Adapting the concept of functionally dominant species for observational data
Bibliographic record
Abstract
• A modified version of the keystone approach is proposed for observational data. • Bootstrapping method is used to identify functionally dominant species (FDS). • It can identify FDS with positive or negative contributions to ecosystem functions. • Confidence intervals can be adjusted to change the detection threshold of FDS. • Testing this procedure on wetland communities revealed its benefits for conservation ecology. Conservation ecologists often rely on surrogate species to identify biodiversity hotspots due to the high cost of monitoring programs. While the keystone species approach is an appealing framework for that purpose, it has been criticized for its lack of a clear threshold to identify functionally important species and for its limited ability to handle observational data variability. Here, we propose a modified version of the functionally dominant species (FDS) framework using a bootstrapping random sampling method implemented with either strict or flexible parameters to identify species that disproportionately contribute to the increase or the decrease of biodiversity. We tested our approach on plant, bird, and fish communities of 37 lake-edge wetlands. We identified eight FDS using a 95% confidence interval, of which two displayed a positive contribution to diversity while six had a negative contribution. Using a 99% confidence interval, we found four FDS, all displaying a negative contribution to biodiversity. Most of the identified FDS had ecological or biological traits that support their disproportionate impact on biodiversity. By addressing the limitations of the keystone species framework and providing a statistical framework for analyzing observational data, our method represents a promising tool for conservation ecology.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 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".