Faster movement in nonhabitat matrix promotes range shifts in heterogeneous landscapes
Bibliographic record
Abstract
Ecologists often assume that range expansion will be fastest in landscapes composed entirely of the highest-quality habitat. Theoretical models, however, show that range expansion depends on both habitat quality and habitat-specific movement rates. Using data from 78 species in 70 studies, we find that animals typically have faster movement through lower-quality environments (73% of published cases). Therefore, if we want to manage landscapes for range expansion, there is a trade-off between promoting movement with nonhostile matrix, and promoting population growth with high-quality habitat. We illustrate how this trade-off plays out with the use of an exemplar species, the Baltimore checkerspot butterfly. For this species, we calculate that the expected rate of range expansion is fastest in landscapes with ~15% high-quality habitat. Behavioral responses to nonhabitat matrix have often been documented in animal populations, but rarely included in empirical predictions of range expansion. Considering movement behavior could change land-planning priorities from focus on high-quality habitat only to integrating high- and low-quality land cover types, and evaluating the costs and benefits of different matrix land covers for range expansion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".