Bridging the scale gap: Predicting large‐scale population dynamics from small‐scale variation in strongly heterogeneous landscapes
Bibliographic record
Abstract
Abstract Often, ecologists are challenged with a mismatch of scales: how do we upscale from local variation and available data to landscape‐level models and predictions? We present a general recipe for coarse‐graining from local‐ to landscape‐scale reaction–diffusion equations when spatial heterogeneity is small in extent compared to dispersal of organisms. Our homogenization approach uses the fundamental ecological concepts of Turchin's residence index and Skellam's dynamic level . Our approach opens avenues to new ecological theory that connects different scales, which we illustrate using predator–prey interactions. It also presents opportunities for using the increasingly available small‐scale data for landscape‐level predictions, such as range expansion rates. We find several unexpected nonlinear relationships between the movement behaviour on the local level and the spatially implicit and explicit outcomes at the landscape level, for example, predator spread rate may increase or decrease when predators move faster locally. Our method provides a mechanistic link for population dynamics and data integration across spatial and temporal scales, addressing a fundamental goal of landscape ecology.
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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 | 0.000 | 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 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".