Is invasion history a useful tool for predicting the impacts of the world's worst aquatic invasive species?
Bibliographic record
Abstract
The ecological impact stemming from a biological invasion is the most poorly understood aspect of the invasion process. While forecasting methods are generally lacking, a potential means of predicting future impacts is to examine the effects caused by a nonindigenous species (NIS) at previously invaded locations, i.e., its invasion history. However, given the context dependence of impact and the scarcity of data, it is uncertain whether invasion history can in fact be used to forecast the effects of most introduced species. Using a sample of 19 aquatic NIS listed with the IUCN's 100 World's Worst Alien Invasive Species, we reviewed the literature to determine (1) the amount of information currently available concerning their ecological impacts, (2) if the effects reported to be caused by each NIS are consistent across multiple studies, and (3) whether their invasion histories provide sufficient quantitative information to assess and forecast the severity of their impacts on recipient environments. As a case study, we conducted a meta-analysis and developed models that relate the severity of the impacts of a well-documented invader, common carp (Cyprinus carpio), to two potential predictor variables: biomass and time since introduction. We then tested whether models developed from one set of observations can predict the severity of impacts reported at other sites. Models incorporating biomass and pre-impact conditions explained 91% of the variation in carp impact severity at new locations (i.e., those not used to build the models). For most other NIS, limited availability of comparable quantitative data currently prevents the development of similar empirical models for predicting the severity of future impact. Nonetheless, invasion history can often be used to develop informative predictions concerning the type and direction of impacts to be expected at novel recipient sites.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".