Brown trout in the Falkland Islands: invasion ecology, population structure and genetic diversity
Bibliographic record
Abstract
Biological invasions are important causes of biodiversity loss, particularly in remote islands. Non-native salmonids, such as brown trout (Salmo trutta), have been widely introduced throughout the Southern Hemisphere, impacting endangered native fauna, particularly galaxiid fishes, through predation and competition. However, due to their importance for sport fishing and aquaculture, they are often protected, and any attempts to curtail their impacts are generally met with limited support, which poses a conservation conundrum. The best prospect of protecting native galaxiids is to predict where and how salmonids might disperse. This thesis aims to answer three main questions about brown trout in the Falkland Islands in order to provide resource managers with information to facilitate conservation planning to minimize the impacts of brown trout on native galaxiids. (1) determine the distribution of invasive brown trout and native zebra trout, Aplochiton zebra and Aplochiton taeniatus. (2) estimate patterns of movement and (3) assess the population structuring and estimate levels of gene flow between different rivers and populations of brown trout in the Falklands. To meet these aims, I used state-of-the-art methods, including SNP genotyping, stable isotope analysis, acoustic tagging, and environmental DNA (eDNA) analysis. The results of this thesis suggest that establishment success (calculated as the proportion of historical introductions where brown trout became established) was ~88% and that brown trout are continuing to spread from their original sites of introduction. The native Aplochiton species have disappeared from most rivers invaded by brown trout. Four genetically distinct clusters of brown trout were identified, with high levels of gene flow indicating widespread dispersal of brown trout across the Falkland Islands. Without strong containment, brown trout are predicted to invaded nearly all suitable freshwater habitats in the Falklands within the next ~70 years, which might put native galaxiids at a high risk of extinction.
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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) | 0.001 | 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".