Science Stories: Adventures in Bay-Delta Data

We all know climate change is going to be rough. We expect increases in temperature, changes in rainfall (where, when, and how much), and local extinctions or migration of plants and wildlife as the climate shifts. Climate change can sound abstract and is often spoken of as a phenomenon of the future, despite the changes we are already seeing in our surroundings. These changes affect the San Francisco Estuary and will eventually make it necessary to adjust the way we manage our water in California if we want to lessen the impact on those ecosystems. To better understand the impacts of climate change and to better inform management strategies, a group of Interagency Ecological Program (IEP) scientists wanted to find out how much is known about climate change in the Sacramento-San Joaquin Delta, Suisun Bay and Suisun Marsh and how management actions can lessen these effects. To do this, they gathered scientists with broad expertise – from zooplankton to aquatic vegetation – and created the Climate Change Project Work Team.

The team decided to start by creating a conceptual model (similar to the Baylands Goals model created for the San Francisco Bay) and synthesize already published research in a technical report. A conceptual model is an organized way of thinking through a particular problem, system, or idea in a visual way to make it easier to see and understand connections. Conceptual models are especially helpful when working in groups as while it is developed everyone participates and has to think through the problem and understands why the model looks like it does when it’s done. The climate change conceptual model made by the group let them see how the Estuary responds to different environmental drivers and that in turn showed what subjects to read about to find the answers they were looking for. The Climate Change conceptual model (Figure 1) started with global-scale changes in the top box, which impact landscape-scale environmental conditions in the Estuary. Those landscape-scale conditions influence site-level environmental change. For example, increases in global air temperature cause increases in water temperature in the rivers and bays, which in turn impact the temperatures experienced by each critter in the rivers. These climate-change effects also interact with landscape management (such as levee construction or wetland restoration) to impact the aquatic environment at a site.

Figure 1. The Climate Change Project Work Team's conceptual model.

Putting together the conceptual model and writing a synthesis of what we know so far is useful in other ways as well. It allowed the team to find out where there are things we need to study more if we want to be able to give better answers about what will happen in our aquatic ecosystems. The model highlighted three aquatic ecosystems in the estuary where organisms will experience different effects from climate change. The largest ecosystem in the Estuary today is open water. Marshes and floodplains make up a much smaller proportion of the habitat, but are still highly important to native species. Three different teams of scientists went on to review literature on the different ecosystems, diving into the current status of fish, benthic invertebrates, plankton and aquatic vegetation, and trying to predict changes and risks.

So, what did the teams find?

Out of the three, the open water ecosystem will be most impacted by drought and warmer temperatures. The changes brought by this will make this ecosystem more suitable for many of the invasive fish, invertebrates and aquatic vegetation, though higher salinity conditions during droughts may also favor some native fishes and aquatic vegetation (Figure 2). Predictions of future Delta temperatures have found that Delta Smelt's spawning window may be greatly restricted, further stressing this endangered fish (Brown et al. 2016).

Figure 2. Impacts of climate change in open water ecosystems include harmful algal blooms, increased invasive clams, increased aquatic weeds, and increased invasive fishes, such as largemouth bass and Mississippi silversides.

Floodplains will experience major changes in timing and magnitude of inundation. Precipitation will become more variable with more frequent extreme floods and droughts. The larger storms we have seen lately benefit floodplains and the native fish that use them to spawn and feed, but only if they occur at the right time. Floods will shift to earlier in the season as more precipitation falls as rain instead of snow, keeping migratory species from being able to use the floodplain when they need it. More frequent droughts will mean the floodplain may not be available at all for years at a time (Figure 3). Management actions that increase the frequency or duration of floodplain inundation, such as the Yolo Big Notch Project, may become more important if floodplains are to be sustainable in the future.

Figure 3. Floodplains, which are important habitat for spawning Sacramento Splittail and juvenile Chinook Salmon will not be inundated as frequently as droughts become more frequent, and may experience earlier flooding as more precipitation falls as rain instead of snow.

Tidal marshes are relatively scarce, but very important habitats. They provide food and nursery habitat for many fish and waterbird species. Whether they will continue to exist where they are will depend on the amount of sediment that will deposit in the marshes to keep up with sea level rise. Some models show that the larger storms will bring more sediment to the Delta which will help the marshes remain, but other models show that much of our tidal marsh will drown, especially if they do not have gentle, sloping transitions to uplands. Restoration planners may need to prioritize areas with adequate transition zones if they want restoration sites to be sustainable in the long-term.

Figure 4. Tidal marshes may drown as sea levels rise unless they have gentle transitions to upland areas. They may also experience the same increases to invasive species and increased temperature as open water ecosystems.

Other members of the Climate Change PWT have been working on looking for temperature trends from our monitoring record. They have found evidence for increased temperatures over the past 50 years (Bashevkin et. al., 2021), lower temperatures during wetter years (Bashevkin and Mahardja, 2022), differences in temperatures at the top and bottom of the water (Mahardja et. al., 2022), and hotter temperatures in the South Delta (Pien et. al., draft manuscript).

