Extreme event impacts on terrestrial and freshwater biota in the arctic: A synthesis of knowledge and opportunities
Bibliographic record
Abstract
Extreme weather events are increasing in frequency and intensity across the Arctic, one of the planet’s most rapidly warming regions. Studies from southern latitudes have revealed that the ecological impacts of extreme events on living organisms can be severe and long-lasting, yet data and evidence from within the terrestrial Arctic biome appear underrepresented. By synthesizing a total of 48 research articles, published over the past 25 years, we highlight the occurrence of a wide variety of extreme events throughout the Arctic, with multiple and divergent impacts on local biota. Extreme event impacts were quantified using a myriad of approaches ranging from circumpolar modelling to fine-scale experimental studies. We also identified a research bias towards the quantification of impacts related to a few extreme event types in the same geographic location (e.g. rain-on-snow events in Svalbard). Moreover, research investigating extreme event impacts on the ecology of arthropods and especially freshwater biota were scant, highlighting important knowledge gaps. While current data allow for hypotheses development, many uncertainties about the long-term consequences of extreme events to Arctic ecosystems remain. To advance extreme event research in the terrestrial Arctic biome, we suggest that future studies i) objectively define what is extreme in terms of events and ecological impacts using long-term monitoring data, ii) move beyond single-impact studies and single spatial scales of observation by taking advantage of pan-Arctic science-based monitoring networks and iii) consider predictive and mechanistic modelling to estimate ecosystem-level impacts and recovery.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".