Science Stories: Adventures in Bay-Delta Data

  • April 26, 2024

Untangling the estuary’s food web with data analysis and synthesis

Blog by Rosemary Hartman. Paper and analysis by Tanya L Rogers, Samuel M Bashevkin , Christina E Burdi, Denise D Colombano, Peter N Dudley, Brian Mahardja, Lara Mitchell, Sarah Perry, and Parsa Saffarinia.

Did you know that one female threadfin shad can lay 5,000 to 20,000 eggs at once?! The California Department of Fish and Wildlife’s Fall Midwater Trawl, which surveys the Sacramento-San Joaquin Delta and upper San Francisco Estuary, caught 1,551 threadfin shad in 2022. If half of those were female, they could have produced three to fifteen MILLION baby fish. But in 2023 the Fall Midwater Trawl only caught 1,922 threadfin shad. What happened to all the baby fish?

Apart from the number of parents, fish populations (and other populations) are usually controlled by a combination of three ecological processes: 1) Individuals cannot find enough food or nutrients to survive and reproduce (what’s known as a ‘bottom-up’ process), 2) they are eaten by predators (what’s known as a ‘top-down’ process), or 3) they encounter unfavorable environmental conditions such as high water temperatures that lower survival. It is frequently very hard to tell which process is dominating, but understanding whether lack of food or too many predators is controlling a population can be very helpful in figuring out how to recover populations (Figure 1).

Diagram of a food web pyramid showing that predators exert top-down pressure on their prey, whereas food exerts bottom-up effects on their predators.

Figure 1. Fish populations depend on the number of fish you start with (parents), amount of food available, number of predators, and suitable environmental conditions such as temperature and salinity. Diagram by Rosemary Hartman, DWR.

To try and figure out which processes might be controlling threadfin shad (and other estuarine fishes), a team of scientists recently applied structural equation models to a long-term dataset of physical variables, phytoplankton, zooplankton, clams, and fishes. The resulting paper “Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient” was recently published in the journal Ecology. It is a great example of how long-term monitoring data, data integration, cutting-edge statistics, and diverse teams can work together to provide new insights about our environment.

The team started by drawing out their hypothesized relationships between ecosystem components in a food-web diagram (Figure 2). They wanted to create a mathematical model that described the relationships between each component in their diagram, but could only do so if there were adequate data available. They compiled data from six different long-term monitoring programs and one model dataset for a combined package of forty years of data across the estuary. Unfortunately, some of the key variables in their conceptual model, such as specific types of phytoplankton, aquatic vegetation, and large, predatory fishes, haven’t been monitored as well as others, so couldn’t be used in the model, but what was available was impressive.

Diagram showing that predatory fish eat smaller fish, smaller fish eat zooplankton, zooplankton eat phytoplankton, and clams eat phytoplankton and zooplankton. Nutrients control amount of phytoplankton.

Figure 2. Conceptual model of the estuarine food web used as a basis for the NCEAS synthesis team's food web model. Diagram adapted by Rosemary Hartman, DWR, from Rogers et al, 2024. 

They applied a modeling technique that allowed them to quantify each of the connections in their food web diagram to determine whether changes in one population were due to top-down effects of their predators, bottom-up effects of their food supply, or environmental variables like temperature and flow, and this was not an easy task! As Tanya Rogers, one of the team members and co-lead author on the final paper said, “It was sometimes hard to find the right balance between detail and interpretability”. Really complicated models have lots of detail, but can be hard to understand, but if the model is too simple it doesn’t describe reality. They eventually settled on a model that was somewhere in between. They found that fish and zooplankton trends were more driven by food supply in freshwater areas of the estuary, but the same populations were more driven by predation in the brackish water areas of the estuary. Abiotic drivers (temperature, turbidity, and flow) were frequently important in all regions and at all levels of the food web and had similar or greater effects than food supply or predation.

This was a great example of using an integrated dataset to address big questions about the ecology of the estuary, but the coolest part of it may have been the team that put it together. This project was part of a synthesis program sponsored by the Delta Science Program and led by the National Center for Ecological Analysis and Synthesis (NCEAS). The Delta Science Program recruited a diverse team of researchers from State and federal resource agencies and local universities with complementary areas of expertise. The team then participated in training workshops run by NCEAS on open science practices, data synthesis, reproducible workflows, data publications, and statistical techniques. Once the team had these new skills, the NCEAS trainers helped them assemble the integrated datasets and analyses used for the project. This workshop resulted in a really cool paper, but more importantly the team gained the skills to do more research like this in the future. Plus, they had fun getting to work with other early career researchers from different organizations who bring different perspectives.

Most exciting of all, the team made several recommendations for future research, such as exploring nutrient dynamics in more detail, testing changes to the dynamics in different salinity zones, exploring different time scales, and filling monitoring gaps such as large predatory fishes. With the team’s new skills and the available data, there are a lot more possibilities to explore.