For a young adult audience interested to learn more about the San Francisco Estuary, the Sacramento-San Joaquin Delta in general and how climate change will affect it and the species living there check out a collection called Where the river meets the ocean – Stories from San Francisco Estuary . Many of the scientists that are on the team who wrote the Climate Change Technical Report also wrote for this collection, published by Frontiers for Young Minds.

Further Reading:

Bashevkin, S. M., and B. Mahardja. in press. Seasonally variable relationships between surface water temperature and inflow in the upper San Francisco Estuary. Limnology and Oceanography

Bashevkin, S. M., B. Mahardja, and L. R. Brown. 2021. Warming in the upper San Francisco Estuary: Patterns of water temperature change from 5 decades of data.

Brown, L. R., L. M. Komoroske, R. W. Wagner, T. Morgan-King, J. T. May, R. E. Connon, and N. A. Fangue. 2016. Coupled downscaled climate models and ecophysiological metrics forecast habitat compression for an endangered estuarine fish. Plos ONE 11(1):e0146724. 

Colombano, D. D., S. Y. Litvin, S. L. Ziegler, S. B. Alford, R. Baker, M. A. Barbeau, J. Cebrián, R. M. Connolly, C. A. Currin, L. A. Deegan, J. S. Lesser, C. W. Martin, A. E. McDonald, C. McLuckie, B. H. Morrison, J. W. Pahl, L. M. Risse, J. A. M. Smith, L. W. Staver, R. E. Turner, and N. J. Waltham. 2021. Climate Change Implications for Tidal Marshes and Food Web Linkages to Estuarine and Coastal Nekton. Estuaries and Coasts.

Dettinger, M., J. Anderson, M. Anderson, L. Brown, D. Cayan, and E. Maurer. 2016. Climate change and the Delta. San Francisco Estuary and Watershed Science 14(3).

Knowles, N., C. Cronkite-Ratcliff, D. W. Pierce, and D. R. Cayan. 2018. Responses of Unimpaired Flows, Storage, and Managed Flows to Scenarios of Climate Change in the San Francisco Bay-Delta Watershed. Water Resources Research 54(10):7631-7650. 2

Mann, M. E., and P. H. Gleick. 2015. Climate change and California drought in the 21st century. Proceedings of the National Academy of Sciences 112(13):3858-3859.

Lots of Interagency Ecological Program (IEP) scientists research fish. Of the 22 surveys in IEP's Research Fleet, 17 are primarily focused on fish. But fish in the San Francisco Estuary are hard to catch these days. Over the past thirty years, Delta Smelt, Longfin Smelt, and even the notoriously hardy Striped Bass have declined precipitously (CDFW FMWT data). To figure out how to reverse these declines, we need an understanding of the “bottom-up” processes that exert control on these populations—we need to study fish food. Therefore, we need to increase our understanding of what pelagic fish eat: zooplankton.

If you’ve spent any time around fish people, you’ve probably heard the word “zooplankton”, but you might not really know what it means. Zooplankton are small animals that live in open water and cannot actively swim against the current (“plankton” means “floating” in Greek). They include crustaceans (copepods, water fleas, larval crabs, etc.), jellyfish, rotifers, and larval fish. Most of them are hard to see without a microscope, so they are easy to overlook – but you’d miss them if they weren’t there because most of your favorite fish rely on zooplankton for food.

Fortunately, the IEP Zooplankton Project Work Team has been tackling the problem head-on. The group got started when Louise Conrad and Rosemary Hartman were both collecting zooplankton samples near the same restoration site. They thought “We’d be able to say a lot more about the restoration site if we combined our data sets!” But with samples collected using different gear and identified by different taxonomists, it proved more difficult than they originally thought. They needed a team of experts to help them figure out how to deal with the differences in their data. So the Zooplankton Synthesis Team was born! The original team included Karen Kayfetz, Madison Thomas, April Hennessy, Christina Burdi, Sam Bashevkin, Trishelle Tempel, and Arthur Barros, but soon grew as more people heard about the discussions they were having.

The team started by identifying the major zooplankton datasets that IEP collects and dealing with tricky data integration questions:

• Can you integrate data sets when the critters were collected with different mesh sizes?
• What do you do when one data set identifies the organisms to genus and another one identifies down to species?
• What if these levels of identification change over time?
• Does preservation method impact the dataset?

To integrate data sets, the team standardized variable names, standardized taxon names, and summarized taxa based on their lowest common level of resolution.

While working through these sticky questions, they compiled what they learned about the individual zooplankton surveys into a technical report (PDF) describing each survey and how they are similar and different. They published a data package integrating five different surveys into a single dataset and Sam put together a fantastic web application that allows users to filter and download the data with a click of a button.

