Landscape, demographic, and dispersal parameters influence the spread and establishment of an aquatic biological invasion
Bibliographic record
Abstract
Abstract Human‐mediated species introductions are contributing to the biotic homogenization of global flora and fauna. Despite extensive research, we lack simple methods of predicting how and where an introduced species will spread and establish, particularly in species with complex life histories in aquatic ecosystems. We predict that spread can be modeled simply using the characteristics of the invading population, specifically species growth rate and dispersal capacity. In addition, we predict that the establishment of introduced species should be explained by the characteristics of the receiving ecosystem. Using the brown trout ( Salmo trutta ) invasion on the Island of Newfoundland as a case study, we fit and test a reaction–diffusion model with brown trout population data collected from the literature. Next, we use statistical models to assess the influence of a suite of abiotic (conductivity, pH, turbidity, calcium), biotic (Atlantic salmon occurrence), and landscape (watershed relief, watershed area, distance to original introduction) variables on brown trout establishment (i.e., presence–absence) patterns. We find that observed coastal spread in Newfoundland is slow (~4.4 km/year), and that it lies on the lower end of the range of predictions made by the reaction–diffusion model parameterized based on the estimates of growth rate and movement from the literature (predicted spread range 1.4 to 92 km/year). Also, we did not find evidence for a relationship between abiotic or biotic variables and brown trout establishment. However, we did observe that landscape variables of the distance to trout introduction point and estuary area may explain establishment patterns along the south coast of Newfoundland. Our results suggest the importance of using population‐specific parameterization and the need to integrate regional landscape factors that are generally applicable across biological invasions (e.g., distance to introduction), and those that are more specific to the ecology of the invader (e.g., estuary area). Our study contextualizes the mechanisms that contribute to a slow invasion by an aquatic species with a complex life history and reveals that future studies need to integrate a variety of methods to elucidate the processes governing invasions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".