Science Stories: Adventures in Bay-Delta Data

What lives in the mud?

(spoiler alert, not just clams)

By Rosemary Hartman, with advice from Betsy Wells.

Benthic samples (things that live in mud)

The Environmental Monitoring Program has been collecting data on water quality, nutrients, zooplankton, phytoplankton, and benthic invertebrates for almost 50 years. Data from the benthic invertebrate sampling program has been key to documenting the invasion of the clam Potamocorbula amurensis and corresponding decrease in phytoplankton (Carlton et al. 1990; Kimmerer and Thompson 2014). However, the program catches a lot more than just clams. They bring up crustaceans, worms, amphipods, isopods, and lot of other critters you have probably never heard of. All of their data are published regularly on the Environmental Data Initiative website (Wells 2024), and there is a lot to be learned by looking through it.

What does sampling look like?

It’s not easy to look at what lives in mud that is 20 feet under water. EMP’s intrepid crew uses a ponar grab – a pair of metal “jaws” that can be held open until it hits a solid surface (like the river bottom). Then the weighted jaws snap shut, picking up a healthy helping of mud and associated critters (Figure 1). The survey crew then dumps the mud out into a mesh tray and slowly washes the mud away, leaving the critters.

Figure 1. A gif demonstrating how a ponar grab works. A pair of metal "jaws" is lowered to the bottom of the water where it springs shut, scooping up a sample of mud and associated invertebrates.

What do they catch?

Well, when we look over the entire time period (1975-2023), 85% of the catch is made up of about 15 taxa (Figure 2, Figure 3). The most common is the invasive overbite clam, Potamocorbula amurensis. Second most common is a tube-dwelling amphipod, Americorophium stimpsoni. Next up is another amphipod, Amplesca abdita, followed by the polychaete worm Manayunkia speciosa. The rest of the “usual suspects” include some more polychaetes, several oligochaete worms, a few more amphipods, the Asian clam Corbicula flumninea, ostracods (also known as “seed shrimp”), and cumaceans (also called “comma shrimp).

Interestingly, there are also 41 species that have only ever been recorded once in the history of the program (Figure 4, Figure 5)! These include several crabs which are probably too fast to show up more frequently (Yellow rock crab – Metacarcinus anthonyi, blue-handed hermit crab – Pagurus samuelis, knobknee crestleg crab- Lophopanopeus leucomanus, and pea crab – Pinnixa scamit), the sea spider – Ammothea hilgendorfi, eleven different species of midge larvae (family Chironomidae), a dragonfly nymph (the blue dasher – Pachydiplax longipennis), and a few more worms and amphipods.

Figure 2. Percent of total catch over the entire history of the EMP program (1975-2023) made up by the 15 most common taxa. (Click to enlarge)

Figure 3. Some of the most common taxa collected by EMP's benthic survey. Clockwise from top life: Manayunkia speciosa (a polychaete worm), Limnodrillus hoffmeisteri (an oligochaete worm), Potamorbula amurensis (overbite clam), Nippoleucon hinumensis (a cumacean – comma shrimp), Corbicula fluminea (Asian Clam), and Americorophium spinicorne (Amphipod). All images from DWR's Environmental Monitoring program, used with permission.

Figure 4. A timeline of instances when a species was found once in the EMP program, and never again. (Click to enlarge)

Figure 5. A few taxa from the Delta that have only been seen once! The yellow rock crab, Metacarcinus anthonyi, the sea spider (Ammothea hilgendorfi) and midge larvae (family Chironomidae, several species). Yellow rock crab picture from Harmonic at English Wikipedia, (used under license CC BY-SA 3.0). Sea Spider picture from The Trustees of the Natural History Museum, London (used under license CC BY). Midge larvae image from CDFW's Stockton lab.

Who is Manayunkia speciosa anyway?

