Can subsets of species indicate overall patterns in biodiversity?
Bibliographic record
Abstract
Abstract Resource limitations often allow only a subset of species to be counted. But using subsets may bias inferences on spatial or temporal trends in biodiversity. Using data from a video survey on reefs in the Gulf of Mexico for which all fish species observed were counted (243 species), we investigated how the use of reduced species lists (RSLs) can impact perceived patterns in biodiversity. We estimated four common biodiversity metrics (species richness, and Margalef's, Shannon's, and Simpson's indices) at each of 2115 sampling locations, using the total species list and RSLs. For all diversity metrics, correlations between estimates using the total species list and RSLs increased with the number of species in the list. Using a bootstrap approach, we randomly generated hypothetical lists equal in length to each empirical RSL to evaluate their performance; empirical RSLs tended to perform similar to random lists of equivalent length when estimating species richness or Margalef's index, and tended to outperform most hypothetical RSLs when estimating Shannon's and Simpson's indices. To understand how to create better performing RSLs, we extended the bootstrap analysis to select RSLs of all possible lengths, using four different selection methods related to species commonness; the functional relationships between correlation and number of species in an RSL were similar among metrics but were very different among selection methods. With each hypothetical RSL, we tested common biodiversity hypotheses such as relationships with depth and latitude and compared the outcomes with the best estimate of true relationships identified using the total list. Longer lists comprised of the most common species more often identified the true relationship, but results showed complex patterns. Many short lists of the most common species yielded results opposite the true relationships, and many lists of intermediate length failed to identify any relationship while the total list showed a significant trend. Overall, these analyses show that sampling methods used for biodiversity studies should be as unselective as possible, and datasets based on more selective methods should be interpreted carefully and should not be expected to reflect true patterns in biodiversity.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".