My family is not the least bit outdoorsy. My mom came out to the field with me once before she died, but refused to step foot out of the car once I parked at the edge of the trail. At times my father has shown interest in my research. However, once I tell him I’m not trying to find new ways to grow larger, more tasty fish, he quickly loses interests. “What good is your research if the fish don’t get large enough to eat?” is a question that I hear all too often.
Little does he know, wrapped up in his disappointment is some interesting science that has lately been receiving a lot of attention. In addition to my dad (who doesn’t really fish, by the way), a common complaint among anglers is that fish aren’t getting as big as they used to. To add gas to the fire, technology makes it easy to find historic images of people proudly displaying their catch of 2+ foot long brook trout, surely caught with little more than a stick and line.
It’s 2017. Managers should be able to get the fish are large as we want them, right? Unfortunately, it’s not that easy. Yes, things like climate change, habitat loss, and invasive species have caused declines in the maximum growth of many fish species. But, we can restore and protect habitats to help minimize some of those impacts. What we can’t do is reverse time, and the reason we can’t get large fish today has a lot to do with harvest regulations (or the lack thereof) hundreds of years ago.
For most fish species, state and federal biologist have done a lot of math in order to determine the minimum harvestable size. This number is ultimately a compromise. You want the minimum size to be small enough that anglers have a good chance of being able to keep a fish, but you also want it large enough that the population remains robust and juveniles are not harvested before they can reproduce.
So, you go fishing. You check minimum harvestable size, and when you catch a big fish you put in your cooler. When you catch a small fish, you return it to the water so it can grow larger, reproduce, and be ready for you next year. The logic seems sound, right?
Not entirely. When you only harvest the biggest fish, you’re not only removing the oldest fish. You’re also removing the fish that are genetically programmed to grow faster and larger. Put another way, by keeping the big fish, you’re harvesting both the grandparents and the “tall kids” from the population. After many generations of anglers keeping only the big fish, the genes responsible for rapid growth are simply gone from the population. At that point, no amount of habitat restoration or food supplementation is going to end in larger fish. The population has lost the genetic capability of producing big fish.
This idea isn’t new to fisheries science, but has more prominence in marine ecosystems where biologists first recognized the need to protect both the smallest and the largest fish from harvest. Many marine fish species are regulated with slot limits, where only fish of a certain mid-range size are allowed to be harvested. Slot limits help protect both young juveniles, large pre-spawn females, but also the young fish with the “tall kid” genes.
Slot limits can help preserve some of the genetic integrity of a population. However, scientists have lately realized that traditional harvesting regulations are probably doing more than just removing genes. For example, it’s been shown that angling selectively targets largemouth bass with bold personalities, that populations exposed to heavy angling have altered rates of gene expression (recall: gene expression can be important for many things, including allowing fish to survive stressful situations like high stream temperatures), and that reproduction declines with increases angling pressure. Consequently, biologists are now predicting that angling is indirectly reducing overall population health and future evolutionary potential.
So, should you stop fishing? Absolutely not. But, it does highlight the need to rethink management goals and harvest regulations. We can’t just think about fish size, but need to start considering the more subtle effects that angling has on genetics, reproduction, and behavior.
This past weekend I went on a camping trip to New York with a few others to celebrate the joint birthdays of myself and another friend. It was a great weekend of many camp fires, little technology, and general acceptance of camp funk from the camp fires and lack of technology. Brings me back to field seasons.
Camping just outside Buffalo, we felt a little obligated to make the quick trip north to Niagara Falls. Walking around, I was asked several times whether fish could make the journey down the nearly 170-foot fall and survive. The answer is yes, absolutely! (Turtles can too, but this isn’t a turtle blog.) Generally speaking, as long as fish are falling in water (as opposed to falling through the air and hitting water at the bottom), they probability of surviving the fall is high.
But, what about getting back up? It goes without saying that a trip down a fall as large as Niagara is a one-way journey. But, smaller falls can potentially be traversed by fish moving in both directions. This is especially true in salmonids (including trout), which are natural-born high jumpers and are able to leap several feet into the air if they can get a good “running” start.
What makes a waterfall navigable by upstream migrants isn’t so cut and dry. A fall that is a straight drop is not going to be passable by even the most agile fish. But, smaller falls and falls made-up of a series of small step-downs can often be traversed. And, somewhat ironically, the ability of fish to navigate a waterfall increases with higher stream flows. Flooding increases water velocity in the center of the channel, which can give fish the extra boost of swimming speed needed to jump higher and further. High flows also cause streams to spill over onto the banks where it is normally dry, and fish seeking refuge in these temporary habitats often find new, easier routes, up waterfalls.
So, how do we determine if a fall is navigable? Genetics! Specifically, we can look at the results from a program called STRUCTURE.
