Science Stories: Adventures in Bay-Delta Data

Protecting Outmigrating Juvenile Spring-run Chinook—Part 3

Pete Nelson, PhD
Department of Water Resources

First… it’s been a while! I wrote two previous posts (Science for Salmon Part 1 and Part 2) about developing our annual forecast for the number of juvenile Spring-run Chinook salmon (“spring-run”) entering the Delta. Since then, the team developing this Juvenile Production Estimate (JPE) has been focused mainly on the modeling that uses various fish counts and environmental conditions to predict the number and timing of outmigrating spring-run. The modeling component is crucial because this is how we finally arrive at a forecast, but it’s a challenge to explain because it requires a lot of advanced mathematics.

Sometimes, scientists will point to a model into which the data are dumped and treat it as a bit of a “black box” from which emerges, sans further explanation, the answer (Figure 1). Our question—how many juvenile spring-run enter the Delta each year?—is a tough one, and we want to make the process used to determine our answer less opaque. So, this article is going to be a balancing act between that “black box” approach and an attempt to explain the beauty behind the math.

Spring-run Chinook salmon have gone from being one of the most abundant Chinook runs in California to being one of the scarcest; consequently, they are listed as threatened under both the California and Federal Endangered Species Acts. The Sacramento-San Joaquin Delta is the Chicago O’Hare International for California Chinook, the hub for adult fish returning from the ocean to their natal streams to spawn, and for the juveniles migrating downstream back to the ocean. Salmon biologists, water managers, and fishermen worry about the gauntlet of threats these anxious travelers experience, including warm water, predators, and taking the “wrong turn” into water diversions. Managing the risk posed to the spring-run population by these diversions is the responsibility of the Department of Water Resources(DWR), and to do this we need to know when and how many salmon youngsters are entering the Delta each year.

The Modeling Architecture: More Than Just a Guess

A spring-run JPE isn't a single number pulled from thin air (or a fish pulled from a barrel); it is generated by a multi-model framework that combines several sophisticated statistical sub-models. To understand how we get to the final answer, we’re going to look at the four main challenges: counting out-migrating juvenile salmon, identifying which of those fish are actually spring-run, estimating their probability of surviving their journey to the Delta, and finally forecasting how many will actually reach that stage in their life-long journey.

Challenge 1: Counting the Invisible (BT-SPAS-X)

The first step is estimating the total number of outmigrating juvenile salmon passing by sampling locations at sites like Knights Landing on the mainstem Sacramento River and on various tributaries. Figure 2, above, shows the expected abundance of outmigrating juvenile Chinook and their weekly capture probability using an example from Lower Clear Creek, 2008. There’s a lot going on in these figures: The upper graph shows weekly abundance estimates, and the lower graph shows weekly capture probabilities in a rotary screw trap. Both figures show the flow or discharge rate in green, the abundance estimates from a modified Peterson model in blue circles and the predicted fish numbers from the BT-SPAS-X model using vertical bars, gray when the estimates are based on trap captures and pink where no data were available. The thin black bars show the range around each weekly estimate within which we’re quite certain the “true” number lies; where that span is narrow, we think we’re close to reality, but where that span is broad our confidence is much reduced. There’s a bit more to the figure, but I’m going to leave it there. If you’re curious about every last detail (nice!), get in touch and I’ll help you out.

Anyway, all of this depends on Rotary Screw Traps which are floating funnels placed in the current, facing the outmigrating salmon as they travel downstream to catch a portion of the passing fish (see Figure 5 for a simplified illustration). We mark a bunch of the fish we catch and then release them upstream of the trap so they have to pass the trap again. We expect, then, to catch a mixture of a few those marked fish and more unmarked fish (i.e., not previously caught). The ratio of marked to unmarked fish tells us the trap “efficiency.” But trap efficiency is strongly affected by both the trap placement and stream conditions, giving results that are sometimes distressingly poor, catching as few as 5 in 2000 marked fish moving down the big mainstem river. These methods traditionally only look at one rotary screw trap at a time, and rely on regular sampling and efficiency tests, but the accuracy can be really poor if there are any interruptions in sampling.