The team had put together the data, but there was more work to do. They realized they needed to do more if they wanted people to use their data. Lots of data on zooplankton get collected, but few research articles are published about zooplankton, and zooplankton data are rarely used to inform management decisions. To get the broader scientific community excited about zooplankton in the estuary, the ZoopSynth team worked with the Delta Science Program to host a Zooplankton Ecology Symposium with zooplankton researchers from across the estuary and across the country (you can watch the Symposium recording on YouTube.). From this symposium they learned a few important lessons to help increase communication and visibility of zooplankton data and research:

• Managers and scientists should work together to develop clear goals and objectives for management actions. Is there a threshold of zooplankton biomass or abundance to achieve? Or is the goal simply higher biomass of certain taxa? This will make it easier to design a study that provides management-relevant results.
• Scientists should understand the management goals and keep the end goal in mind. If the end goal is fish food, study taxa that are most common in fish diets. If the primary interest is contaminant effects, focus on sensitive species.
• We need to start using new tools like automated imagery and DNA along with traditional microscopy to collect better data faster.
• We need to maximize the accessibility of zooplankton data to scientists and managers. Scientists should share data in publicly available places in easy-to-read formats. Similarly, managers should share lessons learned from management actions widely, and use them for adaptive management. Both scientists and managers should be encouraged to ask questions of each other to ensure both understand the best uses for zooplankton data.

These lessons, (and more!) are summarized in a recent essay published in San Francisco Estuary and Watershed Sciences. If that’s too much reading, the team also produced some fact sheets summarizing the major take-home messages of the essay and the symposium:

• Fact Sheet for Managers
• Fact Sheet for Zooplankton Researchers

The team has expanded into an official IEP Project Work Team that meets monthly to discuss new zooplankton research ideas, share analyses, look at cool pictures of bugs, and talk about trends. If you’re interested in joining, contact Sam at Sam.Bashevkin@Deltacouncil.ca.gov

• Kayfetz K, Bashevkin SM, Thomas M, Hartman R, Burdi CE, Hennessy A, Tempel T, Barros A. 2020. Zooplankton Integrated Dataset Metadata Report (PDF). IEP Technical Report 93. Sacramento, California: California Department of Water Resources.
• Bashevkin S.M., R. Hartman, M. Thomas, A. Barros, C. Burdi, A. Hennessy, T. Tempel, and K. Kayfetz. 2020. Interagency Ecological Program: Zooplankton abundance in the Upper San Francisco Estuary from 1972-2018, an integration of 5 long-term monitoring programs ver 1. Environmental Data Initiative.
• Hartman R, Bashevkin SM, Barros A, Burdi CE, Patel C, Sommer T. 2021. Food for Thought: Connecting Zooplankton Science to Management in the San Francisco Estuary. San Francisco Estuary and Watershed Science. 19(3):art1.
• Kimmerer W, Ignoffo TR, Bemowski B, Moderan J, Holmes A, Bergamaschi B. 2018. Zooplankton Dynamics in the Cache Slough Complex of the Upper San Francisco Estuary. San Francisco Estuary and Watershed Science. 16(3).

Prologue

Smelt population was crashing for sure,

While managers franticly searched for a cure.

A synthesis team was tasked with the goal

Of testing if outflow could fill in the hole.

   

Though nothing is ever as clear as it seems

The FLOAT-MAST was faultless in chasing their dreams.

They looked at the plankton, the fish and the flow,

But temperature left them with nowhere to go.

 

Fall outflow may manage a critical part,

But ecology complicates things from the start.

High outflow alone was never enough,

To find a solution may always be tough.

 

But FLOAT will continue in showing the way

For science and synthesis even today.

Act I

It was a dark and stormy night. Actually, it wasn’t stormy, which was the problem. The curtain rises on the intrepid scientists of the Interagency Ecological Program (IEP) in 2016 as California’s drought continues. They are wrestling with a critical question: Is high fall outflow the key to improving habitat conditions for Delta Smelt and benefiting the population? Scientists and resource managers in the San Francisco Estuary are desperately trying to save the endangered Delta Smelt, but the best research to date had resulted in “It’s complicated” (Sommer et al. 2007). However, many studies pointed to high Delta outflow (freshwater flowing out of the estuary) was an important part of the story (IEP-MAST et al. 2015; Thomson et al. 2010). Delta Smelt had positive population growth rates only twice since 2002, and both years had high flows.

Enter from stage left, our heroes: The Flow Alteration Management Analysis and Synthesis Team (FLOAT-MAST). Their task was to evaluate all aspects of Delta Smelt population, health, life history, and habitat to see whether high flows would again allow Delta Smelt to rebound. This effort would build on previous synthesis efforts summarizing what we know about Delta Smelt and fall low-salinity-zone habitat (Brown et al. 2014; IEP-MAST et al. 2015). The report on their work “Synthesis of data and studies relating to Delta Smelt biology in the San Francisco Estuary, emphasizing water year 2017” (FLOAT MAST 2021), just came out, but the punchline was clear from the contents of the [empty] fish nets. High outflow alone was not enough to cause population recovery for Delta Smelt. However, the team was able to show which of our ideas about Delta Smelt habitat requirements still have merit, and which need to be revised. This is their story.

