Adaptive responses to habitat change: Theory and tests with field experiments
Bibliographic record
Abstract
Habitat loss and change are often implicated as the primary causes of species extinction. Although any population can be instantly imperiled by catastrophe, most habitat loss occurs gradually, thus enabling affected individuals an adaptive advantage to occupy the best of their dwindling opportunities. I demonstrate how to infer the advantage between two habitats for any density and frequency-dependent strategy of habitat selection. I explore the concept of an Adaptive Dispersal Strategy Landscape to reveal the Evolutionarily Stable Strategy separately for ideal-free and ideal preemptive habitat selectors. Both solutions reveal an initially counterintuitive expectation that individuals living at high density gain insufficient adaptive advantage to disperse from a deteriorating habitat. Adaptive dispersal is constrained at high density because habitats of better quality are fully occupied. I test the theory with measures of movement and foraging in crossover experiments on a seminatural population of meadow voles. The experiment allowed the voles to choose among patches and between enclosures in which I differentially manipulated food and shelter. Although photographs from an infrared camera documented voles venturing from one habitat to the other, none became resident. Voles preferentially foraged in the richer of the two enclosures, even when I reversed treatments, and they foraged more in patches protected by mulched straw. The adaptive advantage of dispersal using a surrogate fitness proxy based on the voles' giving-up densities mirrored that generated by theory. The convergence between theory and experiment yields much-needed insight into our ability to test, predict, and hopefully resolve, the ecological, evolutionary, and conservation consequences of habitat loss.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".