Defining Distinct Nearshore Marine Biotopes Coastwide in British Columbia
Bibliographic record
Abstract
Nine nearshore epibenthic biotopes along with three physiotopes based on environmental conditions were defined and mapped for the coast of BC. The definition of the biotopes includes correlated species with seven environmental conditions (substrate, slope, depth, exposure, temperature, salinity, and tidal current) occurring within each biotope. Three physiotopes were defined as unique environmental areas and were not defined using species data. 72 Species Distribution Models (single-SDMs) were included in the analysis to define the biotopes. Many species included in the survey are widely distributed across the BC coast and may not be useful in differentiating biotopes (e.g., red sea urchin, Mesocentrotus franciscanus, was correlated with five biotopes) therefore indicator species were not able to be identified. After 10 years of conducting the benthic habitat mapping (BHM) survey, gaps in our understanding of nearshore species still remain. We lack knowledge of many nearshore species distributions, what drives their distribution, and what species they are associated with. Continued data collection could refine the species list and improve biotope classification. Additional species, including rarer species, should be considered in future surveys and data collection should focus on lower-level taxonomy (i.e., genus, species). There are limited environmental predictors available for the nearshore. Many predictors used in marine SDMs are derived from oceanographic models with kilometre scale resolution which only provide broad-scale species-environmental relationships. The scale disparity between the survey data and environmental covariates may limit the utility of oceanographic variables in modeling (i.e., no relationships are found) or restricts their applicability to finer scales. Out of the seven environmental covariates, only three (substrate, depth, and exposure), have a range of values that are not overlapping across all biotopes. Except for slope, the remaining predictors (salinity, temperature and current) are derived from oceanographic models for which the nearshore is not well resolved. This indicates that higher predictor resolution might result in better resolved relationships with species distribution, as well as better definitions of the biotopes, and\or an increase in the number of biotopes. While the maps provide valuable insights into biotope distribution, they should be interpreted with an understanding of their resolution limitations and the potential for finer-scale variability in nearshore environments. Over the course of the 10 years of this project, the team consisted of numerous biologists who were very experienced in species identification, with extensive experience working in intertidal and subtidal habitats throughout the BC coast. Their experience and knowledge were invaluable for validating the resulting biotopes in terms of species inclusion and environmental associations. The analysis is reliable and repeatable and, as such, the biotope outputs can be used to support management decisions, including environmental incidents and marine spatial planning initiatives, in the Pacific Region.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".