If the goal was Science for Science’s sake, it would have been one thing, but the science surrounding flow in the Delta is tightly tied to water management. The stakes were high. In 2016, USFWS asked for increased Delta outflow in the fall to benefit Delta Smelt above what was required by Water Rights Decision 1641 (D-1641). There wasn’t enough water available to increase Delta outflow in 2016, but IEP scientists began laying the framework to analyze future actions. In 2017, they lucked out. It was the wettest water year (measured from October 1st to September 31st) on record in Northern California (DWR 2019 (PDF)), so Delta Smelt were going to have lots of outflow! Now was the chance for FLOAT-MAST to test their hypothesis that high fall outflow is helpful for Delta Smelt!

Why did they think fall outflow was important? Well, previous work investigating ideal habitat for Delta Smelt discovered that smelt like water in a particular range of salinity and turbidity (Sommer and Mejia 2013). They like to hang out in the “Low Salinity Zone” (LSZ, between 0.5-6 ppt) during the fall, though some fish hang out in fresh water year round (no one told them that smelt prefer 0.5-6ppt; (Hobbs et al. 2019c)). Salinity in the estuary can swing widely if the amount of freshwater outflow increases or decreases, meaning the Low Salinity Zone is dynamic, sometimes occurring upstream in the Sacramento and San Joaquin rivers, sometimes occurring downstream in Suisun Bay and Suisun Marsh. However, good habitat for Delta Smelt is about more than just salinity. Areas with stationary habitat aspects (things that are not dynamic or changing over time) that include lots of extended shallows, narrow channels with tidal wetlands, and sandy shoals are also thought to be better for smelt because they have more places to rest and more food (Bever et al. 2016; Hammock et al. 2019). High Delta outflow lets the dynamic region of good salinity overlap with the areas of good stationary habitat with tidal wetlands and shallow shoals, creating the perfect spot for smelt (Figure 1).

Figure 1. The relationship between Delta outflow and Delta Smelt habitat. When outflow is low, the area of good salinity conditions (dynamic habitat) is upstream in the Sacramento River, where the stationary habitat is mostly narrow channels, which aren’t too comfortable to Delta Smelt. When outflow is high, the low salinity zone is pushed into Suisun Marsh, where higher turbidity, extended shoals, and marshes provide better habitat.

Act II

The FLOAT-MAST team was made up of scientists, but each came from different organizations with different expertise. To remain as scientifically objective and independent as possible, it was critical to let the data tell the story. Dr. Larry Brown from the US Geologic Survey was chosen to lead the team because he was a foundational leader within the scientific community who was broadly trusted to provide the best scientific, independent leadership.

Larry wanted to let the data speak for themselves, but data tend to babble. The team started by concentrating on fall habitat conditions but realized conditions in summer were also important. So was spring. Likewise, flow was important but was really just an index of temperature and salinity and food and habitat. There were SO MANY THINGS GOING ON! Herding the cats and getting all the various pieces to tell a coherent story was more difficult than previously thought. Ecology is always more complex than we expect, and even the best management action is unlikely to work the same way every time.

To make matters worse, when the final data from 2017 were in, it was clear there would be no happy ending. High temperatures in the summer 2017 caused the Delta Smelt population to crash before the high fall flows could have any benefit. The population was so low going in to 2017 that they needed a really fantastic year, and instead had the lowest population index on record. Much of the exciting analysis the team had planned never made it into the final report because the high temperatures swamped any potential benefits of flow, rendering those analyses pointless, or impossible. The low fish catch meant they could not really look at fish health, size, or distribution because they just couldn’t catch enough of them.

The team had chosen to divide up the work and each write chapters on salinity, temperature, turbidity, phytoplankton, clams, zooplankton, smelt health, smelt distribution, and smelt survival and summarize the different lines of evidence into a final conclusion. Unfortunately, people with different perspectives added these lines of evidence up in different ways. Some lines showed that high fall outflow helped smelt, other lines showed high fall outflow had no effect. Some lines were inconclusive in regards to flow, and some showed a negative relationship. However, they could say conclusively that 2017 was not a good year for smelt, probably due to high temperatures (Table 1).

While they couldn’t make conclusions on the effectiveness of the 2017 flow action, the team achieved a lot of other important wins along the way.

• By partnering with UC Davis researchers, the team could look at details that trawl surveys can’t tell us, like how Delta Smelt eat, grow, move from place to place, and how the environment influences their health (Hammock et al. 2020; Hobbs et al. 2019a; Hobbs et al. 2019b; Teh et al. 2020).
• The data analysis and special studies initiated by the FLOAT team helped to build life-cycle models for Delta Smelt so they could predict smelt responses in future years (Polansky et al. 2019; Smith et al. 2020).
• The team itself provided an awesome opportunity for scientists to learn from one another and think broadly about the big picture. As one team member said “I learned more from one two-hour meeting than a decade of workshops.” Putting a team of experts together allowed them the freedom to talk and think creatively about solutions (and it was fun too!). While most of the ideas and analyses did not make it into the final report, they increased the capacity of our team to tackle similar analyses faster in the future.

As a scientific community we learned the importance of looking at the big picture. To make it easier to look at the big picture, Larry and the Delta Science Program put together a “Smelt Conditions Report” updated annually to see how the year shaped up for Delta Smelt (DSP, 2020).

