Modeling habitat use of young-of-the-year Pacific sand lance (Ammodytes hexapterus) in the nearshore region of Barkley Sound, British Columbia
Bibliographic record
Abstract
Successful management of coastal ecosystems requires an understanding of the distribution of key food web species through space and time relative to environmental predictors. Here, I examined the habitat use of an important forage species, the Pacific sand lance (Ammodytes hexapterus), using an inductive habitat modeling approach. I examined the presence/absence of Young-of-the-Year Pacific sand lance in the intertidal/shallow subtidal habitat of Barkley Sound, British Columbia. I determined sand lance occurrence using a beach seine at low tide, which was preferred to visual and intertidal digging detection methods due to its high detection frequency, ease of use, and ability to physically capture sand lance. I constructed models using environmental data measured at two different scales: 1) empirically measured environmental data (site-specific level) and 2) GIS derived environmental data extracted with a 200m buffer (landscape level). For each scale, I employed both logistic regression and classification tree modeling procedures to construct habitat models of sand lance occurrence at 55 study sites sampled during the summer of 2003. At the site-specific level, both logistic regression and classification tree models performed similar, however, classification trees were easier to construct and interpret as well as revealing interactions among variables undetected by logistic regression. Based on a deviance pruned classification tree, Grain Size Mean, Intertidal Eelgrass Presence, Absence, Major Substrate Low Intertidal, and Grain Size Sorting influenced sand lance occurrence at this scale, with importance values of 100, 79, 75, and 61 respectively. Standardized importance was based on the overall change in node impurity in the classification tree for each variable. At the landscape level, only Coastline Density was significantly related to sand lance occurrence, however, it was difficult to suggest this variables direct relation to sand lance habitat use. Overall, the habitat modeling approach identified important environmental variables influencing sand lance habitat selection at two different scales and stressed the utility of field data to construct and confirm these models.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".