Use of Spectral Analysis to Detect Changes in Spatial Variability of Forest Floor Properties
Bibliographic record
Abstract
Understanding how silvicultural interventions affect soil spatial variability will improve our ability to predict forest ecosystem function in response to different degrees of management intensity. We demonstrate the use of spectral analysis, a geostatistical technique, to understand how management interventions affect soil spatial variability. The technique was applied to determine whether an intensive vegetation control treatment modifies the spatial patterns of soil microclimate and soil quality indicators: microbial biomass C (SMB‐C) and N (SMB‐N) as well as net N mineralization rate. A secondary objective was to investigate the contribution of soil microclimate factors to the explanation of spatial patterns of microbial biomass (C and N). Measurements were performed on transects laid out in two plots: a plot that was undisturbed since clearcut and replanted 11‐yr earlier with white pine ( Pinus strobus L.) and another plot which was subject to annual herbicide application during 4 yr following reforestation with white pine. Forest floor temperature (TEMP) and water content (WC), net N mineralization rate and SMB‐C and SMB‐N concentrations were measured every 25 cm along each transect. Spatial patterns were assessed using spectral analysis. In the two plots, microclimatic variables and SMB‐N presented complex spatial patterns with several scales of spatial dependency, whereas SMB‐C and net N mineralization did not demonstrate a spatial pattern at this scale of observation. In the herbicide‐treated plot, the spatial pattern of SMB‐N was influenced by the plantation grid. Herbicide applications markedly decreased spatial variability of forest floor properties. In some frequencies, SMB‐N was markedly positively correlated with forest floor layer WC but not with forest floor TEMP.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".