Landscape surrogates of forest fragmentation: Synthesis of Australian Montreal Process case studies
Bibliographic record
Abstract
Habitat loss and fragmentation are key biodiversity indicators of the Montreal Protocol for monitoring progress towards ecologically sustainable forest management. Over the last 15 years, an array of landscape metrics have been developed as spatial measures of habitat loss and fragmentation. However, most metrics require rigorous empirical testing if they are to provide scientifically credible information to managers and policy makers. We present a synthesis of three Australian case studies for developing Montreal Indicator 1.1e, fragmentation of forest type, each representing different levels of landscape modification: St Mary State Forest, south-east Queensland; Tumut, southern New South Wales; and the Central Highlands, Victoria. Collectively, the studies found that no single landscape metric captured the response of the target species and fauna assemblages, or served as a reliable ecological surrogate for the conservation of a large set of species. Rather, species demonstrated a diversity of responses to habitat loss and fragmentation. Fragmentation effects were more important for the Tumut study, but not important for the Central Highlands study. Stand-scale habitat variables and area of suitable habitat were dominant explanatory variables for the St Mary study. Differences in observed response are partly explained by: (i) differences in landscape structure, particularly the proportion of preferred forest habitat remaining; (ii) differences in the ecology of target species; and (iii) the insensitivity of the landscape measures. Based on the outcomes of the three case studies, we propose principles for developing landscape surrogates for conserving biodiversity in Australia's eucalypt forest landscapes.
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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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".