Science Stories: Adventures in Bay-Delta Data

By Rosemary Hartman

With help from Arthur Barros and all the zooplankton taxonomists of the Stockton CDFW lab.

Photos by Tricia Bippus (CDFW)

Zooplankton never get as much appreciation as fish (Hartman et al. 2021), but even among zooplankton there are clear favorites. Copepods and mysid shrimp have dozens of publications dedicated to them, but rotifers often get the short end of the stick. Most papers about “zooplankton” in the San Francisco estuary don’t even mention rotifers. However, the Environmental Monitoring Program works very hard monitoring microzooplankton (guys smaller than 150 microns) and the expert taxonomists at CDFW’s Stockton laboratory spend hours counting and identifying rotifers in those samples. Rotifers are an important link in the food chain connecting bacteria, phytoplankton, and particulate organic matter to fish. They are eaten by larger zooplankton and larval fish (Plabbmann et al. 1997, Burris et al. 2022).

What is a rotifer anyway?

Rotifers are one of the simplest multi-cellular animals on earth, sometimes called "wheel animals" because they have a ciliated structure on their head that looks a little like a wheel. They are tiny, usually only half a millimeter long, and they eat phytoplankton and bits of organic material floating in the water.

How are the samples collected? Well, it starts with the field crew going out to long-term monitoring stations throughout the Delta. The crew lowers a pump nearly to the bottom, then raises the pump up slowly, sucking in water and zooplankton as it goes. The water is then passed through a 43-micron mesh net until 75 L of water have been filtered. All the critters in the net are carefully preserved in formalin, with a little bit of pink dye added to make the critters stand out better. See Kayfetz et al. (2020) (PDF) and the Zooplankton EDI publication metadata (Barros 2021b) for more information.

Back in the laboratory, trained taxonomists subsample the critters and carefully identify and count them under a microscope. Rotifers are tricky to identify, so most are only identified to the genus level, or lumped into “other rotifers”. The rotifers we see most frequently are:

Synchaeta spp.

• Swimming form: top-shaped with pointed foot and lateral auricles with bristles at the widest point, bristles around corona.
• Contracted form: roundish to donut-shaped with corona, auricles and foot sucked in. Not much clear space, organs more prominent than in Asplanchna.

Synchaeta bichornis

• Pointed ‘foot’ at posterior end, two ‘horns’ at the anterior end.
• Body usually curved into a shallow “C” shape.

Polyarthra spp.

• Body squarish with feather-like appendages at the “corners”.
• Appendages extend beyond length of the body.

Keratella spp.

• 6 prominent ‘teeth’ or hooks on the anterior margin. Posterior end variable, with zero, one, or two spines.
• Rigid lorica.

Trichocerca spp.

• Mostly cylindrical, more or less curved, tapering at the anterior and posterior ends.
• Toes asymmetrical: one prominently elongated, filament-like, often held up ventrally.

Asplanchna spp.

• Like a clear bag with few organs inside, more clear space than Synchaeta.
• No ‘foot’. Contracted form with corona sucked in at one end.

“Other rotifers”

• Including Branchionus, Playais, colonial rotifers, Notholca, Filinia, and many more!

So, what can we learn from the rotifer data?

Well, we can start by graphing the average rotifer catch at all stations since the zooplankton survey began (Figure 1). The first thing that jumps out at you is that the standard deviation is HUGE! Rotifers (like all zooplankton) are highly variable critters with big changes from station to station, month to month, and year to year. The next thing that probably jumps out at you is that abundances were a LOT higher prior to 1980. What could have driven that decline?

But that is the average catch for ALL the rotifers lumped together. It might be interesting to look at each taxon individually (Figure 2). Here we can see that all species declined after 1980, but the biggest drops were seen Keratella, Polyarthra, and Trichocerca. Synchaeta didn’t show quite as big a drop. We can also see that Synchaeta is usually the most common taxa, while Asplanchna is pretty rare. Lots of other researchers have noticed a big drop in copepods and chlorophyll after 1986 when the invasive clam Potamocorbula amurensis started to take over the area (Kimmerer et al. 1994, Kimmerer and Thompson 2014, Kimmerer and Lougee 2015), but no one has looked at the post-1980 rotifer crash!

