Partial and complete dependency among data sets has minimal consequence on estimates from integrated population models
Bibliographic record
Abstract
Integrated population models (IPMs) are widely used to combine disparate data sets in joint analysis to better understand population dynamics and provide guidance for conservation activities. An often-cited assumption of IPMs is independence among component data sets within the combined likelihood. Dependency among data sets should lead to underestimation of variance and bias because individuals contribute data to more than one data set. In practice, studied individuals often occur in multiple data sets in IPMs (i.e., overlap), which is one way for the independence assumption to be violated. Such cases have the potential to dissuade practitioners and limit application of IPMs to solve emerging ecological problems. We assessed precision and bias of demographic rates estimated from IPMs using a complete gradient (0-100%) of overlap among data sets, wide ranges in demographic rates (e.g., survival 0.1-0.8) and sample sizes (100-1,200 individuals) and variable data sources. We compared results from our simulations with those from IPMs constructed using empirical data on tree swallows (Tachycineta bicolor) where data sets either had complete overlap or included different individuals. Contrary to previous investigators, we found no substantive bias or uncertainty in any demographic rate from IPMs derived from data sets with complete overlap. While variability in demographic rates was greater at low sample sizes (i.e., low capture, recapture, and survey probabilities), there were negligible differences in the posterior mean or root mean square error of demographic rates among IPMs with strong dependence vs. complete independence among data sets. Our simulations suggest IPMs can be designed using only capture-recapture data or harvest and capture-recovery data where population estimates are obtained from the same data as survival and productivity data. While we encourage researchers to carefully consider the modeling approach best suited for their data sets, our results suggest that dependence among data sets does not generally compromise IPM estimates. Thus, violation of the independence assumption should not dissuade researchers from the application of IPMs in ecological research.
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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 | 0.041 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".