Science Stories | underappreciated data

June 28, 2023

Blog by Rosemary Hartman, Data collated by Nick Rasmussen

Our last post gave you an introduction to the water weeds of the Delta. Invasive submerged aquatic vegetation is taking over the waterways, making it difficult for boaters, fish, water project operations, and scientific researchers (Khanna et al. 2019). As we described in the blog “Getting into the weeds”, they are hard to control too. But how do we collect data on aquatic weeds and what do those data look like?

How do we collect data?

There are two main types of data that we can work with (see IEP Technical Report 92 (PDF) for details). The first, is collected with areal photographs, and is known as ‘remote sensing’ (Figure 1). A specialized camera (sensor) is mounted on a platform (a drone, airplane, or satellite), and it can collect a regular-old photograph, or a hyperspectral photograph that records a lot of wavelengths of light that our eyes can’t see. Because different types of plants reflect different spectra of light (they are different colors) these photographs can be used to map location and extent of vegetation (Figure 2).

Diagram of remote sensing process showing an airplane flying over the water. Lines representing light go from the sun to the ground and are reflected to the sensor on the plane.
Figure 1. Diagram showing how remote sensing works. Light from the sun is reflected by objects on the ground. Different wavelengths of light are reflected differently. The sensor (camera) on the platform (airplane, satellite, drone, etc) registers the different wavelengths and stores them as an image file. Later, experts can classify the images based on which wavelengths of light were reflected.

The UC Davis Center for Spatial Technologies and Remote Sensing (CSTARS) has been mapping vegetation in the Delta using hyperspectral imagery collected with airplanes for most of the last 20 years (like the map in Figure 2). They create maps every year and share their data online via the Knowledge Network for Biocomplexity.

False-color map of Suisun Marsh showing different colors for different types of vegetation.
Figure 2. Hyperspectral image map of Suisun Marsh collected by CSTARS. Colors are used to represent different vegetation types (they aren't really that color).

Hyperspectral imagery is very good for identifying floating aquatic vegetation and terrestrial vegetation, but the water makes it hard to identify submersed vegetation. We can map where submersed vegetation is, but not what kind of vegetation is there. To look at community composition, we need to actually get out in the field and check on the weeds directly. To survey submersed weeds, researchers use a thatch rake (Figure 3) – an evil looking tool with sharp tines on one end and a long handle.

Image of a rake with a long pole and very jagged blades on the end.
Figure 3. A thatch rake being used to sample aquatic vegetation. Photo from the Department of Water Resources.

To measure weeds, researchers either lower the rake into the water and twist it around to pick up the weeds, or they drag the rake behind the boat. When they pull the rake in, they rank the coverage of weeds on the rake head and identify the weeds to species. Several different groups have been collecting these data over the past twenty years– the State Parks Division of Boating and Waterways (DBW), UC Davis (including CSTARS), SePRO Corporation, and the Department of Water Resources. Dr. Nick Rasmussen (DWR) recently integrated all these datasets into one data publication available on the Environmental Data Initiative. He developed the data set because he was helping to write a series of reports about the environmental impact of drought and drought-related management actions. Those reports required rounding up aquatic vegetation data quickly, but at the time, virtually none of it was readily available. He wanted to fix that.

It ended up being a fun challenge for Nick because he got to learn how to create a fully reproducible process for integrating the dataset – including some hard decisions about how best to combine data that was collected in very different ways. He did all the cleaning and formatting in R scripts that are made available in a public GitHub repository.

What do the data look like?

Well, because these data were collected by different programs for different purposes, there are pretty big differences in number of samples and distribution of samples over the years (Figure 4). So it’s a little difficult to detect trends.

Bar graph showing lots of samples taken from 2007-2010, no samples taken 2011-2013, and a lower number of samples taken 2014-2021. The Franks Tract Survey only occurred from 2014-2021.
Figure 4. Graph of number of submerged vegetation rake samples per year in the integrated vegetation dataset.

However, when we look at the relative abundance of different species that have shown up in the rake sample data (Figure 5), we can really see an expansion in Potamogeton richardsonii (Richardson’s pondweed, black bars in figure 5) and Najas guadalupensis (southern naiad, brown bars in figure 5) after 2014. It’s not clear whether these species, both native to California, were recent invaders in the Delta or whether early surveys didn’t know how to tell the difference between them and similar looking species such as Potamogeton crispus (curlyleaf pondweed, red bars in Figure 5).

Figure5. Graph of relative abundance of each species across all rake sample surveys by year. No sampling occurred 2011-2013. Click for a version that shows separate plots for each species.

To make the story a little more complicated, these species aren’t found evenly throughout the Delta. Both Potamogeton richardsonii and Najas guadalupensis are found chiefly in Franks Tract (Figure 6) and not in any of the other regions of the Delta. SePRO and DBW conduct extensive sampling every year within Franks Tract (see Caudill et al. 2019 (PDF)), but not as high an intensity in other areas, so high abundance there throws off the Delta-wide data if it is not weighted by location.

Figure 6. Average relative abundance of different types of submerged vegetation by region of the Delta for the entire dataset. Click for a version that shows separate plots for each species.

We can also look at the hyperspectral maps to give us a record of total coverage of submerged weeds by year (Figure 7). We can see that total coverage of weeds really increased between 2008 and 2017, then remained about the same from 2017 to 2022.

Bar graph showing coverage of vegetation across all Delta waterways. Coverage is about 15% from 2007-2020, then jumps to 18-25% between 2017 and 2022.
Figure 7. Graph of percent of waterways in the Delta covered with submerged vegetation by year. Triangles indicate missing years.

