Non-native freshwater fishes in Guatemala, northern Central America: introduction sources, distribution, history, and conservation consequences
Bibliographic record
Abstract
Non-native freshwater fishes have been introduced to Guatemalan freshwater ecosystems since the beginning of the last century without prior risk assessment or subsequent evaluation of their impacts. We synthesized historical records, and distributional data from a literature review, online databases and museum records of non-native freshwater fishes in Guatemala. We found records for 22 non-native freshwater fishes with the oldest records dating back to 1926. Non-native freshwater fishes were recorded in 64% of the river sub-basins in Guatemala and we identified that at least 12 species have established populations. The Jaguar guapote (Parachromis managuensis) and Tilapias (Oreochromis spp.) are the most widespread non-native fishes. The species of non-native freshwater fishes introduced indicates that they are human selected (e.g., for farming purposes). Our work shows that aquaculture has been the major driver of introductions in the country, but aquarium release has become an important source in the last 20 years. Given the potential impact of non-native freshwater fishes on native fauna and ecosystems, we highlight an urgent need to assess their ecological effects, as well as to establish a fish fauna monitoring program in Guatemala to detect new introductions. Government and non-governmental agencies should promote the use of native species to supply fish demands in alignment with environmental policies and the objectives of the fishing agency in Guatemala.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 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".