Combining Species Distribution Models and Value of Information Analysis for Spatial Allocation of Conservation Resources
Bibliographic record
Abstract
1. Managers often have incomplete information needed to make decisions about threatened species management, and do not have the time or funding needed to obtain complete information.Value of Information (VOI) theory has the potential to assist managers in making the decision to monitor or manage, but it has not been applied to assessing spatial allocation of monitoring resources to multiple units across a landscape.2. I use data from species distribution models (SDMs) to apply VOI analysis across a landscape to assess the utility of single and multiple objective decisions.I determine in which situations one should monitor before purchasing land for conservation, and when one should act based on current information.Further, I prioritize units based on cost effectiveness and identify target properties for conservation.3. When managing for a single species, the optimal decision for target management units was to act based on current information when survey accuracy was low.When detectability was high, it was most effective to monitor the majority of units.When managing for multiple species, monitoring was only optimal in 50% of cases.Using VOI to determine when monitoring is warranted, and when one should act on current information led to an increase in the expected number of occurrences protected when an optimization algorithm was used to simulate the selection of management units given a budget of $100,000 CAD. Synthesis and applications.Using SDMs in combination with VOI allows for large scale analysis, and can assist managers in most efficiently distributing limited resources.My results suggest that if managers can utilize VOI to more efficiently monitor, it can lead to a greater number of protected occurrences of threatened species.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".