Reducing uncertainty in species management: forecasting secondary spread with expert opinion and mechanistic models
Bibliographic record
Abstract
Abstract Predicting the spatial and temporal dynamics of invasive species is critical for successful management intervention, yet substantial uncertainty exists about how species will interact with human pathways when introduced to new ecosystems. We demonstrate a novel approach for quantifying uncertainty when predicting the uptake, movement, and establishment of invasive species by combining mechanistic modeling of the spread process with expert opinion of the demographic factors that govern species performance. We demonstrate the utility of this approach using a case study involving the transfer potential of nonindigenous species (NIS) in the Laurentian Great Lakes basin (GLB). A survey using structured expert judgment was completed by 24 North American taxonomic experts, covering 60 species ofNISestablished in theGLB. Experts estimated species‐specific demographic parameters describing population growth and establishment potential, which were incorporated into an existing mechanistic model of human‐mediated spread via ballast water with species‐specific spread rates (number of ports or lakes invaded/year) as outputs. Expert judgments within each group varied widely, indicating that generalizable rates of spread across taxa are unlikely and highlighting the value of cross‐taxon comparisons. Most species were predicted to establish throughout theGLBwithin 10 yr, assuming status quo management conditions. Sensitivity analysis for expert performance‐based weighting demonstrated that most model outputs were insensitive to weighting (<1% difference over baseline) and shows the robustness of the joint model. Overall, the joint expert opinion and predictive modeling method demonstrates a novel means of handling sparse data when forecasting invasion dynamics. Divergent estimates resulted in a range of likely spread rates, but improved upon traditional best‐guess approaches. Incorporating joint methods into ecological decision‐making frameworks has clear implications for invasive species management but may also inform other ecological scenarios where data are scarce and conservation action is urgent.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".