Analyis of Predator Movement in Prairie Landscapes with Contrasting Grassland Composition
Bibliographic record
Abstract
Mammalian predation influences waterfowl breeding success in the U.S. northern Great Plains, yet little is known about the influence of the landscape on the ability of predators to find waterfowl nests. We used radiotelemetry to record nightly movements of red foxes (Vulpes vulpes) and striped skunks (Mephitis mephitis) in two 41.4-km2 study areas in North Dakota. Study areas contained either 15–20% grassland (low grassland composition) or 45–55% grassland (high grassland composition). Grasslands included planted cover, pastureland, and hayland. We predicted that the type and composition of cover types in the landscape would influence both predator movement across the landscape (as measured by the fractal dimension and displacement ratio) as well as localized movement (as measured by the rate of movement and turning angle between locations) within patches of different cover types. Red fox movements were straighter (lower fractal dimensions and higher displacements) across landscapes with a low grassland composition, indicating directed movement between the more isolated patches of planted cover. Striped skunk movements did not differ between landscape types, illustrating their movement along wetland edges, which had similar compositions in both landscape types. The high variability in turning angles by red fox in planted cover and pastureland in both landscape types is consistent with restricted-area foraging. The high rate of movement by red foxes in planted cover and by striped skunks in wetland edges suggests that spatial memory may influence movement patterns. Understanding the behavior of predators in fragmented prairie landscape is essential for managing breeding habitat for grassland birds and for predicting the spatial and temporal dynamics of predators and their prey.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".