Implications from Monitoring Gopher Tortoises at Two Spatial Scales
Bibliographic record
Abstract
ABSTRACT Conservation biologists need to effectively monitor species given resource limitations and the inherent challenges of assessing long‐term demographic processes. We assessed gopher tortoise ( Gopherus polyphemus ) abundance at a landscape scale and at the scale of 3 local populations within the Conecuh National Forest (CNF), Alabama, USA, between 1991 and 2017. We collected landscape‐level data from line transect distance sampling arranged uniformly across the CNF during a single season (2011); we obtained data for local populations from long‐term mark‐recapture of individuals at 3 sites selected based on prior knowledge of high density at each. At a landscape scale, we estimated 5,242 (95% CI = 3,538–7,768) tortoises occurred across the approximately 34,000‐ha forest, yielding a density of 0.14–0.32 tortoises/ha. These low densities across the landscape suggest that, on average, management activities across the property have not allowed tortoise populations to retain the social structure needed for long‐term persistence. The 3 local populations, however, contained 25–60 individuals and densities of 1.9–6.9 tortoises/ha. Over the study period, populations at 2 sites were stable and the third experienced significant population growth. Mean annual survival of individuals was 0.89 and invariant across size classes. Overall, line transect distance sampling is important for assessing landscape‐scale abundance of tortoises but may fail to detect local clusters of high‐density sites important for population persistence. Our mark‐recapture efforts at the local scale revealed that small populations on these high‐density sites can exhibit long‐term stability or growth even though they do not meet current established criteria for viability. Improved models that incorporate immigration and emigration and better reflect the dynamics of peripheral populations would assist in determining how such populations best contribute to species recovery and regional conservation targets. © 2020 The Wildlife Society.
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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 | 0.000 | 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) | 0.001 | 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 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".