Hatcheries...The Bad, The Good?
With genetic analyses mostly complete, it was time to step back and look at the big picture of what the data were telling us. And, what it said was that there might be considerable stocking influence in some populations. So, we need to first figure out which hatcheries are stocking in Loyalsock Creek, sample those fish, and then re-run analyses to see how much hatchery stocks influence natural populations. So, road block..at least for now.
This discussion made me realize another page on this website could use a little updating. Specifically, under the "Research" tab, I have information that answers some important questions that often come up in discussions with anglers and citizens about trout and fish management. So, this week I added to that list and answered the question "how do hatcheries influence brook trout?" Click HERE to go to that page.
How to be a Good Technician
For many field biologists, the start of a new year means that it’s time to get serious about summer field season preparation. There are supplies to order, permits to submit, and, almost certainly, technicians to hire. In fact, I have probably seen close to 50 advertisements for summer technician jobs cross my email this week. Tis’ the season.
If you are currently an undergraduate student, have recently graduated, or might be thinking about making a career change, these seasonal technician jobs are exactly what you should be looking for. They generally only span the summer, so they won’t interfere with classes. And, if you hate the work, you won’t be forced to do it longer than three months. In short, it’s a low-risk way for you to test the waters on a possible career path and gain experience that will move you up in the applicant pool for future jobs, all while getting paid.
But, don’t let the temporary status of the job fool you. Many (most?) biologists got their first “break” in the field by excelling as a technician. While your contract might end in August, there are often opportunities for excellent technicians to continue working part-time after the semester starts, get invited to join other crews, or potentially even have their name on resulting publications. And, great technicians get great recommendation letters for awards, scholarships, jobs, and graduate school. I even know several people who were directly offered graduate positions after working as a technician for their advisor.
So, what do I mean when I say “great” technician? It varies from job to job, and person to person, but I’ve tried to come up with a list of the top 10 things you can to impress your boss as a technician.
1. Be on time. And, by ‘on time’, I really mean at least 10 minutes early. This is especially true if you are doing field work, which requires a lot of packing, hauling, and planning. Supervisors try to prepare all of that in advance, but only rarely does everything go as planned. So, make a habit of showing up early and offering to help. Ten minutes of unpaid time before the start of the day can go a long way. And, it goes without saying, don’t make a habit of being late.
2. Ask questions. If you’re uncertain about what you should be doing, ask. If you want to make sure a number is right, ask. I would much rather answer the same question 50 times and know you are collecting the data correctly than months later find out it was all done wrong (and trust me, someone always finds out eventually). Likewise, if you want to know more about the research project or why the data are being collected, ask. No question is stupid. Personally, I think one of the most impressive things a volunteer or technician can do is ask questions about why I am doing my research. It tells me they are engaged in the science behind the effort, which generally means they are more invested in collecting good data and are interested in the project beyond just the paycheck.
3. Read my mind. Seriously. I know I just harped on asking questions, but eventually you should be able to think one step ahead of your supervisor and do things without being asked. If you go to five sites and the first thing you’re told to do at every site is to fill a bucket with water, then by the sixth site you should hop out of the car and immediately go get water. Take initiative, be proactive, and step-in where you’re needed. The best field crews are those where everyone knows what the goal is for the day and can fluidly, without much direction, work together to achieve those goals. It takes some time to settle into the routine, but, once you do, the work goes a lot faster and the day is much more relaxed and enjoyable.
4. Keep your head in the game. Technician work is often not the most fun or exciting work. It’s data entry, repetitive habitat measurements, video analysis, etc.- we’ve all been there, we can all sympathize with how endless the days feel. To make the work bearable, you’ll need to find something to keep yourself mentally engaged with the project. Maybe you can listen to music, talk with other crew members, volunteer for other projects, or just enjoy the fact you’re working outside. Do whatever it takes. People that can stick with the very monotonous jobs are often the people that I rank highest on my list because I know they are interested in the job beyond just the fun stuff.
5. Don’t be disrespectful. While true for all jobs, this tip is geared specifically towards technicians who will be working with a graduate student. The age gap may be small, and in some instances you, as the technician, may actually have more experience than the graduate student. But, don’t suggest you are more knowledgeable about a topic, try to dictate a schedule, or redesign their field study (this may seem ridiculous, but I’ve heard this happen on many occasions, particularly with Master’s students and their technicians). You can, and should, offer a suggestion about a better method or a way to save time. But, at the end of the day, this person is your boss and there’s probably a very good reason behind their study design and methods. Relatedly, if you’re working with a new graduate student, expect some degree of chaos. Leading a field crew and collecting data on your own is hard and stressful. They won’t always have a clear vision of what’s going on, but you can help them tremendously if you try to keep the project organized.
