Simultaneous monitoring of vegetation dynamics and wildlife activity with camera traps to assess habitat change
Bibliographic record
Abstract
Abstract Vegetation phenology and productivity drive resource use by wildlife. Vegetation dynamics also reveal patterns of habitat disturbance and recovery. Monitoring these fine‐scale vegetation patterns over large spatiotemporal extents can be difficult, but camera traps (CTs) commonly used to survey wildlife populations also collect data on local habitat conditions. We used CTs (n = 73) from 2016 to 2019 to assess impacts of habitat change in a boreal landscape of northern Canada, where seismic lines for petroleum exploration disturbed wildlife habitat and prompted vegetation restoration efforts. First, we quantified vegetation dynamics from CTs, comparing them to satellite‐based estimates that are typically used to monitor vegetation at broad spatial scales. We then used understory phenology and productivity estimated from CT time‐lapse images to assess vegetation recovery on seismic lines. Finally, we related vegetation dynamics with the habitat use of three wildlife species: sandhill cranes Grus canadensis , woodland caribou Rangifer tarandus , and white‐tailed deer Odocoileus virginianus . CTs provided unique insight into vegetation dynamics that were different from signals measured by satellites, with temporally inconsistent and even some negative correlations between CT and satellite metrics. We found some indication of vegetation recovery on seismic lines that had received restoration treatment, with understory patterns more similar to undisturbed habitat than to seismic lines that did not receive restoration treatment. CTs also provided inferences about wildlife activity related to vegetation resources, which approaches using satellite data failed to detect. Wildlife habitat use tracked vegetation phenology, but did not always increase with vegetation productivity at weekly, 16‐day, or annual intervals. Instead, associations with vegetation productivity depended on species, temporal scale, and productivity metrics. Given the widespread and growing use of CTs to monitor terrestrial wildlife, we recommend their use to simultaneously monitor habitat conditions to better understand the mechanisms that govern wildlife habitat use in changing environments.
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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) | 0.000 | 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".