Table 1 - Results of analyses of each response variable assessed as part of the FLOAT-MAST report for the most recent high flow-years (2006, 2011, and 2017), low flow years, and specifically for 2017. Arrows represent direction of trend, with sideways arrows indicating varying results. Solid green symbols signify the variable responded as predicted. Red checked symbols signify the variable did not respond as predicted. Grey circles indicate insufficient data to evaluate the variable.

Physical Habitat	Low Flows	High Flows	2017
Fall LSZ Location	Confluence	Suisun	Suisun
Area of LSZ
Turbidity
Water Temperature

Biotic Habitat	Low Flows	High Flows	2017
Phytoplankton
Harmful algal blooms
Zooplankton
Clams
Water Hyacinth

Delta Smelt	Low Flows	High Flows	2017
Distribution
Growth and Survival
Health Metrics
Feeding Success
Life History Diversity

Act III

The first draft of the report was completed by spring of 2019. Larry and the team had done their best to connect the pieces and provide a scientifically robust, comprehensive view on why we did not see the predicted response to fall outflow. The draft report was distributed for peer review. The lengthy review process began, with comments coming in from the IEP Science Management Team, Flow Alteration Project Work Team, various members of the authors’ management chains, and USGS’s external peer review process. Larry took it upon himself to tackle addressing the bulk of the comments and had the report almost ready for distribution when tragedy struck. Larry passed away from a massive heart attack early in 2021, just before his scheduled retirement. Larry was one of the most experienced, respected, and prolific scientists in the Bay-Delta community. He was an important mentor to hundreds of younger scientists, and his loss is felt deeply (Herbold et al. 2021).

The remaining team members finalized the document and distributed it far and wide. They also produced a two-page summary (PDF) that boiled down a 265 page report (with 475 pages of appendices) to a quick fact sheet designed for managers that are in a hurry. They are currently planning their next steps. The new environmental regulations for the State and Central Valley Water Projects include a number of fall flow actions, and the FLOAT-MAST team’s experience evaluating the high flow of 2017 may help evaluate these new actions as well.

What can we take from the FLOAT-MAST experience? A few clear lessons stand out.

• If you are going to have a huge synthesis project tackling lots of hypotheses and topics, and that includes many experts, you better have a stellar champion. You need the leader, the orchestrator. Larry was so excellent at that and this project is just one example of why he is so missed. If we are going to tackle wide-ranging synthesis questions, find the leader first. AND – we need to cultivate synthesis leaders in our community.
• Huge synthesis projects take a long time. If you need a fast answer, projects should be smaller in scope with a clear management question. They should produce quantitative results that answer quantitative questions, when possible, instead of relying on descriptive analyses.
•  Partnerships yield tremendous benefits in synthesis projects, especially large ones like this one. We can’t be afraid to reach out to experts that might make our work better, in ways we can’t even predict.
• Ecology is always more complicated than we want to admit. We have an anthropogenically altered system, and climate change will decrease our ability to predict the outcome of our management actions. Flow is one of the few variables we can change through management actions, but other stressors (over which we may not have as much control), such as temperature, will frequently mask the effect of flow.
• Scientists need opportunities to think creatively about the big picture. Large synthesis projects are opportunities to train early-career scientists to put together multiple analyses to answer a management question.

Epilogue

Tiny Delta Smelt

Need more than Delta Outflow

Water must be cool.

