Mortality of Ash in Forested Riparian Zones Drives Prolonged ET Depression and Hydric Soil Formation
Bibliographic record
Abstract
Emerald ash borer (EAB) ( Agrilus planipennis Fairmaire), an invasive, phloem-feeding beetle native to Asia, has killed hundreds of millions of ash ( Fraxinus spp.) trees in the USA and Canada since it was detected in southeast Michigan in 2002. Consistently high mortality of black ash ( Fraxinus nigra ) and green ash ( F. pennsylvanica ) is a particular concern given the role both species play in regulating soil moisture and shallow groundwater levels in riparian forests. Here we present the first longitudinal observations documenting hydrologic effects resulting from EAB-caused ash mortality in a riparian zone at the W.K. Kellogg Experimental Forest in southwest Michigan. From 2018-2022, we monitored soil moisture, depth to groundwater and meteorological observation at 15-min intervals throughout the growing season in two adjacent plots (gap, forest) in the Augusta Creek riparian zone. We estimated groundwater evapotranspiration (ET G ) using a groundwater level fluctuation (WLF) method. Significant differences in volumetric soil moisture content (16-26% higher in the gap than forest), average depth to water (10 cm in the gap vs 70 cm below land surface in the forest) and mean daily ET G (0.6 in the gap vs 3.0 mm per day in the forest) persisted across four growing seasons. Prolonged saturation of the near surface is driving hydric soil formation, contributing to an ecosystem regime shift from forested riparian to herb and sedge-dominated wetland. These changes have important implications for riparian zone ecosystem services including nutrient cycling, sediment transport, and greenhouse gas emissions, especially when considering the extent of ash mortality already sustained in much eastern North America.
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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 | 0.000 |
| 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) | 0.001 | 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".