On the RIP: using Relative Impact Potential to assess the ecological impacts of invasive alien species
Bibliographic record
Abstract
Invasive alien species continue to arrive in new locations with no abatement in rate, and thus greater predictive powers surrounding their ecological impacts are required. In particular, we need improved means of quantifying the ecological impacts of new invasive species under different contexts. Here, we develop a suite of metrics based upon the novel Relative Impact Potential (RIP) metric, combining the functional response (consumer per capita effect), with proxies for the numerical response (consumer population response), providing quantification of invasive species ecological impact. These metrics are comparative in relation to the eco-evolutionary baseline of trophically analogous natives, as well as other invasive species and across multiple populations. Crucially, the metrics also reveal how impacts of invasive species change under abiotic and biotic contexts. While studies focused solely on functional responses have been successful in predictive invasion ecology, RIP retains these advantages while adding vital other predictive elements, principally consumer abundance. RIP can also be combined with propagule pressure to quantify overall invasion risk. By highlighting functional response and numerical response proxies, we outline a user-friendly method for assessing the impacts of invaders of all trophic levels and taxonomic groups. We apply the metric to impact assessment in the face of climate change by taking account of both changing predator consumption rates and prey reproduction rates. We proceed to outline the application of RIP to assess biotic resistance against incoming invasive species, the effect of evolution on invasive species impacts, application to interspecific competition, changing spatio-temporal patterns of invasion, and how RIP can inform biological control. We propose that RIP provides scientists and practitioners with a user-friendly, customisable and, crucially, powerful technique to inform invasive species policy and management.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".