Science Stories: Adventures in Bay-Delta Data

By Rosemary Hartman

Pop quiz – what are the three longest-running monitoring programs in Sacramento Delta? The Summer Townet Survey started in 1959 to monitor young-of-year striped bass, the Fall Midwater Trawl survey started in 1967 to monitor juvenile striped bass after the Summer Townet finished for the year, and the Adult Striped Bass Survey started in 1969 to monitor adult striped bass (males reach sexual maturity at 2 to 4 years old, when they are about 11 inches long, and females at 4 to 8 years old, when they are 21-25 inches long). Data from Summer Townet and Fall Midwater Trawl have been used for tons of other projects, and are now used to monitor many species of native and non-native pelagic fish (Kimmerer 2002, Mac Nally et al. 2010, Sommer et al. 2011, Bever et al. 2016, Mahardja et al. 2021, Smith et al. 2021, Tempel et al. 2021), but not many people know about the Adult Striped Bass Dataset – and it’s just recently been published online (Stompe and Hobbs 2024)!

Why has so much of the monitoring in the Delta started for striped bass? Well, the striped bass – Morone saxatilis – was introduced into the Sacramento River in 1879 (Stevens et al. 1987) and is a popular sport fish. It originally came from the East Coast of North America, where it was a food source for the Indigenous people of the region and has been a favorite of fishermen after colonization as well. Overfishing has caused a decline in abundance on the East Coast (though the fishing rate has been reduced to better manage the stock)(Richards and Rago 1999, Fabrizio et al. 2017), but how are they doing in California?

The young-of-the-year fish picked up in the Summer Townet and Fall Midwater Trawl have declined a lot since the 1980s, with a particularly sharp downturn in the early 2000s (Sommer et al. 2007), though they have not declined as sharply as native pelagic fishes (Mac Nally et al. 2010, Nobriga and Smith 2020).

Figure 1. Population index for young-of-year striped bass in the Fall Midwater Trawl (FMWT) and Summer Townet (STN) from 1959-2023. Black circles and solid lines are FMWT index, dashed blue lines and triangles are STN index.

The adult striped bass data are a little harder to work with. There have been a lot of changes over the course of the survey, so comparing the data from 1970 to that of 2020 isn’t an apples-to-apples comparison. Analyses of the adult striped bass survey data from 1985 showed that the adult population had declined by 75% from 1970 to 1982, with droughts, overfishing, lower food supplies, contaminants, and water diversions implicated in the decline (Stevens et al. 1985) . Extending this analysis through 1995 revealed the decline continued, with food limitation partially to blame (Kimmerer et al. 2000, Lindley and Mohr 2003), but the population recovered somewhat in the late 1990s (Loboschefsky et al. 2012), probably because of the string of wet years, and numbers of age-3 fish in the early 2000s were higher than would be predicted from the age-0 trawl surveys in recent years (Nobriga and Smith 2020).

Figure 2. A) Adult striped bass population estimates from 1969-2004. Reproduced from Loboschefsky et al. 2012, with permission. B) ratio of age 3 adult striped bass to the fall midwater trawl index from three years prior. Reproduced from Nobriga and Smith 2020, with permission.

Looking at more recent data, we can’t calculate abundance in the same way. The older datasets used to use the Bay-Delta Creel Survey to recapture tagged fish, and that program stopped in the early 2000s. However, we can still pick up a few basic trends. First of all, the average length of the fish has declined over time (Figure 3). This is common in populations where the larger fish are removed from the population by fishing (Law 2000). There is often selective pressure to mature at a smaller size to make sure they reproduce before being caught by a fisherman. This hasn’t been studied specifically in California striped bass, but it might be part of the reason behind the change in size. Another factor contributing to the decreased size of fish is the decreasing proportion of female striped bass in the catch. There are always more male fish than female fish, but the percentage of female fish has been declining over time (Figure 4). Why? We don’t really know, but capture of larger fish might be part of the story.

Figure 3. Average fork length of all striped bass caught by the Adult Striped Bass Survey from 1969 to 2022. Green area represents the standard deviation in length, and black line shows the trend.

Figure 4. Percent of annual striped bass catch that are male or female over time from 1969-2022.

