Monitoring with multiple goals: Bayesian methods for changing objectives
Bibliographic record
Abstract
Long-term monitoring is essential for wildlife conservation. Most wildlife population attributes require long-term monitoring to evaluate. Over the time for attributes to resolve through monitoring, however, information needs change. Existing frameworks to accommodate information need changes, such as adaptive monitoring and management, are built for large-scale, programmatic changes. Often, smaller, rapid changes are necessary. Fortunately, information needs can change predictably in wildlife monitoring, even when little is known about populations. Predictable changes include the desire to answer: 1) is the species present?; 2) are multiple individuals present?; 3) is breeding occurring?. We suggest long-term monitoring can accommodate these changes. We propose Goal Efficient Monitoring (GEM), an approach that uses a Bayesian integrated population model (BIPM) to accommodate changing information needs through: a BIPM that links population state changes (e.g., present, multiple individuals present) to population dynamics (e.g., abundance, demographic rates); and sampling rules to allocate effort observation effort based on current knowledge. To test the efficacy of a GEM approach, we ask two research questions: 1) can implementing a GEM approach provide robust population estimates?; and 2) do GEM sampling rules in multiple long-term monitoring settings (i.e., population sizes) accommodate changing questions while providing continual, reliable population inference? To answer these questions, we built a BIPM and conducted a simulation study for a rare species in the US, Canada lynx ( Lynx canadensis ). We simulated lynx populations under five different starting conditions and simulated a GEM approach (10 years of simulated observations with GEM sampling rules), then used our BIPM model to produce estimates and predictions. In 93 % of simulations, 95 % credible intervals for BIMP estimates contained the true value for all biological (abundance of all sexes and age classes, birth events, survival, state transition probabilities) and observation variables (detection probabilities). We demonstrate how a GEM approach can provide reliable long-term inference while being responsive to shifting information needs.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".