That’s a wrap, folks. In just a few hours the clocks will strike midnight and for the next several weeks I’ll write the incorrect year on every document I sign.
This week my computer has been busy running a very long genetic analysis. Notice I say my computer was busy- it took about five minutes for me to start the analysis, but it’s going to take close to two weeks for my computer to churn out the results. And, while new and upgraded, my computer doesn’t have enough oomph for me to run other programs while genetics voodoo happens in the background. Luckily, this was just a trial run. Eventually I’ll have to do a much more extensive analysis that will take even longer to run. I can’t wait.
I’m always a little nostalgic on New Year’s Eve. Sure, I could be wishful and think about all my resolutions for 2017. But, let’s be honest. Those are empty promises. I won’t read or vacation more, it’s unlikely I’ll get back to running a 10k a week, and I just don’t have the self-control to be less grumpy. But, 2016 still holds some magic. It’s like a story where the last line is about to be inked and the chapter closed. We survived another one, and there’s no better way to spend this day than retelling the story of 2016 with some pictures I never got to share.
If I’m telling I story, I think it needs a title, and I think “Wayward Wanderer” captures the essence of 2016 pretty well. I traveled. A lot. And I didn’t have any idea what I was doing most of the time.
In January I was finishing a three-month stint in West Virginia where I was collaborating with the USGS Leetown Science Center. Using their experimental stream lab, we were mimicking climate change to see how individual fish differed in their response to rapid increases in stream temperature. I’ll report on those data eventually, but suffice to say fish aren’t swimming robots and do react much differently from one another. We are exploring the data to see if we can figure out if some fish are predictably better than others at tolerating increases to temperature, but it will be awhile before we get that data.
While in West Virginia I got snowed in under 43 inches of snow, and then less than a week later I was on a plane headed for Panama City, Panama. I volunteered to help someone else in my lab with their fieldwork, which entailed a six-week trip to the rainforest. I can still remember sitting on the plane, taking off from Richmond, Virginia thinking to myself “what have I got myself into.”
And, to this day, I still don’t really know what I go myself into. I’ve done a lot of field work, but nothing prepared me for that experience. Hiking miles upstream to remote sample sites that few people in this world had ever seen, spying on howler monkeys, getting surprised by boars while collecting data, and of course the snakes. So many snakes. It felt like a real life nature documentary. But, it wasn’t just about the science. I was living along the Panama Canal in a small neighborhood shared among canal workers and a handful of other American scientists who are some of the most intelligent and fun people I’ve ever interacted with. Some days were spent roaming the streets of Panama City trying to remember enough Spanish to order food and find my way around. It was an experience that will no doubt rank among the most memorable times I had while in graduate school.
Leaving Panama was bittersweet. After six weeks I had grown a little tired of watching out for dangerous creatures (I came within inches of stepping on a fleur de lance, one of the most venomous snakes in South America, and had a caiman lunge at me from the banks), and I was there at the height of the Zika scare. Someone on my field crew even contracted the disease, and I was working on a species of fish that feeds on mosquito larvae. But, I wasn’t ready to say goodbye to the cultural and social experience. Plus, as long as I was there, I didn’t have to worry about my own pending field season.
But, all good things must come to an end and in mid-March I boarded a plane back to the United States, spent an unexpected night with the pigeons of the Newark airport (seriously, Newark, get it together), and then finally got back to State College. I had a little over a month to apply for permits, finalize field sites, organize crews, buy supplies, etc. And, within hours of getting back to the office, we were awarded a grant to work on the gene expression project that is now showing early promising results. So, I also needed to work on my poker face, because I had no idea was I was doing. Like, zero clue. I had never done telemetry or tissue sampling, had no real idea how the fish were going to behave, nor, honestly, did we know if the fish would survive everything we were doing to them. While I had a long list of people that could provide guidance with a few pieces of the puzzle, I felt the pressure to make it work.
And I did. Usually. Telemetry officially started in early May and for seven months I felt like I was making it up at I go. But, as normally happens, the things you stress out about the most turn out to be the things that aren’t that difficult. Tagging and sampling? Psss…a breeze. And the fish survived. But, tracking every day? That turned out to be a bit harder. It shouldn’t have been a surprise; field work is a long string of judgement calls that can make or break your entire project. The longer you are in the field, the more of those calls you have to make. No pressure, right? Things like should I wait the rain out, is it really too dark to keep going, are flows too high to wade, should I dig after this tag that seems to be moving in the bank? There’s a fine line between good data collection and stupid data collection, and it takes practice to find it, flirt with it, and ultimately make good calls. While I have a few years under my belts, I always feel guilty and think I could have done more or better.
