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.