However, it is often more interesting to look at a smaller area of the Delta and see how vegetation shifts from year to year (Figure 8, 9). For example, distribution of weeds in Franks Tract – a large, open-water area of the Delta – changed dramatically when a barrier was installed in West False River in 2015 and 2021-2022. The open-water area in the middle of the tract filled in during 2015, but the area on the eastern side of the tract started to clear out during 2021-2022 (see Hartman et al, 2022 (PDF) for more information).

Map showing the location of the Delta in the central valley of California and Franks Tract in the center of the Delta.
Figure 8. Map showing the location of Franks Tract - a large, open-water area with extensive vegetation and the site of many vegetation surveys by DBW and SePRO. Maps of vegetation in Franks Tract through the years are in Figure 9, below.

Figure 9. Hyperspectral image of Franks Tract showing installations of a barrier in West False River during 2015, 2021, and 2022, and resulting changes in distribution of submerged aquatic vegetation. Click to enlarge.

Between all these surveys, we’ve collected a lot of data on weeds, but we haven’t done an extensive analysis. There are lots more questions waiting to be asked! How does the distribution of weeds change with floods and droughts? Which species of weeds grow in shallow versus deep water? Are any species expanding in range? Download the dataset yourself and take a look!

What is a rotifer anyway?

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

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

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

Synchaeta spp.

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

Microscopic photo of synchaeta in both swimming and contracted form.

Synchaeta bichornis

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

Polyarthra spp.

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

Keratella spp.

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

Microscopic image of Keratella (rotifer).

Trichocerca spp.

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

Microscopic image of Trichocerca (rotifer).

Asplanchna spp.

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

“Other rotifers”

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

Microscopic image of Brachionus and an unidentified rotifer

So, what can we learn from the rotifer data?

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

Area plot of rotifer catch per unit effort by year from 1975-2021. There is a drop in catch around 1980. — **Figure 1. Average catch per unit effort (number of rotifers per thousand cubic meters) of all rotifers per sample (dark green area). Standard deviation of catch per year (light green area).**

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

Bar plot of rotifer catch per unit effort by year for each of the six major rotifer taxa. — Figure 2. Catch of major species of rotifers caught by EMP over time. You can see that the abundance of many species of rotifers declined sharply around 1980. You can also see that *Synchaeta*, *Keratella*, and *Polyarthra* were the most common species.

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

Bar plot showing rotifer catch per unit effort by year with bars labeled with different water year types. — **Figure 3. Catch per unit effort of each rotifer taxa over time, with bars color-coded with water year type.**

Scatter plot showing rotifer catch per unit effort versus the Sacramento Valley Water Year index with a positive correlation. — Figure 4. Plot of total rotifer catch per unit effort versus Sacramento Valley Water Year index with different shapes and colors indicating water year type. The line indicates a linear model showing an increase in rotifer abundance with increased flow.

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

Map of the estuary showing rotifer abundance in different regions with pie charts. — Figure 5. Map of mean rotifer CPUE from 2017, which was one of the biggest years for rotifers since the 1970s. Each pie chart represents one of EMP’s long-term monitoring stations, with the size of the pie chart corresponding to the total rotifer abundance. The South Delta and Suisun Marsh stations were especially high in rotifers, with more *Synchaeta* in the Marsh and more *Polyarthra* and other rotifers in the South Delta.

Are you interested in finding more patterns in the data?

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

References and further reading

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

Categories: Underappreciated data

Tagged: zooplankton rotifers underappreciated data flow

Feeling Crabby

August 29, 2022

any small crabs running around on a tray

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.

a large crab is held by the back of its shell and is being measured with calipers — **Figure 1. Each crab is carefully measured using calipers. This is where experienced biologists have to practice holding the crabs carefully to avoid being pinched.** Image credit, Lynn Takata, Delta Science Program.

tray full of several dozen small crabs — **Figure 2. Lots of little crabs! Juvenile crabs can be particularly hard to catch, and particularly hard to tell apart.** Image credit: Kathy Hieb, CDFW.

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).

line graph showing annual average catch per trawl of five species of crabs caught by Bay Study in each region of the Estuary (South Bay, Central Bay, Suisun, and the West Delta) - click to enlarge in new window — Figure 3. Annual mean catch per trawl of the most common species of crabs across each region of the estuary. Dungeness crabs are the most frequently caught, with peaks in South Bay, Central Bay, and San Pablo in 2013 and 2016. Chinese mitten crabs had a spike in abundance in Suisun and the West Delta around 2002, but are rarely caught before or after. The red rock crab, graceful rock crab, and Pacific rock crab are only caught in South Bay, San Pablo, and Central Bay, and then only in low abundances. *Click image to enlarge.*

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.

dot plot showing the salinity at which each species of crab is caught - click to enlarge in new window — Figure 4. Dot plot showing the salinity of each trawl where each species was found from 1995-2005. The Pacific rock crab, graceful rock crab, and red rock crab mostly occur at high salinity (25-32 PSU), but the Dungeness crab is often found in brackish water (10-32 PSU), and the Chinese Mitten crab was found in fresh to brackish water and mostly absent from high salinity water (anything greater than 28 PSU). *Click image to enlarge.*

Some data just needs a little love

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

Yolo Bypass Fish Monitoring Program’s Drift Invertebrate survey

By Nicole Kwan, Brian Schreier, and Rosemary Hartman

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

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

A woman in a life jacket stands on the deck of a screw trap with a rectangular net held in the flow at the surface of the water.
Figure 1. YBFMP scientist Anji Shakya sampling drift invertebrates in high flows next to the fish trap. Image credit - Naoaki Ikemiyagi Department of Water Resources.

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

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

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

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

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

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

Science Stories: Adventures in Bay-Delta Data