We do know that female fish are always bigger than male fish – even at the same age. You can see with the trend line in Figure 5 that when they are young – two or three years old – they are about the same size. However, by age four females are a bit larger, and by age 6 females are consistently 8-12 cm (2-4 inches) larger than males, on average. This is pretty common in fish, since females need more resources to produce eggs (Parker 1992). It's possible that the larger females are being taken by fishermen at a higher rate, which may cause the change in sex ratios.

Figure 5. Fork length of all fish caught by the adult striped bass survey versus age (as determined from scales) for female fish (pink circles and solid pink line) and males (blue triangles and dashed blue line).

While we can’t calculate abundance indices like the ones used in the first part of the project’s history, we can determine the number of fish caught per hour in our fish traps. Since we began tracking how much effort is being spent on fishing, we’ve seen a slight increase in number of adult striped bass (Figure 6), but it’s highly variable, and may be due to changes in sampling methods and locations. However, the survey tagged less fish in later years, and we have seen a decline in the number of tagged fish that we’ve recaptured (Figure 7). We use the number of fish we've tagged and the number we've recaptured to estimate population size, but due to a reduction in funding and changes to management, we no longer have enough recaptures for accurate population estimates. Despite the changes, the sport fish monitoring programs have provided valuable insights into Delta’s ecology and the role of striped bass within it.

Figure 6. Catch per unit effort of striped bass in the fyke trap on the Sacramento River from 1994-2022, within linear fit line shown in blue.

Figure 7. Percentage of tagged fish recaptured per year from 1969 to 2022 with trend line shown in blue.

References and further reading

• NOAA Fisheries Species Profile on striped bass.
• CDFW Species Profile on striped bass
• Bever, A. J., M. L. MacWilliams, B. Herbold, L. R. Brown, and F. V. Feyrer. 2016. Linking hydrodynamic complexity to Delta Smelt (Hypomesus transpacificus) distribution in the San Francisco Estuary, USA. San Francisco Estuary and Watershed Science. 14 (1).
• Fabrizio, M. C., T. D. Tuckey, and S. Musick. 2017. A Brief Guide to Striped Bass Ecology & Management in Chesapeake Bay.
• Hartman, R., E. Stumpner, C. Burdi, D. Bosworth, A. Maguire, and IEP Drought Synthesis Team. 2024. Dry me a river: Ecological effects of drought in the upper San Francisco Estuary. San Francisco Estuary and Watershed Science. 22 (1).
• Kimmerer, W. 2002. Physical, biological, and management responses to variable freshwater flow into the San Francisco Estuary. Estuaries. 25(6B):1275-1290.
• Kimmerer, W. J., J. H. Cowan, Jr., L. W. Miller, and K. A. Rose. 2000. Analysis of an estuarine striped bass (Morone saxatalis) population: influence of density dependent mortality between metamorphosis and recruitment. Canadian Journal of Fisheries and Aquatic Sciences. 57 (2):478-486.
• Law, R. 2000. Fishing, selection, and phenotypic evolution. ICES Journal of Marine Science. 57 (3):659-668.
• Lindley, S. T., and M. S. Mohr. 2003. Modeling the effect of striped bass (Morone saxatilis) on the population viability of Sacramento River winter-run chinook salmon (Onchorhynchus tshawytscha). Fishery Bulletin. 101 (2):321-331.
• Loboschefsky, E., G. Benigno, T. Sommer, K. Rose, T. Ginn, A. Massoudieh, and F. Loge. 2012. Individual-level and population-level historical prey demand of San Francisco Estuary Striped Bass using a bioenergetics model. San Francisco Estuary and Watershed Science. 10 (1):25.
• Mac Nally, R., J. R. Thomson, W. J. Kimmerer, F. Feyrer, K. B. Newman, A. Sih, W. A. Bennett, L. Brown, E. Fleishman, S. D. Culberson, and G. Castillo. 2010. Analysis of pelagic species decline in the upper San Francisco Estuary using multivariate autoregressive modeling (MAR). Ecological Applications. 20 (5):1417-1430.
• Mahardja, B., V. Tobias, S. Khanna, L. Mitchell, P. Lehman, T. Sommer, L. Brown, S. Culberson, and J. L. Conrad. 2021. Resistance and resilience of pelagic and littoral fishes to drought in the San Francisco Estuary. Ecological Applications. 31 (2):e02243, 02216 p.
• Nobriga, M. L., and W. E. Smith. 2020. Did a Shifting Ecological Baseline Mask the Predatory Effect of Striped Bass on Delta Smelt? San Francisco Estuary and Watershed Science. [accessed 2023 Feb 13]. 18 (1).
• Parker, G. A. 1992. The evolution of sexual size dimorphism in fish. Journal of Fish Biology. 41 (sB):1-20.
• Richards, R. A., and P. J. Rago. 1999. A Case History of Effective Fishery Management: Chesapeake Bay Striped Bass. North American Journal of Fisheries Management. 19 (2):356-375.
• Smith, W. E., L. Polansky, and M. L. Nobriga. 2021. Disentangling risks to an endangered fish: using a state-space life cycle model to separate natural mortality from anthropogenic losses. Canadian Journal of Fisheries and Aquatic Sciences. 78 (8):1008-1029.
• Sommer, T., C. Armor, R. Baxter, R. Breuer, L. Brown, M. Chotkowski, S. Culberson, F. Feyrer, M. Gingras, B. Herbold, W. Kimmerer, A. Mueller-Solger, M. Nobriga, and K. Souza. 2007. The collapse of pelagic fishes in the upper San Francisco Estuary. Fisheries. 32 (6):270-277.
• Sommer, T., F. H. Mejia, M. L. Nobriga, F. Feyrer, and L. Grimaldo. 2011. The spawning migration of delta smelt in the upper San Francisco Estuary. San Francisco Estuary and Watershed Science. 9 (2):16 pages.
• Stevens, D., H. Chadwick, and R. Painter. 1987. American shad and striped bass in California's Sacramento-San Joaquin River system. American Fisheries Society Symposium. 1:66-78.
• Stompe, D. K., and J. A. Hobbs. 2024. California Department of Fish and Wildlife Adult Striped Bass Study, Sacramento-San Joaquin Watershed, California, 1969-2022 Environmental Data Initiative. ver. 1.
• Tempel, T. L., T. D. Malinich, J. Burns, A. Barros, C. E. Burdi, and J. A. Hobbs. 2021. The value of long-term monitoring of the San Francisco Estuary for Delta Smelt and Longfin Smelt. California Fish and Wildlife. Special CESA Issue:148-171.

