Increasing the credibility of conservation plans through citizen science
Bibliographic record
Abstract
Plans for protected area systems (hereafter, prioritizations) need to identify cost-effective priority areas. They must also be supported by information that stakeholders value as credible. Although field observations are often considered highly credible, species distribution models are generally required to overcome sampling gaps and biases. Here we investigate how field observations collected through citizen science could help improve the credibility of prioritizations. Examining a case study in southern Ontario (Canada), we obtained expert survey and citizen science data for 14 plant species and fitted species distribution models. We generated conventional prioritizations following standard conservation planning approaches. We then generated prioritizations that allocated increasing budgets for representing species through priority areas with confirmed occurrences from expert surveys. We also generated prioritizations with confirmed occurrences from both expert surveys and citizen science. Assuming that greater coverage of confirmed occurrences conveys greater credibility, we assessed the putative credibility of prioritizations according to their ability to meet species representation targets with confirmed occurrences. We found trade-offs between minimizing the cost of prioritizations and maximizing their putative credibility. Although such trade-offs were most acute under limited budgets, prioritizations generated with confirmed occurrences from expert surveys and citizen science achieved a moderate increase in putative credibility for only a minor increase in cost. Additionally, our results showed that prioritizations generated with expert surveys and citizen science had greater putative credibility than those generated with expert surveys alone. By considering the perceived credibility of supporting data, conservation planning exercises may achieve greater approval by stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".