Ecological consequences of shifting habitat mosaics within and across years in an intermittent stream
Bibliographic record
Abstract
Abstract Intermittent streams that cease to flow during dry periods represent more than half of the global river network, and are particularly common in arid and semi‐arid regions. They are characterised by high spatial and temporal variability in aquatic habitat, forming a shifting habitat mosaic that supports diverse assemblages of native and endemic species. Climate projections for much of the world predict greater variation in precipitation and increasing drought severity, suggesting a need to better understand species' responses to habitat variability within intermittent stream ecosystems. Here we explored changes in the distribution and abundance of aquatic vertebrates in response to a wide range of annual hydrologic conditions within a Mediterranean‐climate intermittent stream in California. We conducted wetted habitat and vertebrate abundance surveys during the dry season for 7 years, spanning both extreme wet and extreme dry annual precipitation conditions, and characterised the variation in the size, persistence, and spatial configuration of wetted habitats in relation to the observed abundance and composition of fishes, amphibians, and aquatic reptiles. We characterised the habitat mosaic using a k‐means clustering approach, and identified three habitat types with distinct size, persistence, and connectivity distributions. We found that some wetted habitat units persisted across all years, regardless of antecedent precipitation, whereas others dried in all but the wettest years. We determined that persistent pools, a subset of wetted habitats present in the stream, supported diverse assemblages of native species even during extreme dry conditions, while transient pools acted as important habitat for particular species and life stages, including a young‐of‐year minnow species. Linear mixed models indicated that species' abundances were influenced by habitat quantity, quality, and connectivity. Our study ‐ considers intermittent streams as shifting habitat mosaics and uses multi‐year surveys to understand the ecological consequences of habitat variability within and across years to advance our understanding of intermittent stream ecology and the implications of climate change for spatial and temporal patterns of refuge habitat.
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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 | 0.001 |
| 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 | 0.000 |
| 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".