Establishing a baseline: insights gained from studying a large population of a threatened species
Bibliographic record
Abstract
Data on threatened species are understandably biased as we prioritize researching populations undergoing declines rather than the few remaining stable populations. This prioritization bias reduces the availability of demographic benchmarks, resulting in continually shifting baselines and can result in flawed inferences when gathering range-wide demographic data for quantitative conservation modelling. This is particularly problematic for long-lived endangered species with relatively large ranges and spatially variable threats such as the Wood Turtle ( Glyptemys insculpta (Le Conte, 1830)). We identified a suspected large and stable population of Wood Turtles and conducted a mark–recapture study from 2019 to 2022, and used capture data from 2003 to 2005 to establish benchmark demographic parameters to guide range-wide recovery efforts of declining populations. We captured 135 turtles (55F-32M-48J), and predicted the population size to be 119 (87–170) individuals over 5.5 km of river. We predicted high apparent annual survival of females (96.8%, SE = 2.5) and juveniles (86.4%, SE = 16.5), but lower than expected rates for males (82.5%, SE = 5.7). We estimated 15-year minimum apparent annual adult survival between 2004 and 2019 to be 93.8%. We hypothesize that our low apparent male survival in 2019–2022 is due to connectivity outside our immediate study area because we regularly captured new turtles up (5M-1F-4J) and downstream (2M-8F-5J) of the defined study areas. Despite our low annual male apparent survival, we predict a stable population of Wood Turtles between 2023 and 2053 using a population viability analysis. We demonstrate that Wood Turtle populations should have among population connectivity and naturally high juvenile proportions and survival. Populations lacking these demographic traits risk future declines even with high adult survivorship. Unbiased demographic and vital rates from stable populations are essential for setting appropriate population targets by conservation and recovery programs.
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 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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".