Ultimately, the biggest hurdle with telemetry was the mental game needed to stay engaged and committed. I walked the exact same streams every day tracking the same fish, often to the same exact spot. Every day. For seven months. But we made it- through dropped tags, harsh weather, wildlife encounters, human encounters, and broken bones. And, I think we got a great dataset. And, now that I’m on the other side of the hurdle, I think back to all the times I was standing beside the stream, tired and wanting to call it a day, but took a deep breath and continued on. Stubbornness is one of my best properties, and it helped that Savannah, Dan, and David kept me entertained.
During telemetry I basically lived in Loyalsock where phone service is non-existent and internet is sparse. It made communication difficult, particularly in summer when I was working on publishing a manuscript (which was finally accepted, woohoo!). But, as an upside, it’s a great way to disconnect and motivation to work hard during the day to guarantee an early bedtime. But, I was still largely living out of a suitcase. I think I packed in November 2015, and it wasn’t until September 2016 that I fully unpacked, bought perishables from the grocery store, and enjoyed a full week at my apartment. Even then, I was still making regular trips to Loyalsock, a 4-hour roundtrip commute, so I was still a stranger to the office.
Telemetry season ended in November, and since then it’s been more travel, only this time to spend holidays in Virginia with my ‘research assistant.’ As the year comes to a close, we are working hard to analyze and publish data for the genetics of brook trout in Loyalsock. Where will we go after that? Your guess is as good as mine.
Finally, perhaps motivated by lack of communication and entertainment this summer, but mostly interactions with interested anglers and citizen scientists, I started this website in June. I didn’t really know what to expect, but I can say the response has been far more receptive than I imagined. In less than six months this website has gotten nearly 17,000 views. Most importantly, it has connected me to people with questions about stream ecology, organizations like Trout Unlimited, news stations, and other academics. I also received an award for scientific communication. So, thanks to all of you for joining me on this ride, and I’m looking forward to seeing where it takes us in 2017.
So, did I earn my paycheck this year?
^ Pun intended
Last week we talked about how genetics can aid in the management of threated fish species by quantifying genetic diversity (remember, the more the better) and identifying isolated populations (again, recall that population isolation is usually bad, but read last week’s post for a contrary example). But, genetic studies can be important for management of even the most common fish species by helping identify the right scale of conservation and management efforts.
What does ‘scale of management efforts’ mean? Let’s think about brook trout. Though sometimes locally common, brook trout is a species of considerable concern throughout its native range extending from Maine to Georgia. In just about every state on the east coast fisheries managers are tasked with creating a management plan that accomplishes two fundamental goals: secure long-term population persistence in the face of climate change, invasive species, and habitat loss AND increase angling opportunities. If those goals seem in opposition, welcome to fisheries management. Conservation and recreation sometimes don’t go hand in hand. But, I digress…
So, plans are created. But, how much area should each plan cover? The entire range of brook trout? It is, after all, the same species. But, that doesn’t really make sense. Threats to brook trout are different in Maine than they are in Georgia (Georgia is a lot hotter, for starters). Plus, different states receive funding from different locations, have different levels of involvement from citizen groups, and have different levels of angling pressure. So, to make one really big management plan that encompasses the entire range of brook trout and all possible threats would not be very usable to local managers. Plus,depending on who you talk to, some believe brook trout are actually multiple species along the east coast. So, it needs to be more fine-tuned that than.
What about a brook trout management plan unique to each state? This gets closer. Across a state you would expect climate-related threats to be reasonably similar and funding sources to be shared within state boundaries. But, this scale is also problematic. Streams can cross state boundaries, there’s different interest in cold and warmwater fisheries across the state, and land use (and threats related to changes to land use) are different across the state. So, we still need something smaller.
Okay, how about a management plan for every individual stream? While this would definitely provide a lot of protection to localized threats, it is only practical for the largest streams and rivers. Biologists haven’t even surveyed many small headwaters, so we don’t know if they contain brook trout, or what the threats may be. So, to inventory the threats and population sizes of each stream and then create and implement a plan of action is not possible. Even if we did make those plans, a single fisheries manager is often tasked with covering a large chunk of the state and so they couldn’t possibly enforce a management plan that is unique to each and every stream. Further, it compartmentalizes individual streams when we need to focus on connectivity between waterways.
