From marine snails to marine spatial planning : the science of human impacts and relationships with marine ecosystems
Bibliographic record
Abstract
Extractive human systems are driving unprecedented biodiversity loss and exacerbating social inequity. The magnitude of the intertwined climate, biodiversity, and social inequity crises has prompted the development of interdisciplinary research approaches to address these complex problems. One such approach, social-ecological systems (SES), aims to understand the relationships between coupled human and ecological systems. This thesis applies an SES lens to understand the science of human impacts on and relationships with marine ecosystems and inform characterizations of system vulnerability. First, I examined the sensitivity of marine ectothermic animals to climate change by conducting a meta-analysis of the effects of ocean acidification and warming. My synthesis of nearly five hundred factorial studies demonstrates the negative effects of these two drivers, identifies specific taxonomic groups (molluscs), life- history traits (adults, sessile), and latitudes (tropical and temperate) that are more sensitive, and refutes two common assumptions about the drivers’ interactive effects. Next, I tested whether populations of a marine snail vary in their vulnerability to ocean warming based on thermal sensitivity and local rates of ocean warming. Using coupled lab and field experiments with snails from two regions in the middle of their range that differ in thermal characteristics, I found that snails from the warmer Salish Sea, an urban sea, showed greater vulnerability to ocean warming than those from the cooler central coast of British Columbia, Canada. Finally, to inform how humans can mitigate our impacts while sustaining complex relationships with the ocean, I partnered with the Sḵwx̲wú7mesh Úxwumixw (Squamish Nation) and regional stewardship organizations on a marine spatial planning project in the Salish Sea. I employed a mixed- methods community-based participatory mapping approach to characterize place-based values and outline opportunities to decolonize research and mapping processes. The results contribute important social data about place-based values, reveal value interactions, reflect knowledge system plurality, and identify avenues to advance reconciliation. Overall, this thesis highlights the vulnerability of marine life, particularly life within urban seas, to climate change and provides a roadmap for researchers and decision-makers to meaningfully steward the health and well-being of coastal social-ecological systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".