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 of NIS established in the GLB . 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 the GLB within 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".