Delineating marine ecological units: a novel approach for deciding which taxonomic group to use and which taxonomic resolution to choose
Bibliographic record
Abstract
Aim Ecological maps are increasingly used to support marine management and conservation. However, the biological datasets used to produce these maps are typically limited to taxonomic groups identified to the specific taxonomic levels available. Ecological units should, however, reflect the broader marine ecosystem, independent of the datasets used. This study assessed the influence of taxonomic groups and taxonomic resolution on the process of ecological mapping. Location Estuary and Gulf of St Lawrence (EGSL), Canada. Methods A dataset of more than 200 taxa of benthic macrofauna was used to create a set of biological matrices corresponding to different taxonomic groups (i.e. vertebrates, invertebrates, arthropods, echinoderms, molluscs, all taxa) and different taxonomic levels from species to class. Multivariate regression trees (MRTs) were used to identify environmental drivers of taxa distribution and to create ecological maps. Similarity between maps was assessed using pairwise comparisons. First, the relationships between the two classification legends were assessed using association plots on the partitions in the corresponding trees. Then, the spatial agreement of ecological units believed to represent the same habitat types was quantified and mapped. Results The comparison across different taxonomic groups showed a substantial level of similarity between ecological maps, indicating that ecological units defined for a specific taxonomic group can be considered to some extent as representative of the entire benthic macrofauna. Moreover, little information was lost when working at the family rather than species level, and common patterns of community distribution could still be distinguished at the class level. Main conclusions Using a novel spatially explicit approach for comparing ecological maps, this study demonstrates that datasets limited by taxonomic breadth or resolution can perform nearly as well as more extensive datasets. These simplifications should improve our ability to manage marine ecosystems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".