Habitat change and the scale of habitat selection: shifting gradients used by coexisting Arctic rodents
Bibliographic record
Abstract
The conservation and understanding of biodiversity requires development and testing of models that illustrate how climate change and other anthropogenic effects alter habitat and its selection at different spatial scales. Models of fitness along a habitat gradient illustrate the connection between fine‐scale variation in fitness and the selection of habitat as discontinuous patches in the landscape. According to these models, climate change can increase fitness values of static habitats, shift the fitness value of habitat patches along underlying gradients of habitat quality, or alter both fitness and habitat quality. It should be possible to differentiate amongst these scenarios by associating differences in the abundance and distribution of species with metrics of habitat that document the gradient while controlling for changes in density at larger scales of analysis. Comparisons of habitat selection by two species of lemmings, over an interval of 15 years, are consistent with the theory. The pattern of habitat selection at the scale of wet versus dry tundra habitats changed through time. The change in habitat selection was reflected by different, but nevertheless density‐dependent, patterns of association with the structure and composition of habitat. Abundant collared lemmings abandoned stations where altered habitat characteristics caused a shift to new locations along the wet‐to‐dry gradient. The confirmation of scale‐dependent theory provides new insights into how one might begin to forecast future habitat selection under different scenarios of climate and habitat change.
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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) | 0.003 | 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".