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).

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.

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.

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.

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).

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.

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.

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).

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.

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

• Borgnis E, Boyer KE. 2015. Salinity tolerance and competition drive distributions of native and invasive submerged aquatic vegetation in the upper San Francisco Estuary. Estuaries and Coasts.39(3)1-11.
• Boyer KE, Safran SM, Khanna S, Patten MV. 2023. Landscape Transformation and Variation in Invasive Species Abundance Drive Change in Primary Production of Aquatic Vegetation in the Sacramento–San Joaquin Delta. San Francisco Estuary and Watershed Science. 20(4).
• Caudill J, Jones AR, Anderson L, Madsen JD, Gilbert P, Shuler S, Heilman MA. 2019. Aquatic plant community restoration following the long-term management of invasive Egeria densa with fluridone treatments. Management of Biological Invasions. 10(3):473-485.
• Christman MA, Khanna S, Drexler JZ, Young MJ. 2023. Ecology and Ecosystem Effects of Submerged and Floating Aquatic Vegetation in the Sacramento–San Joaquin Delta. San Francisco Estuary and Watershed Science. 20(4) 
• Hartman R, Rasmussen N, Bosworth D, Berg M, Ateljevich E, Flynn T, Wolf B, Pennington T, Khanna S. 2022. Temporary Urgency Change Petition of 2021 and Emergency Drought Salinity Barrier; Impact on Harmful Algal Blooms and Aquatic Weeds in the Delta (PDF). Report to the State Water Resources Control Board. Sacramento, CA. 
• IEP Aquatic Vegetation PWT, Khanna S, Conrad JL, Caudill J, Christman M, Darin G, Ellis D, Gilbert P, Hartman R, Kayfetz K et al. 2018. Framework For Aquatic Vegetation Monitoring in the Delta (PDF). Sacramento, CA: Interagency Ecological Program. Technical Report 92.
• Khanna S, Acuna S, Contreras D, Griffiths WK, Lesmeister S, Reyes RC, Schreier B, Wu BJ. 2019. Invasive Aquatic Vegetation Impacts on Delta Operations, Monitoring, and Ecosystem and Human Health (PDF). IEP Newsletter. 36(1):8-19.
• Khanna S, Ustin SL, Hestir EL, Santos MJ, Andrew M. 2022. The Sacramento-San Joaquin Delta genus and community level classification maps derived from airborne spectroscopy data. Knowledge Network for Biocomplexity. 
• Rasmussen NL, Conrad JL, Green H, Khanna S, Wright H, Hoffmann K, Caudill J, Gilbert P. 2022. Efficacy and fate of fluridone applications for control of invasive submersed aquatic vegetation in the estuarine environment of the Sacramento-San Joaquin Delta. Estuaries and Coasts. 45:1842-1860.
• Rasmussen N. 2023. Submersed aquatic vegetation community composition in the Sacramento-San Joaquin Delta integrated across four surveys ver 1. Environmental Data Initiative.

