Behavioral flexibility and species invasions: the adaptive flexibility hypothesis
Bibliographic record
Abstract
Behavioral flexibility is an important adaptive response to changing environments for many animal species. Such plasticity may also promote the invasion of novel habitats by introduced species by providing them with the ability to expand or change their ecological niche, a longstanding idea with recent empirical support. At the individual level, flexibility may arise through innovation, in which an individual invents a new behavior, or through social learning, in which an individual adopts a behavior used by others. There is increasing evidence that the adaptive value of these two modes of learning, and the overall expression of behavioral flexibility, may vary with social and environmental context. In this paper, we propose that invasive species may change the degree to which they express behavioral flexibility in an adaptive manner during the different stages of invasion. Specifically, the “adaptive flexibility hypothesis” predicts that the expression of behavioral flexibility, and thus the diversity of behaviors observed in a population, will be high during the initial stage of introduction into a novel environment due to innovation, followed by a decline in behavioral diversity during the establishment and growth of a founding population due to social learning of successful behavioral variants. We discuss several alternatives to this hypothesis and suggest empirical and theoretical tests of these hypotheses. This “adaptive flexibility hypothesis” suggests that a more nuanced approach to the study of the behaviors employed by individuals in populations at different invasion stages could generate new insight into the importance of such flexibility during species invasions, and the evolution of behavioral plasticity in general
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".