Cost-effectiveness of leaf energy and resource investment of invasive Berberis thunbergii and co-occurring native shrubs
Bibliographic record
Abstract
Photosynthetic energy gain and biomass energy and resource investment represent trade-offs between potential enhancements and limitations to plant productivity, respectively. We compared these characteristics in the exotic invasive Berberis thunbergii DC. with that of co-occurring natives Kalmia latifolia L. and Vaccinium corymbosum L. in a northeastern United States forest. We hypothesized that invasion by B. thunbergii could be facilitated by a lower leaf construction cost (CC) and reduced leaf nitrogen content (N) relative to photosynthetic rate (A) and maximum photosynthetic capacity (Amax), which would afford it greater energy-use efficiency (EUE) and nitrogen-use efficiency (NUE), and maximums of these variables (EUEmaxand NUEmax), compared with native shrubs. Although B. thunbergii and K. latifolia exhibited similar peak-season A and Amax, EUE, EUEmax, and NUEmaxwere greater in B. thunbergii, which exhibited lower leaf CC and density. In contrast, EUE, EUEmax, NUE, and NUEmaxdid not differ between B. thunbergii and V. corymbosum given their similar A, Amax, and area-based leaf CC and leaf N. Considered with leaf phenology, our results suggest two distinct physiological mechanisms could influence B. thunbergii invasion. Specifically, deciduous B. thunbergii exhibited greater cost-effectiveness than evergreen K. latifolia, while a longer payback time for photosynthetic energy gain could offset instantaneous similarities in cost-effectiveness of earlier leafing B. thunbergii and deciduous V. corymbosum.
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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 | 0.001 | 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 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".