A conditional probability index to quantify the amplitude and the direction of spatiotemporal changes in communities
Bibliographic record
Abstract
Abstract Monitoring and mapping species diversity using indicators can allow the detection of changes in communities. Conclusions regarding these changes greatly depend on the choice of indicator. Here, we propose a new metric, the distance biochange index (DBCI), that enables the characterization and quantification of both the level and direction of change in biological communities relative to a given reference state. The proposed metric uses conditional probabilities to assess the probability of observing a complete change in a given community and can be decomposed into four conditional probabilities of change: no change, complete change in species composition only, complete change in species richness only, and complete change in both species composition and richness. In this study, we compared the properties of DBCI and BCI, a similarity version of DBCI, with those of other widely used indices. We also proposed a new approach, based on the use of partial derivatives, to assess the sensitivity of six similarity indices over a wide range of contrasting scenarios of change. Finally, we extended the application of DBCI to a simulated case study of the predicted evolution of suitable habitats for 20 species under climate change. Results from this simulation demonstrated that DBCI provides an accurate assessment of the level and direction of change. Results also show that DBCI can be used to reflect the effect of ecological gradients on species composition and species richness in biological communities.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".