Non‐optimal animal movement in human‐altered landscapes
Why this work is in the frame
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Bibliographic record
Abstract
Summary I synthesize the understanding of the relationship between landscape structure and animal movement in human‐modified landscapes. The variety of landscape structures is first classified into four categories: continuous habitat, patchy habitat with high‐quality matrix, patchy habitat with low‐quality matrix, and patchy, ephemeral habitat. Using this simplification I group the range of evolved movement parameters into four categories or movement types. I then discuss how these movement types interact with current human‐caused landscape changes, and how this often results in non‐optimal movement. From this synthesis I develop a hypothesis that predicts the relative importance of the different population‐level consequences of these non‐optimal movements, for the four movement types. Populations of species that have inhabited landscapes with high habitat cover or patchy landscapes with low‐risk matrix should have evolved low boundary responses and moderate to high movement probabilities. These species are predicted to be highly susceptible to increased movement mortality resulting from habitat loss and reduced matrix quality. In contrast, populations of species that evolved in patchy landscapes with high‐risk matrix or dynamic patchy landscapes are predicted to be highly susceptible to decreased immigration and colonization success, due to the increasing patch isolation that results from habitat loss. Finally, I discuss three implications of this synthesis: (i) ‘least cost path’ analysis should not be used for land management decisions without data on actual movement paths and movement risks in the landscape; (ii) ‘dispersal ability’ is not simply an attribute of a species, but varies strongly with landscape structure such that the relative rankings of species’ dispersal abilities can change following landscape alteration; and (iii) the assumption that more mobile species are more resilient to human‐caused landscape change is not generally true, but depends on the structure of the landscape where the species evolved.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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