Macroecological drivers of zooplankton communities across the mountains of western North America
Bibliographic record
Abstract
Disentangling the environmental and spatial drivers of biological communities across large scales increasingly challenges modern ecology in a rapidly changing world. Here, we investigate the hierarchical and trait‐based organization of regional and local factors of zooplankton communities at a macroscale of 1240 mountain lakes and ponds spanning western North America (California, USA, to Yukon Territory, Canada). Variation partitioning was used to test the hypothesized importance of climate, connectivity, catchment features, and exotic sportfish to zooplankton beta‐diversity in the context of key functional traits (body size and reproductive dispersal potential) given the pronounced environmental heterogeneity (e.g. thermal gradients), topographic barriers, and legacy of stocked fish in mountainous regions. Dispersal limitation was inferred from multispecies patch connectivity estimates based on nearest and average distances to occupied patches. Environmental heterogeneity best explained community composition as catchment/lake features (morphometry, land cover, and lithology) collectively captured greater variation than did climate (temperature, precipitation, and solar radiation), local stocking, or connectivity; however, single climatic variables captured the most variation individually. Macrospatial variation by larger obligate sexual species was better explained than that by smaller cyclically parthenogenetic asexual species. Our results provide several novel insights into the macroecology of zooplankton of the North American Cordillera, demonstrating their stronger associations to climatically driven aquatic‐terrestrial habitat coupling than dynamics arising from introduced salmonids, human land‐use, or species dispersal. These findings highlight the clear and important role of these communities as bioindicators of the limnological impacts of accelerating rates of climate change, as their responses appear relatively not confounded by local human perturbations or dispersal limitation.
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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 | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 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 teacher head, 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".