To improve our abundance estimates, we’ve started using a model with penalized splines—mathematical curves that act like a flexible ruler—to smooth out the trends in outmigration, allowing us to predict the trap efficiency when the trap wasn't even in the water, or when efficiency tests didn’t happen. The model "borrows" information from all the other years at that site when the traps were running, and across other sites, as well as using current environmental conditions like stream flow to help fill in the gaps. It also accounts for "process error," recognizing that there will always be some unexplained variation in efficiency.

Challenge 2: Who is Who? (The PLAD Model)

The next challenge is that multiple runs of Chinook salmon—fall, late-fall, winter, and spring—all use the Sacramento River watershed and, based on appearance, the juveniles are indistinguishable. Traditionally, biologists used Length-at-Date (LAD) criteria, which basically assumed that if a fish was a certain size on a certain day, it belonged to a specific run. However, using length-at-date for run assignment is notoriously inaccurate, often misidentifying half of the fish it tries to categorize.

To fix this, our colleague, Noble Hendrix, developed the Probabilistic Length-at-Date (PLAD) model. Instead of a "yes or no" assignment, PLAD uses genetic data from a subset of fish to build a "mixture model.” This model looks at the distribution of fish sizes from a genetically-determined sample of juvenile salmon and calculates the probability that a juvenile is a spring-run based on its length, when it was caught, and where it was caught (Figure 3). It’s like a more accurate version of a growth chart that acknowledges that some "late bloomers" or "early birds" from different runs might overlap in size, and allows us to assign each fish captured a probability regarding its run-type (e.g., 60% likelihood spring run and 40% winter run). By using this probabilistic approach, we can assign run types even to fish that weren't genetically tested, while being honest about how certain we are in that assignment.

Challenge 3: Estimating Survival (Smolt Survival)

The previous models help us to estimate the abundance and timing of juvenile spring-run produced each year in the Sacramento River watershed, but before these fish reach the Delta they must survive something of a gauntlet as they migrate from smaller, higher elevation streams to the confluence of the Sacramento and the San Joaquin rivers. This passage is fraught with danger, ranging from potentially poor water conditions to hungry predators, and estimating the probability of their survival is critical to developing our ultimate estimate (Figure 4).

Our survival model uses fish detection data from acoustic telemetry studies to estimate survival rates for juvenile salmon migrating from their natal tributaries, down the Sacramento River or the Feather River, and eventually to the Delta. Fish tagged with tiny sound-emitting devices are detected as they pass a series of underwater listening devices along the migration path, and each detection is logged as an individual fish of known origin at a specific date and time. So, yes, the government is listening. Just not to you. A sequence of detections provides a measure of downstream passage speed, and a failure of detection suggests the likely demise of that fish somewhere upstream. The primary determinants of survival in the model are fish size (bigger fish have an improved probability of success) and environmental conditions, such as flow or water temperature. For this reason, survival probabilities vary from year to year and from fish to fish, and the model integrates newly acquired data with the previous results each year.

Challenge 4: Predicting the Future (Stock-Recruit and In-Season Models)

Now that we have a decent idea of how many juvenile salmon are moving from their rearing habitat high in the watershed and we have an estimate of their survival probabilities for different flow conditions and fish sizes, we can use models to forecast how many of those are likely to make it to the Delta. We have two general ways of doing this:

First, we can use Spawner-Outmigrant Stock-Recruit models to predict how many outmigrating juveniles that today’s parents will produce over the coming year. This is like trying to guess how many apples an orchard will produce based on how many trees were planted and what the weather is expected to be like. We include environmental conditions in our forecast models, like water temperature and peak flows during spawning and rearing. Our findings show that cooler water and higher flows generally lead to more productive salmon populations.

