Science Stories: Adventures in Bay-Delta Data

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.

Authors: Mallory Bedwell and Sarah Stinson

On any given day in the San Francisco Estuary (SFE) it’s a common sight to see scientists checking water quality, surveying the diverse species that live there, or conducting a myriad of other monitoring and management activities. The SFE is truly one of the most intensely studied ecosystems in the world. Recently, a new monitoring tool has gained traction among scientists as a promising way to complement traditional monitoring and research approaches. By collecting DNA from the environment and analyzing it with molecular techniques, scientists can detect any number of target species of interest. Have you heard of environmental DNA? If not, then consider this a brief introduction.

What is eDNA?

Environmental DNA (abbreviated eDNA) describes the genetic material that an organism sheds or excretes into its environment (e.g., skin cells, hair, mucus, blood, gametes, waste products, pollen, leaves, fungal spores). Once released, eDNA can be collected and extracted from environmental samples such as soil, sediment, water, snow or air. Once extracted, eDNA can be analyzed by several genetic methods. Depending on the method used, researchers can choose to target a single species (e.g., invasive or endangered), a particular community (e.g., fishes), or even multiple communities (e.g., all animals). For studies targeting multiple species, a common approach is to use a reference database to link the genetic sequence obtained in the eDNA sample to a particular species or taxonomic group. By scanning a reference sequence database for a match, scientists can identify which organism(s) the DNA in their sample came from. This is akin to scanning the barcode on any item in your local grocery store. When the item is scanned, information in the database links the barcode with the price of the item, etc. If the item isn’t listed, they won’t know how much to charge you.

Figure 1. eDNA is the genetic material of living things shed into the environment. We can collect from different substrates including soil, water, and even air.

How is it used?

In recent years, eDNA has been used in a wide array of applications.

Some closely related species can be difficult to differentiate in the field, and accurate identification often requires collecting tissue from the organism, followed by several days of processing in a molecular biology lab. A new technique called SHERLOCK (Specific High-sensitivity Enzymatic Reporter unLOCKing) is a sensitive, rapid method that can provide species identification (such as the difference between endangered Delta Smelt and the visually similar, non-native Wakasagi) without invasive sampling and can be done in the field in an hour or less. Pairing eDNA or other non-invasive DNA sampling with SHERLOCK allows managers to make rapid and accurate decisions, a critical necessity for protecting listed species.

To understand the migratory dynamics of salmonids in a creek just south of the SFE, researchers from the Monterey Bay Aquarium Research Institute (MBARI) deployed an autonomous robotic sampler (Video) to collect 750 eDNA samples over the course of one year. The creek provides habitat for endangered Coho Salmon and Steelhead Trout but is at risk from non-native species including Striped Bass. This sampler will not only filter eDNA samples but perform the molecular reactions and transmit the results, ultimately giving scientists the ability to monitor species of concern in near real time.

California encompasses many unique ecosystems considered to be biodiversity “hotspots,” or areas with high biodiversity. To protect these areas, California recently developed a “30 x 30” initiative to conserve 30% of California’s lands and coastal waters by 2030. Cataloging the biodiversity of an entire ecosystem, especially for the many diverse hotspots of California, is a tall order. To aid this effort, scientists are enlisting the help of community members and students to participate in “Bioblitz” events. Teams collect eDNA samples from diverse areas throughout the state, including the SFE. This is part of the CALeDNA program run by the University of California Conservation Genomics Consortium. By involving the community in monitoring efforts, scientists not only increase their ability to collect precious data, but the community gets to learn about these innovative new technologies and gain a deeper appreciation of their local ecosystems.

The previous three examples provide a snapshot of the different types of applications for using eDNA to monitor the diverse ecosystems of the SFE and beyond. Outside of the ecological applications of eDNA, there are additional important uses. For example, to aid efforts to mitigate and monitor COVID-19 dynamics, human health researchers have begun incorporating eDNA techniques to track the virus in wastewater. Far from being a comprehensive list of eDNA applications, these studies are just the tip of the iceberg.

How eDNA can help management?

Environmental DNA sampling can be an effective management tool in the SFE. Due to its sensitivity, eDNA sampling methods can be used to help find rare and hard to find species that are difficult to detect with traditional sampling methods, like trawls or seines. Further, it can be used to detect invasive species that may be present in low numbers or to track an invasion front. Anywhere that you can collect water, you can collect eDNA. This approach can be used to sample hard to access areas or where traditional surveys cannot be utilized. An additional benefit of eDNA sampling is that it provides a way to obtain information without the need to visually observe or handle organisms. Collection and handling can have negative effects, particularly for sensitive species. Using genetic identification through eDNA sampling can also help when species are challenging to identify visually. Different scales of analysis allow managers to ask different questions or carry out different management tasks, such as: habitat use of a rare species over space and time; initial site evaluation for a species of interest to see if further, more intensive sampling is needed; and to evaluate habitat restoration on a community wide scale. The utility and ease of sampling for eDNA make it a good compliment to traditional sampling methods and can even be more efficient in terms of time, labor, and expense. For more information about eDNA sampling in estuaries and how it can help with management needs, with a focus on those of the SFE, please see Nagarajan et al. (2022).

