Application of a variance decomposition method to compare satellite and aerial inventory data: a tool for evaluating wildlife–habitat relationships
Bibliographic record
Abstract
Summary Researchers frequently rely on forest inventories derived for other purposes when characterizing wildlife–habitat associations. When studied populations are remote and inventory coverage is incomplete for one or more data sets, researchers must often choose between the need for greater detail in habitat information and complete coverage of the land base. Forest inventories are often limited to canopy‐level information; however, different aspects of the overstorey and understorey may be important to wildlife. We compared the explanatory power of a forest resource inventory (FRI) and Landsat‐derived inventory to account for variation in vegetation communities available to woodland caribou Rangifer tarandus caribou. Forest canopy and understorey data were collected from stands in the Clay Belt region of Ontario, Canada, and cluster analysis and ordination were used to identify discrete community types. Canonical correspondence analysis revealed strong relationships between forest inventories and vegetation communities derived from field data. The integration of field‐based species and structural data with remote sensing land cover information provided sufficient detail among vegetation communities to identify features of known importance to woodland caribou. Variance decomposition revealed that FRI and Landsat variables had different capacities to explain variation in community composition, and overlap existed in the explanatory power. FRI species factors had greater explanatory power than Landsat habitat classes; however, Landsat structural variables accounted for more variation than FRI structural variables. Synthesis and applications. The results support the use of forest inventory attributes to infer vegetation characteristics in the overstorey and understorey, thus providing a tool for wildlife–habitat management. The need to account for woodland caribou habitat needs in forest management planning can be facilitated by using readily available vegetation descriptions within the FRI and Landsat data sets. This approach to community classification is of particular benefit for characterizing wildlife habitat in remote areas where collection of detailed vegetation information is impractical. When using habitat inventories derived from different data sources, variance decomposition can aid the researcher in identifying how explanatory power is structured within different data sets, quantifying the independent and confounded components of explained variation.
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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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".