Land‐use legacies influence tree water‐use efficiency and nitrogen availability in recently established European forests
Bibliographic record
Abstract
Abstract Forest regrowth following farmland (agriculture and pasture) abandonment has been positively associated with a number of processes including the regulation of hydrological cycling, the enhancement of soil functioning and an increase in forest productivity and carbon (C) sequestration. Although these changes in ecosystem functioning post‐farmland abandonment have been observed in multiple locations and studies, the ecophysiological basis underpinning these patterns remains unclear. Here, we examine whether increased forest expansion following pastureland abandonment is associated with greater water‐use efficiency (WUE) and legacies from previous land use in terms of nitrogen (N) availability. We thus explored differences in leaf traits and N availability between recently established (post‐1950) beech Fagus sylvatica (L.) forests on former pastureland and long‐established beech forests (pre‐1950). The investigated leaf traits were SLA, leaf N concentration (%N) and intrinsic WUE (iWUE, i.e. the ratio between photosynthesis and stomatal conductance); as well, leaf and soil stable N isotope composition (δ 15 N) and total %N were used to assess changes in N availability. Finally, we compared the correlation strength between the above‐mentioned parameters and those associated with tree productivity (wood density and basal area increment, BAI) and the richness of ectomycorrhizal fungi (ECM) in these two forest types. Recent forests had greater iWUE than long‐established forests, which was associated more with lower SLA than leaf %N. Leaf and soil δ 15 N were more robust proxies than %N for detecting differences in N availability. Less negative leaf and soil δ 15 N values in recent versus long‐established forests suggest, on the one hand, greater N availability, probably due to higher historical N input originating from animal excreta on these former pasturelands, and, on the other hand, an increase in N loss pathways. Our results point to greater correlations between leaf δ 15 N, tree iWUE and productivity in recent forests than in long‐established forests, thereby suggesting a close link between C and N cycles. Our findings also highlight different N dynamics between the two forest types, with recent forests showing ‘leaky’ N cycling wherever lower N retention by trees and associated ECM fungi occurs as a legacy of previous land use. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.001 |
| 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 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".