One of the top players in our benthic team is the polychaete worm, Manayunkia speciosa (first picture in Figure 3). If you’re not familiar with polychaetes, they are in the same phylum as earthworms (the annelids) but a different class (Polychaeta, not Oligochaeta). You can tell the difference because the oligochaetes are very “worm shaped” without a clear head and with only a few hairs. Polychaetes, on the other hand, have a lot of spines and hairs all over them. They sometimes have leg-like fins that ungulate along their sides, and they always have a distinct head. In the case of M. speciosa, he is a tube-dwelling worm, which means he sticks a bunch of sand and mud into a little house in the bottom of the river and lets his long, wavey feelers stick out, catching bits of food as they wave by. Most types of polychaetes are salt-water critters, but M. speciosa prefers freshwater, so he is found primarily in the freshwater stations sampled by EMP (Figure 6). M. speciosa is particularly important to the broader ecology of the Delta because they can carry the nasty salmon disease Ceratonova shasta, a myxozoan parasite (Foott 2017; Stocking et al. 2006).

Figure 6. Average catch per meter squared (log-transformed) of M. speciosa at all of EMP’s freshwater stations since 2000. (Click to enlarge)

One of the curious things about M. speciosa is that he can be very common, but not in every year. Looking at the average catch per m² from all the freshwater stations, it can vary from a low of 7 individuals in 1978, to a high of 4,387 individuals per square meter in 1991 (Figure 7)! But why do we see these big swings? A lot of critters in the Delta have population swings based on how much rain we get, so we see patterns based on water year type (broad categories of precipitation from critically dry to wet, indicated by colored point shapes on Figure 7). We see that a lot of the really high population spikes in M. speciosa are during critically dry years. Other researchers have found that M. speciosa seems to do better in slow-moving water (Alexander et al. 2014), so maybe they get flushed out during high-flow years? But other high population years are categorized as “wet” or “above normal”, so that can’t be the only factor. An experiment by Malakauskas et al. (2013) found that while they can get dislodged at high flows, they have high survivorship after being dislodged, so high flow events might just spread them around.

The highest abundance of M. speciosa occurs in the late winter and spring (Figure 8) – the periods of highest flow in the Delta. This is a little different than the pattern of abundance in the Great Lakes – one of the few other places they’ve been studied – where the peak abundance was in May-August (Schloesser et al. 2016). A study of lab-reared M. speciosa found they have an annual life cycle and can reproduce throughout the year, but had highest egg production in the spring and summer, with babies staying in their mother’s tube for 4-6 weeks before emerging (Willson et al. 2010).

Figure 7. Average CPUE of M. speciosa in all the freshwater stations sampled by EMP from 1975-2023. (Click to enlarge)

Figure 8. Mean CPUE of M. speciosa by month for all the freshwater stations sampled by EMP, 2000-2023. (Click to enlarge)

M. speciosa seems to prefer fresh water, and California has a lot of fresh water outside of the Delta. Where else is it found? The Surface Water Ambient Monitoring Program (SWAMP) conducts benthic invertebrate surveys all over the state – sponsored by the State Water Board and implemented by CDFW. It turns out that in over 34,000 samples collected by SWAMP since the year 2000, M. speciosa has only been found 118 times, and most of those detections were in the Delta (Figure 7). However, research conducted on the Klamath River in northern California has found a lot of M. speciosa on that river, particularly in the slower reaches downstream of a major dam (Alexander et al. 2014; Stocking and Bartholomew 2007), so the lack of detections may be more “not knowing what to look for” than not being there. M. speciosa is also quite small, and may be too small to be caught in SWAMP’s sampling gear on a regular basis.

Figure 9. Samples collected by the Surface Water Ambient Monitoring Program from 2000-2023 showing catch of polychaetes (including M. speciosa). Grey points indicated samples without polychaetes, colored circles indicating samples with polychaetes, with larger circles having more individuals. (Click to enlarge)

I wish I could end this blog post with a clear graph of something that is driving abundance of M. speciosa, but after two days of playing with the data, I haven’t found anything useful. So I will leave you with links to the data and so you can figure it out for yourself! Let me know if you have any ideas.

Check out EMP's website for more annual reports and more background information!

