Contemporary perspectives on the niche that can improve models of species range shifts under climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pioneering efforts to predict shifts in species distribution under climate change used simple models based on the correlation between contemporary environmental factors and distributions. These models make predictions at coarse spatial scales and assume the constancy of present correlations between environment and distribution. Adaptive management of climate change impacts requires models that can make more robust predictions at finer spatio-temporal scales by accounting for processes that actually affect species distribution on heterogeneous landscapes. Mechanistic models of the distribution of both species and vegetation types have begun to emerge to meet these needs. We review these developments and highlight how recent advances in our understanding of relationships among the niche concept, species diversity and community assembly point the way towards more effective models for the impacts of global change on species distribution and community diversity.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it