Increased retention after harvest better maintains carabid abundance in boreal mixedwood forests under two climate change scenarios
Bibliographic record
Abstract
Abstract Climate models predict shifting temperature and precipitation regimes across the boreal forest, which may lead to losses of biodiversity in managed landscapes. Mitigating climate change stress on forest biodiversity will depend on developing sustainable approaches to forest management that preserve forest organisms over long timeframes. Silvicultural strategies that retain standing trees may better maintain forest organisms under climate change as compared to conventional practices such as clear‐cutting and may provide an effective and achievable strategy for conservation of biodiversity. We predicted the long‐term, combined impacts of partial‐retention harvesting and climate change on the abundance of carabid beetles, an important group of generalist predators, in boreal mixedwood forests. We combined repeated sampling of carabid communities in replicated harvesting treatments over 20 years from a large‐scale experiment in northeastern North America with site‐specific models of forest stand dynamics and meteorological data to predict changes in carabid species over the next two decades under emission scenarios SSP1‐2.6 and SSP5‐8.5. Abundance of most carabid species was greatest in stands with more standing retention. Additionally, most species increased in abundance with increased precipitation throughout the breeding season but declined with warming winters. Under SSP1‐2.6, most species were expected to increase through the coming decades, particularly in stands with significant post‐harvest retention. High‐retention treatments conferred a numerical advantage for carabid populations, which in turn were more resilient to short‐term periods of stress such as pest outbreaks or senescence of overstory trees. Under SSP5‐8.5, many species increased initially but then declined. While carabid abundance was highest in the high‐retention treatments, populations within all treatments reached low density by ca. 2038. Synthesis and applications . Our findings suggest that under lower emission scenarios, high‐retention harvesting is an effective strategy to conserve carabids over the next two decades and can increase species' resilience throughout short‐term periods of stress. Under more extreme emissions scenarios, high‐retention harvesting can be used to conserve carabid populations for approximately the next decade and may permit species with poor dispersal abilities increased time to develop adaptations to rising temperatures.
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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".