As the migration season begins, we switch to the other way of forecasting the expected number of outmigrants, using an In-Season Outmigrant Model. This is similar to how a GPS updates your expected arrival time as you drive. Basically, at any given date during the outmigration season, the In-season Outmigrant Model counts how many outmigrants have passed a rotary screw trap and then uses the outmigration timing recorded from previous years to forecast how many more outmigrants we expect to pass by season’s end. Early in the season (say, late December), very few outmigrants have passed most of the rotary screw trap sites, so the forecast is broad and uncertain. But as more fish pass the traps, say by the end of February or March, the model "learns" more about the current season’s migration and environmental conditions, and the forecast becomes much more precise.

Bringing it All Together: The Integrated JPE

The final "answer" comes from the integrated JPE model, which stitches all these pieces together. It takes the spawner counts, expands the rotary screw trap catch counts to estimate outmigrant abundances with BT-SPAS-X, identifies the spring-run among those outmigrants with PLAD, forecasts total season outmigrant numbers with Stock-Recruit and In-season Outmigrant models, and then uses survival models to forecast how many of those expected outmigrants will make it as they swim from their natal creeks down the mainstem Sacramento River to the legal boundary of the Delta where the river flows under the I-Street bridge in the city of Sacramento (Figure 5).

 

This modeling process relies on a massive "field-to-cloud" effort that pipes data from over 40 different information streams overseen by dozens of data stewards to ensure that the data we put into the models are reliable and updated on a weekly basis for real time management decisions. Well…that’s the system that’s being built and should be in place by the end of 2026. Because each step—from counting a single egg to tracking a juvenile past a trap—has some uncertainty, our models propagate that error throughout the entire process. This means we won’t just give management teams a single JPE number wrought in stone (the answer!); we will give them a distribution of possible JPEs centered on a highest probability estimate that expresses our degree of confidence in the JPE forecast. And we can update that forecast through the season as more data roll in and our confidence in the forecast increases.

Patience truly does lead to beauty. By moving away from simple guesses and toward these sophisticated, probabilistic tools, we are creating a more transparent and robust way to protect our threatened spring-run Chinook salmon. For us, the answer to how many juvenile spring-run are entering the Delta is going to be different every year, each time generating additional, challenging questions: What was the effect of rainfall this time? How did ocean conditions affect the health of returning adults? And how should we ensure that there are sufficient water resources for this incredible fish every year?

Table 1. This table displays each of the icons used in Figure 5, a schematic of the spring run JPE, accompanied by text describing what each icon represents.

Image	Explanation
	Sacramento River and its tributaries
	Sierra Nevada mountains with snowpack
	Dams and other diversions give us the means to store and regulate water use
	Chinook salmon are an anadromous fish, juveniles hatching and rearing in freshwater but completing most of their growth in the ocean; adults return to freshwater to spawn
	Pacific Ocean offers salmon abundant food, vast space and rapid growth potential
	Data: numbers, size, genetics and more; this information is crucial to understanding salmon ecology, determining fisheries policy, and making water use decisions
	Rotary Screw Trap: these capture juvenile salmon as they migrate downstream, holding them so that we can count and measure them before releasing them back into the river
	Scientists collect data in the field, analyze samples in the lab, enter those data into the system, and develop and run the models to understand the patterns
	Cloud storage for the data provides a central repository accessible to all
	Models, including the response of salmon production to positive environmental conditions, help to make sense of lots of data
	State capital where policy is developed, such as that determining how water use decisions are made
	Decisions like a mandatory reduction in water exports to prevent drawing migrating juvenile salmon into pumping plants, for example

Note: Dr. Josh Korman, who led the technical team developing these models, died on 25 May 2026. Josh was one of the rare quantitative ecologists who also spent major amounts of time in the field—with a mask and snorkel, pulling nets and counting fish—and his keen enthusiasm for the challenge of understanding threatened fish populations was inspiring. We lost Josh before the completion of this project, but we were lucky to work with him. As we finish, I trust that we’d have made Josh proud.

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.
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• 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).
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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).