Data from: Experimental study of species invasion – early population dynamics and role of disturbance in invasion success
Bibliographic record
Abstract
Much of our understanding of natural invasions is retrospective, based on data acquired after invaders become established. As a consequence, we know little about the characteristics of the early population growth and habitat use of the invaders during establishment. Here we report on experimental introductions of guppies into natural streams in which we conducted monthly censuses of each population. Two of the four introductions were in streams with thinned canopies, which mimics a common form of habitat disturbance. We conducted similar censuses of natural populations to characterize natural population densities and generate a null distribution against which we could test a priori hypotheses about the establishment of the experimental invaders. We constructed a pedigree for one population, which enabled us to quantify lifetime reproductive success. Population simulations predict that the nature of the introduced population’s life history, in combination with reduced risk of predation in the introduction sites, will result in explosive population growth; however, populations of introduced invaders instead grew to match densities observed in natural streams with intact canopies. Experimental populations in streams with thinned canopies grew to densities that often exceeded those of natural streams with intact canopies. High population densities were associated with the increased use of marginal habitat. Adult females and males that moved into marginal habitat suffered no apparent fitness loss, suggesting lower population densities found there compensated for lower habitat quality. Our results suggest that the ecological setting in which invasions occur plays a role at least comparable in importance to that of the invader’s inherent characteristics in shaping early population growth and habitat use.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".