Condors in space: an individual-based population model for California condor reintroduction planning
Bibliographic record
Abstract
Abstract Context Existing demographic models of California condors have not simultaneously considered individual condor movement paths, the distribution and juxtaposition of release sites, habitat components, or the spatial distribution of threats. Objectives Our objectives were to develop a dynamic spatially explicit and individual-based model (IBM) of California condor demography and to evaluate its ability to replicate empirical data on demography and distribution from California (1995–2019). Methods We built a female-only spatially explicit California condor IBM in HexSim, using a daily timestep that allowed us to simulate the foraging behavior of condors, changes in food distribution and availability, and the ephemeral threat of lead in decaying food resources. Results Simulated population size was highly correlated with annual population census data once the population became established with > 50 females ( r 2 = 0.99). Mean simulated fecundity and mortality estimates were not significantly different from empirical trends ( p > 0.05), although empirical data had higher interannual variability. The geographic distribution of modeled condors was similar to the empirical distribution with an overall accuracy of 79%, a commission error of 27%, and an omission error of 9%. Simulated movement density corresponded moderately well to the density of observed GPS locations (weighted kappa = 0.44). Conclusions We developed a spatially explicit California condor IBM that is well-calibrated to empirical data from California. Given its mechanistic underpinnings and flexibility to incorporate a variety of spatial and demographic inputs, we expect our model to be useful for assessing the relative risks and benefits of future condor reintroduction and management scenarios.
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) | 0.000 | 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".