The third dimension: How fire-related research can advance ecology and evolutionary biology
Bibliographic record
Abstract
Most of the Earth’s vegetated surface is fireprone but the relevance of fire in understanding how nature works is not always recognized. We aim to show that, by adding the fire dimension to observations on biological phenomena, interpretations can be im-proved; how fire-related research can be used to answer ‘fundamental’ questions in ecology; and how theories/models developed for fireprone ecosystems can be applied to advancing disturbance ecology, biogeography and evolutionary biology more generally. We compiled lists from the world-wide web of the most highly cited papers in fire ecology, and examined papers that had been approached from multiple viewpoints, including fire. We show that great advances over the last 20 years have been made in our understanding of the pivotal role of fire as a driver of many ecological processes and a powerful selective agent/evolutionary trigger among biota. We document 21 sets of observations originally interpreted in the context of the two traditional dimensions, prevailing environment and biotic interactions, but can also be shown to have a strong, if not dominant, historical link to fire. We note that fire-related research is able to address 55 of the 100 questions considered ‘fundamental’ in ecology and that many have already received some attention in fireprone ecosystems. We show how theories/ models that had their origins in fireprone systems can be applied to other disturbance-prone systems and thus have wide application in ecology and evolutionary biology. Fire and other disturbances should be included as variables in research about possible critical environmental and biotic constraints controlling ecosystem function in general. Adding this third dimension to research endeavours greatly enriches our understanding of how nature works at the global scale in an era where ecosystems are changing rapidly and novel species-environmental interactions are emerging.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".