Climate change, fisheries, and aquaculture: trends and consequences for Canadian marine biodiversity<sup>1</sup>This manuscript is a companion paper to Vander Zwaag et al. (doi:10.1139/a2012-013) and Hutchings et al. (doi:10.1139/er-2012-0049) also appearing in this issue. These three papers comprise an edited version of a February 2012 Royal Society of Canada Expert Panel Report.
Bibliographic record
Abstract
Climate change, fishing, and aquaculture have affected and will continue to influence Canadian marine biodiversity, albeit at different spatial scales. The Arctic is notably affected by reduced quality and quantity of sea ice caused by global warming, and by concomitant and forecasted changes in ocean productivity, species ecology, and human activity. The Atlantic has been especially impacted by severe overfishing and human-induced alterations to food webs. Climate change, fishing, and aquaculture have all affected, to varying degrees, biodiversity on Canada’s Pacific coast. Past and projected trends in key biodiversity stressors reveal marked change. Oceanographic trends include increasing surface water temperatures, reduced salinity, increased acidity, and, in some areas, reduced oxygen. Reductions in Canada’s fishery catches (those in 2009 were half those of the late 1980s), followed by reductions in fishing pressure, are associated with dramatic changes in the species composition of commercial catches in the Atlantic (formerly groundfish, now predominantly invertebrates and pelagic fish) and the Pacific (formerly salmon, now predominantly groundfish). Aquaculture, dominated by the farming of Atlantic salmon, grew rapidly from the early 1980s until 2002 and has since stabilized. Climate change is forecast to affect marine biodiversity by shifting species distributions, changing species community composition, decoupling the timing of species’ resource requirements and resource availability, and reducing habitat quality. Harvest-related reductions in fish abundance, many by 80% or more, coupled with fishing-induced changes to food webs, are impairing the capacity of species to recover or even persist. Open-sea aquaculture net pens affect biodiversity by (i) habitat alteration resulting from organic wastes, chemical inputs, and use of nonnative species; (ii) exchange of pathogens between farmed and wild species; and (iii) interbreeding between wild fish and farmed escapees. Physical and biological changes in the oceans, along with direct anthropogenic impacts, are modifying Canadian marine biodiversity with implications for food security and the social and economic well-being of coastal communities. To assess the consequences of changes in biodiversity for Canada’s oceans and society, it is necessary to understand the current state of marine biodiversity and how it might be affected by projected changes in climate and human uses.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".