While my lab blog is usually reserved for science, I’ve been having conversations with colleagues about how we’re managing our labs during these uncertain times and I think there are a few things that are useful to share.
The thing I’ve been sharing with folks in my lab is that there are three buckets into which everything in your life fits at this point:
I typically start my lab meeting with a round-robin where everyone reports their accomplishments from the prior week and priorities for the current week. We start our virtual lab meetings with a round-robin with stating how each person is doing and by naming one “exhilarating” thing that each person has done in the past week. It is pretty entertaining to hear what counts as exhilarating these days. We’ve also added a weekly virtual lab social. It is unstructured and a “show up if you want” event. I’ve also added 30 minute weekly meetings for everyone in my lab. This has been especially important because without being on campus there isn’t the opportunity for impromptu chances to talk about our research.
The final thing that I’ve learned through these past few weeks is that it is my job to make sure that people don’t get stressed out over their drop in productivity. Productivity is going to vary and you’ll have good days and bad. This gets back to bucket 3. You cannot control, on any given day, whether you’ll have the focus to be productive. Take it as it comes.
Additional advice for students and postdocs
Do not expect that if you’re having a tough time with something that your advisor will pick-up on it. Be upfront with what is going on. We’re all dealing with a whole bunch of stuff that wasn’t a concern before the pandemic started.
Be a source of support for your lab-mates. Each individual’s ability to deal with all that is going on will ebb and flow. Check-in with your lab mates regularly. If you think of something your advisor can do to facilitate you engaging with each other, let them know.
When you’re having a period where you can’t focus on your work, don’t force it. Take a walk, do something else in bucket 1, do something to help someone else, sit on the couch and devour a pint of ice cream while binging on some show… You get the point. There are going to be things that you stress over (e.g. data collection, etc) that you don’t have control over and belong in bucket 3. Leave them in bucket 3 because it is not worth playing out the potential scenarios when there are too many variables that are rapidly changing. You can rest assured that there will be plenty of time to figure out that stuff out when we have better information.
We’re all having to make decisions with imperfect information. There will be fallout from some of those decisions that we’ll have to deal with at a later date. Right now, that fallout belongs in bucket 3. I’ve had to make some decisions that are likely to cause problems for me down the road and I don’t care. I’m making the best decisions I can with the information I have available. When this is all over, all that matters to me is that I did the best job that I could to take care of the folks in my lab and that we as a group did the best we could, given the circumstances.
The area burned by wildfire in the southwestern US has increased by over 400% since the 1980s. While many of the forests in the southwest are adapted to deal with frequent-fire, we’ve had a long fire-free period because of fire suppression. This has allowed forests to grow dense and fuels to increase. As a result, we are now seeing increased area burned by large, hot, tree-killing fire, which is uncharacteristic for many southwestern forest types. This is problematic for a number of reasons, but becomes especially problematic when a large, hot wildfire burns through a watershed that serves a community.
The Santa Fe Fireshed is approximately 111,000 acres and encompasses the City of Santa Fe’s municipal watershed. The Santa Fe Fireshed Coalition is a collaborative group working to develop and implement management strategies to reduce the chance that a large, hot fire impacts the municipal watershed.
In a recent study led by Dan Krofcheck, we ran simulations to quantify the effects of different management options on fire severity and carbon dynamics in the Fireshed under future climate and future fire weather. We used the Fireshed Coalition’s implemented, planned, and proposed treatments to develop and simulate a treatment scenario we called the prioritized scenario. We also ran a series of simulations without management (No-Management) to identify the areas with the greatest risk of tree-killing fire. We used these scenarios to develop the optimized scenario. The optimized scenario differs from the prioritized scenario by only thinning the areas with the largest chance of burning at high-severity and increasing the area that is only treated with prescribed fire.
We then ran simulations using future climate from different climate models, ending with a total of 6250 simulation years of data for each management scenario (No-Management, Prioritized, Optimized). We compiled all of the data for each scenario to determine if the optimized scenario was as effective as the prioritized scenario at reducing high-severity fire. We found that it was actually a bit more effective because the area that was treated with prescribed burning was expanded to include dry mixed-conifer forest.
We also looked at the effects of these different scenarios on carbon, because forests are important for helping to regulate the climate. Since thinning treatments reduce the amount of carbon stored in the forest and prescribed burning causes emissions of carbon to the atmosphere, we expected both treatment scenarios to cause the amount of carbon stored in the forest to initially decrease, relative to the no-management scenario. However, because the management scenarios decrease the amount of tree-killing fire, we expected that over time, the carbon stored on the landscape would increase relative to the no-management scenario. We found the while both management scenarios ended up storing more carbon by 2050, the optimized scenario carbon storage surpassed the no-management scenario in approximately 25 years. In fact, the optimized scenario ended up storing approximately 0.3 teragrams more carbon than the no-management scenario. That is equivalent to the annual carbon emissions from 15,000 average Americans.
The reason that the optimized scenario carbon storage surpassed the no-management scenario twenty years earlier than the prioritized scenario is that we thinned less area in the optimized scenario. By only thinning areas with the largest chance of burning at high-severity, we reduced the thinned area by 54%. It is important to note that the only reason the optimized scenario was as effective as the prioritized scenario was because the area burned with prescribed fire increased by 27%. Our results suggest that in this southwestern landscape, restoring regular surface fire will provide more climate benefit than leaving the forests dense and running the risk that they will burn at high-severity.
We are seeking a motivated and independent postdoc to advance the state of the art in remote sensing and geospatial data integration in the field of ecosystem ecology. The successful candidate will work with the Landsat and Sentinel archive in conjunction with very high resolution drone acquired imagery to investigate how vegetation and topography govern microclimatic variability in post-wildfire landscapes. The objective of this project is to quantify influences on post-disturbance microclimatic variability and its effects on tree seedling survival. The Earth Systems Ecology Lab (www.hurteaulab.org) is an interdisciplinary group of ecosystem ecologists in the Department of Biology at the University of New Mexico. We work collaboratively to tackle a range of question related to global change and forest ecosystems.
We are seeking an individual with a quantitative ecosystem ecology or remote sensing background that is fluent in R or Python, has extensive geospatial analytic experience using any GIS, and experience with model-data integration. Familiarity with UAS data acquisition and processing using Agisoft and with geodetics (GNSS, RTKLIB) are a plus. Starting salary is $48,000 plus benefits. The position is initially for one year with the potential for extension. Preferred start date is fall 2019. To apply please send your CV, two-page statement of research interests, and list of three references to Matthew Hurteau (email@example.com) and Dan Krofcheck (firstname.lastname@example.org). We will begin reviewing applications 23 September.