Incorporating genetic gain into growth and yield projections for Alberta’s white spruce and lodgepole pine tree improvement programs
Bibliographic record
Abstract
Forest Management in Alberta, Canada, has been facing challenges from a shrinking forest land-base over the past few decades. Tree improvement is recognized as one of the most efficient approaches in addressing this issue. However, there are still some knowledge gaps limiting the application and benefit assessment of tree improvement programs. Given that white spruce (Picea glauca (Moench) Voss) and lodgepole pine (Pinus contorta var. latifolia Dougl.) are the two most important commercial tree species in Alberta, these species are the focus of this thesis. In this thesis, five chapters are included, with three data chapters (Chapters 2-4) focusing on estimating: 1) genetic gain at rotation age and corresponding growth and yield from improved white spruce and lodgepole pine seedlots; 2) climate change effects on improved white spruce and lodgepole pine performances; and 3) early growth of improved white spruce in mixedwood stands in northeastern Alberta. In Chapter 2, taking advantage of the latest height measurements from progeny trials in the province of Alberta, I adjusted and compared two available age-age correlation equations developed previously by Lambeth (1980) and Rweyongeza (2016). The results indicated that the adjusted Lambeth equations, with re-estimated parameters, were the most accurate for both species and should be incorporated into Alberta’s growth and yield models. The phenotypic age-age correlation showed no significant deviation from the genetic age-age correlation for either species. The stand volume generated from the growth and yield projection system (GYPSY) model using the newly adjusted Lambeth equations showed that white spruce had a higher age-age correlation when given the same selection and rotation ages, and therefore, a higher percentage improvement in volume per hectare compared to lodgepole pine regardless of rotation age. In Chapter 3, the most recent height measurements from progeny and provenance trials, and three Representative Concentration Pathways (RCPs) were selected to incorporate climate change into growth and yield predictions for both species. An adjusted Pooled Transfer Function (PTF) was developed, which relates standardized population height with population climate transfer distance and population climate and was merged with GYPSY using the newly adjusted Lambeth equations to predict the effects of climate change on the growth and yield of unimproved and improved stands in Alberta. The simulation results indicated that height growth was strongly influenced by the mean coldest month temperature (MCMT, averaged over the daily mean temperature) for white spruce and mean annual precipitation (MAP) for lodgepole pine. By 2090, climate change-related growth expansions for white spruce stands are expected to be greater in areas with low provenance MCMT than in areas with high provenance MCMT for both improved and unimproved seedlots, regardless of the RCPs. Unimproved and improved lodgepole pine stands, however, are expected to show decreased height growth in most regions in Alberta. For both species under all three RCPs, improved seedlots will be outgrown by unimproved seedlots in locations where climate change favours height growth, while improved seedlots will retain their growth advantage over unimproved seedlots in locations where climate change shows an overall negative effect on height growth. In Chapter 4, data collected from four Forest Management Units (FMUs) in northeastern Alberta were used. The results indicated that, in the mixed white spruce and trembling aspen (Populus tremuloides Michx.) stands, the improved white spruce seedlot, which originated from a tree improvement program with an approved height gain of 1.9% at a 100-year rotation, did not show any advantage in height or diameter at an early stage. A distance-independent competition index based on Lorimer’s index, that included size ratio between competitor aspen and subject spruce, accounted for most of the variation in averaged diameter and height increments from 2016-2018 (age of trees 8-10 years), when a power function was used in the competition analysis (competition index was the explanatory variable, and averaged diameter and height increments were the response variable). The competition effects on height and diameter growth differed significantly. For both unimproved and improved seedlots across ecosites, height growth was less sensitive to the competition effects than diameter growth. These results in this thesis fill some of the current knowledge gaps, through providing accurate age-age correlation equations and an adjusted PTF to estimate growth and yield of improved forest stands under climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".