Further reading

Categories: General
  • January 27, 2023

By Rosemary Hartman

Delta Smelt

Small fish with large eye, small pectoral fin, and adipose fin.
Picture of Delta Smelt, photo by Rene Reyes, US Bureau of Reclamation


Small fish with short pectoral fin, adipose fin, and upright dorsal fin. Very similar to the Delta Smelt.
Picture of a Wakasagi, photo by Rene Reyes, US Bureau of Reclamation

You’ve probably heard of Delta Smelt (Hypomesus transpacificus), and you may have heard of their cousin, the Longfin Smelt (Spirinchus thaleichthys), but there is a third osmerid in the estuary. The Wakasagi (Hypomesus nipponensis), also known as Japanese Smelt, is in the same genus as Delta Smelt, and was once thought to be the same species. It is native to Japan, but was introduced to reservoirs in California by the California Department of Fish and Game in the 1950s, and now it is established throughout the watershed, including the Delta.

But what does this brother of the Delta Smelt do? Is there sibling rivalry? A group of IEP scientists was curious, so they decided to look at all of our existing data to see when, where, how big, and how many Wakasagi are in the Delta and how their environmental tolerances and diet compares to Delta Smelt. A paper about their analysis recently came out in the Journal San Francisco Estuary and Watershed Sciences.

In order to compare Delta Smelt and Wakasagi, they looked at all the data from thirty different fish datasets from San Francisco Bay, Suisun Marsh/Suisun Bay, the Delta, and the watershed (see map below). This resulted in a dataset with over 250,000 individual Wakasagi! They also looked at data from special studies of Wakasagi and Delta Smelt growth and diets in the Yolo Bypass.

Map of the San Francisco Estuary showing hundreds of sampling points in the San Francisco Bay and Delta with scattered points upstream.
Maps of the San Francisco Bay-Delta Estuary. (A) Four long-term CDFW monitoring surveys and region assignments used for the comparative Delta Smelt and Wakasagi analysis. (B) Sampling locations for a subset of additional surveys used to assess Wakasagi catch, as well as Yolo Bypass surveys used to assess life-history traits including growth, phenology, and diet. Map reproduced from Davis et al, 2022, with permission.

They found some similarities between delta smelt and Wakasagi – both fish really like hanging out in the Sacramento Deep Water Ship Channel and both like eating calanoid copepods (a particularly tasty variety of zooplankton). They spawn at about the same time, but Wakasagi are usually a little earlier (though this varies from year to year), and Wakasagi usually grow a little faster. They are similar enough that they sometimes interbreed and produce hybrid offspring.

Wakasagi aren’t the same as Delta Smelt though. Wakasagi aren’t actually very common in the Delta, instead finding their homes further upstream in reservoirs (they especially seem to like the Feather River, the screw trap there catches tens to hundreds of thousands of Wakasagi per year!). In the Delta they are mostly in the northern region, which might be just them washing in from upstream. Though they were mostly found in freshwater reaches of the Delta, Wakasagi can actually tolerate a wider range of salinity and temperatures than Delta Smelt, but they seem to prefer cooler temperatures.

So, are Wakasagi competing with Delta Smelt for limited food resources? Maybe a bit, but while they play a similar ecological role when they do overlap, they don’t overlap spatially very often, and both Delta Smelt and Wakasagi are rare in the Delta. However, they overlap enough that areas that are good for Wakasagi are probably good for Delta Smelt too. Delta Smelt are becoming more and more endangered, so we can use Wakasagi as indicators of good Delta Smelt conditions and as substitutes for smelt in some laboratory experiments.

Major similarities and differences between Delta Smelt and Wakasagi
Delta Smelt Wakasagi Comparison
Annual life span Annual life span Checkbox that indicates the items are similar
Spawn later Spawn earlier Two arrows pointing in opposite directions that indicate the items are different
Eat calanoid copepods Eat calanoid copepods Checkbox that indicates the two items are similar
Grow slower Grow faster Two arrows pointing in opposite directions which indicates the items are different
Narrower tolerances Wider tolerances Two arrows pointing in opposite directions which indicate the items are different
Endangered More common Two arrows pointing in opposite directions which indicate the items are different
Native Non native Two arrows pointing in opposite directions which indicate the items are different
Mostly semi-anadromous Mostly freshwater Two arrows pointing in opposite directions which indicate the items are different
Small and silver Small and silver Checkbox which indicate the items are similar
Loves the North Delta Loves the North Delta Checkbox which indicates the items are similar
Smells like cucumber Smells like fish Two arrows pointing in opposite directions which indicates the items are different


Further reading

Categories: General
  • December 30, 2021

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.

Magnifying glass with cartoon images of several zooplankters

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?

diagram of three data sets being put into a machine and turning into one data set

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:

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

diagram of organism giving presentation

Categories: BlogDataScience, General
  • August 13, 2021

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?

A group of fish get together and look at a diagram that says: Surveys produce Data that inform decisions that inform mandates.
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?

Image of a classification tree with four groups of fish labeled: Brackish, Fresh, Marine, and Grunion.
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!

Image of a midwater trawl with two fish talking about whether there are any biases in their fishing. They agree that the boat probably catches fewer fish than we think it does.
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

Categories: BlogDataScience, General
  • April 1, 2021

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).

Bar chart showing periods of drought and non-drought from 1967-2017.
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.

Graphic show responses of American Shad, Delta Smelt, Striped Bass, Chinook Salmon, Splittail, Bluegill, and Silversides to drought.
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

Categories: General