Changes in ocean health in British Columbia from 2001 to 2016.

Effective management of marine systems requires quantitative tools that can assess the state of the marine social-ecological system and are responsive to management actions and pressures. We applied the Ocean Health Index (OHI) framework to retrospectively assess ocean health in British Columbia annually from 2001 to 2016 for eight goals that represent the values of British Columbia's coastal communities. We found overall ocean health improved over the study period, from 75 (out of 100) in 2001 to 83 in 2016, with scores for inhabited regions ranging from 68 (North Coast, 2002) to 87 (West Vancouver Island, 2011). Highest-scoring goals were Tourism & Recreation (average 94 over the period) and Habitat Services (100); lowest-scoring goals were Sense of Place (61) and Food Provision (64). Significant increases in scores over the time period occurred for Food Provision (+1.7 per year), Sense of Place (+1.4 per year), and Coastal Livelihoods (+0.6 per year), while Habitat Services (-0.01 per year) and Biodiversity (-0.09 per year) showed modest but statistically significant declines. From the results of our time-series analysis, we used the OHI framework to evaluate impacts of a range of management actions. Despite challenges in data availability, we found evidence for the ability of management to reduce pressures on several goals, suggesting the potential of OHI as a tool for assessing the effectiveness of marine resource management to improve ocean health. Our OHI assessment provides an important comprehensive evaluation of ocean health in British Columbia, and our open and transparent process highlights opportunities for improving accessibility of social and ecological data to inform future assessment and management of ocean health.

Blue Growth Potential to Mitigate Climate Change through Seaweed Offsetting.

Carbon offsetting-receiving credit for reducing, avoiding, or sequestering carbon-has become part of the portfolio of solutions to mitigate carbon emissions, and thus climate change, through policy and voluntary markets, primarily by land-based re- or afforestation and preservation [1, 2]. However, land is limiting, creating interest in a rapidly growing aquatic farming sector of seaweed aquaculture [3-5]. Synthesizing data from scientific literature, we assess the extent and cost of scaling seaweed aquaculture to provide sufficient CO2eq sequestration for several climate change mitigation scenarios, with a focus on the food sector-a major source of greenhouse gases [6]. Given known ecological constraints (nutrients and temperature), we found a substantial suitable area (ca. 48 million km2) for seaweed farming, which is largely unfarmed. Within its own industry, seaweed could create a carbon-neutral aquaculture sector with just 14% (mean = 25%) of current seaweed production (0.001% of suitable area). At a much larger scale, we find seaweed culturing extremely unlikely to offset global agriculture, in part due to production growth and cost constraints. Yet offsetting agriculture appears more feasible at a regional level, especially areas with strong climate policy, such as California (0.065% of suitable area). Importantly, seaweed farming can provide other benefits to coastlines affected by eutrophic, hypoxic, and/or acidic conditions [7, 8], creating opportunities for seaweed farming to act as "charismatic carbon" that serves multiple purposes. Seaweed offsetting is not the sole solution to climate change, but it provides an invaluable new tool for a more sustainable future.

Our path to better science in less time using open data science tools.

Reproducibility has long been a tenet of science but has been challenging to achieve-we learned this the hard way when our old approaches proved inadequate to efficiently reproduce our own work. Here we describe how several free software tools have fundamentally upgraded our approach to collaborative research, making our entire workflow more transparent and streamlined. By describing specific tools and how we incrementally began using them for the Ocean Health Index project, we hope to encourage others in the scientific community to do the same-so we can all produce better science in less time.

Aligning marine species range data to better serve science and conservation.

Species distribution data provide the foundation for a wide range of ecological research studies and conservation management decisions. Two major efforts to provide marine species distributions at a global scale are the International Union for Conservation of Nature (IUCN), which provides expert-generated range maps that outline the complete extent of a species' distribution; and AquaMaps, which provides model-generated species distribution maps that predict areas occupied by the species. Together these databases represent 24,586 species (93.1% within AquaMaps, 16.4% within IUCN), with only 2,330 shared species. Differences in intent and methodology can result in very different predictions of species distributions, which bear important implications for scientists and decision makers who rely upon these datasets when conducting research or informing conservation policy and management actions. Comparing distributions for the small subset of species with maps in both datasets, we found that AquaMaps and IUCN range maps show strong agreement for many well-studied species, but our analysis highlights several key examples in which introduced errors drive differences in predicted species ranges. In particular, we find that IUCN maps greatly overpredict coral presence into unsuitably deep waters, and we show that some AquaMaps computer-generated default maps (only 5.7% of which have been reviewed by experts) can produce odd discontinuities at the extremes of a species' predicted range. We illustrate the scientific and management implications of these tradeoffs by repeating a global analysis of gaps in coverage of marine protected areas, and find significantly different results depending on how the two datasets are used. By highlighting tradeoffs between the two datasets, we hope to encourage increased collaboration between taxa experts and large scale species distribution modeling efforts to further improve these foundational datasets, helping to better inform science and policy recommendations around understanding, managing, and protecting marine biodiversity.