Using data from camera traps and autonomous recording units to evaluate and improve species-habitat inferences
Bibliographic record
Abstract
In vast and remote regions such as the boreal forest of northern Canada, population dynamics of most wildlife species are poorly understood because of limited sampling. Habitat models are a key tool to identify important habitat types, predict species occurrences in unsampled areas, and ultimately inform conservation actions. There are many sampling methods available to generate data to fit habitat models, each of which has pros and cons. We evaluated the use of data from two non-invasive sampling methods in the Northwest Territories, Canada: autonomous recording units (ARUs, n = 160) and camera traps (n = 229), to create habitat models for Sandhill Cranes Antigone canadensis. Specifically, we tested for differences in model fit between sampling methods using habitat information at different spatial scales (300 and 2000 m around sampling points). We considered the smaller scale to represent the home range of a pair of cranes and the larger to represent the landscape scale. We also tested whether an integrated habitat model that combined data from both sampling methods would improve predictive performance over single sampling method models. Both methods sampled cranes well, and we found similar directions and magnitudes of parameter estimates generated from all three models (ARU, camera trap, and integrated). Models using ARU data estimated higher overall occupancy probability than those using camera trap data and had better fits at the 2000 m scale, while the camera trap models had better fit at the 300 m scale. The integrated model combined inferences from both sampling methods but did not improve predictive performance. For cranes, we recommend that ARUs be used when the best estimate of landscape scale occupancy is required, and that camera traps be used when information is needed on home range use at a smaller scale. Using both sampling methods can strengthen inferences across scales and improve knowledge of understudied species in remote regions, but the utility of integrating the data into one modeling framework was not shown and should be further evaluated.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".