References and further reading:

• Alexander JD, Hallett SL, Stocking RW, Xue L, Bartholomew JL. 2014. Host and Parasite Populations After a Ten Year Flood: Manayunkia speciosa and Ceratonova (syn Ceratomyxa) shasta in the Klamath River. Northwest Sci. 88(3): 219-233, 215. .
• Carlton JT, Thompson JK, Schemel LE, Nichols FH. 1990. Remarkable invasion of San Francisco Bay (California USA) by the Asian clam Potamocorbula amurensis. I. Introduction and dispersal. Mar Ecol Prog Ser; 66: 81-95.
• Foott JS. 2017. Ceratonova shasta and Parvicapsula minibicornis (Phylum Cnidaria: Myxosporea ) infectivity for juvenile Chinook salmon (Oncorhynchus tshawytscha) in the Upper Sacramento River: July – November 2016. Anderson, CA. : US Fish and Wildlife Service. California-Nevada Fish Health Center.
• Kimmerer WJ, Thompson JK. 2014. Phytoplankton growth balanced by clam and zooplankton grazing and net transport into the low-salinity zone of the San Francisco Estuary. Estuaries Coasts; 37: 1202-1218.
• Malakauskas DM, Willson SJ, Wilzbach MA, Som NA. 2013. Flow variation and substrate type affect dislodgement of the freshwater polychaete, Manayunkia speciosa. Freshwater Science. 32(3): 862-873.
• Schloesser DW, Malakauskas DM, Malakauskas SJ. 2016. Freshwater polychaetes (Manayunkia speciosa) near the Detroit River, western Lake Erie: Abundance and life‐history characteristics. Journal of Great Lakes Research. 42(5): 1070-1083.
• Stocking RW, Bartholomew JL. 2007. DISTRIBUTION AND HABITAT CHARACTERISTICS OF MANAYUNKIA SPECIOSA AND INFECTION PREVALENCE WITH THE PARASITE CERATOMYXA SHASTA IN THE KLAMATH RIVER, OREGON–CALIFORNIA. Journal of Parasitology. 93(1): 78-88. doi: https://doi.org/10.1645/ge-939r.1
• Stocking RW, Holt RA, Foott JS, Bartholomew JL. 2006. Spatial and Temporal Occurrence of the Salmonid Parasite Ceratomyxa shasta in the Oregon–California Klamath River Basin. Journal of Aquatic Animal Health. 18(3): 194-202.
• Wells E, Interagency Ecological Program. 2024. Interagency Ecological Program: Benthic invertebrate monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 1975-2023. ver 4. Environmental Data Initiative.
• Willson SJ, Wilzbach MA, Malakauskas DM, Cummins KW. 2010. Lab Rearing of a Freshwater Polychaete (Manayunkia speciosa, Sabellidae) Host for Salmon Pathogens. Northwest Sci. 84(1): 183-191, 189.

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.

By Pete Nelson

What’s this post about?

A Juvenile Production Estimate (JPE), as we discussed in Part 1 of this essay, is an estimate of the number and timing of outmigrating juvenile spring-run Chinook Salmon (“spring run”) as they enter the Sacramento-San Joaquin Delta. It is an important tool for protecting these fish because it helps water managers anticipate when these salmon may be at risk of becoming entrained in water diversions as well as serving as an important check on the status of this population. In this part, I’ll describe the cutting-edge genetic and modeling tools we’re using to distinguish spring run from the other Central Valley Chinook. This series will finish with a final installment full of the quantitative modeling we’re developing to pull in all the salmon and environmental data and actually produce a forecast of juvenile spring-run production.

Distinguishing spring-run Chinook from other salmon

Ronald Reagan once said, “A tree’s a tree: How many more do you need to look at?” I don’t know about Mr. Reagan, but most of us could probably tell that there’s a difference between a valley oak from a ponderosa pine (hint: one has pinecones and the other has acorns); however, even the best fish biologists can’t tell a spring-run from a fall-run Chinook based on looks alone.

Table 1. Central Valley Chinook salmon life cycle timing.

