Combining current and historical biodiversity surveys reveals order of magnitude greater richness in a British Columbia marine protected area
Bibliographic record
Abstract
The value of biodiversity and of documented biodiversity surveys is well established. Extracting historical biodiversity data and synthesizing them with current data can provide a more comprehensive estimate of total diversity and guide future monitoring. We demonstrate the utility of compiling historical and recent biodiversity data to better characterize taxon richness and composition. Our focus is an otherwise unmonitored habitat in an unmonitored British Columbia provincial park, in a heavily impacted region of the Salish Sea that was designated a United Nation Biosphere Reserve in 2021. We conducted surveys and compiled historical records that together spanned three intertidal habitats and 43 years. From these combined data we report a total of 99 taxa, an order of magnitude increase over the number listed in the park’s Master Plan. These include seven non-native species, of which four are newly reported here. Rarefaction, extrapolation, and multivariate dissimilarity analyses revealed the roles of methods and habitat types in contributing to differences in taxon richness and composition among surveys. This data compilation illustrates many of the challenges and opportunities in aligning and assembling independent space-time snapshots of alpha (i.e., local) diversity to better understand the gamma (i.e., regional) diversity of a marine protected area and provides the foundational data needed to design effective future monitoring at molecular to ecosystem scales.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".