So, what is the solution? The truth is there’s often no easy answer, and it often depends on the ecology of the species. A highly mobile fish can, and should, be managed at larger scales than a smaller fish that moves shorter distances. But, there are trade-offs. At larger scales you may miss some of the local problems, and at smaller scales you may be unable to manage the entire range of the species effectively and not pay attention to process that happen at larger scales.
But, for most species and for many managers, the easiest balance of scales is found at the unit of a watershed. And, ‘watershed’ can be somewhat ambiguous because every small stream has its own watershed. But, when I say watershed, I generally mean the area of land that is drained by a reasonably large river. Larger watersheds represent an area of land that is easy to identify and has similar threats and recreational uses. Further, species composition within a watershed is likely to vary little, such that a watershed with brook trout is unlikely to also be a trophy largemouth bass fishery (because largemouth are a warmwater species).
But, while watersheds can make the most practical sense, are they biologically the most appropriate? For highly mobile species, they probably are. However, while brook trout are mobile, we also know that populations tend to quickly become genetically distinct. Several studies have found that brook trout populations from neighboring streams often show genetic separation, even in the absence of a barrier. This would indicate to us that nearly every stream in a brook trout watershed is its own independent unit. And, if you’re following my studies in Loyalsock Creek, preliminary results suggest that every population that I sampled is genetically distinct from one another.
So, should we let genes dictate our management efforts and manage each genetically distinct population? Maybe, but, again, it really isn’t feasible and perhaps not necessary. But, what we can do is factor that information into our watershed’s management plan. As we learned from our last post, being genetically distinct isn’t a guarantee that the population is completely isolated, or at risk of collapse. It just means there are not that many fish moving in and out of the population. And, we are quantifying genetic similarity from microsatellites, which are neutral genetic markers and therefor do not suggest that there are specific, important genes absent or present in some populations. So, there’s no reason to suggest that genetic distinction is indicative of reduced long-term survival (there are other metrics we need to look at to start making inferences about how genetically “safe” a population is…a post for later).
So what does it tell us? The fact that populations are genetically distinct could suggest that brook trout are locally adapting to the environment at small spatial scales. In the future, hatchery stocking and translocations from one population to another are going to become increasingly popular tools for managing brook trout However, stocking populations that are genetically incompatible with the environment can either cause immediate mortality or lead to outbreeding depression, which occurs when a native individual mates with a foreign (be it stocked or a migrant) individual, and the resulting offspring have genotypes that make them maladapted to local conditions. So, considering local adaptation is necessary for ensuring successful management outcomes.
The fact that even neighboring populations of brook trout are prone to becoming genetically distinct also highlights the importance of maintaining connectivity. The reality is that few trout move, and then reproduce, outside their native streams. But, in this case, the minority of the population takes high priority. It may not result in two populations becoming genetically similarit, but the handful of trout that do move and reproduce elsewhere are increasing genetic diversity at the new site. Ultimately, increased genetic diversity is what we want to see as it increasing resilience to habitat loss. It’s nature’s own management plan!
So, for brook trout, we can’t let genetics decide our management scale, but we can use it to improve our management practices and help us better understand the ecology of the species.
This may be my last post of 2016, so next time we'll take a look back at the year that was. Until then, I think I need to watch Home Alone for the upteenth time. Happy holidays to all!
Last week I broke down some of the nuts and bolts behind genetics studies, explaining what a microsatellite is and why they are useful for genetics studies. If that seemed confusing or uninteresting, stick with me! That information, while important and useful, isn’t required to understand what I’m doing. What is important is an appreciation that genetics can tell us a lot about how we should manage our fish populations, and for the next several posts I’ll be delving into some of the reasons genetics studies are so informative.
Let’s start with a thought exercise. You are a fish manager tasked with creating a conservation plan for a threatened fish species that is found in only one river in the United States. Populations of this species are currently isolated from one another by dams, and habitat is significantly reduced from historic conditions. Generally speaking, biologist cringe when they hear this, particularly at the phase ‘population isolation.’ Higher connectivity among populations generally means higher genetic diversity, which usually results in healthier, more stable populations (we’ll dive into those details later…for now just trust me). So, your gut reaction to this problem might seem easy - remove the dams, restore the habitat.
But, wait! Before you say ‘down with the dams!’, you need to consider nonnative species. Restoring connectivity for native fishes will also increase connectivity among populations of nonnative species, thereby improving the health of their populations as well. It will also likely allow nonnative species to invade new habitats were they could compete with, and potentially extirpate, populations of the threatened species you are fighting to restore.
