Science Stories: Adventures in Bay-Delta Data

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.

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).