Using bird species community occurrence to prioritize forests for old growth restoration
Bibliographic record
Abstract
Conservation often focuses on ‘ecologically intact’ habitats with little human influence. But where all such habitats have been lost or modified, identifying promising restoration targets is a key goal. We describe a direct approach to identify high conservation value targets using predictive distribution maps of taxa that, based on habitat affinity, ease of detection and abundance can be used to infer native species richness and prioritize conservation investment. We used 1169 avian point counts in a 1560 km2 study area, remote‐sensed data and models incorporating imperfect detectability to predict habitat occupancy in 18 widely‐distributed native birds; 12 of which were determined by experts to be positive indicators of old‐forest conditions. Forest‐association scores for these 12 species where then used as weights in a composite distribution map of the probability of community occurrence, which corresponded well with the occurrence of old forest stands mapped by aerial photography. Our results indicate that composite maps of widespread indicators improve site prioritization by incorporating the behavioural and demographic responses of a diverse range of indicators to variation in patch size, configuration and adjacent human land use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".