Models of individual tree mortality for trembling aspen, lodgepole pine, hybrid spruce and subalpine fir in northwestern British Columbia
Bibliographic record
Abstract
Density dependent mortality is an important process in forest succession. The overall predictive abilities of forest simulation models are closely related to their ability to predict mortality. Finding appropriate methods for modelling mortality have often proved to be a difficult challenge. The objective of this study was to test a method on adult trees, which was previously used for modelling density dependent mortality for saplings with good results. In the basic model mortality is predicted as a function of recent diameter growth. It was also tested if incorporating tree size into the mortality model improved it. \n \nModels were developed for four species: trembling aspen (Populus tremuloides Michx.), lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia), hybrid spruce (a complex of white spruce (Picea glauca (Moench) Voss) and Engelmann spruce (Picea engelmanii Parry ex Engelm.)) and subalpine fir (Abies lasiocarpa (Hook.) Nutt.). The models were parameterized from field data using a maximum-likelihood method. Field data was gathered from 16 stands in the Sub-Boreal Spruce Zone in northwestern British Columbia and comprised of 337 live and 345 recently dead trees in total. The mortality models were tested by incorporating them into the individual tree, spatially explicit forest simulation model SORTIE-ND. SORTIE-ND simulations of single species even-aged stands were compared to simulations of a commonly used stand level simulation model. Furthermore, SORTIE-ND simulations of permanent sample plots in mixed species uneven-aged stands were compared to remeasurements of the plots. \n \nIt was determined that incorporating tree size into the mortality models gave better fits to the field data. Tolerance to low growth decreases to a minimum at intermediate trees size for all species except for subalpine fir, where it decreases and remains low as trees growth larger. This is probably an effect of the ontogenetic characteristics of the individual species. \n \nTesting the mortality models in SORTIE-ND showed that they contribute to realistic thinning patterns in simulations of both pure even-aged stands and complex stands. However, it was evident that the performance of the mortality models is highly dependent on the underlying growth models as well as mortality models accounting for random mortality. Discrepancies in modelling results were linked to over- and underestimation of growth or inappropriate random mortality rates. \n \nOverall the tested method provides a straight forwards approach to parameterizing growth based mortality models from field data which is relatively easy to obtain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".