Science Stories: Adventures in Bay-Delta Data

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!

By Rosemary Hartman, Brian Mahardja, and Vanessa Tobias

We are now half-way through Water Year 2021 (which started in October of 2020) and it seems likely that California will experience another dry year. The frequency of prolonged drought events is projected to increase due to climate change and the San Francisco Estuary system is expected to have more wild swings between floods and droughts. This was in the mind of a handful of IEP scientists back during the last California drought of 2012-2015. They were wondering, which fish species are negatively impacted by drought, and would they rebound in numbers after the drought is over? Brian Mahardja, Vanessa Tobias, Shruti Khanna, Lara Mitchell, Peggy Lehman, Ted Sommer, Larry Brown, Steven Culberson, and J. Louise Conrad published the results of their study in a recent journal article.

The Delta’s fish community is unique, with a melting pot of native fish and introduced fish. The native fish evolved with California’s regular cycles of droughts and floods. Some of the non-native fish come from ecosystems that are usually less volatile, so may have a harder time resisting the negative effects of drought and bouncing back. However, other introduced fish are considered “weedy” species that take advantage of disturbance. They might be particularly good at bouncing back after a drought.

The IEP researchers used data collected covering previous droughts in California to test these hypotheses. They compared fish abundance as measured by the CDFW Fall Midwater Trawl and the USFWS Delta Juvenile Fish Monitoring Program Beach Seines before, during, and after the droughts of 1976-1977, 1987-1994, 2001-2002, 2007-2010 and 2012-2016 (Figure 1).

Figure 1. Periods of drought in the Sacramento San-Joaquin Delta over the past 50 years. Figure is copied from Mahardja et al. 2021.

Though it sounded simple, the process ended up not being very straightforward. Even the definition of what a “drought” was turned out to be complicated. If you’re from the East Coast of the US, five months without rain is a “drought”. But in California, we would just call that “summer”. For Californians, we need lower-than-normal precipitation for multiple years in a row before we start worrying. Drought is often defined by its impact on people – dry soil conditions, low water storage, and water supply problems – but what does drought mean if you are a fish? The team had to make decisions. They also had to decide which components of the ecosystem to look at. At first they wanted to look at everything IEP monitors – water quality, phytoplankton, zooplankton, and fish, but decided that just doing fish was hard enough!

It was a big team working on the project, and they each brought skills to the analysis. Brian, Lara, Vanessa, and Shruti had a lot of experience with complex statistics and data analysis. Lara was “an R Wizard” who streamlined the data processing. Ted and Larry brought their fish expertise and deep knowledge of the system. Steve and Peggy provided hydrologic and water quality knowledge. Louise was the lead of the IEP synthesis program and organized the overall drought synthesis effort. Even though several authors changed jobs midway through the project, the great thing about working in the IEP community is that the new employers were supportive of seeing the project through, since they knew it would help the IEP community as a whole.

The team found that many fish species declined in abundance during the drought (they had “low resistance to disturbance”), particularly pelagic species such as Delta Smelt, American Shad, and young Striped Bass (Figure 2). Many of these species had the ability to “bounce back” after the drought (they had “high resilience”), however some species never returned to their former abundance (such as the Delta Smelt). Other fish species didn’t decline during the droughts (they had “higher resistance”). These included many of the fish of the littoral habitat, including Largemouth Bass, Redear Sunfish, and Chinook Salmon. A few of the non-native littoral species even increased in abundance during some of the droughts, particularly Mississippi Silversides and Bluegill.

Climate change is likely to increase the frequency and severity of droughts. If these results hold true, we could see a shift in the fish community away from native pelagic fish and towards more non-native littoral fish. Because freshwater flow is a tightly managed commodity, particularly during drought conditions, finding a balance of water use for people and water use for native species will become increasingly difficult.

Figure 2. Fish populations have different responses when it comes to drought. Many of the pelagic species decline, but bounce back. Some don't bounce back quite as well. Many littoral fish don't decline, or even increase in population during droughts. Fish photos from US Bureau of Reclamation.

Learn More

• Durand, J. R., F. Bombardelli, W. E. Fleenor, Y. Henneberry, J. Herman, C. Jeffres, M. Leinfelder–Miles, J. R. Lund, R. Lusardi, A. D. Manfree, J. Medellín-Azuara, B. Milligan, and P. B. Moyle. 2020. Drought and the Sacramento-San Joaquin Delta, 2012–2016: Environmental Review and Lessons. San Francisco Estuary and Watershed Science 18(2). DOI: 10.15447/sfews.2020v18iss2art2
• Department of Water Resources Drought Activities
• Kimmerer, W., F. Wilkerson, B. Downing, R. Dugdale, E. S. Gross, K. Kayfetz, S. Khanna, A. E. Parker, and J. K. Thompson. 2019. Effects of Drought and the Emergency Drought Barrier on the Ecosystem of the California Delta. San Francisco Estuary and Watershed Science 17(3). DOI: 10.15447/sfews.2019v17iss3art2
• National Integrated Drought Information System.
• Mahardja, B., V. Tobias, S. Khanna, L. Mitchell, P. Lehman, T. Sommer, L. Brown, S. Culberson, and J. L. Conrad. 2021. Resistance and resilience of pelagic and littoral fishes to drought in the San Francisco Estuary. Ecological Applications, 31(2): e02243. DOI: 10.1002/eap.2243
• Singer, G. P., E. D. Chapman, A. J. Ammann, A. P. Klimley, A. L. Rypel, and N. A. Fangue. 2020. Historic drought influences outmigration dynamics of juvenile fall and spring-run Chinook Salmon. Environmental Biology of Fishes 103(5):543-559. DOI: 10.1007/s10641-020-00975-8

