Marine biogeographic realms and species endemicity
Bibliographic record
Abstract
Marine biogeographic realms have been inferred from small groups of species in particular environments (e.g., coastal, pelagic), without a global map of realms based on statistical analysis of species across all higher taxa. Here we analyze the distribution of 65,000 species of marine animals and plants, and distinguish 30 distinct marine realms, a similar proportion per area as found for land. On average, 42% of species are unique to the realms. We reveal 18 continental-shelf and 12 offshore deep-sea realms, reflecting the wider ranges of species in the pelagic and deep-sea compared to coastal areas. The most widespread species are pelagic microscopic plankton and megafauna. Analysis of pelagic species recognizes five realms within which other realms are nested. These maps integrate the biogeography of coastal and deep-sea, pelagic and benthic environments, and show how land-barriers, salinity, depth, and environmental heterogeneity relate to the evolution of biota. The realms have applications for marine reserves, biodiversity assessments, and as an evolution relevant context for climate change studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".