6. Be flexible. Sorry in advance. I probably don’t know what your hours are going to be, but it very likely will include night and weekends, and almost certainly over 40 hours a week. Sometimes you won’t get paid for all of those hours. I’ll cancel work within minutes notice because of rain, and sometimes ask you to live in sub-par housing. This entire profession is about being flexible and adaptable, and the more you are willing to roll with the punches the more you’ll impress your supervisor and be given more opportunities.
Talk about flexible. Last year my technicians were hired with the promise of doing laboratory genetics work. Needless to say they never touched a lab. Here, Nate tries to unlock the door after locking his keeps in the car just outside of cell phone service (we ended up breaking the window) and Laurel huddles under a bucket in a rainstorm. A shame they don't actually like fish, because I would rehire them in a heartbeat.
7. Hustle. Field work is all about packing 20 hours of work into a 10 hour day. That means there’s not much time for breaks (my crews are notorious for shocking with one hand and eating with the other), small talk, or correcting mistakes. Move purposefully between tasks. Be efficient. Be mindful. Stay focused. At the same time, make sure you aren’t sacrificing the quality of your work by trying to rush through it.
8. Volunteer to do the dirty work. Carry the heavy stuff, run back to the truck, stay late to prepare for tomorrow. Your supervisor can, and should, do some of this. But, it makes them infinitely happier if there is someone willing to carry the load.
9. Be willing to try new things. Yes, the average day will be too busy for your supervisor to show you a new skill. But, if your supervisor can tell you are committed and invested (because you have exceeded expectations in the areas above), they will find time. The whole point of a technician job is to gain experience, and you won’t do that by sitting on the sidelines. And, don’t worry about being good at everything when you first try it. Everyone starts somewhere, and the best place to learn is when someone is there to help teach you. By the time you start graduate school or take a full-time biologist job, your boss will assume you have certain skills that you can learn as a technician.
10. Have fun. There’s a chance you won’t fall in love with the work you do as a technician (but, you might!). And, you may be signing up for three months of bug bites, sunburns, sweat, and exhaustion. You need to find something to keep the days entertaining and fun. Luckily, you’ll probably be joining a crew of people who have done summer field work long enough that they’ve gone just a little bit crazy. Follow their lead, prepare to learn and work hard, and just have fun. In a few years you’ll look back with fond memories and great stories.
I tried to limit my list to things anyone, regardless of background and experience, can do to be a good technician. If you want bonus points, I would suggest you up your knowledge of species identification for the system you work on, know your way around a toolbox, be comfortable working in remote locations, and try to get a job as early in your undergraduate career as possible. Starting early means you have more opportunities to gain experience, and more chances for me to re-hire you on my crew.
If you think I’ve missed something, leave a comment below!
I’m in Virginia this weekend where I’m currently watching snow accumulate and my little snow pig root around. The ruler is reading around six inches, which in central Virginia is enough to lock the grids and cause chaos. Guess I’ll have to lounge on the couch all day. Shucks.
Readers of my post from last week may recall mention of some genetics models my computer was slowly cranking out. Yep- those are still running. But it’s close to finishing and I think I can predict what the results are going to be. It’s potentially really interesting, but, I don’t want to report any findings, no matter how preliminary, until I’m confident they can hold at least a little weight. Plus, I’m meeting with some collaborators next week and I think they will recommend a few tweaks to the analysis that will help clarify some oddities and rule out other possibilities. But, soon. Promise.
In the meantime, I’ve started the early stages of preparing the publication that will formally report the results from this study. This process is always a little daunting. It usually starts with a blank Word document, a lot of distractions and procrastination, and many frustrations as I try to find the perfect words and the perfect topics to tell the perfect story for the data. I call this stage “unproductive circling,” and for me it can last for days. Then, I break down and just start typing. The resulting text is usually a horrible mess of incomplete thoughts, poor grammar, cuss words, and confusion. But, it stops my wheels from spinning so I can get traction and move forward. From there it’s all about refinement- reread, rewrite, reword. Over and over until the paper you were so excited about becomes utterly boring and dull. That’s when you know you’ve done your job correctly.
In case you're curious, this is an example output from the models that are running on my computer. I'll explain what these mean later, but for now, populations (numbered at the bottom) are more genetically distinct if they are shown as a solid block of color. So, for example population #3 is more genetically distinct than population #4, which COULD indicate that #3 is more isolated than #4.
For those unfamiliar with scientific publications, it starts with an introduction. Usually, it’s a couple pages in length explaining what the scientific community already knows and justifying why your research project was needed to plug an information gap. Many writers will disagree, but I really enjoy writing introductions. There are millions of research articles out there, and it’s your job to figure out why your lone study is still important to science. It’s a messy thought exercise that ends in a succinct story. It’s as close as scientific writing gets to poetry.
