Beyond species richness: an empirical test of top predators as surrogates for functional diversity and endemism
Bibliographic record
Abstract
Using surrogate species to monitor the status of target biodiversity in areas undergoing exceptional habitat loss requires extending the traditional assessment of surrogates for taxonomic diversity to validating surrogates for functional diversity. This validation will be critical to inform about broader ecosystem processes and stability. We compared the surrogacy reliability of the habitat‐specialist Rufous‐legged Owl (Strix rufipes) and the habitat‐generalist Austral Pygmy‐Owl (Glaucidium nana), and we examined potential underlying mechanisms for surrogacy relationships in Andean temperate forests, a global biodiversity hotspot in southern Chile. During 2011–2013, we conducted 1,145 owl surveys, 505 vegetation surveys, and 505 avian point‐transect surveys across 101 sites comprising a range of conditions from degraded forest habitat to structurally complex old‐growth forest stands. The habitat‐specialistS. rufipeswas a reliable surrogate for all avian biodiversity measures, including avian endemism and functional diversity measures (degree of community specialization and density of large‐tree users, understory users, and cavity‐nesters). On the contrary, the habitat‐generalistG. nanadid not function as a surrogate. With increasing occurrence ofS. rufipes, the density of target specialized biodiversity (species, guilds, and communities) increased nonlinearly and peaked at the least degraded sites. This specialist aggregation might be driven by stand structural complexity available in older, more stable, forests. These results suggest that management actions tailored to promote occurrence of habitat‐specialist owls, such as theS. rufipes, may result in enhanced density of endemic species, specialized communities, and likely ecosystem stability.
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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 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".