So what do you do? Do you try to improve connectivity knowing it could increase the abundance of nonnative fish, but potentially improve health of native fish? Or, do you leave things as-is knowing native fish population may be declining, but at least they are protected from nonnative species? Before you make a decision, you may decide you need a better picture of how healthy native fish populations are.
This is a scenario often faced in fisheries management, and one that genetics can help resolve. And, this is exactly what scientists out west did with populations of Oregon chub, a fairly small minnow species that is only found in the Willamette River in Oregon (to read the full paper, click here). Construction of flood control dams and introduction of nonnative species, particularly sunfish and catfish, severally reduced populations and restricted them to isolated areas within the river. Through significant effort, populations of Oregon chub have been showing signs of improvement, and the species was even downgraded from endangered to threatened on the Endangered Species Act. But, populations size isn’t always indicative of long-term stability (as we’ll discuss below), and appropriate next steps for management were uncertain.
Enter genetics. Biologists collected tissue samples (in the form of a small portion of the caudal fin, which is not harmful and grows back) and ran analyses to determine how genetically different each population was from one another and how genetically stable each population was.
The result? Though most populations of Oregon chub were isolated from one another and were genetically different, most still maintained high genetic diversity. And, most populations still had a lot of adults reproducing, which indicates genetic diversity will likely remain high into the future. So, though genetics are not a definitive measure of fish health, the study did indicate that most populations were likely healthy and stable into the near future.
So, as a manager, you let out a sigh of relief. You dodged a huge bullet. Restoring population connectivity, which seems like it’s the right thing to do, would have probably been one of the worst things you could have done. In addition to the cost, benefits to Oregon chub would have been negated by the introduction of nonnative fish which would have reduced population sizes. And, you wouldn’t have known any of that if it wasn’t for genetics suggesting current populations were healthy.
The study also highlighted another important fact. Numbers can be deceiving, and a population that has a lot of individuals isn’t necessarily healthy. One of the largest populations of Oregon chub had the lowest genetic diversity. Why does this matter? Well, one of the recovery strategies for Oregon chub involves creating new populations by taking individuals from one population and putting them in an area of river with good habitat that is currently unoccupied (a practice we call “translocating”). Normally, you take individuals from strong, stable populations, and without genetics you assume population size is a proxy for health. But, that’s not always the case, and translocating individuals from an unhealthy population with low genetic diversity could be setting them up to fail in their new environment.
Genetics also helps identify the correct scale of management. For example, should each stream be managed differently. What about river? Or entire watersheds? Maybe the state of Pennsylvania? You want to manage the largest area possible because it’s cost effective and easier. But, threats affect fish differently at different locations. And, these affects are usually species-specific. So, there’s no easy answer as to what the scale of fisheries management should be, but genetics can help guide the choice.
But, I’ll save that topic for next time as it directly relates to trout management and some of the results we are finding in our studies.
After field work ended the question was ‘now what?’ I have a lot of datasets for several different projects, and I needed to figure out which to tackle first. But, despite exciting observations at the end of the telemetry season, I can’t start there. It’s a really messy dataset that’s going to require a lot of tinkering, input from collaborators, and there are some plans for more data collecting in the spring.
I ultimately decided to dust off a dataset from last year that was collected to determine the genetic structure of brook trout populations across the Loyalsock Creek watershed. But, I hesitated to start here. It’s no secret that I struggle with even the most fundamental concepts in genetics. I have a hard time understanding things I can’t visualize, and I managed to skirt my way around taking genetics classes as an undergraduate. But, the last few years I have grown to appreciate the questions you can answer about trout conservation through genetic studies. So, through a lot of hand holding from my friends who study genetics and our collaborators at the U.S. Fish and Wildlife Service, I am very slowly getting there.
One of the downfalls with genetics is that the topic can very quickly become technical and stray beyond the average person’s interest and understanding. But, that’s where my ignorance actually comes in handy. I don’t know enough to make the conversation technical! For the next few months I’ll be working on analyzing genetics data, and along the way I’ll try to breakdown all the concepts and terminology in a way that is both understandable and (hopefully) interesting.
For starters, let me go back. I am studying the population genetic structure of brook trout across the Loyalsock Creek watershed. What does that mean? Basically, I’m trying to see how genetically similar brook trout are from different locations around the watershed. We would expect that populations that are close to one another would be more similar than populations that are further away. This is because it’s more common for fish to move to, and then reproduce in, a neighboring stream than to make a long-distance movement to a stream many miles away. This is particularly true for species like brook trout which, compared to other species, don’t move very far. Trout also live in cold, headwater streams and warmer mainstem rivers act as barriers to movement, thereby further limiting exchange of individuals among populations.