What researchers are currently doing with eDNA

Environmental DNA sampling is currently being instituted by different agencies and institutions to answer management questions. For example, the Washington Department of Fish and Wildlife has implemented eDNA detection for several projects to evaluate streams after wildfires for Rocky Mountain Tailed Frog, to identify whether redds belong to Coho Salmon or Steelhead, and early detection of invasive mussels and snails in water bodies. Here in the SFE, Ann Holmes, a graduate student in the Genomic Variation Lab (GVL) at UC Davis, studied eDNA detection patterns of Delta Smelt in cages during the first cage deployment experiments at Rio Vista and the Deep Water Ship Channel (manuscript in prep). The California Department of Water Resources (DWR) has several active studies currently underway focused on endangered Longfin Smelt and other listed species. The Longfin Smelt team at the California Department of Water Resources (DWR), in collaboration with Cramer Fish Sciences, has also been investigating whether eDNA sampling could be used to detect larval Longfin Smelt entrained at the Barker Slough Pumping Plant, filtering water that was collected from vegetation. DWR and Cramer Fish Sciences are also planning on utilizing eDNA sampling to monitor Chinook Salmon during droughts. A collaborative group lead by the GVL at UC Davis is working to build a database of reference sequences for fish and invertebrates in the SFE to be used for eDNA based studies. They are also comparing eDNA metabarcoding, or identifying a large group of taxa, with long term fish and invertebrate catch data around the SFE to evaluate the success of eDNA sampling as survey method in these monitoring locations.

Figure 2. eDNA filtration set up in the lab. Replicate water grab samples are filtered using a peristaltic pump. Sites with lower turbidity take only a few minutes to filter. Filters can be stored in ethanol, frozen, or dried before analysis.

Challenges of eDNA in estuaries

If eDNA detection can be a helpful tool in our management toolbox, what is hindering us from applying it further in the SFE? There are a few challenges when it comes to utilizing eDNA captured in an estuarine system. Tidally influenced systems make sampling eDNA more difficult; although unidirectional flows found in streams or rivers mean eDNA is transported in the same direction, the sloshing of the tides can make it hard to know how eDNA is being moved around. This means that sampling location (shore vs. boat) and sample spacing will likely vary by species and habitat. A better understanding of hydrodynamics in estuaries would help us to understand how best to capture and interpret eDNA results.

Additionally, the higher turbidities that are found in our estuary can cause filters that capture eDNA to quickly clog, decreasing the amount of water that is filtered and thus decreasing the probability of capturing the eDNA of our species of interest. Larger pore sized filters can be used to compensate for less volume being filtered at higher turbidities, but there is a risk of allowing smaller eDNA particles to slip through the larger pores. Turbidity also can inhibit reactions in the lab, leading to incorrectly assuming eDNA was not found in the samples (false negatives). An additional clean up step to remove inhibitors may be helpful.

Figure 3. eDNA is happy to help with your monitoring questions!

Considerations for eDNA applications

The field of eDNA is still relatively new and is most powerful when used to answer questions for which it is well-suited. There are many factors that can impact the distribution and detection of eDNA in the environment such as life stage, temperature, flow rate (in aquatic environments), and degradation by microbes. Scientists are still working to understand how these factors play into the ecology of eDNA, and how to optimize their methods to account for them. Another important consideration for any study involving eDNA is the availability of reference databases. The reference databases for various organisms are constantly expanding and improving, but many species are still missing. While eDNA has been used to determine species presence or absence for some time, there is still uncertainty about its ability to estimate biomass or abundance. As the field of eDNA research continues to expand, many of these limitations will likely be overcome, which will improve the utility of the tool.

Future plans

The California Department of Water Resources (DWR) has initiated a new Genetic Monitoring (GeM) lab that will conduct genetic monitoring and molecular ecological studies using eDNA for water management decision-making within the SFE. As part of the State Water Project and Interagency Ecological Program (IEP), GeM research will prioritize the needs identified within the Incidental Take Permit, Biological Opinions, and water rights decisions for the State Water Project. The new lab will use innovative technology and collaborative partnerships to advance management decision-making critical to the State's water supply operation and planning. 

Figure 4. Sampling for eDNA is non-invasive and can allow managers to monitor hard to access locations.

Further Reading

• Miya, M. 2022. Environmental DNA metabarcoding: a novel method for biodiversity monitoring of marine fish communities. Annual review of marine science, 14, 161-185.
• Nagarajan, R. P., Bedwell, M. E., Holmes, A. E., Sanches, T., Acuña, S., Baerwald, M., Barnes, M. A., Blankenship, S., Connon, R. E., Deiner, K., Gille, D., Goldberg, C. S., Hunter, M. E., Jerde, C. L., Luikart, G., Meyer, R. S., Watts, A., and Schreier, A. 2022. Environmental DNA Methods for Ecological Monitoring and Biodiversity Assessment in Estuaries. Estuaries and Coasts. In press.
• Meyer, R. S., Watts, A., and Schreier, A. 2022. Environmental DNA Methods for Ecological Monitoring and Biodiversity Assessment in Estuaries. Estuaries and Coasts. In press.