Preventing a series of unfortunate events: Using qualitative models to improve conservation
Bibliographic record
Abstract
Abstract Biological organisms are increasingly being introduced and eradicated in an effort to maintain biodiversity and ecosystem function in the face of anthropogenic threats. However, these conservation actions can have unintended consequences to non‐target species. Careful vetting of these actions using ecological modelling tools could help predict and avoid unintended consequences. Qualitative modelling tools, such as fuzzy interaction webs (FIWs), allow for qualitative rankings of community properties (e.g. interaction strength = high, medium, low) in combination with quantitative information to predict management outcomes. These tools have lower data requirements than strictly quantitative models, facilitating their use for communities lacking comprehensive parameterization. However, no studies have evaluated the efficacy of FIWs for predicting unintended consequences against empirically documented outcomes. Moreover, there is no process for systematically identifying which species to incorporate in community‐level conservation assessments to overcome model structure uncertainty. Finally, there is a need to make qualitative modelling tools more accessible for conservation practitioners. We applied FIWs to the case study of lake trout introduction into Yellowstone Lake, Yellowstone National Park, to assess its ability to predict documented community‐level outcomes from an intentional species introduction. Next, we used the case study of the intentional red squirrel introduction to Newfoundland to show how a community assessment framework can help define the community interaction web needed for applying a FIW. Lastly, we introduced a user‐friendly web interface ( https://matrix.mpgranch.com/#/ ) for applying FIWs to conservation questions. We found that the FIW predicted previously documented directional changes in the abundance of community components relatively well in the Yellowstone Lake case study, even with minimal knowledge of the system. The community assessment framework provided a formal process for identifying community components for the Newfoundland case study, and the resulting FIW predicted documented unintended consequences. The user interface predicts realistic outcomes in our study system and allows managers to build and apply FIWs for conservation planning. Synthesis and applications. Our community assessment framework and user interface can be used to apply FIWs to identify and avert potential unintended outcomes of species introductions and eradications for improved conservation 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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".