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).

• We always catch more critters in the spring and summer, when days are long and temperatures are warm (Figure 3, Figure 4).

• 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).

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.
  
• 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.
  
• 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.
  
• 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.
  
• Cryptophytes - Single-celled algae with a pocket in one end with two flagella sticking out of it.
  
• Euglenoids - Single cells with a flagellum that are frequently heterotrophic – they can eat other cells or photosynthesize to produce energy.
  
• Crysophytes - Also known as “golden algae”, these guys have two flagella and many are encased in a silica cyst.
  
• 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.
  

Further Reading

• Barros, A.E. 2022. Interagency Ecological Program Zooplankton Study ver 11. Environmental Data Initiative.
• Battey, M. and S. Perry. 2022. Interagency Ecological Program: Discrete water quality monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 1975-2022. ver 8. Environmental Data Initiative.
• Brown LR, Kimmerer W, Conrad JL, Lesmeister S, Mueller–Solger A. 2016. Food webs of the Delta, Suisun Bay, and Suisun Marsh: an update on current understanding and possibilities for management. San Francisco Estuary and Watershed Science. 14(3).
• Cloern JE, Robinson A, Richey A, Grenier L, Grossinger R, Boyer KE, Burau J, Canuel EA, DeGeorge JF, Drexler JZ et al. 2016. Primary production in the Delta: then and now. San Francisco Estuary and Watershed Science. 14(3).
• Lehman P. 2022. The increase of cyanobacteria and benthic diatoms over 43 years in upper San Francisco Estuary, California. Estuarine, Coastal and Shelf Science. 275:107988.
• Lehman PW. 2007. The influence of phytoplankton community composition on primary productivity along the riverine to freshwater tidal continuum in the San Joaquin River, California. Estuaries and Coasts. 30(1):82-93.
• Perry, S.E., T. Brown, and V. Klotz. 2023. Interagency Ecological Program: Phytoplankton monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 2008-2021 ver 4. Environmental Data Initiative.
• Twining CW, Brenna JT, Hairston Jr. NG, Flecker AS. 2016. Highly unsaturated fatty acids in nature: what we know and what we need to learn. Oikos. 125(6):749-760.
• Wells, E. and Interagency Ecological Program. 2023. Interagency Ecological Program: Benthic invertebrate monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 1975-2022. ver 3. Environmental Data Initiative.

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.

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.

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.

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!

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?

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!

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?

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.

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.

More underappreciated data!

This is the second blog in our series on underutilized datasets from IEP.

San Francisco Bay Study’s Crab Catch dataset

Curated by Kathy Hieb and Jillian Burns

The San Francisco Bay Study has been sampling with otter trawls and midwater trawls throughout the San Francisco Bay, Suisun Bay, and Delta since 1980. Their fish data have been used in a number of scientific studies, regulatory decisions, and journal articles. However, did you know they measure and count crabs in their nets too?

