The interacting effects of the historic landscape structure, human landscape change, and species mobility on species extinction risk in human-altered landscapes: an evolutionary perspective
Bibliographic record
Abstract
Mobility is widely thought to be important for extinction risk in human-altered landscapes.However, there are conflicting theories and empirical findings as to whether mobile species are more or less at-risk than sedentary species.My objective was to investigate why mobility increases risk in some cases, but decreases risk in others.My first three data chapters focused on the idea that mobility-extinction risk relationships depend on interacting effects of the historic landscape structure, human landscape change, and mobility.In Chapter 2, I used simulations to demonstrate that landscape structure -particularly non-habitat (matrix) quality and disturbance frequency -can drive evolution of species behaviour at habitat boundaries, an important aspect of mobility that determines rates of movement among populations.In Chapter 3, I found empirical support for an interaction between landscape change and mobility, finding that more mobile North American bird species became less mobile over the 20th century, while less mobile species became more mobile.In Chapter 4, I simulated the interacting effects of the historic landscape, landscape change, and mobility on extinction risk.These simulations suggest that the mobility-risk relationship depends on the (1) mobility metric, because mobility increases risk when measured as emigration, but decreases risk when measured as immigration; (2) rate of landscape change, because mobility increases risk with rapid habitat loss, but decreases risk with slow habitat loss; and (3) historic landscape structure, because some structures drive evolution of species characteristics that increase iii mobility and decrease risk, while other structures favor characteristics that increase both mobility and risk.The rest of my thesis focused on the idea that collinearity between habitat specialization and mobility may cause apparently conflicting mobility-risk relationships, when specialization drives risk and specialization-mobility relationships vary between species groups.I found that temperate breeding bird specialists disperse farther than generalists (Chapters 5 and 6).This was opposite to invertebrate specialization-mobility relationships, providing preliminary support for my hypothesis.My thesis contributes to our understanding of why conflicting mobility-extinction risk relationships occur.Additionally, these results have conservation implications, suggesting conservation should focus on species that frequently emigrate but cannot move long distances among habitat fragments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".