Distribution models of temperate habitat-forming species on the continental shelf In eastern Australia: setting the baseline to monitor and predict future changes
Bibliographic record
Abstract
Habitat-formers (e.g. kelp beds, corals, sessile invertebrate assemblages) are key to the structure and functioning of reef ecosystems worldwide. In southeast Australia, a region identified as a global hotspot for climate-driven ocean warming, the structure and distribution of deep (> 30 m) benthic sessile communities are poorly known given these habitats are hard to quantitatively survey. Using high-resolution imagery of the seafloor from a recent national-scale AUV-based survey program, we establish a critical baseline about the latitudinal gradient in benthic community composition from 27S to 43S on the eastern seaboard of Australia. Over >1,800 AUV images taken across the 7 different survey regions along the eastern seaboard of Australia, we estimated percentage cover of 51 pre-selected invertebrate morphospecies, including two ascidians, four bryozoans, seven cnidarians and 38 sponges. These morphospecies were chosen for their strong features (i.e. size, shape, colour), which facilitated their identification and detectability on the images. Three levels of details (i.e. group, shape, colour) were reported for each record, so as to test the sensitivity of our results to alternative invertebrate classification schemes of increasing resolution. Large-scale latitudinal variability between three major community types (sub-tropical, warm temperate and cool temperate) mostly correlates with primary productivity and temperature climatology, while local scale variability relates well with depth. Using environmental variables that capture past climatology both in terms of means and extremes (frequency and magnitude), we develop alternative distribution models for several habitat-forming species. Our models characterise the thermal tolerance of individual morphospecies in terms of suitable and/or critical boundary conditions. We compare model performance, discriminate between different types of latitudinal distribution (e.g. truncated or continuous), identify indicator morphospecies more likely to respond to climate-driven changes in ocean conditions, and discuss these results in the context of ongoing and future ocean changes. Our study provides an important benchmark to detect and predict future climate-driven changes in southeastern Australia, and our methodology has general applicability for monitoring of deep reef environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".