Until recently, the most practical way to separate the four different runs (remember? there are fall run, late fall run, winter run, and, of course, spring run) was to consider the size of the fish and the day of the year it was observed. Each run spawns during a different time of the year, and if those developing eggs and fry grow at approximately the same rate, you might expect that spring run, with peak spawning in September and October, are going to be a little larger than fall run, which spawn about two months later (Table 1). Similarly, late fall- and winter-run fish, which begin their spawning even later are likely to be smaller still. As you peruse Table 1, you’ll see that the four runs spawn during different times of the year (light blue bars), but there’s a lot of overlap, particularly between spring run and fall run. When you factor in variations in water temperature and food availability across the watershed, which directly affect the duration of egg incubation and larval and juvenile growth, there’s even more potential for overlap in size between the runs. To make run identification more challenging still, the juveniles from different spawning locations are typically moving downstream during roughly the same timeframe (November through July), and juveniles from some runs (especially late fall run and spring run) over-summer where they can find cold enough water and don’t migrate down until the following migration season, but typically earlier in the season and at a much larger size. Nonetheless, observing outmigrating juveniles with the first rains in November, you’d probably expect the largest of these fish to be late fall-run and spring-run fish that delayed migrating to the ocean and spent their first summer in their natal streams, with successively smaller fish being young-of-the-year winter run, spring run, and fall run.

Figure 1. River length-at-date (LAD) run identification curves (colored panels) and the genetic identity of fish (panels A-D) for juvenile Chinook collected at Chipps Island ( from Johnson et al. 2017). Word is, this figure is based on older genetic results and a lot of the bigger fall-run here are probably spring-run or late fall-run (thanks Brett Harvey!).

Counting cards, counting salmon

Now take a look at Figure 1. The background of each panel, made up of curved shapes, is colored to indicate the predicted size ranges on any given day of the year for the four run-types: Fall run as orange, late fall run as green, winter run as blue, and spring run as purple. These colors are generated using a mathematical formula that relates a fish’s length-at-date or LAD to a particular run-type. For example, looking at the blue winter-run curves, you’ll see that they (the fish and the shapes) start small, early in the time period—not even 50 mm long in September—but growing to 200 mm as early as about April. Therefore, if the models are correct, we would expect a small, 50 mm fish in October to be the progeny of parents who returned to fresh water in the winter (winter run). Similarly, a 75 mm fish in October is likely to be a late fall-run fish, and a juvenile 80 mm or larger in October is expected to be a fall-run fish. That’s if the fish play by our rules! Now look at the black dots on these graphs: Each dot represents the size of an actual individual, outmigrating juvenile salmon that was genetically identified as belonging to one of the four run-types, and the dots don’t all stay in the colored curve where we’d expect based on length. Still need convincing that these models aren’t perfect? Figure 2 compares the percentage of outmigrants belonging to each run as determined by LAD versus genetic test, and the success of the LAD approach at predicting run type could only be described as abysmal. Bottom line: LAD models are a less than dependable option for identifying the run of a juvenile salmon. Still, LAD does provide the foundation for another important element of our JPE program: probabilistic length-at-date or PLAD models.

Figure 2. Percent of total juvenile Chinook salmon by field year assigned to each run (rows), based on length-at-date (LAD) criteria versus genetic analysis (from Brandes et al. 2021).

The PLAD approach also uses the size of a juvenile salmon and the date captured to assign run type, but, employing a similar approach to counting cards when trying to beat your nephew during a competitive game of “Hi-Lo”^[1],; PLAD also uses probabilistic modeling to estimate the uncertainty associated with that run assignment. Figure 3 shows how two run types might be distinguished with varying degrees of confidence based on day of capture, size, and our history of genetically determined run types of that date and size combination. Here, the larger the fish, the more likely it is to be from that “blue” run type. Each contour line is defined by a probability measure. That measure of probability relies on our database of juvenile salmon captures where we’ve used genetic techniques to determine the run-type for juveniles sampled across a range of sizes, dates and locations of capture. We’re particularly careful to retain a tissue sample from fish with a low probability PLAD run assignment; later genetic analysis is expected to improve our model’s accuracy. As this database is expanded, our ability to include such factors as the tributary where the salmon was captured and environmental factors (e.g., flow and temperature) likely to affect growth rates will be improved. In other words, the PLAD model does not provide a run-type determination with complete certainty, but it should dramatically improve our ability to distinguish between these runs and it’s cheaper, quicker, and easier than doing the genetics on every single fish we sample.

