Biotic disturbances in Northern Hemisphere forests – a synthesis of recent data, uncertainties and implications for forest monitoring and modelling
Bibliographic record
Abstract
Abstract Aim Biotic disturbances (BD, including insects, pathogens and wildlife herbivory) can alter forest structure and the capability of forests to deliver ecosystem services. Impact assessments, however, are limited by the lack of reliable and timely disturbance data at large spatial scales. This review synthesizes empirical data on the magnitude and distribution of spatio‐temporal impacts of BD. Location Northern Hemisphere. Methods Based on large‐scale, multi‐year BD data sets, covering c . 46% of the global forest, we calculated annual disturbance fractions D f (percentage of forest area affected) and their inter‐annual variability at a grid cell resolution of 1°. The impact of BD on forest carbon pools was determined by overlaying D f with data on forest cover and carbon density. Results Overall, 43.9 million hectares (Mha) ( D f = 2.6%) of forests were affected annually by BD, particularly by insects (36.5 Mha, D f = 2.2%). Our synthesis demonstrates that fractions affected by BD (1) vary greatly over space and time, mainly in response to ephemeral bark beetle and defoliator outbreaks, (2) show temporal trends that are inconsistent across regions, yet are largely increasing over recent decades, and (3) are substantially higher than D f caused by fire and other abiotic disturbances. Tree mortality was estimated over an area of 3.3 Mha year −1 (medium estimate which assumed mortality at 7.5% of the affected area), with associated committed carbon fluxes from living biomass to litter and the atmosphere at 129.9 Mt C year −1 . Main conclusions BD are key drivers of forest dynamics, making a contribution to tree mortality of a similar magnitude to fire. Despite inherent uncertainties, the data reported can be used to improve the representation of BD in global ecosystem models. Our findings call for future forest monitoring approaches to provide accessible, precise and consistent data on the occurrence and severity of BD which are harmonized across jurisdictions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".