Genetic diversity hotspots of trematodes (Platyhelminthes) in Mexico and their overlap with protected natural areas
Bibliographic record
Abstract
Genetic diversity (GD) is a fundamental component of biodiversity that remains largely overlooked in conservation planning, especially for parasitic taxa. Trematodes are among the most diverse and ecologically important parasitic groups, although their GD across regions remains poorly characterized. Here we analyze the nucleotide diversity (π) and haplotype diversity (Hd) of mitochondrial (COI) and nuclear (28S) genes using sequences available in public datasets to: (i) represent the spatial patterns genetic diversity at the family level of trematodes across Mexican biogeographic provinces and Protected Natural Areas (PNAs); (ii) identify regions with the highest GD (hotspots); and (iii) to explore how environmental factors influence genetic diversity patterns. We identified some GD patterns, as well as GD hotspots in center and southeastern Mexico, particularly in the states of Michoacán, Estado de México, Veracruz, Tabasco, Chiapas, and Oaxaca. Correlation and model selection analysis revealed multiples environmental variables that can influence the GD of trematodes, as temperature seasonality (BIO4), max temperature of warmest month (BIO5), annual temperature range (BIO7), precipitation of the wettest quarter (BIO16), precipitation of warmest quarter (BIO18) and vegetation type. Furthermore, we found that 37 of 67 PNAs in the southeast overlapped with cells mapped with high-GD, suggesting that existing PNAs may preserve GD. However, public databases are still limited, highlight the need to promote more targeted studies that include parasitic taxa in conservation initiatives. This work contributes to the integration of genetic indicators into biodiversity monitoring, in line with the objectives of the Kunming-Montreal Global Biodiversity Framework.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".