Since 1980, the biggest years for rotifers were 2017 and 2011, both of which were really wet years. Maybe rotifers like wetter years better? Let’s subset our data so we just have data from after 1980 and see how water year time affects rotifer catch (Figure 3). The pattern isn’t super clear – all taxa had high catches in 2017, but not all wet years had high catches, and some taxa (like Asplanchna) also had high catches during drier years. However, when we graph the average total rotifer catch versus the Sacramento Valley Index (a measure of water availability), we see a positive correlation between water flow and rotifer catch (Figure 4). Why might this be? Are they getting moved in from upstream? Or are they reproducing faster?

Of course, there are lots of different ways to display the data. We can make area plots, bar plots, streamflow plots, pie charts, maps, or pie charts on top of maps (Figure 5)! Different types of graphs help you see the data in different ways and pull out different patterns.

Are you interested in finding more patterns in the data?

You can visualize the data yourself on the ZoopSynth Shiny app (which also lets you download the data). However, before you dig in, be sure to read all of the metadata available on the Zooplankton EDI publication. You can also read some of the most recent Status and Trends reports published in the IEP newsletter for more ideas about useful patterns waiting for you to discover (Barros 2021a). Feel free to reach out if you have any questions or find any cool patterns! We love talking about zooplankton. Consider sharing your findings with the Zooplankton PWT too!

References and further reading

• Barros, A. 2021a. Zooplankton Trends in the Upper SFE, 1974-2018. IEP Newsletter 40:5-14.
• Barros, A. E. 2021b. Interagency Ecological Program Zooplankton Study ver 7. Environmental Data Initiative.
• Burris, Z. P. L., R. D. Baxter, and C. E. Burdi. 2022. Larval and juvenile Longfin Smelt diets as a function of fish size and prey density in the San Francisco Estuary. California Fish and Wildlife 108.
• Hartman, R., S. M. Bashevkin, A. Barros, C. E. Burdi, C. Patel, and T. Sommer. 2021. Food for Thought: Connecting Zooplankton Science to Management in the San Francisco Estuary. San Francisco Estuary and Watershed Science 19:art1.
• Kayfetz, K., S. M. Bashevkin, M. Thomas, R. Hartman, C. E. Burdi, A. Hennessy, T. Tempel, and A. Barros. 2020. Zooplankton Integrated Dataset Metadata Report (PDF). IEP Technical Report 93., California Department of Water Resources, Sacramento, California.
• Kimmerer, W. J., E. Gartside, and J. Orsi. 1994. Predation by an introduced clam as the likely cause of substantial declines in zooplankton of San Francisco Bay (PDF). Marine Ecology Progress Series 113:81-93.
• Kimmerer, W. J., and L. Lougee. 2015. Bivalve grazing causes substantial mortality to an estuarine copepod population. Journal of Experimental Marine Biology and Ecology 473:53-63.
• Kimmerer, W. J., and J. K. Thompson. 2014. Phytoplankton growth balanced by clam and zooplankton grazing and net transport into the low-salinity zone of the San Francisco Estuary. Estuaries and Coasts:1-17.
• Plabbmann, T., G. Maier, and H. B. Stich. 1997. Predation impacts of Cyclops vicinus on the rotifer community in Lake Constance in the Spring. Journal of Plankton Research 19:1069-1079.

More underappreciated data!

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

San Francisco Bay Study’s Crab Catch dataset

Curated by Kathy Hieb and Jillian Burns

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

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

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

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

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

Further reading

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

Some data just needs a little love

IEP collects a lot of data. Most people who work in the estuary have probably heard of FMWT’s Delta Smelt Index, or the Chipps Island salmon trawl, or the EMP zooplankton survey. But those “big name” surveys are only part of what we do at IEP! This is the first blog post in a series on “underappreciated” datasets where we highlight some of the data you might not be familiar with.

