Multiple drivers of contrasting diversity–invasibility relationships at fine spatial grains
Bibliographic record
Abstract
The diversity-invasibility hypothesis and ecological theory predict that high-diversity communities should be less easily invaded than species-poor communities, but empirical evidence does not consistently support this prediction. While fine-scale experiments tend to yield the predicted negative association between diversity and invasibility, broad-scale observational surveys generally report a positive correlation. This conflicting pattern between experiments and observational studies is referred to as the invasion paradox and is thought to arise because different processes control species composition at different spatial scales. Here, we test empirically the extent to which the strength and direction of published diversity-invasibility relationships depend on spatial scale and on the metrics used to measure invasibility. Using a meta-analytic framework, we explicitly separate the two components of spatial scale: grain and extent, by focusing on fine-grain studies that vary in extent. We find evidence of multiple drivers of the paradox. When we consider only fine-grain studies, we still observe conflicting patterns between experiments and observational studies. In contrast, when we examine studies that are conducted at both a fine grain and fine extent, there is broad overlap in effect sizes between experiments and observation, suggesting that comparing studies with similar extents resolves the paradox at local scales. However, we uncover systematic differences in the metrics used to measure invasibility between experiments, which use predominantly invader performance, and observational studies, which use mainly invader richness. When we consider studies with the same metric (i.e., invader performance), the contrasting associations between study types also disappear. It is not possible, at present, to fully disentangle the effect of spatial extent and metric on the paradox because both variables are systematically associated in different directions with study type. There is therefore an urgent need to conduct experiments and observational studies that incorporate the full range of variability in spatial extent and invasibility metric.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".