Estimating Causal Impacts of Human Recreation on Wildlife in the Absence of Experimental Controls
Bibliographic record
Abstract
ABSTRACT Much recent research has focused on the impact of human recreation on wildlife, but relatively few studies have used causal inference approaches; doing so would strengthen recreation management and decision‐making. Here, we use tools from the causal analysis literature and a multi‐year observational dataset to assess how human road‐ and trail‐use affects an apex predator, the grizzly bear ( Ursus arctos horribilis ). Our study leverages a natural experiment that reduced—via access restrictions and changes to tourism operator conditions—peak‐season human recreation by ∼ 85% in 2023 compared to levels in 2018–2022.We used structural time series forecasting to quantify how weekly detection rates of grizzly bears changed in 2023 versus previous years, and “placebo tests” to strengthen causal inference and rule out competing hypotheses. We show that grizzly bear detections and temporal trends were 185% higher in late summer 2023 due to reduced human trail‐use, providing robust evidence that human recreation can cause reduced wildlife activity in protected areas.
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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".