Do animal size, seasons and vegetation type influence detection probability and density estimates of Serengeti ungulates?
Bibliographic record
Abstract
Abstract Accurate detection of individual animals and estimation of ungulate population density might be a function of vegetation cover, animal size, observation radius or season. We assessed the effect of these factors on estimates of detection probability and density using five ungulate species in Western Serengeti National Park, Tanzania. Estimates were derived from information collected using ground surveys involving line transects targeting three resident species (impala, topi and buffalo) and two migrants (wildebeest and zebra) and analysed using DISTANCE, MANOVA, t ‐test and Pearson correlation. Results showed that ground surveys that take observation radii of 100 m would appreciably estimate at least 80% of the available ungulates. Beyond 100 m radii, surveys would leave approximately 43% of individuals undetected, the reason being a substantial influence of animal size, vegetation cover and observation radius on the detection. Animal size and observation radius have interactive effects. On their own seasonal differences, they do not have any effect but in interaction with animal size have significant effects especially on the migrant species. As reliable estimates of detection and density are required for making reasonable inferences, we urge that surveys using DISTANCE approach should consider incorporating both ground and aerial survey methods and ensure adequate sample replication.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".