A Census of Marine Biodiversity Knowledge, Resources, and Future Challenges
Bibliographic record
Abstract
ABSTRACT. The Census of Marine Life (2000–2010) was the largest global research programme on marine biodiversity. This paper integrated the findings of reviews of major world regions by the Census and provides a global perspective on what is known and what are the major scientific gaps. Study metrics were regional species richness, numbers of endemic and alien species, numbers of species identification guides and taxonomic experts, and a state-of-knowledge index. The threats to biodiversity were classified across the regions. A poor to moderate correlation between species richness and seabed area, and sea volume, and no correlations with topographic variation, were attributed to sparse, uneven and unrepresentative sampling in much of the global marine environment. Many habitats have been poorly sampled, particularly in deeper seas, and several species-rich taxonomic groups, especially of smaller organisms, remain poorly studied. Crustacea, Mollusca, and Pisces comprised approximately half of all known species across the regions. The proportion that these and other taxa comprised of all taxa varied sufficiently to question whether the relative number of species within phyla and classes are constant throughout the world. Overfishing and pollution were identified as the main threats to biodiversity across all regions, followed by alien species, altered temperature, acidification, and hypoxia, although their relative importance varied among regions. The findings were replicated worldwide, in both developed and developing countries: i.e. major gaps exist in sampling effort and taxonomic expertise that impair society's ability to discover new species and identify and understand species of economic and ecological importance. There was a positive relationship between the availability of species identification guides and knowledge of biodiversity, including the number of species and alien species. Available taxonomic guides and experts correlated negatively with endemic species, suggesting that the more we study the ocean the fewer endemic species are evident. There is a need to accelerate the discovery of marine biodiversity, since much of it may be lost without even being known. We discuss how international collaboration between developed and developing countries is essential for improving productivity in the discovery and management of marine biodiversity, and how various sectors may contribute to this.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".