Extensive gaps and biases in our knowledge of a well‐known fauna: implications for integrating biological traits into macroecology
Bibliographic record
Abstract
ABSTRACT Aim Ecologists seeking to describe patterns at ever larger scales require compilations of data on the global abundance and distribution of species. Comparable compilations of biological data are needed to elucidate the mechanisms behind these patterns, but have received far less attention. We assess the availability of biological data across an entire assemblage: the well‐documented demersal marine fauna of the United Kingdom. We also test whether data availability for a species depends on its taxonomic group, maximum body size, the number of times it has been recorded in a global biogeographic database, or its commercial and conservation importance. Location Seas of the United Kingdom. Methods We defined a demersal marine fauna of 973 species from 15 phyla and 40 classes using five extensive surveys around the British Isles. We then quantified the availability of data on eight key biological traits (termed biological knowledge) for each species from online databases. Relationships between biological knowledge and our predictors were tested with generalized linear models. Results Full data on eight fundamental biological traits exist for only 9% ( n = 88) of the UK demersal marine fauna, and 20% of species completely lack data. Clear trends in our knowledge exist: fish (median biological knowledge score = six traits) are much better known than invertebrates (one trait). Biological knowledge increases with biogeographic knowledge and (to a lesser extent) with body size, and is greater in species that are commercially exploited or of conservation concern. Main conclusions Our analysis reveals deep ignorance of the basic biology of a well‐studied fauna, highlighting the need for far greater efforts to compile biological trait data. Clear biases in our knowledge, relating to how well sampled or ‘important’ species are suggests that caution is required in extrapolating small subsets of biologically well‐known species to ecosystem‐level studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".