The impacts of contemporary logging after 250 years of deforestation and degradation on forest-dependent threatened species
Bibliographic record
Abstract
Abstract Despite the importance of safeguarding forests and woodlands for achieving global climate and biodiversity agendas, logging continues across most forested countries. Forestry advocates often claim logging has minimal impacts, but rarely consider the cumulative threat deforestation and degradation has had, and continue to have, on species. Using New South Wales (Australia) as a case study, we quantify the extent of deforestation and degradation from 1750 – current. Using these estimates of overall loss as a baseline, we then quantify the relative extent of contemporary (2000 – 2022) logging and the condition of the remaining native forest and woodland (quantified by measuring the similarity of a current ecosystem to a historical reference state with high ecological integrity). Using these data, we measure the impacts on distinct vegetation types and on 484 terrestrial forest-dependent now-threatened species. We show that more than half (29 million ha) of pre-1750 (pre-European colonization of Australia) native forest and woodland vegetation in NSW has been lost. Of the remaining 25 million ha, 9 million ha is degraded. We found contemporary degradation from logging affected 244 forest-dependent now-threatened species that had already been affected by this historical deforestation and degradation, but the impacts varied across species and vegetation types. We found that 70 now-threatened species that were impacted by historical deforestation and degradation and continue to be impacted by logging, now have ≤50% of their pre-1750 extent remaining that is intact (with three species now having <20%). By quantifying the historical impacts of deforestation and degradation, our research sets the impact of contemporary degradation from logging in perspective and highlights shortfalls in current environmental assessments that fail to consider appropriate baselines when reporting on overall impact. Future land management decisions need to consider not only the extent of remaining habitat based on pre-1750 extents, but also its condition. Article impact statement The impact of logging needs to be placed in perspective by considering past losses and degradation due to human land use decisions.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".