the various monitoring programs for the seven spring-run spawning streams identified as most important to the JPE.

This data management system is now a reality, and is designed to provide timely access to machine-readable monitoring data and metadata. To meet the annual deadlines for calculating a spring-run JPE, new RST data must be compatible across programs and reported rapidly. Building the initial dataset took over a year because of historical inconsistencies in data reporting across monitoring programs, but state and federal agencies are collaborating to make newly collected data compatible from the moment of data entry. Data from some monitoring programs are now acquired automatically from digital entry and uploads are occurring directly from the field daily; the rest of the monitoring programs will move to this “field-to-cloud” data entry system over the next several years, improving data quality and the greatly facilitating the ease of access. All historical RST data are now publicly available from the Environmental Data Initiative (use search term “JPE”), and new RST data will be added to this repository on a weekly basis. Indeed, one of the most exciting and novel aspects of the spring-run JPE effort is that it has unified much of the existing data reporting from multiple agencies monitoring along with new monitoring under a common goal and purpose.

The spring-run JPE data management program

  • has now standardized data collection methodologies, schemas, encodings, and processing protocols;
  • produces machine-readable data for all RST monitoring programs (adult data will follow soon);
  • uploads data in near real-time to a shared data management system; and
  • makes data publicly accessible in a simple format.

This system allows us to look at all the different data sources at once to learn new things! For example, if we plot the catch of salmon from the rotary screw traps at Mill Creek, the Feather River, Knights Landing, and Delta Entry from upstream to downstream (Figure 5) we see that the most upstream site (Mill Creek) catches salmon earlier than the downstream sites and catches a lot more of them. Moving downstream the catch gets smaller and smaller as juvenile salmon get lost, eaten, or die along the way. Mill Creek also has juvenile salmon leaving the stream as late as May or June, but very few of these fish make it all the way down to the Delta, indicating that later migrants might have a harder time surviving.

Ridgeline plot showing timing and number of juvenile outmigrants at Mill Creek, Feather River, Knights Landing, and Delta Entry. - link opens in new window

Figure 5. Plot of rotary screw trap catch over time for the spring of 2023 at several locations in the Central Valley. Click to enlarge.

In our next post on the spring-run JPE, we’ll describe the cutting-edge genetic tools we’re using to distinguish spring-run from the other Central Valley Chinook, the quantitative modeling we’re developing that pulls in all of the salmon and environmental data and actually produced a juvenile production estimate along with an indication of our confidence in that estimate, the peer-review process that will critique our program and recommend improvements, and where we expect to take this spring -run JPE program next.

Further Reading

Categories: General
  • 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