Ephemeral Wetland Macroinvertebrate Communities Across Climate Regions Share Similar Functional Trait Composition Despite Near‐Total Taxa Replacement
Bibliographic record
Abstract
ABSTRACT Community assembly in aquatic habitats is heavily influenced by hydrology, but understanding the influence of other habitat conditions is also critical. Most studies focus on comparisons of geographically close communities that exist under diverse hydrological regimes, but this framework limits our ability to understand how conditions other than hydrology shape ephemeral wetland communities. Here, we investigated how macroinvertebrate communities vary with local, landscape, and climate variables in ephemeral wetlands across a large geographic range with few geographic barriers. We sampled ephemeral wetlands in North Dakota, New Mexico, and Texas (USA) in 2021 and in North Dakota and New Mexico in 2022. We used an array of hydrographic, climate, landscape, and spatial variables to relate taxonomic and functional macroinvertebrate community composition and diversity to habitat conditions. Taxonomic composition was overwhelmingly different among states and between years: landscape‐scale refuge availability explained variation in taxonomic composition, but local and climate‐scale variables only explained variation within the context of other variables. Trait composition was similar between most sampling groups, but distinct trait assemblages occurred in the North Dakota 2021 communities. No predictor variable matrix explained trait composition alone, but local, climate, landscape, and spatial arrangement predicted composition when considering the overlapping influence of other variables. Taxa and trait diversity indices were associated with increased refuge habitat at landscape scale. Our results show consistent trait structure across a large geographical scale in hydrologically similar wetlands, despite almost complete taxonomic turnover between regions. Patterns in taxonomic and functional composition imply that incorporating predictor variables at multiple scales is critical in understanding ephemeral wetland community composition. Despite similar hydrological regimes and potential for connectivity via dispersal, taxa replacement is high in ephemeral wetlands across regions within a single grassland macrosystem. Taxonomic composition and overall diversity change with the context provided by a diverse suite of structuring variables. Further, we show that in most cases, ephemeral hydrology elicits a similar trait response across climate regions.
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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 | 0.000 |
| 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) | 0.014 | 0.001 |
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; both teacher heads agree on what is shown here.
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".