Yolo Bypass Fish Monitoring Program’s Drift Invertebrate survey

By Nicole Kwan, Brian Schreier, and Rosemary Hartman

In most of the estuary, we concentrate on invertebrates and other fish food that live under the water. However, in streams and rivers the contribution of terrestrial invertebrates falling into the water from surrounding vegetation and aquatic insects that ‘hatch’ on the surface of the water to metamorphose into their terrestrial adult form are also important food sources for fish, particularly Chinook Salmon and Sacramento Splittail. The Yolo Bypass, a large managed floodplain near Sacramento, is located on the boundary between the estuary and the river. As such, the Yolo Bypass Fish Monitoring Program (YBFMP) tracks both aquatic zooplankton and terrestrial drift invertebrates.

The YBFMP collects drift invertebrates year-round from two sites to compare the seasonal variations in densities and species trends of aquatic and terrestrial invertebrates between the Sacramento River and the Yolo Bypass. The crew piles into a boat and heads out, then tows a rectangular net that sits half-in, half-out of the water for ten minutes along the surface. Sometimes, when flows are really high, they can simply hold the net out on the side of their fish trap for ten minutes and let the water flow through it instead of towing it (Figure 1). The crew then rinses the sample into a bottle, preserves it with formalin, and sends it to a contracted lab for identification and enumeration (counting all the bugs under a microscope).

Figure 1. YBFMP scientist Anji Shakya sampling drift invertebrates in high flows next to the fish trap. Image credit - Naoaki Ikemiyagi Department of Water Resources.

There are a lot of interesting questions we can ask with these data, such as, what time of year do we catch the most chironomids (midges) (Figure 2)? Or, how does community composition and abundance differ between the Sacramento River and Yolo Bypass (Figure 3), and how does that relate to differences in hydrology and water quality?

Figure 2. Log-transformed catch-per-unit-effort of chironomid midges caught in drift net samples in the Yolo Bypass Toe Drain and Sacramento River at Sherwood Harbor. Note the abundances of chironomids in the spring on the Yolo Bypass. The Bypass tends to have higher abundances than the Sacramento River in the spring, but lower abundances in the summer. Sampling in summer and fall only started in more recent years. Click on image to enlarge.

Figure 3. Catch per unit effort of organisms in the drift net categorized by taxonomic order and plotted over time. Insects dominate both the River and the Bypass samples, but the Bypass has consistently higher abundance of drift invertebrates. Click on image to enlarge.

One particularly unexpected thing we’ve seen in the data is high abundances of snails in the samples. Snails normally live on the bottom of the water or on vegetation, so seeing them floating on the surface was surprising. We see a lot of variation in snail abundances between years, and we’re not sure why (Figure 4). The wet years of 2017 and 2019 had particularly high snail catch, but other wet years weren’t similar. A fun mystery for someone to investigate!

Figure 4. Mean (+/- one standard error) CPUE of snails (class Gastropoda) in drift net samples from the Yolo Bypass. Water year classes (Wet - W, Dry - D, or Average - A) is noted with letters under each bar. Notice how snail catch was very high during the wet years of 2017 and 2019, but also during the dry year of 2013 and the average year of 2003. Click on image to enlarge.

If you want to check out this data for yourself, it has been published on the EDI data repository and will be updated regularly. However, keep in mind that sample frequency, contracting labs, and methods have changed slightly over time. Be sure to read the metadata so you fully understand the data before using it. If you have any questions, just reach out! We’re nice people and we love talking about our data and helping others use it.

Further Reading

• Benigno, G. M., and T. R. Sommer. 2008. Just add water: sources of chironomid drift in a large river floodplain. Hydrobiologia 600(1): 297-305.
• Goertler, P. A. L., T. R. Sommer, W. H. Satterthwaite, and B. M. Schreier. 2018. Seasonal floodplain‐tidal slough complex supports size variation for juvenile Chinook salmon (Oncorhynchus tshawytscha). Ecology of Freshwater Fish 27(2):580-593.
• Goertler, P., K. Jones, J. Cordell, B. Schreier, and T. Sommer. 2018. Effects of Extreme Hydrologic Regimes on Juvenile Chinook Salmon Prey Resources and Diet Composition in a Large River Floodplain. Transactions of the American Fisheries Society 147(2):287-299.
• Sommer, T. R., W. C. Harrell, A. M. Solger, B. Tom, and W. Kimmerer. 2004. Effects of flow variation on channel and floodplain biota and habitats of the Sacramento River, California, USA. Aquatic Conservation 14(3):247-261.
• For more key findings from the Yolo Bypass, check out the YBFMP Story Map

