Spatial variability of foliar nitrogen in the Adirondack Park, New York
Bibliographic record
Abstract
This dissertation explores the relationship between the physical environment and the spatial pattern of the concentration of nitrogen in tree leaves, or foliar N. Maps of foliar N are of great interest due to relationships among foliar N, forest production, and N cycling. I propose that the spatial variability of foliar N is the outcome of a localized process involving species functional traits, the environment experienced by the species, and human' effect on that environment. I obtained species-level and whole-canopy foliar N data from a diverse set of seventy-five forest plots within the Adirondack Park, New York. These plots contained a wide range of variation in foliar N and six factors hypothesized to control its spatial distribution (i.e. spatial controls): species composition, atmospheric N deposition, disturbance history, temperature, moisture availability, and bedrock geology. I also used a subset of the field data and imagery from the Hyperion hyperspectral sensor to predict whole-canopy foliar N with thirty-meter spatial resolution across two 185x7.7 km satellite images. Analysis of the field and satellite foliar N datasets against descriptions of spatial controls obtained from field surveys and a geographic information system (GIS) revealed that species composition was the primary control on the spatial pattern of whole-canopy foliar N. In fact, inter-specific variation in the functional trait of mean foliar N accounted for 93% of the variation in the field whole-canopy foliar N data. The remaining intraspecific variability was related to all six hypothesized spatial controls, and especially to the anthropogenic controls of N deposition and disturbance history. Interestingly, the marked species differences in foliar N response to a single spatial control of N deposition, or to multiple spatial controls were strongly related to species' functional traits of leaf mass per area (LMA) and shade tolerance. In sum, I found the inter- and intra-specific sources of variability to explain 97% of the spatial variability in my field whole-canopy foliar N dataset. Given this evidence, I strongly suggest that maps of foliar N can be further developed as tools to discover fundamental ecological principles and indicate ecosystem response to environmental impacts.
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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".