A Good Compromise: Rapid and Robust Species Proxies for Inventorying Biodiversity Hotspots Using the Terebridae (Gastropoda: Conoidea)
Bibliographic record
Abstract
Devising a reproducible approach for species delimitation of hyperdiverse groups is an ongoing challenge in evolutionary biology. Speciation processes combine modes of passive and adaptive trait divergence requiring an integrative taxonomy approach to accurately generate robust species hypotheses. However, in light of the rapid decline of diversity on Earth, complete integrative approaches may not be practical in certain species-rich environments. As an alternative, we applied a two-step strategy combining ABGD (Automated Barcode Gap Discovery) and Klee diagrams, to balance speed and accuracy in producing primary species hypotheses (PSHs). Specifically, an ABGD/Klee approach was used for species delimitation in the Terebridae, a neurotoxin-producing marine snail family included in the Conoidea. Delimitation of species boundaries is problematic in the Conoidea, as traditional taxonomic approaches are hampered by the high levels of variation, convergence and morphological plasticity of shell characters. We used ABGD to analyze gaps in the distribution of pairwise distances of 454 COI sequences attributed to 87 morphospecies and obtained 98 to 125 Primary Species Hypotheses (PSHs). The PSH partitions were subsequently visualized as a Klee diagram color map, allowing easy detection of the incongruences that were further evaluated individually with two other species delimitation models, General Mixed Yule Coalescent (GMYC) and Poisson Tree Processes (PTP). GMYC and PTP results confirmed the presence of 17 putative cryptic terebrid species in our dataset. The consensus of GMYC, PTP, and ABGD/Klee findings suggest the combination of ABGD and Klee diagrams is an effective approach for rapidly proposing primary species proxies in hyperdiverse groups and a reliable first step for macroscopic biodiversity assessment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".