Mammal responses to human footprint vary with spatial extent but not with spatial grain
Bibliographic record
Abstract
Abstract Ecological patterns and processes can vary with scale, causing uncertainty when applying small‐scale or single‐scale studies to regional or global management decisions. Conducting research at large extents and across multiple scales can require additional time and effort, but may prove necessary if it uncovers novel patterns or processes. Knowing the degree to which patterns vary between spatial extents and grains can provide insight into the importance of considering scale, particularly in applied research. Across multiple spatial scales, we evaluated variation in the strength and direction of large mammal responses to human footprint, a measure of human infrastructure (e.g., roads, buildings) and landscape change (e.g., agriculture, forestry). We focused on the response of five boreal mammals: gray wolf ( Canis lupus ), Canada lynx ( Lynx canadensis ), coyote ( Canis latrans ), white‐tailed deer ( Odocoileus virginianus ), and moose ( Alces alces ). Firstly, we asked how responses measured at the regional extent of the boreal forest of Alberta (approximately 400,000 km 2 ) compared to those measured at a nested subregional extent (40,000 km 2 ) and to those reported in previous studies conducted at smaller spatial extents (median 2400 km 2 , mean 46,993 km 2 ). Secondly, we tested whether responses differed across three spatial grains of measurement (250 m, 1500 m, or 5000 m radii) at the regional extent. Using the Alberta Biodiversity Monitoring Institute's snowtrack survey data (2001–2013) and human footprint map, we created a set of generalized linear mixed‐effects models for each species, which related relative abundance to individual and cumulative effects of human footprint and compared these using an information theoretic approach. We found variation across spatial extents in both direction and strength of estimated mammal responses to human footprint, suggesting that some patterns are scale‐dependent. This reinforces the need for regional studies to complement those conducted at smaller extents in order to fully understand, and thus manage for, the impacts of human footprint on mammalian biodiversity. By contrast, we found little variation in direction and strength of responses across spatial grains, indicating that analyses across multiple grain sizes may be of less importance than those conducted across multiple spatial extents.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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 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".