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 (δ15N) 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 δ15N were more robust proxies than %N for detecting differences in N availability. Less negative leaf and soil δ15N 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 δ15N, 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 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 | 0.001 | 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".