Influence of artefacts in marine digital terrain models on habitat maps and species distribution models: a multiscale assessment
Bibliographic record
Abstract
Abstract Remote sensing techniques are currently the main methods providing elevation data used to produce Digital Terrain Models (DTM). Terrain attributes (e.g. slope, orientation, rugosity) derived from DTMs are commonly used as surrogates of species or habitat distribution in ecological studies. While DTMs’ errors are known to propagate to terrain attributes, their impact on ecological analyses is however rarely documented. This study assessed the impact of data acquisition artefacts on habitat maps and species distribution models. DTMs of German Bank (off Nova Scotia, Canada) at five different spatial scales were altered to artificially introduce different levels of common data acquisition artefacts. These data were used in 615 unsupervised classifications to map potential habitat types based on biophysical characteristics of the area, and in 615 supervised classifications (MaxEnt) to predict sea scallop distribution across the area. Differences between maps and models built from altered data and reference maps and models were assessed. Roll artefacts decreased map accuracy (up to 14% lower) and artificially increased models’ performances. Impacts from other types of artefacts were not consistent, either decreasing or increasing accuracy and performance measures. The spatial distribution of habitats and spatial predictions of sea scallop distributions were always affected by data quality (i.e. artefacts), spatial scale of the data, and the selection of variables used in the classifications. This research demonstrates the importance of these three factors in building a study design, and highlights the need for error quantification protocols that can assist when maps and models are used in decision‐making, for instance in conservation and management.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".