Effects of drought and pluvial periods on fish and zooplankton communities in prairie lakes: systematic and asystematic responses
Bibliographic record
Abstract
The anticipated impacts of climate change on aquatic biota are difficult to evaluate because of potentially contrasting effects of temperature and hydrology on lake ecosystems, particularly those closed-basin lakes within semiarid regions. To address this shortfall, we quantified decade-scale changes in chemical and biological properties of 20 endorheic lakes in central North America in response to a pronounced transition from a drought to a pluvial period during the early 21st century. Lakes exhibited marked temporal changes in chemical characteristics and formed two discrete clusters corresponding to periods of substantially different effective moisture (as Palmer Drought Severity Index, PDSI). Discriminant function analysis (DFA) explained 90% of variability in fish assemblage composition and showed that fish communities were predicted best by environmental conditions during the arid interval (PDSI <-2). DFA also predicted that lakes could support more fish species during pluvial periods, but their occurrences may be limited by periodic stress due to recurrent droughts and physical barriers to colonization. Zooplankton taxonomic assemblages in fishless lakes were resilient to short-term changes in meteorological conditions, and did not vary between drought and deluge periods. Conversely, zooplankton taxa in fish-populated lakes decreased substantially in biomass during the wet interval, likely due to increased zooplanktivory by fish. The powerful effects of such climatic variability on hydrology and the strong subsequent links to water chemistry and biota indicate that future changes in global climate could result in significant restructuring of aquatic communities. Together these findings suggest that semiarid lakes undergoing temporary climate shifts provide a useful model system for anticipating the effects of global climate change on lake food webs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".