Importing risk: quantifying the propagule pressure–establishment relationship at the pathway level
Bibliographic record
Abstract
Abstract Aim To build and assess pathway‐level non‐indigenous species ( NIS ) establishment curves generated using a propagule pressure ( PP ) proxy and historical establishment data. Location North America Methods Our analysis examines the utility and behaviour of pathway‐level NIS establishment curves that relate species‐level PP to establishment probability. Using theoretical and empirical methods, we examine the behaviour of pathway‐level establishment models when species are heterogeneous in their ability to establish. Next, we examine the implications of using PP proxy and historical establishment data to parameterize these models. Finally, we test the model by building an establishment curve for aquarium fish establishments in the United States using import data as a proxy for PP . Results First, we show theoretically how species' heterogeneity and the use of a proxy metric for PP affect model parameterization and the interpretation of the establishment curve. Second, we demonstrate that import data are relatively consistent across space and time for aquarium fish species. Finally, we demonstrate how basic import‐level data can improve our ability to predict which species are at risk of establishment using aquarium fish introductions to the United States as a case study. Main conclusions Pathway‐level analyses generated using species‐level PP information can provide a snapshot of establishment probability for use in risk analyses without in‐depth knowledge of species' abiotic and biotic interactions. Proxy data for PP can be a good metric for such analyses, and valid predictions can be expected when the PP data are relatively consistent across the time period for which establishments are recorded.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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 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".