There’s many ways to measure genetic diversity, but we are doing it by looking at differences in sections of DNA called microsatellites. To explain this concept a little further (mostly to myself...the visuals help), I often show the diagram below. Basically, every tissue in an organism’s body is made up of cells. Floating around inside the nucleus of those cells are chromosomes, and every chromosome contains thousands of genes. A gene is made of DNA, and DNA codes for proteins that ultimately produce all features of an organism. Put another way, genes are like instruction manuals, and DNA the step-by-step instructions for how to assemble, in this case, a trout.
The DNA inside genes is made up of base pairs (some of you may remember that there are four base pairs, adenine (A), cytosine (C), guanine (G), thymine (T)). While most of these base pairs code for specific proteins (for example, the base pair string of UCA codes for the protein ‘serine’), there are some sections of DNA that are “silent” and serve almost no biological function. One such case are sections of DNA known as microsatellites, which are sequences of 2-5 base pairs that are highly repetitive and do not code for a protein.
Microsatellites are powerful in population genetics studies for many reasons. First, because they do not code for a specific protein or trait, they are largely conserved in populations. This is important because if we were analyzing coding regions of DNA we wouldn’t know if absence of the DNA region was because of genetic isolation or because the environment was selecting against the trait that was being produce and therefor deleting it from the population.
The other reason microsatellites are useful is because they are prone to mutating. Again, because microsatellites do not have a functional purpose, these mutations are not harmful. But, these mutations work to give each population its own unique “signature” which we can track around the watershed as fish move around.
The location of a microsatellite on a gene is referred to as a ‘locus,’ and we analyze 12 different microsatellite loci to make inferences about population genetic diversity. Basically, we look across all loci to see how different individuals are within a population, and compare that to how different individuals are across all populations. Thankfully there are software programs to do this.
If you’re curious, below is what this data looks like in real life. Every row on that spreadsheet represents an individual, and the columns represent the ‘genotype,’ or genetic composition, for each microsatellite. In this case, the genotype represents the number of times the base pair sequences repeats. So, for example, the microsatellite loci B52, which is a base pair sequence of GCGT, is repeated 207 times in the first individual on that spreadsheet. You'll also see that there are two numbers for each loci because trout are diploid, meaning they get one copy of the gene from their mother and one copy from their father (just like humans). And, if you quickly glance down the spreadsheet, you see a lot of similarities because all of those fish are from the same population.
I’ve spent the last few days trying to run some summary statistics to describe the genetic diversity of all 28 samples sites and determine how similar each site is to one another. I’ll report some of those results in the coming weeks but, for now, don’t worry if all of that seemed confusing. Just remember- we study sections of DNA called microsatellites, and the more similar the microsatellites are the more genetically similar two fish, or two populations, are.
That’s not so bad, is it?
Today it might be snowing. And the days may still be getting shorter. But, before you know it spring will be here and the forest will come back to life. And, when it does, trout anglers will turn into entomologist and the phrase “what’s hatching” will be heard at tackle shops across the nation
“Match the hatch” is a common phrase among trout anglers used to describe the act of matching artificial lures to aquatic insects that are currently hatching from their juvenile into adult stages. When insects are hatching they are abundant, and so the probability of one floating downstream and being seen by a trout is fairly high.
But, trout are picky eaters. They develop a search image for one or two species of insects and become hyper-focused on eating only those species to the exclusion of all other food sources. Search images are helpful because it helps fish quickly tease apart an insect from little pieces of debris or rocks that could potentially look like food as it floats downstream. It’s kind of like putting together a jigsaw puzzle. There could be a pile of pieces in front of you that go together, but if you’ve developed a search image for edge pieces you’ll look past the other pieces for a long time.
Search images can be helpful because it quickly allows fish to categorize floating objects as ‘food’ or ‘not food’. But, they can also be problematic because hatches don’t last for very long. Sometimes by the time a fish has developed a good search image the hatch is nearly over. If that happens, the fish has a search image for an insect that is no longer common, and the fish will wait to see that specific species float by while allowing many other insects to pass by without being consumed.
More problematic is that search images can take a long time to form. It’s a long trial and error process where the fish has to keep trying to eat a lot of things that look similar to the hatching insect before it hones in on the exact characteristics that make the insect look different than a piece of stick or a leaf.
