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They Don’t Make Them Like They Used To

5/27/2017

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PictureYou're not going to catch that many large brook trout, especially from a stream, today.
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.
​ 
Thanks, Dad.
 
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? 

PictureSome of the mostly closely monitored fish populations are in marine species, including snapper.
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.

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The Fate of Fish at Falls

5/21/2017

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PictureStrap a boom to the Maid of the Mist....it needs to go shocking!
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.
 
Picture
Example output from a STRUCTURE analysis on weeping fig by Liu et al. I've added annotations to help explain all the moving parts.
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.
Picture
STRUCTURE output from a population of brook trout living downstream and upstream of a waterfall on Weed Creek.
PictureThe falls on Weed Creek. They don't look all too menacing, but apparently the fish don't like them.
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.

Picture
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. 
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A Year Later

5/12/2017

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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?
  • Collecting telemetry data isn’t THAT hard.  When I started my Ph.D., I never imagined I’d do a telemetry study. And, honestly, I had no desire. The combination of electronics, lingo, and uncertainty really dissuaded me from wanting to purse this costly endeavor. But, I knew it was a great skill set, and I was assured by many colleagues that I’d catch on fast. I was reluctant to believe them (I think I even called many of them liars to their face), but they were right. The first few days were exhausting and confusing, but I caught on. In the end, everything I was worried about didn’t really matter.  What mattered was knowing which rocks absorb tag signals and needed to be tracked carefully, developing expert skills in “I Spy the Antenna” when trying to confirm dropped tags, knowing the appropriate defrost settings to use in the truck when drying out a receiver that took a belly flop into the stream, and finding a way to silence the phantom chirping noises from the receiver that you hear in your sleep (that thing really haunts you at night). 
Picture
In case you're wondering, 30 minutes on medium heat can dry out the inside of a receiver box, but I would not recommend.
  • Analyzing telemetry data is THAT hard. It seems easy. There are points on a graph, and you just need to measure the distance between the points to see how far a fish has moved.  But, I have nearly 3,000 points, and the distance between points needs to be measured with as little error as possible. That’s tough because GPS points collected in remote locations (like Loyalsock State Forest) are inherently inaccurate because satellite reception is limited. So, there’s a lot of work done back in the office to get the points as close to the right location as possible. Then, I have to make judgement calls about whether the movement data truly represent a fish, or whether I think it’s actually a dropped tag. Tags that never move can’t always be trusted, but neither can tags that only move downstream. So, I look at the GPS points and all the notes and make a decision for every individual tag, 180 in total. Only then can I start measuring movement distances. Thankfully there are software programs that can automate this process so I don’t have to individually measure the distances by hand. But, it requires coding data into the computer and double checking that it’s working correctly. I still haven’t finished all of this, but I’m close.  Then I can finally start actual data analysis. I know many of you are interested in hearing about fish movement and our results, but the road to the information is not as smooth as it may seem. 
Picture
A little snapshot of what it looks like to measure fish movement distances.
  • But, it’s all worth it because the telemetry data are really interesting. Without it, we wouldn’t have figured out that brook trout move into Loyalsock Creek after spawning, and that these movements are the basis for a metapopulation (metapopulation is just a fancy word for several smaller populations that are separated, but sometimes share individuals among them). It’s a fairly rare discovery. While a lot of anglers and biologists assume that this type of movement pattern exists in brook trout, it doesn’t happen everywhere and its hard to document. But, it’s extremely important to find these metapopulations so we know how to properly conserve brook trout. Now we know without doubt that Loyalsock Creek is important brook trout habitat, and that we need to preserve connectivity between tributaries and the mainstem in order to conserve brook trout in that region. Or, more simply, brook trout conservation- which includes anything from habitat restoration to considerations about fish stocking- can’t ignore mid-reach waterways if we want to preserve the genetic integrity and health of native populations. 
Picture
The little fished that proved metapopulation dynamics in Loyalsock Creek.
  • Speaking of genetics, somewhere along the way I picked up several projects that focus on genetic and molecular ecology.  My hatred for these topics wanes a little with each day, but I never envisioned I would be where I am today.  It started innocently with population genetics. Though this level of genetic study was (and still is, actually) far beyond my level of understanding, I knew the tools needed to complete it were readily obtained from textbooks and conversations with friends working in that field. Now that the data are largely in hand and results are rolling in, I can’t believe I tried to get out of studying genetics. Without the genetic data, we wouldn’t be able to tell that movement into Loyalsock Creek is effective- meaning fish that move into Loyalsock Creek go on in future years to spawn outside of their home tributaries. This just proves to us that the movement we saw with the telemetry data isn’t random or unintentional.  There are fish out there are that ‘hardwired’ to move back and forth between tributaries and larger mainstems, and they are the ones keeping the metapopulation connected. 
Picture
With a simple clip of the fin, we can get a whole lot of data on genetic diversity and population connectivity.
PictureTo monitor gene expression, we not only have to sample fish in hot temperatures, but also cold. That's okay..eventually you don't feel your fingers, anyway!
  • And then, to this day, I don’t know how this happened.  Maybe it was one of those “no means yes” deals, or just a reckless decision made before the coffee kicked in, but I found myself leading a study of gene expression and microRNA. Unlike population genetics, those aren’t terms you can readily Google. And, I don’t really know anyone that studies gene expression that I can run to with insanely stupid questions (so, sorry to Luke, our collaborator on this project). But, I’m learning as I go.  I’m learning that brook trout can survive just fine without a few gill filaments, that drawing fish blood isn’t all that hard, and that you will get awkward stares when you set up a mobile centrifuging station in the parking lot of a gas station. I’m also learning gene expression data can be really interesting. It turns out, brook trout turn on the machinery (i.e., genes) to express heat shock proteins very early in the year, around mid-April. These proteins help keep cells alive when it’s hot, but “hot” to a trout appears to start around 50°F. It’s important to know when fish start expressing these proteins because, as we’re learning, there is a limit to how long the genes can stay on and how much heat shock protein can be produced.  By the time it’s actually hot in July, the genes are no long actively cranking out heat shock proteins and fish have to use whatever is left to protect their cells. If the genes turn off too soon, then fish could be left without heat shock proteins to keep their cells alive. This result has implications for future climate change, as we’re projecting stream temperatures to warm earlier in the year and reach higher maximum temperatures. So, the question is, will fish have the molecular capacity to keep up with these temperature rises?
 
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!
 

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Big Data at Your Fingertips

5/6/2017

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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. 

Picture
The landing page when you first open the SHEDS website. Don't worry if your's looks a little different than mine, it seems to never load the same way twice.

​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.
 
Picture
You can see that going from HUC8 to HUC12 really decreases the size of the watershed. Also note that HUC12s are so small that all the data can't load at once, so you'll have to define a specific region.

​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. 
Picture
Zooming in and hovering over one of the HUC10s for Loyalsock Creek, you can see that the average summer temperature in 16.1C. My data from this summer would agree!

​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. 

​This was a really quick rundown of an amazing tool.  More information on how to use the web interface can be found at http://ice.ecosheds.org/, and details about some of the models used to generate the data can be found at here and here. 
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