Understanding the adequacy and representativeness of species distribution data
Bibliographic record
Abstract
Aim: Species occurrence data provides the basis for any analysis of species distributions, biodiversity patterns and change, and progress towards biodiversity targets. Yet whilst data has grown exponentially over the last decade, there have been no assessments of how representative this growth is, spatially or taxonomically. We provide the first global comparison of all major distribution databases, systematically analyze how biases vary spatially and taxonomically, assess the taxonomic completeness for different regions, and explore where we are and are not seeing representative growth.,Location: Global,Time Period: 2007-2025,Major Taxa Studied: All taxa,Methods: We collated data from all major species distribution and monitoring databases including GBIF, OBIS, BIEN, BioTime, Predicts and the Living Planet. We assessed spatial and taxonomic coverage within each dataset, as well as how data coverage and representativeness has changed over time.,Results: Despite increasing data volumes, most of the world's most biodiverse regions still lack data. Overall, whilst countries such as Japan and South Korea have seen expansions of spatial data coverage by over 60% over the last decade, major parts of the world including North Africa, Central Asia, and the High-Seas have not witnessed comparable growth. Furthermore, growth in these regions is not sufficiently representative, and in some cases is driven by single research projects on a single taxon. Where representative growth has been witnessed it is underpinned by government efforts, whereas growth in many other areas comes from citizen science data.,Main Conclusions: Different databases display different biases, often reflecting different strategies and priorities in data mobilization. Whilst citizen science has increased spatial coverage, this data is dominated by birds, from accessible regions in high-income economies. Overcoming these biases requires efforts to mobilise, consolidate, and standardise existing data, including published data, and museum and government records, emulating the strategies of countries which successfully remedied previous data coverage challenges.
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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 | 0.180 | 0.589 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".