STRUCTURE output can easily become difficult to understand. But, in short, you feed the genetic data from individual fish into the program, and it produces a diagram that shows you the most likely population assignment for each fish. You can then use the output to answer questions about where a fish was likely born, how connected populations are, and/or the extent an identified barrier (such as a waterfall) blocks fish movement.
I find it best to think about STRUCTURE output with an example. STRUCTURE analyses can be performed on any organism, and the below example is from a paper on the plant, creeping fig. The authors’ sampled 17 populations, and STRUCTURE determined that there were only two genetically unique populations (as represented by the red and green colors). The number of sampled populations is much higher than the number of genetically distinct populations because there is a high degree of population connectivity and movement of individuals among populations prevents genetic isolation.
If you look at the STRUCTURE diagram, you can see that there is a line for each individual, and each line is a stacked bar chart that is color-coded by the probability the individual assigns to each population. So, an individual with high assignment to the red population is represented by a solid red bar, and an individual with high assignment to the green population will be represented by a solid green bar. Bars that are of various degrees of both red and green represent individuals that have ancestors from both populations. For example, if an individual has a parent from each population, then they show up as 50% red and 50% green.
Big picture, the more isolated a population, the higher the probability that individuals will be from a single genetic population. As a result, isolated populations show up as solid blocks of color in STRUCTURE. Populations that are more open have individuals that have lower assignment probabilities to many genetic populations, and they appear as blocks of mixed color in STRUCTURE.
Now, let’s put this information to work using two examples from brook trout in Loyalsock Creek. The first is a fairly sizeable waterfall on Weed Creek. Normally when sampling for population genetics I don’t go above a known movement barrier. I know it is likely to separate a population, even if just a little, and so I would technically be sampling two populations instead of one. But, at this site, for reason that don’t matter, I decided to collect 40 fish downstream of the falls and ten from above. The STRUCTURE diagram looks like this.
Can you guess what’s going on? (Hint: there’s three genetic populations in this diagram, and the fish caught upstream of the waterfall are on the right).
If you guessed that fish weren’t moving upstream of this fall, then you’d be correct. The solid block of green on the right represents fish upstream of the fall, and they are all assigning with high probability to the “green” population.
Downstream of the fall, things are a little more interesting and a lot more confusing. We see several fish with high assignment to the “green” population, which probably represents fish that recently dropped down the falls. As you can see, there’s quite a few fish that make the journey down the falls and survive. There’s also several fish that are represented as mostly red or mostly blue. One of those colors probably represents the genetics of the resident fish of Weed Creek who living downstream of the falls. The other is probably fish that are moving in from another tributary. Which color is which is uncertain with the data at hand.
Lastly, we see fish that are combination of any two colors, or, in some cases, all three colors. This represents individuals that were spawned from some combination of fish dropping downstream, moving from outside of Weed Creek, and/or fish living downstream of the falls in Weed Creek. This is what it looks like to have a genetically diverse population!
Now, let’s look at another example for comparison. This time, I’ve chosen two different streams, separated by no known barriers, and quite literally a stone’s throw away from one another. Before scrolling down, take a second to think about what you might expect the STRUCTURE diagram to look like.
Not what you were expecting, huh? One stream is almost entirely genetically distinct and shows up as a nearly solid blue box. The other stream has a little more diversity, with two genetic populations nearly equally represented by green and red. But, more strikingly, we see almost no fish from the other site, the “blue” site, finding their way into this stream.
What’s going on here? I’m not sure. But, what this genetics data highlights is that movement barriers aren’t always so obvious. Sure, waterfalls, bridge crossings, and dry channels decrease movement. But, sometimes we can’t always see the barrier, and it may not be a physical barrier but could be driven by some sort of behavior. In the examples here, there was more movement downstream of a fairly large waterfall than there was in an open channel. But, now that we’ve uncovered this hidden barrier, we can start looking a little more closely and potentially find a way to reconnect these two populations.
Exactly one year ago, fueled by coffee and angst, I tagged my first telemetry brook trout and started what would be an 11-month study on movement and gene expression of four brook trout populations. I had no idea what I was getting myself into (for starters, it was supposed to only last six months). And, as I continue to analyze the data, I’m not entirely sure what I got myself into. But, what I do know is that one year later, a combination of good fortune and effort led to some pretty cool data.
So, what did I learn in the last year?
Having spent much of the last year working outside, I have to admit that it feels a little weird to not be frantically packing and preparing for field work right now. But, as much as I would rather be out in the streams, it’s time to hang up my waders and get to analysis. After all, I need to graduate eventually!
Ever have one of those weeks where you’re distracted by a million other things and get nothing done. Yea? Well, that was mostly me this week. Thankfully, I’m landing into the weekend with loose ends mostly tied back up and ready to actually work through this cold, rainy, graduation weekend (seriously…it could snow Sunday?).
