Water dynamics in managed pine plantation forests
Bibliographic record
Abstract
Forests provide important ecosystem services and play a dominant role in the global carbon and hydrologic cycles. These ecosystems are becoming more vulnerable to climate change-related threats such as extreme temperature and precipitation events, drought and wildfires. In addition, forest ecosystems have also undergone land use changes and a significant reduction in cover area, specifically in North America. There has been renewed realization to restore and rehabilitate forest ecosystems because they are a major carbon sink and play a key role in sequestering atmospheric carbon dioxide. In response, plantation forests are being widely established to sequester carbon, increase biodiversity, secure water resources and generate economic revenue when harvested. Forest managers employ different management practices such as thinning or retention harvesting to enhance growth, plant structural and species diversity within forest plantations, with the ultimate goal of emulating the characteristics and benefits of natural forests. However, the influence of these forest management practices on the growth, productivity and specifically water cycling in plantation forests is not well studied and reported in the literature. This experimental study investigated the effect of four different variable retention harvesting (VRH) treatments on evapotranspiration and water balance in an 83-year-old red pine (Pinus resinosa) plantation forest in the Great Lakes region in Canada. These VRH treatments included 55% aggregated crown retention (55A), 55% dispersed crown retention (55D), 33% aggregated crown retention (33A), 33% dispersed crown retention (33D) and unharvested control (CN) plot. Tree-level experimental work was conducted in the control plot and showed that most of the water transport (65%) occurred in the outermost sapwood, while only 26% and 9% of water was transported in the middle and innermost depths of sapwood, respectively. These results help to avoid overestimation of transpiration, which may cause large uncertainties in water budgets in pine forests. Study results further showed that the 55D treatment had the highest tree-level transpiration followed by 33D, 55A, 33A and CN plots. During periods of low precipitation, vapor pressure deficit (VPD) was the main driver or control on transpiration in VRH treatments. However, transpiration was more closely coupled with photosynthetically active radiation (PAR) in the control plot. Moreover, the 55D treatment resulted in on average 58% of total water loss from canopy as transpiration and 42% from the understory and ground surface as evapotranspiration. These findings suggest that dispersed or distributed retention of 55% basal area (55D) provides the optimal environmental conditions for forest growth with reduced competition of trees for water as shown by enhanced transpiration. This study will help researchers, forest managers and decision-makers to improve their understanding of thinning impacts on water and carbon exchanges in forest ecosystems and select and adopt viable forest management practices to enhance their carbon sequestration capabilities, water use efficiency and resilience to climate change.
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 | 0.000 |
| 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 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".