Conservation Challenges Under Taxonomic Uncertainty: Introgression Patterns and Environmental Correlates in Mediterranean Brown Trout
Bibliographic record
Abstract
ABSTRACT The Mediterranean brown trout ( Salmo trutta species complex) comprises genetically and ecologically distinct lineages primarily distributed across the Western Mediterranean region. Conservation of the Mediterranean brown trout is complicated by unresolved taxonomy. Although some populations have recently been recognised as distinct species, most—including those in the Iberian Peninsula—remain classified under the nominal S. trutta , which is currently listed as Least Concern by the IUCN. This taxonomic ambiguity masks severe conservation threats, particularly the widespread genetic introgression from stocked Atlantic‐origin trout. In this study, we assessed introgression patterns, environmental drivers and conservation implications for Mediterranean brown trout populations in Catalonia (northeastern Iberian Peninsula). Genetic data from 134 sites were analysed using microsatellite markers and LDH‐C1* allele frequencies, and the influence of historical stocking intensity and climatic variables on introgression was evaluated using hierarchical partitioning and regression models. Our results revealed extensive introgression, with only 10.4% of the populations retaining genetic purity. Stocking intensity was the primary predictor of introgression, followed by temperature and precipitation seasonality. Although the overall distribution of brown trout in the region remains stable, genetically pure Mediterranean populations have experienced severe range contraction, with a 61.5% reduction in extent of occurrence (EOO) and an 89.6% reduction in area of occupancy (AOO). This decline might meet IUCN criterion B for Endangered, although a formal assessment is currently impeded by taxonomic uncertainty and data limitations. The recognition of Mediterranean brown trout as a distinct species, alongside urgent measures to halt ongoing introgression and preserve remaining pure populations, is essential for its long‐term conservation.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".