The Impact of Habitat Impermanence on Metapopulation Viability and Size
Bibliographic record
Abstract
Abstract Habitat is often impermanent causing the amount and spatial distribution of habitat patches available to species to vary through time. Theory calls for metrics that fully account for the impact of habitat impermanence on metapopulations, yet all use averages that homogenize spatiotemporal impacts of habitat impermanence in some manner. We develop a novel modelling approach using a deterministic variation of the widely used spatially realistic Levins model paired with a continuous time Markov chain to capture the stochastic impacts of habitat impermanence on finite metapopulations. From this model we derive analytic expectations of metapopulation viability and size by weighting landscape capacities and equilibrium occupancies by the quasi-equilibrium distribution of habitat configurations exhibited by landscapes characterized by a simplest form of habitat impermanence (i.e. random and independent habitat patch loss and gain at constant rates). These provide expectations of metapopulation viability and size in absence of transient metapopulation dynamics. We then show how dispersal and colonization/extinction rates, relative to rates of habitat change, separately impact metapopulation viability and size. Using simulations of our model, we show the measures we propose invariably improve estimation of metapopulation viability and size from earlier estimates, but ultimately overestimate both when species less able to keep pace with rates of change from habitat impermanence in the landscapes they occupy. Our research identifies the importance of fully accounting for spatial habitat configurations through time, and highlights the importance of transient dynamics as rates of habitat impermanence increase.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".