Miniaturizing landscapes to understand species distributions
Bibliographic record
Abstract
Species’ geographic distributions shape global patterns of biodiversity and therefore have long been of interest to ecology and conservation. Theory has generated valuable hypotheses about how landscape structure, dispersal, biotic interactions and evolution shape range dynamics, but most predictions have not been tested on real organisms because key variables are difficult to isolate, replicate or manipulate in natural ecosystems. An exciting and rapidly emerging approach is to extend classical microcosm and mesocosm systems to create experimental ‘micro‐landscapes’. By enabling researchers to manipulate geographic features of interest, replicate landscapes, control colonization and follow dynamics across evolutionary timescales, micro‐landscapes allow explicit tests of the ecological and evolutionary underpinnings of species distributions. Here we review the micro‐landscape systems being used to advance biogeography, the major insights they have generated thus far, and the features that limit their application to some scenarios. We end by highlighting important questions about species’ biogeography that are ripe for testing with experimental micro‐landscapes, particularly those of immediate concern given rapid global change, such as range contractions and constraints to range expansion.
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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 | 0.001 |
| 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) | 0.056 | 0.003 |
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; both teacher heads agree on what is shown here.
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".