Data for: Putative past, present, and future spatial distributions of deep-sea coral and sponge microbiomes revealed by predictive models
Bibliographic record
Abstract
This dataset includes output rasters of the spatio-temporal model presented in Busch et al. 2024 (Data folder 1) giving cumulative microbial richnesses in deep-sea sponges and corals for the present, and two future scenarios. We also provide point data on the environmental variables used in our predictions (Data file 2): Depth, Slope, Bottom Stress (“BtmStress”), Bottom Current Speed (“BtmCur”), Mixed Layer Depth (“MLD”), Bottom Salinity (“BtmSal”), Sea Surface Salinity (“SSS”), Sea Surface Temperature (“SST”), and Bottom Temperature (“BtmTmp”) averaged for each of the seven time periods (extracted from the Simple Ocean Data Assimilation, SODA, for the past periods 1871-1900, 1901-1930, 1931-1960, 1961-1989; extracted from the BNAM Ocean Model for the present period 1990-2015 and for the future periods 2046-2065 and 2066-2085 under a RCP8.5 scenario). This dataset also contains raw occurrence data compiled from different indicated sources (Data file 3), and presence/absence data (true absences as well as pseudo absences) gridded to be used as model input (Data file 4) of the analysed host species Weberella bursa, Stryphnus fortis, Lophelia pertusa, Desmophyllum dianthus, and Vazella pourtalesii. Another data file (Data file 5) contains an overview of the ENA (European Nucleotide Archive) accession numbers, the original literature sources, and some basic ecological metadata of the used microbial data (16S amplicon data) drawn from the host species. After assigning the microbial abundance status (HMA – high microbial abundance, and LMA – low microbial abundance) to ten key sponge species in the Flemish Cap area area (Asconema foliatum, Geodia barretti, Geodia macandrewii, Geodia parva-phlegraei, Mycale lingua, Stelletta normani, Stryphnus fortis, Stylocordyla borealis, Tentorium semisuberites, and Weberella bursa) we correlated our spatial predictions of status occurrence with predictions of overall ecosystem function, i.e. here nutrient cycling and habitat provision (derived from Murillo et al. 2020, Diversity and Distributions). For our study the original rasters (created by Murillo et al. 2020) were resampled to a 0.088 cell size using Bilinear interpolation as the resampling technique in ArcGIS Pro. Data 6 contains the resampled predictions of overall ecosystem function, as well as our predicted occurrences of the HMA and LMA status, and the respective summed biomasses. In our article (above) we show a biomass network, integrating the generated information on HMA and LMA sponge biomasses with biomass measurements of other sessile filter feeding invertebrates, which occur in high abundances at the Flemish Cap (data taken from Murillo et al. 2020, Diversity and Distributions). Data file 7 contains the underlying biomass data used for the network, covering 116 different species, which are classified according to size (small, medium, medium large, large), functional (passive “PFF”, and active “AFF” filter feeders) and taxonomic (8 phyla) groups.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| 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 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".