Further Reading

• Bever AJ, MacWilliams ML, Herbold B, Brown LR, Feyrer FV. 2016. Linking hydrodynamic complexity to Delta Smelt (Hypomesus transpacificus) distribution in the San Francisco Estuary, USA. San Francisco Estuary and Watershed Science. 14(1).
• Brown LR, Baxter R, Castillo G, Conrad L, Culberson S, Erickson G, Feyrer F, Fong S, Gehrts K, Grimaldo L, Herbold B, Kirsch J, Mueller-Solger A, Slater S, Sommer T, Souza K, Van Nieuwenhuyse E. 2014. Synthesis of studies in the fall low salinity zone of the San Francisco Estuary, September-December 2011. Sacramento, CA: U.S. Department of the Interior, U.S. Geological Survey.
• DWR. 2019. Water Year 2017: What a Difference a Year Makes (PDF).
• Delta Stewardship Council. Delta Plan Performance Measures, Salinity. Outcome Performance Measure 6.2, Last updated: July 14, 2021
• Delta Science Program (DSP) 2021. Smelt Conditions Report, Report Year 2019. 
• FLOAT-MAST (Flow Alteration - Management Analysis and Synthesis Team). 2021. Synthesis of data and studies relating to Delta Smelt biology in the San Francisco Estuary, emphasizing water year 2017. IEP Tech report 95. Sacramento, CA: Interagency Ecological Program.
• Hammock BG, Hartman R, Slater SB, Hennessy A, Teh SJ. 2019. Tidal Wetlands Associated with Foraging Success of Delta Smelt. Estuaries and Coasts. 42:857–867
• Hammock BG, Ramírez-Duarte WF, Triana Garcia PA, Schultz AA, Avendano LI, Hung T-C, White JR, Bong Y-T, Teh SJ. 2020. The health and condition responses of Delta Smelt to fasting: A time series experiment. PLOS ONE. 15(9):e0239358.
• Herbold B, Moyle PB, Mueller–Solger A, Sommer T. 2021. In honor of Larry Brown. San Francisco Estuary and Watershed Science. 19(2). 
• Hobbs JA, Denney C, Lewis L, Willmes M, Schultz A, Burgess O. 2019a. Exploring Life History Diversity of Delta Smelt During a Period of Extreme Environmental Variability. In: Schultz AA, editor. Directed Outflow Project: Technical Report 1 (PDF). Sacramento, CA.: U.S. Bureau of Reclamation, Bay-Delta Office, Mid-Pacific Region. p. 79-123.
• Hobbs JA, Denney C, Lewis L, Willmes M, Xieu W, Schultz A, Burgess OT. 2019b. Environmental and Ontogenetic Drivers of Growth in a Critically Endangered Species. In: Schultz AA, editor. Directed Outflow Project: Technical Report 1 (PDF). Sacramento, CA.: U.S. Bureau of Reclamation, Bay-Delta Office, Mid-Pacific Region. p. 124-146.
• Hobbs JA, Lewis LS, Willmes M, Denney C, Bush E. 2019c. Complex life histories discovered in a critically endangered fish. Scientific Reports. 9(1):16772. 
• IEP-MAST, Baxter R, Brown LR, Castillo G, Conrad L, Culberson S, Dekar M, Feyrer F, Grimaldo L, Hunt T, Kirsch J, Mueller-Solger A, Slater S, Sommer T, Souza K. 2015. An updated conceptual model for Delta Smelt: our evolving understanding of an estuarine fish. IEP Technical Report #90. Sacramento, CA: Interagency Ecological Program.
• Polansky L, Mitchell L, Newman KB. 2019. Using multistage design-based methods to construct abundance indices and uncertainty measures for Delta Smelt. Transactions of the American Fisheries Society. 0(ja).
• Smith WE, Newman KB, Mitchell L. 2020. A Bayesian hierarchical model of postlarval delta smelt entrainment: integrating transport, length composition, and sampling efficiency in estimates of loss. Canadian Journal of Fisheries and Aquatic Sciences. 77(5):789-813.
• Sommer T, Armor C, Baxter R, Breuer R, Brown L, Chotkowski M, Culberson S, Feyrer F, Gingras M, Herbold B, Kimmerer W, Mueller-Solger A, Nobriga M, Souza K. 2007. The collapse of pelagic fishes in the upper San Francisco Estuary. Fisheries. 32(6):270-277. 
• Sommer T, Mejia F. 2013. A place to call home: a synthesis of Delta Smelt habitat in the upper San Francisco Estuary. San Francisco Estuary and Watershed Science. 11(2):25 pages.
• Teh SJ, Schultz AA, Duarte WR, Acuña S, Barnard DM, Baxter RD, Garcia PAT, Hammock BG. 2020. Histopathological assessment of seven year-classes of Delta Smelt. Science of The Total Environment.138333. 
• Thomson JR, Kimmerer WJ, Brown LR, Newman KB, Mac Nally R, Bennett WA, Feyrer F, Fleishman E. 2010. Bayesian change point analysis of abundance trends for pelagic fishes in the upper San Francisco Estuary. Ecological Applications. 20(5):1431-1448.

When you are running a long-term monitoring program, it’s easy to keep plugging away doing the same old thing over and over again. That’s what “long term monitoring” is all about right? But is the survey we designed 40 years ago still giving us useful data? With new sampling gear, new statistics, and new mandates, can we improve our monitoring to better meet our needs? These questions have been on the minds of Interagency Ecological Program (IEP) researchers, so an elite team spent over a year doing a rigorous evaluation of three of IEP’s fisheries surveys to figure out how we can improve our monitoring program. The team was assembled with representatives from multiple agencies who each brought something to the table: guidance and facilitation, experience using the data, regulatory background, quantitative skills, and outside statistical expertise. This wasn’t the first time IEP reviewed itself, but it was the first time they tried to take a really quantitative look at it. The team focused on trying to assess the ability of the datasets to answer types of management questions based on themes, so multiple surveys were reviewed together.

