Assessment of coastal management options by means of multilayered ecosystem models

This paper presents a multilayered ecosystem modelling approach that combines the simulation of the biogeochemistry of a coastal ecosystem with the simulation of the main forcing functions, such as catchment loading and aquaculture activities. This approach was developed as a tool for sustainable management of coastal ecosystems. A key feature is to simulate management scenarios that account for changes in multiple uses and enable assessment of cumulative impacts of coastal activities. The model was applied to a coastal zone in China with large aquaculture production and multiple catchment uses, and where management efforts to improve water quality are under way. Development scenarios designed in conjunction with local managers and aquaculture producers include the reduction of fish cages and treatment of wastewater. Despite the reduction in nutrient loading simulated in three different scenarios, inorganic nutrient concentrations in the bay were predicted to exceed the thresholds for poor quality defined by Chinese seawater quality legislation. For all scenarios there is still a Moderate High to High nutrient loading from the catchment, so further reductions might be enacted, together with additional decreases in fish cage culture. The model predicts that overall, shellfish production decreases by 10%–28% using any of these development scenarios, principally because shellfish growth is being sustained by the substances to be reduced for improvement of water quality. The model outcomes indicate that this may be counteracted by zoning of shellfish aquaculture at the ecosystem level in order to optimize trade-offs between productivity and environmental effects. The present case study exemplifies the value of multilayered ecosystem modelling as a tool for Integrated Coastal Zone Management and for the adoption of ecosystem approaches for marine resource management. This modelling approach can be applied worldwide, and may be particularly useful for the application of coastal management regulation, for instance in the implementation of the European Marine Strategy Framework Directive

Measured and remotely sensed estimates of primary production in the Atlantic Ocean from 1998 to 2005

Primary production (PP) was determined using 14C uptake at 117 stations in the Atlantic Ocean to validate three PP satellite algorithms of varying complexity. An empirical satellite algorithm based on log chlorophyll-a had the highest bias and root-mean square error compared with measured 14C PP and tended to under-estimate PP. The vertical generalised production model improved PP estimates and was the most accurate algorithm in the Eastern Tropical Atlantic (ETRA) and Western Tropical Atlantic (WTRA), but tended to over-estimate PP in eutrophic provinces. A photosynthesis-light wavelength-resolved model was the most accurate over the Atlantic basin, having the lowest mean log-difference error, root-mean square error and bias, and exhibited a superior performance in six out of the nine ecological provinces surveyed. Using this algorithm and mean monthly SeaWiFS fields, a PP time series was generated for the Atlantic Ocean from 1998 to 2005 which was compared with Advanced Very High Resolution Radiometer (AVHRR) sea-surface temperature (SST) data. There was a significant negative correlation between SST and PP in the North Atlantic Subtropical Gyre Province (NAST), North Atlantic Tropical Gyre (NATR), and WTRA suggesting that recent warming trends in these provinces are coupled with a decrease in phytoplankton production.<br/

Iterative Quality Control Strategies for Expert Medical Image Labeling

Data quality is a key concern for artificial intelligence (AI) efforts that rely on crowdsourced data collection. In the domain of medicine in particular, labeled data must meet high quality standards, or the resulting AI may perpetuate biases or lead to patient harm. What are the challenges involved in expert medical labeling? How do AI practitioners address such challenges? In this study, we interviewed members of teams developing AI for medical imaging in four subdomains (ophthalmology, radiology, pathology, and dermatology) about their quality-related practices. We describe one instance of low-quality labeling being caught by automated monitoring. The more proactive strategy, however, is to partner with experts in a collaborative, iterative process prior to the start of high-volume data collection. Best practices including 1) co-designing labeling tasks and instructional guidelines with experts, 2) piloting and revising the tasks and guidelines, and 3) onboarding workers enable teams to identify and address issues before they proliferate

Do Cities Have Broad Shoulders? Does Motown Need a Haircut? On Urban Branding and the Personification of Place

Once regarded as dens of iniquity, injurious to human health and social welfare, cities are increasingly seen as a savior for our species. The world is becoming ever more urban and the benefits of city living – ecological benefits, educational benefits, financial benefits, well-being benefits (Glaeser 2011) – are ever more widely recognized. Marketing too is embracing the urban imperative. Recent years have witnessed a surge in geo-branding and scape-based scholarship generally. This essay reflects on the proliferation of place marketing publications and draws macromarketers’ attention to a hitherto overlooked aspect of the literature. Namely, our propensity to personify places, to treat them as living things, as organic entities – as people, in effect – that grow, flourish and finally pass away. Metaphors also suffer from the ravages of time, as do ostensibly healthy academic disciplines like marketing