Assessing coastal species distribution models through the integration of terrestrial, oceanic and atmospheric data
Bibliographic record
Abstract
Abstract Aim When considering multiple species in distribution models, environmental variables should be selected that describe the group of species' environmental requirements. This can, however, be challenging in locations such as coastal areas, where different species may respond to terrestrial, oceanic and/or atmospheric conditions. Here, we evaluate the use of remotely sensed ( RS ) terrestrial, oceanic and interpolated climate variables, as well as a more detailed shore‐zone data set, as a means of modelling the distributions of coastal bird species with diverse habitat requirements. Location Coastal British Columbia, Canada. Methods Boosted regression trees were used to model the distributions of 60 species of coastal birds using each environmental variable group, run individually and in combination. Models were assessed for their predictive ability and model fit, as well as for model overfitting. Results Models that incorporated terrestrial data were found to produce the highest model fit and predictive ability of the broad‐scale environmental groups. Incorporating the fine‐scale shore‐zone data offered little improvement, as did selecting the best model in terms of predictive ability by species. Model fit and predictive ability were also found to vary by functional feeding group, with insectivores and benthivores being the best‐modelled. Main conclusions Having access to both broad‐scale RS environmental data and more detailed coastal shore‐zone data produced modest improvements over employing RS environmental data alone. Models using only the terrestrial data set, however, performed similarly to the best single model type (terrestrial + shore‐zone), indicating that broad‐scale environmental data also offer an effective means of estimating coastal bird distributions. Testing multiple environmental variable groups in different combinations and selecting the best model allowed models to be optimized by species; conversely, the results of a ‘one model fits all’ approach were comparable to those of the best models, indicating that a single‐model approach is also valid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".