From canopy to seed: Loss of snow drives directional changes in forest composition
Bibliographic record
Abstract
Abstract Climate change is altering the conditions for tree recruitment, growth, and survival, and impacting forest community composition. Across southeast Alaska, USA, and British Columbia, Canada,Callitropsis nootkatensis(Alaska yellow‐cedar) is experiencing extensive climate change‐induced canopy mortality due to fine‐root death during soil freezing events following warmer winters and the loss of insulating snowpack. Here, we examine the effects of ongoing, climate‐driven canopy mortality on forest community composition and identify potential shifts in stand trajectories due to the loss of a single canopy species. We sampled canopy and regenerating forest communities across the extent ofC. nootkatensisdecline in southeast Alaska to quantify the effects of climate, community, and stand‐level drivers onC. nootkatensiscanopy mortality and regeneration as well as postdecline regenerating community composition. Across the plot network,C. nootkatensisexhibited significantly higher mortality than co‐occurring conifers across all size classes and locations. Regenerating community composition was highly variable but closely related to the severity ofC. nootkatensismortality.Callitropsis nootkatensiscanopy mortality was correlated with winter temperatures and precipitation as well as local soil drainage, with regenerating community composition andC. nootkatensisregeneration abundances best explained by available seed source. In areas of highC. nootkatensismortality,C. nootkatensisregeneration was low and replaced byTsuga. Our study suggests that climate‐induced forest mortality is driving alternate successional pathways in forests whereC. nootkatensiswas once a major component. These pathways are likely to lead to long‐term shifts in forest community composition and stand dynamics. Our analysis fills a critical knowledge gap on forest ecosystem response and rearrangement following the climate‐driven decline of a single species, providing new insight into stand dynamics in a changing climate. As tree species across the globe are increasingly stressed by climate change‐induced alteration of suitable habitat, identifying the autecological factors contributing to successful regeneration, or lack thereof, will provide key insight into forest resilience and persistence on the landscape.
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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 | 0.000 |
| 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) | 0.001 | 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".