Connectivity and Adaptation Patterns of the Deep-Sea Ground-Forming Sponge <i>Geodia hentscheli</i> Across Its Entire Distribution
Bibliographic record
Abstract
Geodia hentscheli, a species forming sponge grounds in the North Atlantic and Arctic Oceans, is a common deep-sea organism, that plays a fundamental role in forming biogenic habitats. However, there is little information about gene flow and adaptation patterns of this species, which is crucial to develop effective management/conservation plans under current global change scenarios. Here, we generated ddRADseq data from 110 specimens of G. hentscheli, together with microbial profiling, transcriptomics, and metatranscriptomics for a selection of specimens to investigate their genetic diversity, molecular connectivity, and local adaptations. Sampling covered the species' entire distribution within a wide bathymetric range. We obtained 1,115 neutral SNPs and identified long-distance genetic connectivity among regions separated 1,000s of km, but strong genetic structure segregating populations by depth at ca. 1,300 m, in line with our microbial analyses. Coalescent analyses inferred the split of these depth-related genetic entities ∼10 KYA, coincident with the last postglacial maximum. Analyses of SNPs under selection, combined with transcriptomic and metatranscriptomic data highlight the presence of several sponge genes and microbial metabolic pathways involved in adaptation to depth, including heat shock proteins and fatty acids, among others. The physiological plasticity of the sponge and its microbiome as a function of depth suggest the existence of a host-microbiome metabolic compensation for G. hentscheli. This study provides a multiscale paradigmatic example of the depth-differentiation hypothesis, a phenomenon mainly caused by changes in environmental conditions at different depths, mainly related to the presence of water masses with different characteristics that drive local adaptations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".