FISHGLOB: Fish biodiversity facing global change
Bibliographic record
Abstract
Global change is causing redistribution of marine species worldwide, modifying fish population and stock structure, as well as community compositions. These changes may have strong impacts on marine fish, associated fisheries, and ecosystem functioning and services. However, our capacity to assess and monitor short and long-term changes in species distribution and biodiversity is hampered by data availability and heterogeneity around the globe. This presentation will introduce FISHGLOB, launched in 2019 and an UN Ocean Decade project since 2023, that has collected and combined a unique data set of scientific bottom trawl surveys (SBTS) conducted regularly during the last decades across the globe. SBTS are ecological observation programs that sample marine communities associated with the seafloor. These surveys report taxa occurrence, abundance and/or biomass in space and time, and they can contribute substantially to understanding responses of species and communities to global change. Because combining these data together remains challenging, FISHGLOB enhances access and visibility of SBTS around the world and integrates these data across regions through an international network of experts. Currently, the integrated FISHGLOB dataset encompasses public and private surveys. Metadata comprises over 280,000 samples (hauls) from the last two decades of 95 surveys performed across all continents. Biodiversity data covers more than 3,000 fish taxa collected since 1963 from 65 surveys. FISHGLOB biodiversity data is accessible on github.com and osf.io for the 26 surveys that are public. We are now growing the consortium as an international community of practice, maintaining the core FISHGLOB database, and using the database to address research questions about global change impacts on fishes – focusing on marine heatwaves, community turnover, species’ extinction risk, and more – at an unprecedented scale. None of this would have been possible without open and collaborative data science, which enabled our “big data” approach to studying and managing species and community changes. FISHGLOB sets the stage for a long-term international collaborative platform bringing together marine data and experts from data science, ecological research, government agencies, and management in order to support biodiversity and fishery management adaptation in a time of global change.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.030 |
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".