Search images where the first thing that got me interested in studying trout. At the start it seemed a little silly and a waste of time for a trout to need a search image. But, if you think about it, it makes a lot of sense. If a trout’s is willing to eat everything and has a search image that is too broad, then it will spend a lot of time chasing down little sticks or, worse yet, eat something potentially toxic. But, if their search image is too narrow, it will not eat enough to survive. It’s an interesting problem to have.
More interesting was that, at the time, no one had studied specifically how trout develop search images. Yes, at some level it’s a trial and error process. But, trout live in pools with other trout. And, they have eyes capable of watching what those other trout do. So, my advisor had a hunch. Perhaps they learn search images socially. That is, they watch other fish test out food of various shapes and sizes and that helps cut down the time it takes for a fish to develop a search image.
That was the theory we tested in 2008 in what still remains my favorite study I’ve ever done (a link to the publication that resulted can be found below). We started by going out and electrofishing trout from a small stream in Virginia. But, we had a bit of a unique problem. To track search image development, we needed to not only monitor fish behavior, but be able to trace the behavior of each individual over time. In short, we needed some sort of external that had unique identification for each fish and could be seen from about 50ft away from the stream bank.
The solution was ribbon tags- small pieces of plastic glued to a needed. After the fish is anesthetized, you thread the needle under the top layer of skin, out through the other side, and then tear the needle off the plastic tag. The plastic remains under the fish’s skin (and don’t worry, these tags fall out a few months after they are put in). So, now we had about a hundred fish in the stream swimming around with a little extra bling.
We then installed feeders in two pools that had a lot of fish. The idea was to train some fish to develop a search image and then move these trained fish to new pools to see if they helped new, untrained fish develop search images. The feeders were a series of PVC pipes, a small, battery-operated toy motor, and a photocell. Every 5 minutes the feeder would turn on, spin a little brush in the PVC tube, and out would come some mealworms.
The mealworms were their own story. They came in a can and were intended to be fed to reptiles. And, relative to hatching insects, these mealworms were king size candy bar- high in fat, calories, and exactly what a starving fish wants. But, there was a problem. The worms were fairly moist, and so when they hit the water they would sink. The majority of trout diet is made up of floating insects, and so we needed these mealworms to float. So, out came the frying pan and camp stove, and we fried the mealworms to a crispy golden brown (in what may or may not have been the same skillet we cooked dinner in).
So, with fish tagged and feeders in place we sat and watched. And every time the feeder went off we noted the behavior of every fish in the pool. And, for a long time, their behavior was to do nothing. Feeder goes off, worms float downstream (often right over top the trout), no one eats them. Repeat. For days. And, we did these observations from about 7am-7pm.
But, after a few days things started to change. It started with one brave fish finally eating the worm, but then spitting it up. There was clearly some hesitation. Then a few days later, the fish took the worm swallowed. After that the fish knew it had saddled up to a buffet and it would sit at the feeder anxiously awaiting the next round. In total, it took about 14 days for fish to develop search images. Remember that number.
Below is a video of what this whole process looks like. Look carefully for the tagged fish (his color code is blue-blue-blue) sitting on the far right of the screen. He is sitting in the current waiting for the feeder (the large blue bag) to release a worm, which happens around the 50-second mark. Continue watching after the tagged fish feeds and you'll see him try to fight with another untagged fish.
After these fish were trained, we electrofished out of their home pools and moved them to new pools throughout the study area and installed feeders in these new pools. Here is where we crossed and fingers and hoped. How long would it take a naïve fish to develop a search image for mealworms if they could watch another fish that already had a search image?
Maybe less. We didn’t immediately start observations, but by the time we did untrained fish were already consuming mealworms. To put it another way.
Without social learning it takes 14 days to develop a search image. With social learning it takes less than a day.
Why does that matter? Trout basically starve during summer. They need to be able to quickly switch their search images in order to consume the most calories possible, and social learning is a mechanism they have evolved to use to speed the process up as fast as possible.
Does this study help in trout conservation? Probably not. But, it does showcase how complex the species is socially and intellectually. When asked to give public seminars I often present on this study for several reasons. First, because it’s so unique and different from typical fish research. And, second, because it’s entertaining and, without fail, the audience really connects with the story. So, maybe this research does help in trout conservation. Not with directly improving population health, but in helping from empathetic connections with what is not the most charismatic of animals.
And, now trout anglers can blame social learning when they don’t get a bite.
To read more about this study, click here.