Combination of targeted monitoring and Breeding Bird Survey data improves population trend estimation and species distribution modeling for the Common Nighthawk
Bibliographic record
Abstract
Abstract Standardized monitoring is critical for conservation because reliable survey data are used to inform the necessity, type, and effectiveness of conservation actions. Many of the avian monitoring data used for conservation are collected by “comprehensive” programs that survey for all species observed; however, there are some species that have low availability for detection during comprehensive surveys and are instead monitored with targeted programs. Unfortunately, those targeted programs are rarely evaluated relative to existing programs and management objectives to inform allocation of effort. We assessed the statistical performance of the comprehensive North American Breeding Bird Survey (BBS), the targeted Canadian Nightjar Survey (CNS), and the two programs combined for the Common Nighthawk (Chordeiles minor). First, we used parameters from the existing datasets to simulate population declines and determined the probability of detecting those declines. Analyses that combined both datasets resulted in higher probability of detecting a 30% population decline (BBS: 38%, CNS: 64%, combined: 69%). Next, we built probability of occurrence models and assessed the predictive performance of those models. Combined analyses had similar predictive performance to the CNS and moderated poor performance of the BBS in the north (mean Cohen’s kappa; BBS: 0.40, CNS: 0.46, combined: 0.50). Our results suggest that data from targeted monitoring is important for evaluation of Common Nighthawk population trend and habitat relationships but is best combined with BBS data. Comprehensive and targeted monitoring programs may be better when considered together, and exploration of data combination should become the rule, not the exception. We suggest that the framework we present can be used as a starting point for evaluating targeted monitoring programs relative to defined objectives and existing programs, with the potential to explore hypothetical management scenarios.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".