Experimental evidence that density dependence strongly influences plant invasions through fragmented landscapes
Bibliographic record
Abstract
Populations of range expanding species encounter patches of both favorable and unfavorable habitat as they spread across landscapes. Theory shows that increasing patchiness slows the spread of populations modeled with continuously varying population density when dispersal is not influence by the environment or individual behavior. However, as is found in uniformly favorable landscapes, spread remains driven by fecundity and dispersal from low density individuals at the invasion front. In contrast, when modeled populations are composed of discrete individuals, patchiness causes populations to build up to high density before dispersing past unsuitable habitat, introducing an important influence of density dependence on spread velocity. To test the hypothesized interaction between habitat patchiness and density dependence, we simultaneously manipulated these factors in a greenhouse system of annual plants spreading through replicated experimental landscapes. We found that increasing the size of gaps and amplifying the strength of density dependence both slowed spread velocity, but contrary to predictions, the effect of amplified density dependence was similar across all landscape types. Our results demonstrate that the discrete nature of individuals in spreading populations has a strong influence on how both landscape patchiness and density dependence influence spread through demographic and dispersal stochasticity. Both finiteness and landscape structure should be critical components to theoretical predictions of future spread for range expanding native species or invasive species colonizing new habitat.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".