The Team

• Dr. Steve Culberson, IEP Lead Scientist - Guidance and Facilitation
• Stephanie Fong, IEP Program Manager – Guidance and Facilitation
• Dr. Jereme Gaeta, CDFW Senior Environmental Scientist – Quantitative Ecologist
• Dr. Brock Huntsman, USGS Fish Biologist – Quantitative Ecologist
• Dr. Sam Bashevkin, DSP Senior Environmental Scientist – Quantitative Ecologist
• Brian Mahardja, USBR Biologist – Quantitative Ecologist and Data User
• Dr. Mike Beakes USBR Biologist – Quantitative Ecologist and Data User
• Dr. Barry Noon, Colorado State University – Independent statistical consultant
• Fred Feyrer, USGS Fish Biologist – Data User
• Stephen Louie, State Water Board Senior Environmental Scientist – Regulator
• Steven Slater, CDFW Senior Environmental Scientist - Principal Investigator – FMWT
• Kathy Hieb, CDFW Senior Environmental Scientist – Principal Investigator – Bay Study
• Dr. John Durand – UC Davis, Principal Investigator – Suisun Marsh Survey

The Surveys

• Fall Midwater Trawl (FMWT) – One of the cornerstones of IEP since 1967, this California Department of Fish and Wildlife (CDFW) survey runs from September-December every year and was originally designed to monitor the impact of the State Water Project and Central Valley Project on yearling striped bass.
• San Francisco Bay Study – On the water since 1980, Bay Study was also run by the CDFW and runs year-round from the South Bay to the Confluence. It also monitors the effects of the Projects on fish communities.
• Suisun Marsh Survey – Starting in 1979, the Suisun Marsh Survey is conducted by UC Davis with funding from the Department of Water Resources. This survey describes the impact of the Projects and the Suisun Marsh Salinity Control Gates on fish in the Marsh.

The Gear

• The otter trawl – A big net towed along the ground behind a boat, this type of net targets fish that hang out on the bottom (“demersal fishes”). This net only samples the bottom in deep water, but will sample most of the water column in shallower channels (less than 3 meters deep).
• The midwater trawl – Another big net, but this one starts at the bottom and is pulled in toward the boat while trawling, gradually reducing the depth of the net so all depths are sampled equally. This net targets fish that like living in open water (“pelagic fishes”).

The question: Can we make it better?

Figure1. The team assembled to see how surveys could generate the best data to inform decisions and fulfill their regulatory mandates.

The question seemed simple – but the answer was unexpectedly complex. While the surveys all targeted similar fish, used similar gear, and went to similar places, they all had enough differences in their survey design, mandates, and institutional history that looking at them together wasn’t easy.

The first step in the review process was, perhaps, the most difficult. The team had to get the buy-in from all the leaders of the surveys under review, all the regulators mandating that the surveys take place, all the people critical of the surveys as they currently stand, and the supervisors of the team who were going to devote a large percentage of their time to the effort. Getting trust from multiple interest groups was challenging, but it was also one of the most rewarding and exciting parts of the process. Stephanie reflected: “We plan to incorporate more of their recommendations in upcoming reviews and increase our collaboration with them… it also would have been helpful if we could have spent more time up front with getting buy-in from those being reviewed and those critical of the surveys.”

Once everyone was on board, the team took a deep dive into the background behind each survey. Why was it established? How have the data been used in the past? Has it made any changes over time? How are the data currently shared and used? Putting together this information gave them a great appreciation for the broad range of experience within IEP. In particular, the team needed to pay attention to the regulatory mandates that first called for the surveys (such as Endangered Species Act Biological Opinions and Water Rights Decisions), to make sure the surveys were still meeting their needs.

The next step was putting the data together, and here’s where it got hard. The team had to find all the data, interpret the metadata, and convert it into standard formats that were comparable between surveys. Even basic things like the names of fish were different. In the FMWT data, a striped bass was “1”, in the Suisun Marsh data a striped bass was “SB”, and in the Bay Study data a striped bass was “STRBAS”. The team quickly identified a few easy steps that could improve the programs without changing a single survey protocol!

1. Make all data publicly available on the web in the form of non-proprietary flat files (such as text or .csv spreadsheets)
2. Create detailed metadata documents describing all the information needed to understand the survey (assume the person reading it is a total stranger who knows nothing about your program!)

Figuring out better methods of storing and sharing data is relatively easy, but how do we decide whether we should change when and where and how the surveys actually catch fish? The surveys were all intended to track changes in the fish community, but community-level changes are complex, with over 100 fish species in the estuary. The team decided to divide the task into three parts:

1. Figure out how to quantify bias between the surveys for individual fish (seeing if some surveys are more likely to catch certain species than the other surveys, Figure 2).
2. Create a better definition of the “fish community” by identifying which groups of species are caught together more often (Figure 3).
3. See what happens when we change how often we sample or how many stations we sample. Do we lose any information if we do less work?

Figure 2. The quantitative ecologists used a form of hierarchical clustering to figure out which groups of fish are most frequently caught together, and which species is most indicative of each group. The indicator species are the ones with the gold stars. Figure adapted from IEP Survey Review Team (2021).

Going through this process involved pulling out all the fancy math and computer programing. Sam, Brian, Jereme, Mike, and Brock explored the world of generalized additive models, principle tensor analysis, Bayesian generalized linear mixed models, hierarchical cluster analysis, and things involving overdispersion in negative binomial distributions. If there were a way to Math their way to the answer, they were going to find it!

Figure 3. The team also evaluated biases in sampling gear. Sampling bias occurs when the gear doesn't sample all fish consistently. Sometimes they miss fish of certain sizes, fish that live in certain habitats, or fish that can evade the nets.

