Indicator Taxa, Rapid Biodiversity Assessment, and Nestedness in an Endangered Ecosystem
Bibliographic record
Abstract
To prioritize areas for conservation, biologists and managers need information on species diversity in threatened habitats. The resources available for such inventories remain severely limited, increasing the need to develop speedier ways to estimate the status of target habitats. We present a study of the use of such techniques in the highly fragmented oak savannas of southern Ontario, including selection of indicator taxa, use of rapid biodiversity assessment based on morphospecies, and analysis of community structure. We found that butterflies and skippers can be used to predict richness among Hymenoptera in the study sites, which is consistent with the hypothesis that these easily surveyed Lepidoptera are good candidates for indicator status. Richness values for hymenoptera morphospecies in these savanna remnants were strongly correlated with species richness scores as estimated by systematists, although nonspecialists tended to "split" species into more than one morphospecies. Finally, both the Hymenoptera and Lepidoptera communities in these oak savannas exhibited a high degree of nestedness, suggesting that local extinctions, mostly undocumented, are important determinants of the richness patterns across these widely separated savanna study sites. We found no evidence of significant spatial autocorrelation, probably because of the wide separation of study sites.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".