Demographic meta‐analysis: synthesizing vital rates for spotted owls
Bibliographic record
Abstract
Summary Effective resource management ultimately influences vital rates of fecundity and survival for target species. Meta‐analysis can be used to combine results from multiple demographic studies replicated in time and space to obtain estimates of vital rates as well as metrics of population growth. Workshop formats were used to conduct meta‐analyses of mark–recapture experiments on spotted owls Strix occidentalis in the western USA. The implied motivation for demographic studies of spotted owls has been that changes in vital rates and population growth, λ, reflect the success of conservation strategies, but how to interpret results may not be obvious. Demographic analysis is of little practical utility until vital rates can be linked to management. In the case of spotted owls, future meta‐analyses must focus on co‐variation between vital rates and habitat variables, and experiments will be necessary. Sensitivity of population growth to variation in vital rates is central to demographic analysis, but results must be interpreted cautiously because these sensitivities are not likely to identify the vital rates most responsible for variation in population size, and cannot reveal which vital rates will be most responsive to conservation investments. Difficulties in documenting dispersal seriously compromised estimates of juvenile survival and thereby biased estimates of λ pm from a projection matrix, a problem that was resolved in later workshops by estimating λ RJS directly using a reparameterized Jolly–Seber mark–recapture method. Several sources of bias for estimates of vital rates and λ were reviewed. Bias exists in meta‐analysis estimates of λ combined over spatial replicates because λ is a non‐linear function of vital rates. Bias also exists in estimates of average population growth where λ t varies over time. This problem can be reduced by calculating the geometric mean of λ. Research to measure biases associated with the estimation of vital rates and the selection of study areas will be necessary to validate meta‐analyses of demography for spotted owls. Synthesis and applications. Meta‐analysis is ideally suited to studies of the demography of long‐lived species because of the large areas involved, high costs for each individual study, and multiple jurisdictions within which the organisms occur. Mixed models selected using information–theoretic approaches provide a powerful way to combine research results from several studies in a meta‐analysis.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".