Pathway‐level models to predict non‐indigenous species establishment using propagule pressure, environmental tolerance and trait data
Bibliographic record
Abstract
Summary Non‐indigenous species ( NIS ) establishments are a growing concern. Current quantitative methods for NIS risk assessment generally focus on only one species or only one of the main drivers of establishment [propagule pressure (PP), environmental suitability, or species' traits]. There is a need for quantitative models that estimate establishment probability for species at the pathway level; models would be particularly relevant if they could utilize available data, combine multiple predictors and were made accessible to managers. We present and evaluate methodology that uses establishment data, PP proxy data and any available trait data for the suite of species present in an introduction pathway to generate a joint pathway‐level establishment model where species' establishment probabilities are influenced by their traits and environmental tolerances. The consequence of using our joint model is that if traits are predictive and species differ in the number of propagules needed to establish, a family of species‐specific PP‐establishment curves is estimated. Theoretical results revealed that our model performs well and makes accurate predictions even when trait data are incomplete and/or extraneous data are used in fitting. An empirical analysis of freshwater fish introductions to the United States identified species that have a high chance of establishment. The inclusion of species' trait and environmental data significantly improved upon predictions made with a PP‐only model. Further, by considering both PP and species traits, we were able to predict which species have been observed in the U.S. and which species were more likely to persist. Synthesis and applications . Managers can use the methodology presented herein to generate quantitative pathway‐level models of establishment probability. This methodology is especially appealing because it gives managers the ability to make quantitative estimates of how proposed management actions will affect establishment probabilities, allows managers to control risk without completely restricting trade, can generate predictions for new species in a pathway given knowledge of the species' trait values and informs on the consequences associated with substitutions for species with trade restrictions.
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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) | 0.004 | 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".