Floodplain land use gradients have a stronger effect on soil microbial enzyme activity than spatiotemporal variability
Bibliographic record
Abstract
Abstract Climate change is leading to flood events with higher frequency and longer duration. Changes in seasonal flooding that affect water saturation of soils can impact soil microbial extracellular enzyme activity (EEA) that mediates nutrient and carbon cycling. Understanding controls on soil functional potential in floodplain ecosystems helps identify optimal land use practices in these biodiverse ecosystems often under threat from land use intensification. Here, we assess some of the abiotic controls on soil microbial EEA within a floodplain and determine how sensitive the relationship between EEA and land use is across spatial scales and time. We collected soils across land use gradients within the Lake Saint Pierre floodplain, a UNESCO World Biosphere Reserve in Québec, and the largest freshwater floodplain in eastern Canada. Land uses included conventional and conservation agriculture, new and established managed perennial grasslands, and natural grasslands and forests. Within each land use, soils were sampled at three time periods, at three elevations representing different exposures to flood, and at four regions around the lake's shoreline to capture temporal and spatial variability. We found that EEAs declined with increasing land use intensity as expected, primarily associated with soil moisture and soil organic carbon. Notably, perennial agriculture practices had EEAs and nutrient and carbon concentrations falling between those under the annual agricultural and natural sites and could be an appropriate compromise to converting conventional agricultural practices back to natural areas. We also found that the gradient of decreasing EEA with increasing land use intensity was largely conserved across spatial scales and time. The exception for this conserved enzyme–land use relationship was in lower elevation soils, located close to the lakeshore that experience the highest flood frequency. In these locations, the land use characteristics that otherwise supported higher EEA seem to be overridden, as we did not observe any relationship between EEA and land use. Our results suggest that the influence of land use on supporting microbial nutrient and carbon cycling is largely stronger than the inherent spatial and temporal variation within a heterogeneous and biodiverse ecosystem like floodplains, highlighting the importance of land use management across scales.
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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 | 0.000 |
| 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 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".