Estimating non‐indigenous species establishment and their impact on biodiversity, using the Relative Suitability Richness model
Bibliographic record
Abstract
Summary Key questions in invasion biology include where will a non‐indigenous species (NIS) establish, and how will it affect biodiversity? Scientists have addressed the first question, largely through correlating species’ distributions with environmental factors (i.e. species distribution models, SDM). Conceptually, SDMs reflect a species’ abiotic constraints, but in reality, they measure the realized rather than fundamental niche. This is a limitation of SDMs, but analysed correctly, it may also be a strength since SDMs already incorporate the outcome of species interactions. We postulate that we can use the relative predicted probabilities of occurrences in each location from SDMs to predict the outcome of interactions between species. Based on this idea, we develop the Relative Suitability Richness (RSR) model, and generate two protocols to (i) assess theoretically whether we can predict the probability of establishment, and (ii) by conducting counterfactual analyses, the extent to which we can infer the impact of NIS on biodiversity. Species distribution models based solely on abiotic factors have the potential to predict species establishment well, even when biotic interactions are strong. However, predictions improved with inclusion of biotic data, even with only partial information. Even with weak environmental predictors and no community data, macro‐scale predictions could still be strong and one can estimate loss of native populations, as long as the fitting environment was representative of the prediction environment. If environmental conditions change, predictions were still possible so long as either good environmental predictors and/or biotic information was available. Policy implications. Risk analyses are often recommended to guide management practices; they depend upon forecasting the likelihood and severity of an invasion. The Relative Suitability Richness protocols developed here predict non‐indigenous species occurrences and impact, using even partial biotic data on native species occurrences, to provide key information needed to estimate invasion risk, prioritize management effort and advance invasive species policy.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".