Environmental filtering and habitat (mis)matching of riverine invertebrate metacommunities
Bibliographic record
Abstract
Abstract Aim Metacommunities are assembled through a combination of local and regional processes, with the relative importance of the drivers of assembly depending on ecological context. Global change can alter community assembly at both local and regional levels, potentially shifting communities into disequilibrium with their local environmental conditions. In this study, we evaluated the spatial variation in environmental filtering and habitat matching of 1078 riverine macroinvertebrate communities distributed across nine ecoregions within the conterminous United States. Location Conterminous United States. Taxon Freshwater macroinvertebrates. Methods We first quantified spatial patterns in environmental filtering, habitat matching, and functional trait diversity. We then used boosted regression trees to identify (1) functional trait predictors of environmental filtering and habitat matching and (2) environmental, landscape, and network variables that predict functional trait abundances. Results Our results demonstrated that environmental filtering but not habitat matching varied strongly by ecoregion. We also found that functional trait diversity varied by ecoregion, but not as strongly as the signatures of environmental filtering. We did not identify consistent functional trait predictors for both environmental filtering and habitat matching, with trait predictors instead varying by individual traits, trait categories, and ecoregions. Notwithstanding inconsistent trait predictors, environmental filtering was primarily influenced by habitat preference traits while habitat matching was primarily influenced by both habitat preference and dispersal traits. Predictors of functional traits also varied by trait category and ecoregion, with habitat preference and dispersal traits primarily influenced by network variables. Main conclusions Our study demonstrates the contingent patterns and drivers of environmental filtering and habitat matching on a macroecological scale. We aim for this work to provide the foundation on which trait-environment relationships can be further quantified and causal explanations established in the context of community disequilibrium and applied to conservation and management of freshwater systems.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".