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 (mhurteau@unm.edu) and Dan Krofcheck (krofcheck@gmail.com).  We will begin reviewing applications 23 September.

Across the western US, the area burned by wildfire has been increasing as a result of higher temperatures and earlier spring snowmelt.  When temperature goes up, ecosystems dry out and become more available to burn.  This relationship has formed the basis for projecting future area burned as a result of climate change.  With the projected increase in area burned, the expectation is that wildfire emissions will also increase.  In a prior study, we estimated a 19-101% increase in emissions from wildfires burning in California by the end of this century.  However, the majority of fire projections, including our future work makes the assumption that there will be vegetation available to burn if a fire occurs.  The problem with that assumption is that fire is a self-limiting process, meaning for some period of time after a wildfire occurs, there is not enough vegetation available to support another fire.  Further, even if enough vegetation is present to support a second fire, the amount of vegetation may be lower than the first fire and result in fewer wildfire emissions.  To determine the effects of prior wildfires on future wildfires, we modeled this vegetation-fire feedback by simulating forest growth and wildfire under future climate across three transects in the Sierra Nevada (Figure 1).  We re-estimated wildfire size distributions at each decade from 2010-2100 to account for the effect of prior wildfires on future fire size.  We used the area burned and the type of vegetation that it burned to estimate the emissions from wildfire.  

We found that when we accounted for the vegetation-fire feedback, the cumulative area burned was 9.8-21.8% lower than our simulations that only accounted for the effect of climate on wildfire (Figure 2).  Scaled to the entire mountain range, this equals a 14.3% reducing in cumulative area burned through 2100.

​The largest wildfires are typically the most impactful to both society and ecosystems because large wildfires typically occur under extreme weather conditions, which cause them to spread rapidly.  When we accounted for the effects of prior wildfires on fire size (dynamic), we found that by mid-century the largest wildfires were significantly smaller than the largest wildfires in the climate-only (static) simulations (Figure 3). However, by late-century, vegetation recovered in the previously burned areas and the largest wildfire sizes were no longer statistically different. 

​The data in Figure 3 are plotted on a log-scale to meet the assumptions of the statistical test we used to compare them.  By mid-century, the dynamic simulations had a median fire size of 24,053 acres and the static simulations had a median value of 53,389 acres.  The biggest fire we simulated in the dynamic scenario occurred during early-century and was 210,452 acres.  Whereas, in the static scenario, the biggest fire we simulated occurred in late-century and was 440,754 acres.  For comparison, the 2013 Rim Fire burned 257,314 acres in the Sierra Nevada and estimated to have emitted the equivalent of 12 Tg of carbon dioxide, equivalent to 2.7% of California’s total emissions.  When we calculated the emissions that our simulated wildfires would emit, we found that even when we account for the effects of previous wildfires limiting future wildfire size, total emissions in the Sierra Nevada are equivalent to one Rim Fire occurring every 3.8 years.  This could have significant implications for air quality in California.

​This blog post is in response to all of the interest in the Twitter post I made about my student’s opinions regarding science communication on Twitter.  

About my course
My course is called science-policy and it is focused on how to effectively engage as a scientist in the decision-making process.  We spend the first third of the class discussing different philosophies behind engagement and the roles that scientists can play in the process.  The middle third is a mix of case studies (e.g. Northwest Forest Plan, experimental flooding in the Grand Canyon, etc) and guest visits from congressional staffers.  The final third is focused on effective communication. This involves writing research briefs, giving short presentations, and the elevator speech. I like to experiment with a different assignment each year and see how it works.  I make them worth a very small fraction of the course grade in case they are a flop.  I ask the students at the end what they thought of the assignment and it either evolves or is eliminated.  This year I had them follow ten scientists on Twitter for the semester and report three things they thought worked well for communicating science and three things they thought did not work well.  

I tweeted the common ones out because I thought they were interesting and the one-off ones that made me chuckle while grading.  These are absolutely the opinions of my students.  They did not get any more guidance than I stated above.  

​Stats on the scientists they followed
Total = 82 (20 female, 62 male, 17 early career)
The fields of expertise were diverse and ranged from psychology to neuroscience to space to climate to ecology and many more.
58 are at academic institutions and the balance is a mix of government, NGO, and independent
Location: US 65; UK 9, Canada 4, 1 each in Spain, Portugal, Germany, Australia
Stats on number of followers: Max 13M;  Q3 77,400;  Median 9,557;  Q1 945;  Min 195
Three of the early career (< Assistant Professor) had more followers than the median

​Probably the thing that caused the most consternation within the twitterverse was that scientists are people too and many tweet personal stuff as well.  Drum roll please…
Exactly 4 of the 58 had anything “personal” in their description and only 2 stated that this was their personal account.

Unpacking “politics not science”
Given the character limitation, I boiled down something nuanced to three words.  Unfortunately, this yielded statements about my students being biased, privileged, and entitled.  I found the privileged and entitled comment so preposterous that I shared it with my colleagues and we all got a laugh.  No one has ever described students at our university as privileged and entitled.  

​What that three word summary represented was the fact that students who mentioned politics very much appreciated when scientists brought their expertise to the discussion about a politically hot-button topic such as climate change.  They thought folks like Katharine Hayhoe did a fantastic job of communicating the science, addressing the policy context, and answering questions.  What they found ineffective was when scientists mixed their political views on stuff unrelated to their research in with their tweets about science. 

Note: Katharine Hayhoe was listed as an example of a top-notch science communicator by several students.  This also happens to be my opinion.

My Opinion
The tweet summary represents the opinions of my students.  I tweeted the common ones out because I found them interesting.  The wonderful thing about the First Amendment is that if you are a US-based scientist you’re entitled to tweet out whatever you want.  Non-science audiences are as diverse as the people who do science professionally. If you take anything away from this very limited survey of my students it should be this: If you want to effectively communicate your science, figure out what audience you are trying to reach and tailor your approach to that audience.  You may turn some others off, but if you’re reaching your intended audience, who cares.  I work on forests, fire, and climate change.  My intended audience is policy-makers and natural resource managers.  Some other folks, that don’t fall into those groups, appreciate the information I share.  I dabble on Twitter, but that is not my primary tool for communicating science with my intended audience.  I’m always happy to answer anyone’s questions, as long as they are respectful. 

​Take what you want from this and leave the rest.  If you use a similar exercise and find something that improves on what I did, please share it.  If you’re put off by my student’s opinions, I recommend you do some self-reflection to figure out why you are all twisted-up over the opinions of a group of young people you’ve never met who haven’t singled you out in some way. My personal opinion is that my expertise on feedbacks between forests, fire, and climate doesn’t make me an expert on any other topic.  Thus, my opinion on another topic isn’t any more valid than anyone else’s.  

Any comments require my approval and I am pretty bad about paying attention to the notifications.