Behavioral responses to resource heterogeneity can accelerate biological invasions
Bibliographic record
Abstract
The abundance and spatial distribution of resources in a landscape and the behavioral response of individuals determines whether and how fast an invasive species spreads in an environment. Whether and how landscape manipulations can be used to slow invasive species is of great interest, in particular in forest ecosystems, where tree removal, thinning, and increasing tree diversity are discussed as management options. Classically, the focus is on availability and accessibility of resources; more recent considerations include individual-level behavioral movement responses to a spatially heterogeneous resource distribution. We derive a novel model for insect-host dynamics that includes three common behavioral aspects of foraging: higher movement rate in resource-poor areas, lower ovipositioning rate in resource-poor areas, and movement preference for resource-rich areas. We show that each of these basic mechanisms can increase the speed of invasion in a source-sink landscape above that in a homogeneous landscape with larger overall resource availability. We parameterize our model and illustrate our results with data for emerald ash borer, a recent highly destructive forest pest in North America. Our results highlight the importance of empirical work on movement behavior in different landscape types and near the interface between types.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".