Reconciling contradictory relationships between mobility and extinction risk in human‐altered landscapes
Bibliographic record
Abstract
Summary Some empirical and theoretical studies suggest that more mobile species are less at risk in human‐altered landscapes, while others suggest the opposite. We propose three novel hypotheses to explain these contradictory findings: (i) extinction risk increases with increasing mobility when mobility is measured as emigration, but decreases with increasing mobility when mobility is measured as immigration; (ii) the most mobile species (whether measured by emigration or immigration) in unaltered landscapes are least mobile in human‐altered landscapes, so the relationship between mobility and risk is opposite when mobility is measured in unaltered and altered landscapes; and (iii) the mobility–risk relationship is ∩‐shaped; thus, the relationship is apparently positive when studied species have low‐to‐intermediate mobility, but negative when species have intermediate‐to‐high mobility. We also evaluate a fourth hypothesis that had been previously theorized: that some landscape structures drive evolution of dispersal characteristics that increase both mobility and risk, while others drive evolution of characteristics that increase mobility and decrease risk. To evaluate these four hypotheses, we created an individual‐based, spatially explicit simulation model to simulate population dynamics, evolution of dispersal characteristics and species response to habitat loss. Our simulations supported the first and fourth hypotheses. Extinction risk increased with emigration rate but decreased with immigration rate. Species in landscapes with historically more abundant, less fragmented habitat and more frequent disturbance had increased risk and mobility during habitat loss, while species in landscapes with lower matrix quality had increased risk and decreased mobility. Synthesis . This is the first study to investigate why some studies find that more mobile species are less at risk in human‐altered landscapes, while others find the opposite. Our results suggest that these contradictory relationships occur because of differences in how mobility is measured and differences in the historic structure of species' landscapes. Understanding how mobility and the historic landscape influence extinction risk can help us identify species of conservation concern. Our results suggest that we should focus on species with high emigration rates but low immigration rates, and those that evolved in landscapes with non‐fragmented habitat and high‐risk matrix.
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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) | 0.003 | 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".