Authors: Rosemary Hartman and the IEP Data Utilization Work Group

Here at IEP, we collect a lot of data, and we do a lot of science. However, people haven’t always realized how much data we collect because it hasn’t always been easy to find. For scientists that were able to find the data, sometimes it was difficult to understand or it was shared in a hard-to-use format. That’s why IEP’s Data Utilization Work Group (DUWG) has been pushing for more Open Science practices over the past five years to make our data more F.A.I.R (Findable, Accessible, Interoperable, and Reusable). And wow! We’ve come a long way in a short time.

This image was created by Scriberia for The Turing Way community and is used under a CC-BY license. DOI: 10.5281/zenodo.3332807

What is Open Science anyway? Well, I was going to call it “the cool-kids club” but really it’s the opposite of a club! It’s the anti-club that makes sure everyone has access to science – no membership required. Open science means that all scientists communicate in a transparent, reproducible way, with open-access publications, freely shared data, open-source software, and openness to diversity of knowledge. Open science encourages collaboration and breaks down silos between researchers – so it’s a natural fit for a 9-member collaborative organization like IEP.

For IEP, the ‘open data’ component is where we’ve really been making strides. While “share your data freely” sounds easy, it’s actually taken a lot of work to make our data FAIR. As government entities, theoretically all of the data we collect is held in the public trust, but putting data in a format that other people can use is not simple. Here are some of the things we have done to make IEP data more open:

Data Management Plans

The first thing the DUWG did was get all IEP projects to fill out a simple, 2-page data management plan outlining what was being done with the data in short, clear sections:

• Who: Principal investigator and point of contact for the data.
• What: Description of data to be collected and any related data that will be incorporated into the analysis.
• Metadata: How the metadata will be generated and where it will be made available.
• Format: What format the data will be stored in and what format it will be shared in, which may not be the same. For example, you may store data in an Access database but share it in non-proprietary .csv formats.
• Storage and Backup: Where you will put the data as you are collecting it and how it will be backed-up for easy recovery. This is about short-term storage.
• Archiving and Preservation: This is about long-term storage to keep your data for someone years down the line. This is best done with publication on a data archive platform, such as the Environmental Data Initiative (EDI).
• Quality Assurance: Brief description of Quality Assurance and Quality Control (QAQC) procedures and where a data user can access full QAQC documentation.
• Access and Sharing: How can users find your data? Is it posted on line or by request? Are there any restrictions on how the data can be used or shared? 

You can find instructions (PDF) and a template (PDF) for Data Management Plans on the DUWG page. All of IEP’s data management plans are also posted on the IEP website.

Data Publication

Many IEP agencies were already sharing data on agency websites, but most of this was done without formal version control, machine-readable metadata, or digital object identifiers (DOIs), making it difficult to track how data were being used. Now IEP is recommending publishing data on EDI or other data archives. Datasets now have robust metadata, open-source data formats (like .csv tables instead of Microsoft Access databases), and DOIs for each version so studies using these data can be reproduced easily.

• We have instructions (PDF) for publishing your data to EDI on the DUWG page.
• We also have recommended vocabulary and crosswalks (PDF) for vocabulary used by many of IEP’s surveys.
• Over 30 IEP datasets are now published to EDI!
• These datasets are also linked to the California Natural Resources Agency Open Data Portal so that we can comply with AB1755 – the Open and Transparent Water Data Act.
• Other datasets have been published to the USGS Science Base archive, or the Knowledge Network for Biocomplexity.

