Interactions between fire, climate change and forest biodiversity.
Bibliographic record
Abstract
Abstract The interactions between climate change, fire and forest biodiversity can be characterized as a cascading relationship. Changes in climate directly affect fire frequency and severity. As a result of climate change, a significant increase in fire occurrence and behaviour is predicted to occur over the next 70 years. Climate change will also directly impact the composition of current ecosystems by shifting the fundamental niche of species to higher elevations and/or latitudes, with the changes being most apparent in temperate and boreal latitudes and on mountains in the tropics and subtropics. The alteration and shift of forest ecosystems will be facilitated by an increase in the frequency and severity of forest fires, as these ecosystem disturbances will create the opportunity for the development of new ecosystems. Warmer, drier conditions, coupled with a change in fire behaviour will reduce the resilience of existing ecosystems. The reduced ability to resist and/or recover from disturbance will result in the reduction or complete loss of ecosystem structures required by the fauna and flora that constitute the biodiversity we currently know today. Areas of climatic refuge that exist within landscapes may maintain the ecosystem structures required for some species in the face of direct climate change impacts. Areas that act as both fire and climatic refugia will provide habitat for biodiversity associated with late-successional forests. Species that require late-successional habitat in ecosystems that are intolerant of fire or drought are at greatest risk to climate change and its interaction with fire. The current composition of biodiversity within forested landscapes will change over time and space as species and habitat are both lost and gained in direct or indirect response to climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".