Consistent metacommunity structure despite inconsistent drivers of assembly at the continental scale
Bibliographic record
Abstract
A fundamental goal of community ecology is to understand the drivers of community assembly and diversity. Local factors acting on community assembly are typically related to environmental conditions while regional factors are typically related to dispersal. Previous research has not consistently demonstrated the importance of local or regional factors, but this is likely because these factors act in concert and not in isolation. Studies that simultaneously integrate local and regional factors into analyses of community assembly can be a useful avenue to further our understanding of this core concept in community ecology. Here, we aimed to identify metacommunity structure and diversity and the local and regional drivers of community assembly at the continental scale. We evaluated metacommunity structure and drivers of assembly of macroinvertebrate communities in 941 rivers and streams nested within nine ecoregions distributed across the conterminous United States. Pattern-based metacommunity analyses and boosted regression tree techniques were used to (a) assign metacommunity structures and (b) identify the environmental, landscape and network drivers of assembly. We also evaluated how biodiversity scaled across hierarchical levels and varied among ecoregions. Metacommunity structures were consistent for the conterminous United States and each of the nine ecoregion subsets, with each ecoregional metacommunity displaying a Clementsian structure. Environmental variables were the predominant drivers of assembly, suggesting the importance of species sorting and environmental filtering on community structure; however, the identity of the most influential environmental variables differed among ecoregions and suggested hierarchical filtering on assembly. Partitioned diversity was found to be lower at the local and ecoregional levels, but turnover in diversity among ecoregions was higher than expected. Our results demonstrate contingencies in community assembly, notwithstanding consistency in metacommunity structure and support the importance of environmental control over community assembly and biodiversity. Moreover, biodiversity at the continental scale is likely maintained through this inherent variation in the drivers of assembly and concomitant changes in community composition among ecoregions. We suggest that further work should evaluate the assembly of other facets of community structure and the underlying mechanisms of the contingency in assembly drivers.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".