A phylogeographic analysis of the North American medicinal leech, <i>Macrobdella decora</i> (Say, 1824)
Bibliographic record
Abstract
Abstract In spite of their important roles in many ecosystems, data regarding population structure and biogeographic patterns of leeches are scarce. To begin to address this knowledge gap, we herein perform a phylogeographic analysis of the North American medicinal leech, Macrobdella decora (Say, 1824). A total of 224 M. decora specimens were collected from 35 localities across large swaths of USA and Canada and covering most of the known range of the species. Using four loci (mitochondrial cytochrome c oxidase subunit I [COI] and NADH dehydrogenase I [ND1], as well as nuclear 18S rRNA [18S] and 28S rRNA [28S]), we construct phylogenetic trees using several optimality criteria and superimpose geographic patterns onto the trees in order to tease out any potential structure among the populations. Rather surprisingly, given the large geographic range of the species and abundance of potential geographic barriers to gene flow, the analyses showed a conspicuous lack of structure among the different populations of M. decora . However, an AMOVA did show statistically significant differences between the genetic variation within populations and between populations (COI: FST = 0.65412, p < .00001; ND1: FST = 0.69245, p < .00001), which was largely driven by only 6 out of the 35 populations, and indicated a potential barrier for dispersal across the Appalachian Mountains. Finally, a Mantel test showed a weak, but significant, correlation between geographic distance and genetic distance (COI: r = 0.209, p = .027; ND1: r = 0.1289, p = .030); however, this correlation was primarily driven by a single locality. The overall weak structure suggests that M. decora is panmictic throughout its range, and we discuss this in light of previous population level studies in both bloodfeeding and non‐bloodfeeding species, concluding that the lack of structure in M. decora might be due to its high capacity for dispersal via hosts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 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".