Much of this week was spent “circling.” I did all I could do to avoid writing, but I finally opened the blank Word document and stared. The problem I had was trying to decide what about my project was interesting. I know that sounds a little backwards. After all, shouldn’t you know your research is interesting before you do it? Yes, but that’s often not how research works. It’s only after it’s all done that you realize you got an unexpected result, or you collect data knowing it will be of interest to someone, but don’t know who that someone is until you’re nearly finished. So, I kept circling between two fundamental questions- is this study interesting because it’s brook trout, or is it interesting because it’s a genetics study of an aquatic organism?
While the answer is both, it turns out that the impact of the study is probably further reaching if I stretch my mind beyond brook trout. Sure, the study will be important for brook trout management. But, can it tell us more about genetics in general?
As I struggled with this question I do what I normally do- I started chatting with someone online. Luckily for me, my friend, Will, happened to be on. He studies genetics in many several wildlife species and knows far more about the topic that I ever hope to. He has been my go-to person during this process because he can give me insights into why something isn’t working or a next step. As we were talking he turns my attention back to a paper that has been sitting in my “to read” library for a long time.
To be honest, there is nothing earth shattering about the paper. It describes ways organisms (and their genes) can distribute throughout streams. But, the paper is incredibly necessary because streams are a special type of habitat. Many genetics studies are conducted on terrestrial organisms that can disperse in any direction. But, aquatic organisms are restricted to streams (for at least part of their life) and so have very different controls on how, when, and where they can move.
In some ways, you’d think streams make genetics models easier. Movement can only be up and down. But, that’s not entirely true for organisms like salamanders and macroinvertebrates that can fly or walk on land. Even for fish, which really are restricted to in-stream movements, models easily get confusing when you meet a tributary. Now movement isn’t just up and down, but the fish can pick any number of tributaries to occupy.
Taken together, suffice to say streams quickly complicate gene models. But, this paper was helpful because it packages just about every possible way stream-dwelling organisms move into four models. (And, don’t get scared away by the term “model.” It this case a model doesn’t involved any complex math or equations, it’s just a possible scenario for how the world works.)
The four models included:
Imagine from Hughes et al. (full link to the paper below). In these images, imagine that there is a stream flowing north, and another flowing south. The dots represent populations where colors represent genetic similarity. Under the death valley model (A), there is no exchange of individuals between populations. For the stream hierarchy (B), organisms move within a waterrshed, but do not cross watershed boundaries (so genetics are similar for populations in the north flowing stream, but different between the north and south flowing stream). In the headwater model, organisms look for the next closest headwater, which is often in an adjacent watershed (so genes are shared among populations that are close in overland distance, regardless of watershed). And, in a widespread gene flow model, distance doesn't prevent movement and all populations are genetically similar.
Conceptualized models, such as those described above, may not completely explain a single dataset, but it does help quickly put a dataset into perspective. We know that trout shouldn’t conform to the widespread gene flow or headwater models. Both of those require overland dispersal or very, very far stream distance dispersal.
Theoretically, trout should conform to the stream hierarchy model. We know that trout move, and they are more likely to move within their watershed than outside. And, previous studies have found evidence to support that trout genetics follow other requirements of the of a stream hierarchy model.
But….(and here is why my study is special), most trout genetics studies are done at fairly small spatial scales. Usually 10 sites that are near one another and that are part of a larger watershed network. We studied 28 populations located throughout the Loyalsock Creek watershed. Near as I can tell, there isn’t a published study on brook trout that sampled as many fish from such a large distribution. So, hopefully, we can tease apart which genetics model most explains brook trout.
Why does this matter? From a practical view, it can help in management. Management is most often applied at a watershed scale, but that’s not often the scale scientists do genetic studies. So, our gene data, which is arguably one of the most powerful tools we have, is not informative to our management efforts- at least not without a lot of assumptions. But, if we know that brook trout genes, at a watershed scale, tend to conform to one of the aforementioned models, then suddenly we can make more informative guesses (notice I say guesses) about how trout are moving in watersheds where we haven’t done a robust genetics analysis.
From an ecological perspective, understanding which model brook trout conform to could be just the kind of interesting quirk ecologists dream about. From the telemetry data, we know brook trout move among tributaries. But, if they still conform to a death valley model, it could mean that they return back to their home tributaries. Brook trout, after all, are closely related to salmon. Or, it could be a mix of models, in which case we ask why some populations have adopted different behaviors that has then lead to distinctly different models of gene flow.
I have a hunch as to what is going on. But, only time will truly tell…
If you’re interested in reading the paper I described above, it can be found here.