Effects of fine-scale and landscape-level habitat features on a sagebrush breeding birds of the southern Okanagan and Similkameen Valleys, British Columbia
Bibliographic record
Abstract
I determined habitat associations for five species of songbirds breeding in sagebrush of the southern Okanagan and Similkameen Valleys, British Columbia. I examined the relative importance of scale for the presence and relative abundance of these species, through the measurement of vegetation floristics and structure at a local level (<100 m), and habitat context at three landscape scales (500 m, 1 km and 2 km). Vegetation and bird survey data were collected at 245 point count stations in 1998. Local-level habitat variables were derived from field surveys, while landscape-level variation was classified from a single Landsat Thematic Mapper ( TM ) image from 1996. Within sagebrush habitat the Landsat TM image was also classified at a fine-scale to determine if local-level habitat variation could be mapped by satellite data. Accuracy of the classification was assessed in 1999 by ground-truthing. Overall accuracy was 85%, and 78% for the sagebrush 'subtypes'. Local-level models from Landsat TM classified sagebrush habitat subtypes agreed with habitat associations identified from vegetation survey data, indicating that satellite data may be used as a surrogate for field data, although these associations were relatively weak. Performance of local, landscape, and local + landscape-level models was assessed from ranked Akaike's Information Criteria (AIC) scores. For all songbird species, logistic regression models showed the strongest habitat associations at a local level. Floristic variables were often more important than vegetation structure variables. Brewer's Sparrow was associated with large tufted perennials: parsnip-flowered buckwheat (Eriogonum heracleoides) and lupine (Lupinus sericeus or sulphureus). Lark Sparrow was positively associated with sand dropseed grass (Sporobolus cryptandrus), Vesper Sparrow (Pooecetes gramineus) was positively associated with lupines, and Western Meadowlark was positively associated with needle-and-thread grass (Stipa comatd). The addition of landscape-level variables usually improved the predictive ability of survey-derived local habitat association models. Habitat associations varied markedly for each species, and songbird relative abundance responded differently to the scale of measurements. Herb layer species identified as important in habitat associations were in turn correlated with rangeland management practices. Management recommendations are presented to direct conservation efforts in this highly threatened area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".