Potential influence of climate change on ecosystems within the Boreal Plains of Alberta
Bibliographic record
Abstract
Ecosystems within the subhumid Boreal Plains of Northern Alberta host ecologically and commercially significant habitat and natural resources. However, these ecosystems exist under a delicate hydrologic balance that may be upset as the climate warms by 2 to 5 °C over the next century. In this study, numerical simulations were used to predict climate change impacts at a catchment composed of a mosaic of Boreal Plains ecosystems including a small pond, peatlands with sparse black spruce, and hillslopes with predominantly aspen forests. Simulations were conducted with a fully integrated groundwater–surface water code using a 2-D model previously calibrated to a decade of hydrologic data that included a range in climatic conditions. Projections from 13 climate change scenarios were simulated from 2011 to 2090 and compared to a base case scenario that assumed no climate change. Results indicate peatland water levels may decline by up to 1 m; however, sensitivity simulations indicate that the decline in water levels may be moderated by several feedback mechanisms that restrict evaporative losses and moderate water level changes. In contrast, higher evapotranspiration losses from the aspen hillslopes are predicted to result in near-surface soils becoming increasingly drier. Thus, the aspen may frequently be water stressed and increasingly susceptible to secondary maladies such as pests and disease. Reduced pond water levels are also predicted with the development of frequent ephemeral conditions in warmer and drier scenarios. Concurrent decreases in stream flow may further impact downstream ecosystems. Further research into the regional health and sustainability of Boreal Plains ecosystems is warranted and could benefit from the development of improved numerical tools capable of extending the processes considered.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".