For better or worse, Math and a 1-year pilot effort will only get you so far. The team could develop some recommendations, scenarios, and new methods, but it will be up to management to decide how to continue the review effort and then implement change. Their results highlighted a few key points that will be useful in reviewing the rest of IEP’s surveys and making decisions about changes:

1. Involving stakeholders early in the review process will increase transparency, facilitate sharing of ideas, and promote community understanding.
2. We need to characterize the biases of our sampling gear in order to make stronger conclusions about fish populations.
3. Identifying distinct communities of fish helps us track changes over time and space.
4. We can use Bayesian simulation methods to test the impacts of altered sampling designs on our understanding of estuarine ecology.
5. These sort of reviews take time and effort by a highly skilled set of scientists, so IEP will need to dedicate a lot of staff to a full review of all their surveys.

Further Reading

• IEP Long-term Survey Review Team. 2021. Interagency Ecological Program Long-term Monitoring Element Review: Pilot approach and methods development (2020). IEP Technical Report #96. 206 pp. (request document by contacting iep@wildlife.ca.gov)
• Tempel, T. L., et al. (2021). "The value of long-term monitoring of the San Francisco Estuary for Delta Smelt and Longfin Smelt (PDF)." California Fish and Wildlife Special CESA Issue: 148-171.
• Stompe, D. K., et al. (2020). "Comparing and Integrating Fish Surveys in the San Francisco Estuary: Why Diverse Long-Term Monitoring Programs are Important." San Francisco Estuary and Watershed Science 18(2).

One of life’s greatest joys is playing with data. However, not everyone has the time or experience needed to make fancy graphs. Fortunately, availability of on-line web applications that allow people with no data analysis experience to visualize status and trends of data across space and time has exploded in recent years.

Figure 1. Fish love data, but they need a little help making their graphs.

One of the first data visualization tools was the mapping widgets on the CDFW website. These maps allow you to plot the catch for different fish species as different size bubbles, and have been available since the late 1990s:

• 20mm Survey
• Fall Midwater Trawl
• Summer Townet
• Spring Kodiak Trawl

But we needed better ways to display data from multiple surveys at once at the click of a button. The website Bay Delta Live was launched in 2007 as a home for Bay-Delta data and data visualizations. It includes summaries, graphs, and interactive visualizations for water quality, operations, fish monitoring, and special studies.

A similar website, SacPas, was built specifically for synthesizing, summarizing, and displaying data for salmonids in the Central Valley. It allows a user to visualize data on salmon abundance, temperature thresholds, river conditions, and hydrologic conditions. It also lets you play with a nifty Chinook Salmon population model and download all the underlying data.

Figure 2. FLowWest's Central Valley Instream Rearing Habitat Calculator shiny app

Custom-built websites like Bay Delta Live and SacPas are great, but they are built by web developers, not fisheries scientists. Now, thanks to user-friendly data display tools such as Tableau and the increase in coding literacy among environmental scientists, more and more people can create their own on-line data visualizations. This means the number of data visualizations apps has grown astronomically in the past few years, and many apps are custom-built for specific scientific questions.

The Delta Science Program now hosts a number of these visualizations built with the R package “shiny’

Figure 3. You can now map all the stations monitored by IEP's long-term surveys.

• NEW: Map all the monitoring stations sampled by IEP’s long-term monitoring surveys (My personal favorite!)
• Put together all of the IEP zooplankton data with the Zooplankton integrated data set.
• Look at Salmon acoustic telemetry stations and release locations for the Sacramento River.
• Check out the future with the Delta Social Climate Change Vulnerability Risk app and Delta Flood Risk app.
• Look at Long-term Temperature Trends using point values from IEP surveys going back to 1959.

Other Shiny apps have launched recently on a variety of other platforms:

• Avery Kruger and Dylan Stompe (UC Davis) integrated all the data from IEP’s long-term fish monitoring in their Bay Delta Live SF Estuary Integrated Fish Survey Visualizer V1.00 app.
• Check out Delta imports and exports app.
• ICF’s incidental take app looks at hydrodynamics and fish salvage.
• Cramer Fish Sciences has made a Delta Hydrodynamics app. (Because you can never have too many ways of looking at hydrodynamics)
• Cramer’s Loss Calculator app.
• Cramer also produced an Emigrating Salmonid Habitat Estimation app, if you want to see how baby fish move out of the river.
• And if you haven’t had enough salmon, there is also the Winter Run Life Cycle Model Shiny app.
• The FlowWest Central Valley Instream Rearing Habitat Calculator app.
• The CalFishTrack acoustic telemetry Shiny app, and
• FlowWest’s SHOWR dashboard for looking at Shasta Reservoir operations to protect winter run Chinook Salmon.

Figure 4. CalFishTrack includes a Shiny App of their Survival Travel time And Routing Simulation (STARS).

USGS has developed several new dashboards for mapping water and water quality data:

• Water Dashboard
• Groundwater
• Water Quality

With all these tools out there, it’s one big data playground! If you’re interested in making your own, it’s easy to get started with Shiny. Visit the Learn Shiny video tutorial!