Predicting non-inventoried forest elements using forest inventory data: The case of winter forage for woodland caribou
Bibliographic record
Abstract
Growing development pressures and expectations that forest managers provide future wildlife habitat require better understanding of species' habitat needs, particularly food, cover, and space requirements, and an ability to spatially depict these needs. In forest management in Canada, the primary data used to identify and quantify wildlife habitat reside in remotely sensed forest resource inventories (FRI) that were originally developed to assess timber values for merchantable tree species. Although FRI- and field-based sampling do not always show strong agreement, research has shown that FRI can be informative for wildlife habitat assessments. However, much uncertainty remains when investigating forest characteristics that are not visible to the interpreters, such as sub-canopy features. Here, we used 152 plots in northwestern Ontario to compare the ability of field-based and remotely sensed forest inventories to predict Cladonia lichen cover, a primary winter food source for woodland caribou. The best model for field-based data, which included percentage of jack pine and black spruce in the tree canopy, tree height, stand age, soil moisture, and stem density, correctly predicted 92% of cases where Cladonia spp. were absent (n = 107 plots) and 62% of cases where they were present (i.e., cover >1%; n = 45 plots). FRI performed poorly by contrast, with corresponding percentages of 96 and 19%. FRI provide weak data support for differentiating winter forage availability for woodland caribou, an important habitat factor at the stand level. These findings have important implications for predictions of herd productivity, and suggest that improved remote-sensing capabilities are required in order to assess woodland caribou winter habitat.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".