Response diversity, nonnative species, and disassembly rules buffer freshwater ecosystem processes from anthropogenic change
Bibliographic record
Abstract
Integrating knowledge of environmental degradation, biodiversity change, and ecosystem processes across large spatial scales remains a key challenge to illuminating the resilience of earth's systems. There is now a growing realization that the manner in which communities will respond to anthropogenic impacts will ultimately control the ecosystem consequences. Here, we examine the response of freshwater fishes and their nutrient excretion - a key ecosystem process that can control aquatic productivity - to human land development across the contiguous United States. By linking a continental-scale dataset of 533 fish species from 8100 stream locations with species functional traits, nutrient excretion, and land remote sensing, we present four key findings. First, we provide the first geographic footprint of nutrient excretion by freshwater fishes across the United States and reveal distinct local- and continental-scale heterogeneity in community excretion rates. Second, fish species exhibited substantial response diversity in their sensitivity to land development; for native species, the more tolerant species were also the species contributing greater ecosystem function in terms of nutrient excretion. Third, by modeling increased land-use change and resultant shifts in fish community composition, land development is estimated to decrease fish nutrient excretion in the majority (63%) of ecoregions. Fourth, the loss of nutrient excretion would be 28% greater if biodiversity loss was random or 84% greater if there were no nonnative species. Thus, ecosystem processes are sensitive to increased anthropogenic degradation but biotic communities provide multiple pathways for resistance and this resistance varies across space.
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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 | 0.001 | 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) | 0.002 | 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".