Predicting present and future habitats using LiDAR to integrate research and monitoring with landscape analyses
Bibliographic record
Abstract
Managed forests contribute to both economic and non-timber values, but the ecological role of managed, including planted, forests to biodiversity objectives at the landscape scale needs to be better understood. In this project in collaboration with J.D. Irving, Limited, we: 1) used airborne LiDAR and field data to identify terrestrial habitats; 2) monitored selected taxa by 18 stand type/seral stage habitat types in intensively and extensively managed forests and reserves; 3) assessed effects of management intensity on water quality and aquatic habitat; and 4) projected forest and wildlife habitat under planned management and natural disturbance scenarios. Taxa studied included songbirds, bryophytes and beetle species associated with mature-overmature forests, and several listed ground vegetation species. LiDAR-based enhanced forest inventory provided forest structure variables that improved bird habitat models and spatial predictions of bird habitat, metrics explaining bryophyte composition and richness, and variability in beetle abundance and richness. There was no evidence of negative landscape-level effects of increasing management intensity on bird communities in mature forest stands, suggesting that managed spruce-fir-tolerant hardwood landscapes provide habitat for bird species that need old forest. Richness, diversity, and composition of bryophyte guilds in reference stands in Mount Carleton Provincial Park unmanaged reserve did not differ from stands in the intensively managed District. The landscape focus and stratification into stand type/seral stages were important to understand habitat requirements. Catchments with greater forest management did not show any consistent signs of biological impairment from smaller to larger scales, and all sites had good or very good biological water quality based on the aquatic insect communities. This study helped to evaluate forest management effects on habitat areas, detected with airborne LiDAR data, that need to be addressed to enhance decision making processes.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".