Bay Study’s stations are all categorized as “Shoal” (shallow areas) or “Channel” (deeper samples). Crabs are collected by otter trawl, which is towed along the bottom of the water, scraping up whatever demersal fishes and invertebrates it comes across. Truth be told, it’s not the best way to catch crabs, because most crabs like hiding under rocks where they are out of the way of the net, but it does give us a metric of status and trends of some of the most common species of crabs, including the Pacific rock crab (Cancer productus), the graceful rock crab (Cancer gracilis, also known as the slender rock crab), the red rock crab, and everyone’s favorite, the Dungeness crab (Metacarcinus magister).

After the net has been towed on the bottom for five minutes, it’s brought on board the boat and the biologists count, measure, and sex the crabs they’ve caught (Figure 1). This can be tricky, because crabs can be FAST! Especially the smaller Dungeness crabs (Figure 2). The biologists have to be careful and pick up the crabs by their back side to avoid getting pinched by their claws, which definitely takes practice.

Once all the crabs are counted and measured, they are entered into a database that goes back to 1980. Bay's Study's Dungeness crab data have been used to help manage the commercial crab fishery because fisheries-independent data is valuable. From 1975 to 1978, an estimated 38-82% of the Dungeness crabs in the central California region rear in the San Francisco Estuary each year (Wilde and Tasto 1983). This dataset was also very helpful in tracking the introduction, expansion, and decline of the Chinese mitten crab (Eriocheir sinensis), which briefly took over the brackish regions of the estuary but declined as rapidly as it arrived (Figure 3. Rudnick et al 2003). Bay Study's crab data has also been combined with other datasets to see how the estuarine community as a whole responds to climate patterns and human impacts (Cloern et al. 2010).

However, a lot of questions remain to be asked of this dataset. Why did we see such high catch of Dungeness crabs in 2013 and 2016? What are the drivers between the lesser-studied crabs, such as the graceful rock crab? How does the salinity preference of each species of crab differ (Figure 4)? If you want to investigate these questions yourself, data are available on the CDFW file library website. But be careful, the data have a few hiccups in them, such as changes to sampling sites over time, missing samples during period of boat break-downs, and other caveats. Be sure to read the metadata and make sure you understand the data before using them.

Further reading

• Baxter, R., K. Hieb, S. Deleon, K. Fleming, and J. Orsi. 1999. Report on the 1980-1995 fish, shrimp and crab sampling in the San Francisco Estuary (PDF), Interagency Ecological Program Technical Report 63. Stockton, California. 503 pages. 
• Cloern, J. E., K. A. Hieb, T. Jacobson, B. Sanso, E. Di Lorenzo, M. T. Stacey, J. L. Largier, W. Meiring, W. T. Peterson, T. M. Powell, M. Winder, and A. D. Jassby. 2010. Biological communities in San Francisco Bay track large-sale climate forcing over the North Pacific (PDF). Geophysical Research Letters 37(L21602):6 pages.
• Cloern, J. E., and A. D. Jassby. 2012. Drivers of change in estuarine-coastal ecosystems: Discoveries from four decades of study in San Francisco Bay. Reviews of Geophysics 50(4):RG4001. 
• Rudnick, D. A., K. Hieb, K. F. Grimmer, and V. H. Resh. 2003. Patterns and processes of biological invasion: The Chinese mitten crab in San Francisco Bay. Basic and Applied Ecology 4(3):249-262.
• Rudnick, D., T. Veldhuizen, R. Tullis, C. Culver, K. Hieb, and B. Tsukimura. 2005. A life history model for the San Francisco Estuary population of the Chinese mitten crab, Eriocheir sinensis (Decapoda: Grapsoidea). Biological Invasions 7(2):333. doi:
• Rudnick, D. A., K. M. Halat, and V. H. Resh. 2000. Distribution, Ecology and Potential Impacts of the Chinese Mitten Crab (Eriocheir sinensis) in San Francisco Bay. Technical Completion REPORT UCAL-WRC-W-881, University of California. 
• Wild, P. W, & Tasto, R. N. (1983). Fish Bulletin 172. Life History, Environment, and Mariculture Studies of the Dungeness Crab, Cancer Magister, With Emphasis on The Central California Fishery Resource. UC San Diego: Library – Scripps Digital Collection.