This image was created by Scriberia for The Turing Way community and is used under a CC-BY license. DOI: 10.5281/zenodo.3332807

Metadata Standards

The term “metadata” can mean different things to different people. Some people may think it simply means the definitions of all the columns in a data set. Some people may think it means a history of changes to the sampling program. Some people think it’s your standard operating procedures. Some people may think it means data about social media networks. What is it? Well, it’s the “who, what, where, when, why, and how” of your data set. It should include everything a data user needs to understand your data as well as you do. The DUWG developed a template for metadata that includes everything we think you should include in full documentation for a dataset. Some of it might not apply to every dataset, but it is a good checklist to get you started.

You can find the Metadata template (PDF) on the DUWG page.

QAQC standards

The DUWG is just starting to dig into QAQC. Quality assurance is an integrated system of management activities to prevent errors in your data, while quality control is a system of technical activities to find errors in your data. QAQC systems have become standard practice in analytical labs, but the formalization and standardization of QAQC practices is new for a lot of the fish-and-bug-counters at IEP. The DUWG QAQC sub-team developed a template for Standard Operating Procedures (PDF), and is working to provide guidance for QAQC of all types of data, and for integrating QAQC into all sampling programs. This promotes consistency across time, people, and space, increases transparency, and gives users more confidence in your data.

Dataset integration

One of the great things about laying down the framework for open data that includes data publication, documentation, and quality control is that it then becomes much easier to integrate datasets across programs. The IEP synthesis team (spearheaded by Sam Bashevkin of the Delta Science Program) has developed several integrated datasets that pull publicly accessible data, put them in a standard format, and publish them in a single, easy-to-use format.

• Zooplankton
• Discrete Water Quality
• Fishes
• Continuous Water Temperature
• With Phytoplankton and Aquatic Vegetation coming soon!

Spreading the Word

We’re also making sure EVERYONE knows about how great our data are.

• We’ve revamped the data access webpage on our IEP site.
• Publishing data on EDI makes it available on DataOne, which allows searches across multiple platforms.
• Publishing data papers is a relatively new way to let people know about a dataset. For example, this zooplankton data paper was recently published in PLOSOne.
• We’ve made presentations at the Water Data Science Symposium and other scientific meetings.
• We published an Open Data Framework Essay in San Francisco Estuary and Watershed Sciences.
• We also put on a Data Management Showcase (video) that you can watch via the Department of Water Resources YouTube Channel.
• Plus, we have lots more data management resources available on the DUWG website.

Together, we're putting IEP Data on the Open Science Train to global recognition. 

Questions? Feel free to reach out to the DUWG co-chairs: Rosemary Hartman and Dave Bosworth. If you have any suggestions for improving data management or sharing, we want to hear about it.

This image was created by Scriberia for The Turing Way community and is used under a CC-BY license. DOI: 10.5281/zenodo.3332807

Further Reading

• Baerwald MR, Davis BE, Lesmeister S, Mahardja B, Pisor R, Rinde J, Schreier B, Tobias V. 2020. An Open Data Framework for the San Francisco Estuary. San Francisco Estuary and Watershed Science. 18(2).
• DataONE. Primer on Data Management: What you always wanted to know. 2012.
• Borer ET, Seabloom EW, Jones MB, Schildhauer M. 2009. Some simple guidelines for effective data management. The Bulletin of the Ecological Society of America. 90(2):205-214.
• Hampton SE, Anderson SS, Bagby SC, Gries C, Han X, Hart EM, Jones MB, Lenhardt WC, MacDonald A, Michener WK. 2015. The Tao of open science for ecology. Ecosphere. 6 (7):1-13.
• The Turing Way Community, Becky Arnold, Louise Bowler, Sarah Gibson, Patricia Herterich, Rosie Higman, Anna Krystalli, Alexander Morley, Martin O'Reilly, & Kirstie Whitaker. (2019). The Turing Way: A Handbook for Reproducible Data Science (Version v0.0.4). Zenodo. https://doi.org/10.5281/zenodo.3233986
• National Center for Ecological Analysis and Synthesis Learning Hub Curriculum NCEAS materials for reproducible research and synthesis
• USGS data management website

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.