Spatial distribution of marine invasive species: environmental, demographic and vector drivers
Bibliographic record
Abstract
Abstract Aim The introduction of potentially invasive species remains a global threat to biodiversity and ecosystem services. The spatial distribution of introduced species can provide insight into present and historical vectors of invasion. Here, we aim to investigate the influence of environmental, demographic and vector variables on the spatial distribution of non‐indigenous species (NIS) in coastal marine ecosystems. Location Coastal British Columbia, Canada. Methods We used subtidal settlement plates to sample NIS richness at 81 sites. Spatial patterns for seventeen environmental, population, and vector variables were created using a Geographic Information System (GIS). We used multiple regression with model selection and spatial autocorrelation to define a statistical model that best explained the spatial distribution of NIS. Results Four variables, salinity, human population density, port arrivals and marina propulsiveness (probability of boater travel from home marina), best explained the observed spatial distribution of subtidal NIS. Aquaculture, an original global introduction pathway, did not significantly explain the contemporary distribution of NIS. Results suggest that recreational boating is the most probable pathway of fouling NIS spread in this region, driving their current distribution. Spatial autocorrelation was significant for environmental, demographic, and aquaculture variables. However, marina propulsiveness and attractiveness were not autocorrelated, suggesting that boater behaviour varies on a finer scale. Main conclusions A simple model using a combination of vector, demographic, and environmental characteristics can explain 43.6% of the variation in the spatial distribution of NIS. Our study provides further evidence that recreational boating is a significant pathway for the contemporary spread of NIS in marine environments. With projected increases in human population, we expect a continued rise in introduction rates and spread in this region and elsewhere in the world.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".