I hope absence makes the heart grow fonder. After a long hiatus, I’m back with another research update. And, I have to say, it might be the most interesting (and hopefully most influential) project I worked on for my Ph.D.
But, first, let me set the stage for those of you who may be new to this blog. I work on brook trout ecology in the Loyalsock Creek watershed in Pennsylvania. The watershed is mostly forested, making it a great home for my Ph.D. research on brook trout population response to climate change. I’ve previously reported on other findings from my research showing that interbreeding between wild and hatchery fish was fairly minimal throughout the watershed and also some preliminary telemetry results showing that some brook trout seem to move from the small tributaries into the mainstem river after spawning season.
But, the question always remained, what happens to fish that get into the mainstem? There are a lot of predators in the mainstem and water temperatures in the summer far exceed brook trout thermal tolerance. So, many speculated that fish that got into the mainstem probably died within a few months. Moreover, our telemetry observations only found that a handful of fish seemed to have this migratory behavior. So, even if they did survive, could their behavior really drive any sort of population-level response?
The answer is a resounding yes.
I’m basing that response on a study we (myself, my advisor, and very importantly a collaborator in the statistics department) just completed that looks at genetic connectivity of brook trout populations across the Loyalsock Creek watershed. As you may recall from previous posts, maintaining and increasing connectivity among populations is one of the most important management tools we have for increasing population persistence and resiliency to future disturbance. And, we can measure the degree of connectivity between two populations by measuring the degree of genetic similarity. This isn’t so hard- we take a little fin clip from a bunch of individuals in each population of interest, from the fin we identify the genes present in each population, and then using some computer software we estimate the degree of connectivity.
Knowing if two populations are genetically dissimilar- and thus disconnected- is great, but it doesn’t necessarily explain why those populations are isolated from one another. Sometimes it’s easy. If there is a large waterfall that separates two populations, then it’s reasonable to assume that few individuals are moving back and forth between those populations and therefor connectivity is low. Other times it’s not so clear. There could be a hidden barrier (perhaps a road crossing with bad fish passage or an area with a steep slope), or it could be that our assumptions about what limits fish movement (and thus population connectivity) are wrong. That last point is important, because if we don’t know what we are looking for then we will never be able to identify and fix areas of stream that are reducing population connectivity or conserve areas that are important movement corridors.
So, we used some really fancy models (hence the phone-a-friend to the stats department) to essentially determine how various features in the watershed either resist or increase gene flow. We call this a riverscape genetics study- essentially seeing what features of the riverscape (which is like a landscape, only for streams and rivers) are responsible for producing the observed patterns in genetic connectivity. And, remember, individual fish are just bundles of genes, so this analysis is a proxy for determining which features of the watershed increased and decrease fish movement.
To run the analysis, we identify a bunch of variables we think could influence gene flow, and then let the model tell us whether there is actually a high probability that gene flow is influenced by each variable. So, we thought about it and decided to include 12 variables. This included some of the usual suspects like stream slope, road crossing density, and large barriers (like waterfalls), as well as some more unusual variables like distance to mainstem Loyalsock and a few things that essentially measure the location of a stream within the watershed. After it was all said and done, we found support for just four variables that influence gene flow in Loyalsock Creek, including:
Why am I so excited bout this study? First, for any fish biologists reading this post, the model we used is new, and I’m hoping it provides a framework for future riverscape genetics analyses (so, contact me for details!). Second, and most importantly, it definitively shows that the mainstem is not only brook trout habitat but may be some of the most important brook trout habitat in the watershed. Because larger rivers are thermally unsuitable for coldwater fishes during summer and don’t have large resident trout populations, they generally don’t receive the same conservation status as small tributaries. However, these rivers are critical migration corridors that are responsible for increasing population connectivity.
This study also gives some insights into how future disturbance could influence brook trout population connectivity. With climate change we are generally expecting increased floods and droughts- both of which will change stream flow patterns and could limit the ability of brook trout to move through the mainstem. This is particularly true given that there is only a small window of time where thermal conditions are suitable for brook trout to use the mainstem, and so disruption of flow for even a short period could have large effects on trout populations. Additionally, human disturbances that alter flow patterns, either through direct water withdrawals or watershed disturbances that result in a lowering of the water table, could influence flow patterns in larger rivers as well as increase the periodicity of flow in intermittent stream channels. So, if we want to maintain future brook trout population connectivity, we probably need to start thinking beyond just removal of physical barriers and conservation of natural stream flow patterns.
Finally, a word of caution. This study was conducted in Loyalsock Creek and, while some of the findings likely do translate to other watersheds, I would expect the results to change depending on the location. For example, as a largely undeveloped watershed, variables like road crossings and watershed development were not important for explaining population connectivity. These features undoubtedly influence brook trout populations, they are just uncommon in Loyalsock. But, I’m looking forward to this model being applied elsewhere and seeing how the results change across watersheds.