Extended density‐dependent mortality in mature conifer forests: causes and implications for ecosystem management
Bibliographic record
Abstract
Understanding processes driving mortality in forests is important for comprehension of natural stand dynamics and for informing natural disturbance-based ecosystem management. There has been considerable study of mortality in forests during the self-thinning phase but we know much less about processes driving mortality in stands at later successional stages. We addressed this through study of five 1-ha spatially explicit permanent plots in mature (111-186 yr old in 2012) Pinus contorta stands in the Canadian Rocky Mountains using data from repeated measurements over a 45-yr period, dendrochronological information, and point pattern analysis. We tested the hypothesis that these stands had completed the self-thinning/density-dependent mortality stage of succession. Contrary to our expectations, the self-thinning phase can persist for more than 140 yr following stand establishment. Our findings suggest this was attributable to prolonged post-fire establishment periods due to surface fires in three of the plots while in the other two plots moist conditions and slow growth most likely delayed the onset of competition. Several pieces of evidence indicated the importance of density-dependent mortality in these stands over the study period: (1) The diameter distribution of individuals changed from initially right-skewed toward normality as a result of mortality of smaller-diameter stems. (2) Individuals of lower canopy positions were proportionally more affected by mortality. (3) When compared to the pre-mortality pattern, surviving stems in all stands had an increasingly uniform spatial distribution. In two of the plots, recent windthrow and/or ingrowth initially hindered our ability to detect density-dependent mortality but our dendrochronological sampling and permanent plot data allowed us to untangle the different processes at play; in doing so we demonstrate for the first time how density-independent processes can mask underlying density-dependent mortality processes in older stands. Mortality of larger dominant canopy trees increased over the study period and mortality of dominant stems was a random process in all stands suggesting these stands were approaching the end of the self-thinning stage and that density-independent processes might soon become more important. Our results provide an improved understanding of mortality processes that can be applied to natural disturbance-based ecosystem management.
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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 | 0.000 | 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) | 0.000 | 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".