By: Rosemary Hartman (CA Department of Water Resources), Pascale Goertler (Delta Science Program), Brian Mahardja (US Bureau of Reclamation), and Ted Sommer (CA Department of Water Resources).

A new synthesis study indicates Striped Bass could start swimming up the Sacramento River earlier in the year as climate change progresses. You probably know “stripers” as popular game fish, and die-hard fishermen will tell you all about the best times and places to try and catch a big one. However, Striped Bass do not stay put. Stripers are “anadromous”, meaning that just like salmon, they begin their life in fresh water, migrate to the estuary and ocean, and eventually return to freshwater as adults to spawn. This study, by IEP Scientists Pascale Goertler, Brian Mahardja, and Ted Sommer, looked at one of our oldest data sets to examine fish migration patterns. Scientists in other systems have found a number of changes to migration timing of many species linked to climate change, and Pascale, Brian, and Ted were curious whether there had been any changes in our system. They found that adult Striped Bass migrated into the Central Valley later in the year when there was higher Delta outflow and cooler sea surface temperatures (Figure 1).

Figure 1. Diagram of how environmental conditions influence Striped Bass Migration. In the top panel, warm ocean temperatures and low freshwater flow correlate with earlier upstream migration. In the bottom panel, lower ocean temperature and higher flow mean Striped Bass hang around in the ocean or estuary for longer before migrating.

In order to figure this out, the team started with data from the CDFW Adult Striped Bass Survey. This survey has captured and tagged Stripers using gill nets and fyke traps throughout the Delta and the Sacramento River since 1969 (Figure 2). Having a really long-term data set let them correlate migration to climate factors, which has not been studied very often in migratory fishes, but it also caused problems. This is a “presence-only” data set, which means the researchers didn’t know whether there was no record for a given date because the monitoring crews didn’t sample, or because they sampled, but failed to catch any fish. The monitoring crews also didn’t record how long they were out in a consistent way. Did they only catch one fish because there weren’t many fish? Or because they only put the net in the water for five minutes instead of an hour? They didn’t have the crews from the 1970s to ask.

Figure 2. CDFW scientists have been monitoring Striped Bass populations using fyke traps such as this one since the 1960s. CDFW photo.

While the patterns Pascale and her colleagues found have held true over the past forty years of the data set they analyzed, migration timing could change in the future. Based on trajectory of climate change, we expect warmer sea surface temperature and lower outflow during late spring and early summer (as snowpack level decreases), which could mean earlier bass migration. Striped Bass are a voracious predator, so adult fish entering the estuary earlier in the year could mean a head-on collision with juvenile salmon moving out of the estuary. While Striped Bass have coexisted with salmon for the past 150 years, they are not native to California, and changes in predation patterns could affect already stressed salmon populations.

Learn More:

• Goertler, P., B. Mahardja, and T. Sommer. 2021. Striped bass (Morone saxatilis) migration timing driven by estuary outflow and sea surface temperature in the San Francisco Bay-Delta, California. Scientific Reports 11(1):1510.
• Morris Jr., J. A., R. A. Rulifson, and L. H. Toburen. 2003. Life history strategies of striped bass, Morone saxatilis, populations inferred from otolith microchemistry. Fisheries Research 62:53-63.
• Nobriga, M., and F. Feyrer. 2008. Diet composition in San Francisco Estuary striped bass: does trophic adaptability have its limits? Environmental Biology of Fishes 83(4):495-503 (PDF).
• Sabal, M., S. Hayes, J. Merz, and J. Setka. 2016. Habitat alterations and a nonnative predator, the Striped Bass, increase native Chinook Salmon mortality in the Central Valley, California. North American Journal of Fisheries Management 36(2):309-320.
• Sommer, T., F. Mejia, K. Hieb, R. Baxter, E. Loboschefsky, and F. Loge. 2011. Long-term shifts in the lateral distribution of age-0 striped bass in the San Francisco Estuary. Transactions American Fisheries Society 140:1451-1459.
• Stompe, D. and P. Moyle. Striped Bass in the San Francisco Estuary: Insight Into a Forgotten Past. California WaterBlog. UC Davis Center for Watershed Sciences. 11/18/2018.