Assessing the relative potential ecological impacts and invasion risks of emerging and future invasive alien species
Bibliographic record
Abstract
Invasive alien species (IAS) cause myriad negative impacts, such as ecosystem disruption, human, animal and plant health issues, economic damage and species extinctions. There are many sources of emerging and future IAS, such as the poorly regulated international pet trade. However, we lack methodologies to predict the likely ecological impacts and invasion risks of such IAS which have little or no informative invasion history. This study develops the Relative Impact Potential (RIP) metric, a new measure of ecological impact that incorporates per capita functional responses (FRs) and proxies for numerical responses (NRs) associated with emerging invaders. Further, as propagule pressure is a determinant of invasion risk, we combine the new measure of Pet Propagule Pressure (PPP) with RIP to arrive at a second novel metric, Relative Invasion Risk (RIR). We present methods to calculate these metrics and to display the outputs on intuitive bi- and triplots. We apply RIP/RIR to assess the potential ecological impacts and invasion risks of four commonly traded pet turtles that represent emerging IAS: Trachemysscriptascripta, the yellow-bellied slider; T.s.troostii, the Cumberland slider; Sternotherusodoratus, the common musk turtle; and Kinosternonsubrubrum, the Eastern mud turtle. The high maximum feeding rate and high attack rate of T.s.scripta, combined with its numerical response proxies of lifespan and fecundity, gave it the highest impact potential. It was also the second most readily available according to our UK surveys, indicating a high invasion risk. Despite having the lowest maximum feeding rate and attack rate, S.odoratus has a high invasion risk due to high availability and we highlight this species as requiring monitoring. The RIP/RIR metrics offer two universally applicable methods to assess potential impacts and risks associated with emerging and future invaders in the pet trade and other sources of future IAS. These metrics highlight T.s.scripta as having high impact and invasion risk, corroborating its position on the EU list of 49 IAS of Union Concern. This suggests our methodology and metrics have great potential to direct future IAS policy decisions and management. This, however, relies on collation and generation of new data on alien species functional responses, numerical responses and their proxies, and imaginative measures of propagule pressure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".