Science AMA series: I’m Brian Buma, Assistant Professor at the University of Alaska. I study forest disturbances around the world, focusing on emerging mortality associated with climate change and carbon/water/forests as an integrated system. AMA!
Bibliographic record
Abstract
Hi reddit! I am a disturbance ecologist (think fires, windstorms, landslides) that primarily studies the response of forested ecosystems to emerging disturbances triggered by climate change. I’m particularly interested in how resilient forests may be to these new stresses - and if that resilience is a good thing. Will our forests recover from future disturbances? What will that recovery look like? Does this recovery – or lack thereof – help or hinder species ability to migrate in response to climate change? What new disturbances are emerging? One striking example of all of these issues is the emerging mortality of species along the remote southeast Alaskan and Canadian west coast, where 400,000 ha (so far) of trees have died due to low snow conditions brought about by warming winters. The cause is surprisingly related to freezing – the soil is no longer insulated by snow, so cold snaps can kill. This is an emerging disturbance that we are just beginning to study, and it’s dramatically changing the forest community. But it also appears to be associated with migration in other, less climate sensitive species. So perhaps this disturbance, and others, are facilitating the migration of species into more favorable climates. It’s a complex ecological story of adaption/maladaptation and creative destruction (so to speak), and great fun to investigate. Most of my work involves a focus on either forest biodiversity, forest carbon, or water resources, and I’ve worked in Hawaii, the Rocky Mountains, and Alaska, and collaborated on projects around the world using a combination of fieldwork, remote sensing/satellites, GIS, and modeling. I am also the caretaker of what is believed to be the longest running permanent study plots studying primary succession in the world in Glacier Bay, Alaska (100 years and counting). So the data comes from a lot of sources, and I’m happy to discuss integration of methods as well. For more info, [check out this website.](www.brianbuma.com) I will be answering your questions at 1 PM ET, AMA! Edit: Thanks everyone! Some really interesting and thought provoking questions in here, and it’s humbling and exciting to see so many people concerned and interested in the state of the world’s forests. There were lots of great ideas for next steps, projects, etc mentioned here and I’d love to hear how those progress. I have to run for a meeting but I’ll check back in tonight (it’s only 1PM in Alaska right now, after all, lots of time) and keep on doing what I can. Edit 2: And I’m back for a bit. This is really fun. …off for dinner. Will log in later to reply further. In the meantime, most of my work is posted on my website, and for those great questions about coastal forests I would encourage you to check out the Alaska Coastal Rainforest Center (http://acrc.alaska.edu/) for all things coastal forest related. Feel free to email with questions as well, I’m always looking for interested students and research collaborations, in addition to partners in management and policy. Alrighty, it’s late in Alaska, so I’m done (and Denzel is online…). Thanks so much for your questions!
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.236 | 0.116 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".