Overcoming the challenges of translating mental health instruments into signed languages

The Strengths and Difficulties Questionnaire (SDQ) is widely used in Child and Adolescent Mental Health Services (CAMHS), and has been translated into over sixty spoken languages. British Sign Language (BSL) is a visuo-gestural language, and the first language of between 50-100,000 Deaf people in the UK. Translating diagnostic tools into BSL is important to provide valid assessment of common mental health problems in Deaf signing young people. We report the process of translation from a written language (English) into a visual language (BSL) using adapted, existing methodologies. We highlight the challenges we faced, with particular reference to the difficulties in translating for a population of signing Deaf young people, followed by suggestions of how to overcome these difficulties

A probabilistic approach to mapping the contribution of individual riverine discharges into Liverpool Bay using distance accumulation cost methods on satellite derived ocean-colour data

Assessments of the water quality in coastal zones often rely on indirect indicators from contributing river inputs and the neighbouring ocean. Using a novel combination of distance accumulation cost methods and an ocean-colour product derived from SENTINEL-3 data, we developed a probabilistic method for the assessment of dissolved inorganic nitrogen (DIN) in Liverpool Bay (UK) for the period from 2017 to 2020. Using our approach, we showed the annual and monthly likelihood of DIN exposure from its 12 major contributory rivers. Furthermore, we generated monthly risk maps showing the probability of DIN exposure from all rivers, which revealed a seasonal variation of extent and location around the bay. The highest likelihood of high DIN exposure throughout the year was in the estuarine regions of the Dee, Mersey, and Ribble, along with near-shore areas along the north Wales coast and around the mouth of the rivers Mersey and Ribble. There were seasonal changes in the risk of DIN exposure, and this risk remained high all year for the Mersey and Dee estuary regions. In contrast, for the mouth and near the coastal areas of the Ribble, the DIN exposure decreased in spring, remained low during the summer and early autumn, before displaying an increase during winter. Our approach offers the ability to assess the water quality within coastal zones without the need of complex hydrodynamic models, whilst still having the potential to apportion nutrient exposure to specific riverine inputs. This information can help to prioritise how direct mitigation strategies can be applied to specific river catchments, focusing the limited resources for coastal zone and river basin management

Optimizing Monitoring Programs: A Case Study Based on the OSPAR Eutrophication Assessment for UK Waters

The data and results of the UK second application of the OSPAR Common Procedure (COMP) for eutrophication were used as a case study to develop a generic system (i) to evaluate an observational network from a multi-variable point of view, (ii) to introduce additional datasets in the assessment, and (iii) to propose an optimized monitoring program to help reduce monitoring costs. The method consisted of tools to analyse, by means of simple statistical techniques, if any reduction of the available datasets could provide results comparable with the published assessments, and support a reduced monitoring program (and limited loss in confidence). The data reduction scenarios included the removal of an existing dataset or the inclusion of freely available third-party data (FerryBox, satellite observations) with existing datasets. Merging different datasets was problematic due to the heterogeneity of the techniques, sensors and scales, and a cross validation was carried out to assess possible biases between the different datasets. The results showed that there was little margin to remove any of the available datasets and that the use of extensive datasets, such as satellite data, has an important effect, often leading to a change in assessment results with respect to the thresholds, generally moving from threshold exceedance to non-exceedance. This suggested that the results of the original assessment might be biased toward sampling location and time and emphasized the importance of monitoring programmes providing better coverage over large spatial and temporal scales, and the opportunity to improve assessments by combining observations, satellite data, and model results

Toward a European coastal observing network to provide better answers to science and to societal challenges : The JERICO research infrastructure

The coastal area is the most productive and dynamic environment of the world ocean, offering significant resources and services for mankind. As exemplified by the UN Sustainable Development Goals, it has a tremendous potential for innovation and growth in blue economy sectors. Due to the inherent complexity of the natural system, the answers to many scientific and societal questions are unknown, and the impacts of the cumulative stresses imposed by anthropogenic pressures (such as pollution) and climate change are difficult to assess and forecast. A major challenge for the scientific community making observations of the coastal marine environment is to integrate observations of Essential Ocean Variables for physical, biogeochemical, and biological processes on appropriate spatial and temporal scales, and in a sustained and scientifically based manner. Coastal observations are important for improving our understanding of the complex biotic and abiotic processes in many fields of research such as ecosystem science, habitat protection, and climate change impacts. They are also important for improving our understanding of the impacts of human activities such as fishing and aquaculture, and underpin risk monitoring and assessment. The observations enable us to better understand ecosystems and the societal consequences of overfishing, disease (particularly shellfish), loss of biodiversity, coastline withdrawal, and ocean acidification, amongst others. The European coastal observing infrastructure JERICO-RI, has gathered and organized key communities embracing new technologies and providing a future strategy, with recommendations on the way forward and on governance. Particularly, the JERICO community acknowledges that the main providers of coastal observations are: (1) research infrastructures, (2) national monitoring programs, and (3) monitoring activities performed by marine industries. The scope of this paper is to present some key elements of our coastal science strategy to build it on long term. It describes how the pan-European JERICO community is building an integrated and innovation-driven coastal research infrastructure for Europe. The RI embraces emerging technologies which will revolutionize the way the ocean is observed. Developments in biotechnology (molecular and optical sensors, omics-based biology) will soon provide direct and online access to chemical and biological variables including in situ quantification of harmful algae and contaminants. Using artificial intelligence (AI), Internet of Things will soon provide operational platforms and autonomous and remotely operated smart sensors. Embracing key technologies, high quality open access data, modeling and satellite observations, it will support sustainable blue growth, warning and forecasting coastal services and healthy marine ecosystem. JERICO-FP7 is the European 7th framework project named JERICO under Grant Agreement No. 262584. JERICO-NEXT is the European Horizon-2020 project under Grant Agreement No. 654410. JERICO-RI is the European coastal observing research infrastructure established and structured through JERICO-FP7 and JERICO-NEXT, and beyond