Figure 3. Conceptual depiction of probabilistic length-at-date (PLAD) juvenile salmon size ranges for two runs (from Nelson et al. 2023).

Genetics

Thus far, we’ve been blithely referring to the use of “genetics” for distinguishing amongst Chinook run types without a care in the world for the hard-working folks who extract the DNA from these fish and examine the sequence of molecules contained therein. So how do they do it? DNA sequences can be used for differentiating amongst groups at varying levels—blood found at a crime scene might be examined first at the species level: Is this blood from a human victim or is this merely evidence of poor kitchen practices where someone butchered that darned rooster who persisted in crowing at 4 AM every morning? DNA can be used to determine if this is poultry or human blood, and it can also be used to determine which human left that blood. As you might imagine, distinguishing between individual humans requires more detail (more genetic markers, more time and expense) than determining foul versus fowl. The laboratory techniques for making these determinations are continually being improved, and DWR is now using a method that allows field crews to take a tissue sample from a fish and right there, in as little as 30 minutes, learn whether that fish is a spring-run salmon or not. The fish is unharmed, the sampling is quick, and the process is relatively inexpensive.

Our team has several genetic tests that we employ, each giving results with varying levels of precision and each with different demands in terms of time, effort and cost. Our initial test uses a CRISPR-based technique known as SHERLOCK (Baerwald et al. in press). It’s fast and cheap—less than $2.26 per sample—and gives one of three results: Early/Early, Late/Late, and Early/Late. These three terms relate to Central Valley Chinook run timing and the knowledge that these behaviors have largely been attributed to a single genetic region. Like humans, each salmon has two copies of each gene; each juvenile salmon inherits either an Early or a Late gene from dad, and an Early or Late gene from mom. Thus, each juvenile salmon ends up with either two Early genes (Early/Early or “homozygous Early”) or two Late genes (Late/Late or “homozygous Late”), or the juvenile may end up with one of each gene (Early/Late—“heterozygous” as the geneticists would say). It turns out that both winter- and spring-run salmon are Early/Early, and Late/Late fish are either fall-run or late fall-run salmon. Additional testing can distinguish between winter and spring run. What about the Early/Late heterozygotes? Yet another genetic test may allow us to assign those fish to a specific run, but this test costs more and must be processed in the laboratory, which takes more time.

Not only are these genetic tests critical to the ostensibly simple counts (“three more spring run and one more winter run”) that go into calculating a JPE, but they also serve to improve our PLAD model, testing and refining our ability to predict how to make run assignments using only size and date. After all, the PLAD models are still the quickest and cheapest way to determine how many juvenile salmon are produced by each of the runs.

What next?

Maryam Mirzakhani, the amazing Iranian-born mathematician, said, “The beauty of mathematics only shows itself to more patient followers.” Similarly, our efforts to forecast the number of juvenile spring run each year requires a little patience...and requires what ecologists refer to as quantitative modeling. This is where we take the monitoring data, PLAD results and the output of genetic analyses, and join it with environmental information—principally water temperature and flow rates—to generate a final JPE number. But for this, you’ll have to wait for Part 3.