With little appreciable progress on my end this week, I’m turning to some outside sources to showcase cool science. Lately, it’ been tough to turn a blind eye to the fact that funding for many government programs that support scientific research, and potentially my future career, is getting overhauled, sometimes completely nixed. Recent international marches for science and climate have brought light to the fact that many are opposed to these changes. But, have you stopped to hear out the science critics? I can’t say I agree, or even know, all of their views, but some of their arguments hold a lot of weight. For example, one of the biggest oppositions I’ve seen to government-funded science is that the average person feels “left out” of what is being said.
I hear you. I feel left out of science, and I’m a scientist. But, as I’ve campaigned before, it doesn’t have to be that way. And, there are some great resources out there to showcase some of the efforts of federal scientists. Case in point- if you’re interested in brook trout, stream temperature rise, climate change, or geography, go ahead and click on the website for the Spatial Hydro-Ecological Decisions System, or SHEDS http://ice.ecosheds.org/sheds/.
This website was produced by a team of scientists, many employed through the United States Geological Survey, a bureau of the Department of the Interior, to produce a visual tool to display hard to access data and the results of really complex models. If you click of the website, you’re brought to an interface that shows a map from Maine to Virginia. Though native brook trout extend as far south as Georgia, data on populations south of Virginia are more sparse and not easily incorporated into this larger dataset.
On the left is a series of drop-down menus where you can customize which data are displayed. It starts by selecting a spatial extent based on HUC watershed. For those unfamiliar with HUCs (short for Hydrologic Unit Code), just know that the larger the HUC, the smaller the watershed. So, for example, a HUC4 watershed is much larger than a HUC8, and HUC8 larger than HUC12. After you select a HUC size, the watershed outlines will appear on the map and the next drop-down menu is automatically populated to show the states that your selection covers. You can also select the state(s) you’re interested in directly from this second menu.
Now, after you find the watershed of most interest to you, the fun really starts. The “map variable” section has TONS of information to characterize the current and future habitat in and watershed and occupancy probability for brook trout. With a click of the mouse, you can have information such as average elevation, percent of forested land in the watershed, and average summer temperature. These are all variables that biologists have determined are the best predictors for brook trout occupancy, which you can also plot on the map. Simply select which variable you’re interested in, and hover over your watershed to see the value appear.
But, what about the future? If you look at the last three variables in that drop down list, you see occupancy probabilities with 2, 4, and 6°C increases in July temperature. Clicking on these values will show results of models that predict brook trout occupancy based on these three projected levels of stream temperature rise. For example, brook trout occupancy probability in Loyalsock Creek is currently 91%. But, it decreases to 84% and 58% with 2°C and 6°C increase in stream temperature, respectively.
Change in brook trout occupancy probability from present (left), 2C increase (middle), and 6C increase (right) in temperature. Click on each picture to view the full model output.
The fun doesn’t stop there. So far, we’ve been characterizing the habitat and occupancy for specific watersheds. What if you flipped that line of thought, and looked for watersheds that met specific habitat and occupancy values? For examples, what if you wanted to see all watersheds that currently have a >90% probability of brook trout occupancy? Easy! Simply go to the drop down menu on the right and select ‘probability of brook trout occupancy.’ A histogram of all the data will appear. Take the little plus sign cursor, and click on the blue line going through average value, and drag it to 100%. You’ll notice a box appear to highlight watersheds within the desired occupancy probability. By moving the upper and lower limit of that box, you can highlight watersheds with desired occupancy probabilities.
By using the drop down tools in the "Catchment Filters and Histograms" menu, you can start narrowing down watersheds that meet certain criteria.
Play around with the other variables. One of my favorite comparisons is to see how stream temperature rise might affect brook trout occupancy probability. It’s particularly interesting to zoom out at larger scales, and see larger trends in projected declines. Look how many watersheds are expected to lose quality brook trout habitat.
Watersheds with >80% probability of brook trout occupancy today (left), with 2C (middle), and 6C (right) increase in stream temperature. See how the number of watersheds declines with temperature? Click on the pictures for a closer look at model results.
Finally, what if wanted to play around with two variables? For example, we know that brook trout like forested watersheds, and you can visualize that relationship by clicking on both “% forest cover’ and ‘probability of brook trout occupancy.’ Again, play around with the sliding rules and see how forest cover affects occupancy.
You can click on multiple attributes in the "Catchment Filters and Historgrams" menu (as seen on the right). From there, you can slide the histograms around to see, for example, the watersheds that have a >60% probability of brook trout occupancy when there is <50% (middle) and >50% (right) forest cover in a watershed. As you can see, the number of watersheds with higher occupancy probabilities increases considerably with more forest cover.