Predicting consumer biomass, size-structure, production, catch potential, responses to fishing and associated uncertainties in the world's marine ecosystems

Existing estimates of fish and consumer biomass in the world’s oceans are disparate. This creates uncertainty about the roles of fish and other consumers in biogeochemical cycles and ecosystem processes, the extent of human and environmental impacts and fishery potential. We develop and use a size-based macroecological model to assess the effects of parameter uncertainty on predicted consumer biomass, production and distribution. Resulting uncertainty is large (e.g. median global biomass 4.9 billion tonnes for consumers weighing 1 g to 1000 kg; 50% uncertainty intervals of 2 to 10.4 billion tonnes; 90% uncertainty intervals of 0.3 to 26.1 billion tonnes) and driven primarily by uncertainty in trophic transfer efficiency and its relationship with predator-prey body mass ratios. Even the upper uncertainty intervals for global predictions of consumer biomass demonstrate the remarkable scarcity of marine consumers, with less than one part in 30 million by volume of the global oceans comprising tissue of macroscopic animals. Thus the apparently high densities of marine life seen in surface and coastal waters and frequently visited abundance hotspots will likely give many in society a false impression of the abundance of marine animals. Unexploited baseline biomass predictions from the simple macroecological model were used to calibrate a more complex size- and trait-based model to estimate fisheries yield and impacts. Yields are highly dependent on baseline biomass and fisheries selectivity. Predicted global sustainable fisheries yield increases ≈4 fold when smaller individuals (< 20 cm from species of maximum mass < 1kg) are targeted in all oceans, but the predicted yields would rarely be accessible in practice and this fishing strategy leads to the collapse of larger species if fishing mortality rates on different size classes cannot be decoupled. Our analyses show that models with minimal parameter demands that are based on a few established ecological principles can support equitable analysis and comparison of diverse ecosystems. The analyses provide insights into the effects of parameter uncertainty on global biomass and production estimates, which have yet to be achieved with complex models, and will therefore help to highlight priorities for future research and data collection. However, the focus on simple model structures and global processes means that non-phytoplankton primary production and several groups, structures and processes of ecological and conservation interest are not represented. Consequently, our simple models become increasingly less useful than more complex alternatives when addressing questions about food web structure and function, biodiversity, resilience and human impacts at smaller scales and for areas closer to coasts

Assumed selectivities and fishing mortality rates for modeled species.

<p>Different selectivities, fishing mortality rates (<i>F</i>) and relative fishing mortality rates between species (<i>F</i>_<i>rel</i>) were assumed in the four scenarios. Asymptotic length (<i>L</i>_<i>∞</i>) in cm was estimated from <i>M</i>_∞ in g assuming <i>M</i> = 0.01<i>L</i>³ and length at first capture (<i>L</i>_<i>c</i>) was based on an assumed age at first capture [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133794#pone.0133794.ref062" target="_blank">62</a>]. When values are shown in bold they apply only to areas inside LME and not to the FAO areas.</p

Predicted effects of fishing on global consumer biomass.

<p>Predicted changes in global biomass as a function of <i>F</i> (expressed as a proportion of <i>F</i>_<i>MMSY</i> for all consumers of body mass >100g) and selectivity. Each column presents results for one selectivity scenario (A to D, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133794#pone.0133794.t003" target="_blank">Table 3</a>) and panels show biomass by consumer for (a) all individuals >100g (total), (b) 100g to 1 kg (small), (c) 1 kg to 10 kg (medium) and (d) > 10 kg (large).</p

Predicted relationship between cumulative biomass of consumers and ocean area.

<p>The colour scale (extended vertically for visibility) indicates sea surface temperatures in 0.5° grid cells contributing biomass values at each point on the cumulative relationship (black line).</p

Predictors of phytoplankton community size structure.

<p>Means and standard deviations of the α, <i>β</i>₁ and <i>β</i>₂ coefficients used to predict the size structure of the phytoplankton community as a function of primary production and temperature [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133794#pone.0133794.ref043" target="_blank">43</a>].</p

Predicted effects of temperature on production and biomass.

<p>Modelled relationships between temperature <i>T</i>_<i>C</i> and daily primary production <i>P</i>_<i>P</i> and (a) median estimated consumer biomass <i>B</i> (g m^-2 in <i>M</i> range 1 to 10⁶ g), (b) median estimated consumer production <i>P</i>_<i>C</i> (g m^-2 in <i>M</i> range 1 to 10⁶ g), (c) ratio of primary production (g m^-2 yr^-1) to consumer biomass <i>B</i>_<i>C</i> (g m^-2 yr^-1 in <i>M</i> range 1 to 10⁶ g) and (d) ratio of primary production (g m^-2 yr^-1) to consumer production <i>P</i>_<i>C</i> (g m^-2 yr^-1 in <i>M</i> range 1 to 10⁶ g). In all simulations <i>Z</i>_<i>e</i> was assumed to be fixed at 50 m and <i>Z</i> at 200 m. Chlorophyll concentration was estimated from primary production using a relationship established from the GCM outputs. Values of <i>T</i>_<i>C</i> and <i>P</i>_<i>P</i> that fell outside ranges including 99.99% of GCM outputs for the world’s oceans are masked. Contours indicate combinations of <i>T</i>_<i>C</i> and <i>P</i>_<i>P</i> that include 70% (black), 90%, 95% and 99% (pale grey) of GCM outputs.</p