Biodiversity and ecosystem functioning in contrasting marine habitats: patterns, drivers, and implications for conservation planning
Bibliographic record
Abstract
The patterns and drivers of marine biodiversity and ecosystem functioning and how biological communities influence ecological processes remain poorly understood, especially for deep-sea and other remote environments. Such constraints impair effective protection of important organisms and ecosystem functions from human impacts and global change through conservation strategies such as Marine Protected Areas (MPAs). This thesis explores different aspects of biodiversity and ecosystem functioning in deep-sea sedimentary habitats, focusing on macrofaunal biodiversity and organic matter remineralization, which can be quantified through measurement of inorganic nutrient flux rates at the sediment-water interface. I examine the roles of biogenic (e.g., sea pen fields) and geophysical (e.g., submarine canyons) habitats along the Northwest Atlantic continental margin in regulating biodiversity and functioning. Through literature review and experimentation, I explore how biological traits of organisms influence the ecology and functioning of biological communities and can potentially inform MPA design and improve conservation outcomes. My findings demonstrate the important role of biogenic and geophysical habitats in shaping macrofaunal communities, mostly by altering food availability and creating habitat heterogeneity, and the central role of food availability in driving macrofaunal diversity at regional scales. The interacting effects of several abiotic and biotic factors that act over different spatial and temporal scales complicated efforts to discern patterns of organic matter remineralization. Some macrofaunal taxa and measures of biodiversity clearly influenced variation in benthic flux rates, reiterating the importance of biological communities in driving ecosystem processes. Biological trait expression analysis helped in understanding patterns and underlying drivers of community structure, despite poor correlations between traits and benthic flux rates, highlighting the need for further studies. The findings of this study highlight the importance of protecting multiple ecologically important and sensitive marine habitats in order to maintain biodiversity and functions, but also punctuate the need for further studies to characterize biological traits of under-studied organisms and effectively apply trait-based approaches to improve conservation outcomes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".