Genetics Make Terrible Guests

Results of genetics assignment tests for six streams near the telemetry study area. Each pie chart represents the proportion of fish in a population that that were born in that stream. For example, Double Run shows a mixture of fish from Double (black), Shanerburg (red), Pole Bridge (green) and locations outside of the study (grey).

Last week I presented some preliminary genetics results using the image to the right, and used it as evidence that brook trout moving into Loyalsock Creek likely spawn outside of their home stream in later years.  To put it another way, the fact that we see a mixing of genes at most locations (except for Mill Run), means that these populations were at least historically connected (and the telemetry data suggest at least some of them probably still are now).
 
Genetics data are such terrible guests for a blog. They come in unannounced, make a mess of the place, and don’t know when to leave.  In this case, the mess they created was a lot of questions about what we expected this data to look like. While I’m excited that people want to know more, the answer just isn’t that easy.  
 
For starters, my expectations of this dataset were zero.  As you may recall, I hate genetics.  My feelings towards the field are starting to evolve into a past-tense form of repulsion, but every day remains a struggle.
 
Those with a basic understanding of population genetics may have looked at that figure with doom and gloom and wondered why in the world those populations are so isolated.  Separated by only a stone’s throw, we would expect all of those populations to be genetically very similar. This is particularly true when we think of land dwellers, which are generally more mobile than aquatic species.  In fact, for many terrestrial species and larger-bodied fishes, we’d probably have to separate populations by many miles before we start seeing genetic isolation similar to the above diagram. 

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But, those that study headwater fishes may have found the results to be of no surprise. Most headwater fishes have very limited dispersal, owning to the fact that downstream habitats become increasingly wider and hotter with faster flows, making them less suitable environments for species that have evolved to live in tiny streams. There are also more predators downstream, so small bodies that are perfect for small streams quickly get eaten by larger fish like bass, pike, etc.
 
All this to say, interpreting genetics data is a not entirely straight forward.  We can get numbers that tell us things about genetic diversity, population isolation, and anything else you might be interested in knowing. But, that’s not the full story, and those numbers are actually meaningless, and potentially dangerous, if used out of context. Our expectations for what the results should be really depend on the species, study location, historic stream use, stocking, etc., etc. It’s a bit of a detective game. So, for those interested in specific numbers describing diversity, FST, AR, NE, rxy, you’re out of luck. They’ll appear in a publication eventually, but only sparingly in this blog.
 
What I will do is compare, in broad terms, how our data stack up against other studies of brook trout. For starters, our genetics data cover far more than just the sites in that figure.  We sampled 28 streams across the Loyalsock Creek watershed, making our study one of the largest scale studies of brook trout population genetics. 

We covered some ground in 2015 when we did our genetics study. Our 28 sites have a good mix of locations nearby and sites really far away allowing for some interesting comparisons.

Usually changing the scale of a dataset means that you can use your results to answer new questions, questions that may be more appropriate for how we manage species.  For example, genetics data are usually collected at the scale of a single stream or maybe a few streams in a small area.  However, we don’t generally manage fish at this small of a scale. We generally make management decisions for entire watersheds. So, given that we now have genetics data for an entire watershed, it makes sense that we can now shed new light into the population genetics and management of brook trout at a watershed-level.
 
 Wrong. But, don’t feel bad. I led you on.
 
The reason our dataset isn’t all that revolutionary, at least at the surface, is because brook trout populations are known to quickly isolate, even at fairly small spatial scales. Even side-by-side tributaries can be isolated from one another. So, if we see isolation at small scales, it’s not that surprising to see isolation at large scales. And, it’s no secret that that is what we’ve found in much of our dataset. Sites that are separated by 3+ miles are isolated from one another, and that’s pretty typical for brook trout.
 
That said, brook trout genetics are very diverse, particularly at sites separated by less than a mile. Sometimes they are genetically different, and other times they aren’t.  To me, this is where things get interesting.  If two sites are separated by the same distance and similar habitat, why do we sometimes see isolation and other times connectivity?
 
I don’t have an answer, but we’re hoping to explore this question with our dataset.  And, we have some preliminary ideas.  For example, we see that sites near a mainstem river system seem to have more connectivity than sites that are connected by a mid-reach run. What is it about mainstem rivers that makes them better for connecting fish populations?  Or, to really wig your brain, what is it about fish living near a mainstem that makes them different (i.e., more mobile) than fish living higher in the headwaters? 

Salters and coasters are known for getting large and, as you can see in this salter, losing coloration during migration.

That last question may seem a bit far-fetched, but brook trout are known for having a diverse range of life histories.  Many of you may be familiar with “coasters,” brook trout living in Lake Superior that make long-distance migrations into the lake’s tributaries to spawn.  There are also “salters” which spend a significant portion of their life in saltwater before returning to smaller freshwater tributaries to spawn.
 
Are fish moving between headwaters and mainstems (like we see in Loyalsock) a true life history variant? If so, how cool would that be? We’re a really long way from being able to say anything about behavioral variation in populations, but I will say that we aren’t the first study to document such dramatic differences in individual behavior.  So, there’s support out there for the idea.
 
But, going back to the main question, how do our data compare to other brook trout population genetics studies? That’s an easy, albeit unsatisfactory, answer. Previous studies showed a lot of variation, and our study shows a lot of variation.  So, Loyalsock Creek, as a whole, is not more or less isolated than we would expect given other studies. When we zoom in we see patterns were certain sites do seem oddly disconnected, and others more connected than we would have thought. And, seeing if we can explain that variation is going to be what makes our study so interesting. 

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