Assessing landscape relationships for habitat generalists
Bibliographic record
Abstract
Abstract: The importance of landscape heterogeneity for the abundance and distribution of wildlife is well recognized. General relationships have been developed to link landscape pattern to demographic processes, although these relations are best demonstrated for species with specialized habitat requirements and often in landscapes that can be generalized to a simple habitat-matrix structure. Habitat generalists may interact in more complex ways with a mosaic of landscape features. A novel method for quantifying the habitat relationships of generalist species using thematic vegetation maps was proposed by Brotons et al. (2005) and based on a theoretical model by Andrén, Delin, and Seiler (1997). We tested the efficacy of this approach on moose (Alces alces) distribution in the heterogeneous landscapes of the Foothills Natural Region, Alberta, Canada, using 8 broad vegetation types. Fecal pellet group data, an index of moose occurrence, was compared across pre-selected sites. Sites were selected to represent the variable amounts and combinations of the different vegetation types available in the study area. Moose habitat preference was determined using a Chi-square test and Bonferroni confidence intervals. Moose preferred shrublands and deciduous forests. Shrubland was considered primary moose habitat as it had the highest observed proportion of pellet groups of the preferred habitats. Each vegetation type was assessed regarding its role in habitat amount, habitat compensation, supplementation, complementation, and fragmentation models using general linear modelling. Habitat amount and fragmentation were related to moose pellet occurrence. However, there was no indication of supplementation, compensation, or complementation. This mosaic approach effectively revealed habitat relationships and the potential impacts of habitat change for a generalist species at the landscape scale. Nomenclature: Kays & Wilson, 2002.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".