Further Reading

• Baerwald, M. R., E. C. Funk, A. M. Goodbla, M. A. Campbell, T. Thompson, M. H. Meek, and A. D. Schreier. Rapid CRISPR-Cas13a genetic identification enables new opportunities for listed Chinook salmon management. Molecular Ecology Resources. in press.
• Brandes, P. L., B. Pyper, M. Banks, D. Jacobson, T. Garrison, and S. Cramer. 2021. Comparison of Length-at-Date Criteria and Genetic Run Assignments for Juvenile Chinook Salmon Caught at Sacramento and Chipps Island in the Sacramento–San Joaquin Delta of California. San Francisco Estuary and Watershed Science. [accessed 2023 Jan 3]. 19 (3).
• Johnson, R., S. Windell, P. L. Brandes, J. L. Conrad, J. Ferguson, P. A. L. Goertler, B. N. Harvey, J. Heublein, J. A. Israel, D. W. Kratville, J. E. Kirsch, R. W. Perry, J. Pisciooto, W. R. Poytress, K. Reece, and B. G. Swart. 2017. Science Advancements Key to Increasing Management Value of Life Stage Monitoring Networks for Endangered Sacramento River Winter-Run Chinook Salmon in California. San Francisco Estuary and Watershed Science.  15 (3).
• Nelson, P. A., M. Baerwald, O. Burgess, E. Bush, A. Collins, F. Cordoleani, H. DeBey, D. Gille, P. A. L. Goertler, B. Harvey, R. C. Johnson, J. Kindopp, E. Meyers, J. Notch, C. C. Phillis, G. Singer, and T. Sommer. 2022. Considerations for the Development of a Juvenile Production Estimate for Central Valley Spring-Run Chinook Salmon. San Francisco Estuary and Watershed Science. 20 (2).

1. Hi-Lo is a simple card game in which the dealer turns over the top card in a full deck and the player then guesses whether the next card in the deck will be higher or lower than the first card. Guess right and the player wins; guess wrong and the dealer wins (if the cards are equal, then neither player wins). The process continues as each card is revealed in succession. To give a very simple example, if you count the face cards (Jacks, Queens, Kings—12 in the deck, total), and the dealer has revealed 10, chances are very good that a Queen is going to be followed by a lower card.

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).

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.

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

• Tanya L Rogers, Samuel M Bashevkin, Christina E Burdi, Denise D Colombano, Peter N Dudley, Brian Mahardja, Lara Mitchell, Sarah Perry, Parsa Saffarinia. 2024. Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient. Ecology. e4274.
• Brown, L. R., W. Kimmerer, J. L. Conrad, S. Lesmeister, and A. Mueller–Solger. 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).
• Durand, J. R. 2015. A conceptual model of the aquatic food web of the upper San Francisco Estuary. San Francisco Estuary and Watershed Science.13 (3).

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.

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.

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).

Monitoring

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).

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.

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.

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

• Atlas WI, Ban NC, Moore JW, Tuohy AM, Greening S, Reid AJ, Morven N, White E, Housty WG, Housty JA et al. 2020. Indigenous Systems of Management for Culturally and Ecologically Resilient Pacific Salmon (Oncorhynchus spp.) Fisheries. BioScience. 71(2):186-204.
• Nelson PA, Baerwald M, Burgess O, Bush E, Collins A, Cordoleani F, DeBey H, Gille D, Goertler PAL, Harvey B et al. 2022. Considerations for the Development of a Juvenile Production Estimate for Central Valley Spring-Run Chinook Salmon. San Francisco Estuary and Watershed Science. 20(2).
• Cordoleani F, Satterthwaite WH, Daniels ME, Johnson MR. 2020. Using Life-Cycle Models to Identify Monitoring Gaps for Central Valley Spring-Run Chinook Salmon. San Francisco Estuary and Watershed Science. 18(4).
• Harvey BN, Jacobson DP, Banks MA. 2014. Quantifying the Uncertainty of a Juvenile Chinook Salmon Race Identification Method for a Mixed-Race Stock. North American Journal of Fisheries Management. 34(6):1177-1186.
• Michel CJ. 2019. Decoupling outmigration from marine survival indicates outsized influence of streamflow on cohort success for California’s Chinook salmon populations. Canadian Journal of Fisheries and Aquatic Sciences. 76(8):1398-1410.
• Satterthwaite WH, Cordoleani F, O'Farrell MR, Kormos B, Mohr MS. 2018. Central Valley Spring-Run Chinook Salmon and Ocean Fisheries: Data Availability and Management Possibilities. San Francisco Estuary and Watershed Science. 16(1).
• Singer GP, Chapman ED, Ammann AJ, Klimley AP, Rypel AL, Fangue NA. 2020. Historic drought influences outmigration dynamics of juvenile fall and spring-run Chinook Salmon. Environmental Biology of Fishes. 103(5):543-559.