The geography of metacommunities: landscape characteristics drive geographic variation in the assembly process through selecting species pool attributes
Bibliographic record
Abstract
Abstract Metacommunity ecology traditionally disregards that the dominant life-histories observed in species pools are selected by the characteristics of landscapes where the assembly process takes place. Recognizing the importance of this relationship is relevant because it integrates macroecological principles into metacommunity theory, generating a greater understanding about the ecological causes underlying broad-scale geographic variation in the relative importance of assembly mechanisms. To demonstrate that, we employed simulation models in which species pools with the same initial distribution of niche breadths and dispersal abilities interacted in landscapes with contrasting characteristics. By assessing the traits of species that dominated the metacommunity in each landscape type, we determined how different landscape characteristics select for different life-history strategies at the metacommunity level. We also analyzed the simulated data to derive predictions about the causal links between landscape characteristics, dominant life-histories in species pools, and their mutual influence on empirical inferences about the assembly process. We provide empirical support to these predictions by contrasting the assembly process of moth metacommunities in a tropical versus a temperate mountainous landscape. Collectively, our simulation models and empirical analyses illustrate how our framework can be formalized as an inferential tool for investigating the geography of metacommunity assembly.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".