Benthic habitat mapping: A review of three decades of mapping biological patterns on the seafloor
Bibliographic record
Abstract
What is benthic habitat mapping, how is it accomplished, and how has that changed over time? We query the published literature to answer these questions and synthesize the results quantitatively to provide a comprehensive review of the field over the past three decades. Categories of benthic habitat maps are differentiated unambiguously by the response variable (i.e., the subject being mapped) rather than the approaches used to produce the map. Additional terminology in the literature is clarified and defined based on provenance, statistical criteria, and common usage. Mapping approaches, models, data sets, technologies, and a range of other attributes are reviewed based on their application, and we document changes to the ways that these components have been integrated to map benthic habitats over time. We found that the use of acoustic remote sensing has been surpassed by optical methods for obtaining benthic environmental data. Although a wide variety of approaches are employed to ground truth habitat maps, underwater imagery has become the most common validation tool – surpassing physical sampling. The use of empirical machine learning models to process these data has increased dramatically over the past 10 years, and has superseded expert manual interpretation. We discuss how map products derived from these data and approaches are used to address ecological questions in the emerging field of seascape ecology, and how remote sensing technologies and field survey logistics pose different challenges to this research field across benthic ecosystems from intertidal and shallow sublittoral regions to the deep ocean. Outstanding challenges are identified and discussed in context with the trajectory of the field.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".