The Geography of Metacommunities: Landscape Characteristics Drive Geographic Variation in the Assembly Process through Selecting Species Pool Attributes
Bibliographic record
Abstract
AbstractThe nonrandom association between landscape characteristics and the dominant life history strategies observed in species pools is a typical pattern in nature. Here, we argue that these associations determine predictable changes in the relative importance of assembly mechanisms along broadscale geographic gradients (i.e., the geographic context of metacommunity dynamics). To demonstrate that, we employed simulation models in which groups of species with the same initial distribution of niche breadths and dispersal abilities interacted across a wide range of landscapes with contrasting characteristics. By assessing the traits of dominant species in the species pool in each landscape type, we determined how different landscape characteristics select for different life history strategies at the metacommunity level. We analyzed the simulated data using the same analytical approaches used in the study of empirical metacommunities to derive predictions about the causal relationships between landscape characteristics and dominant life histories in species pools, as well as their reciprocal influence on empirical inferences regarding the assembly process. We provide empirical support for these predictions by contrasting the assembly of moth metacommunities in a tropical versus a temperate mountainous landscape. Together, our model framework and empirical analyses demonstrate how the geographic context of metacommunities influences our understanding of community assembly across broadscale ecological gradients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".