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Key traits of functional biodiversity are examined for 31,134 species of fishes. These traits are maximum body weight, productivity, and trophic level. A new, simple framework is presented that shows the combined usage of these traits, in ordinal categories, for close to 90% of extant species of fishes. Most species are clustered tightly along an evolutionary axis in size-productivity-trophic space (SPT-space) from few large, evolutionary old species with very low productivity to many medium-sized newly evolved species with high productivity, superseding Cope's rule of a within-lineages trend towards larger size and lower productivity. The across-lineages evolutionary axis is also found in the subsets of marine, freshwater, and Arctic species. Another notable prediction is the five-fold increase in top predators in Arctic waters in 2100, which could cause the extinction of endemic species. The main purpose of this study is to demonstrate the usefulness of the SPTframework for comparing functional biodiversity patterns in ecosystems by salinity, geography or time. Also, the SPT-framework was used to explore correlations with other traits such as body shape, and to display the position of individual species, represented by pictograms of body shape and habitat, within SPT-space. [ABSTRACT FROM AUTHOR]

Infant cry is one of the first distinctive and informative life signals observed after birth. Neonatologists and automatic assistive systems can analyse infant cry to early-detect pathologies. These analyses extensively use reference expert-curated databases containing annotated infant-cry audio samples. However, these databases are not publicly accessible because of their sensitive data. Moreover, the recorded data can under-represent specific phenomena or the operational conditions required by other medical teams. Additionally, building these databases requires significant investments that few hospitals can afford. This paper describes an open-source workflow for infant-cry detection, which identifies audio segments containing high-quality infant-cry samples with no other overlapping audio events (e.g. machine noise or adult speech). It requires minimal training because it trains an LSTM-with-self-attention model on infant-cry samples automatically detected from the recorded audio through cluster analysis and HMM classification. The audio signal processing uses energy and intonation acoustic features from 100-ms segments to improve spectral robustness to noise. The workflow annotates the input audio with intervals containing infant-cry samples suited for populating a database for neonatological and early diagnosis studies. On 16 min of hospital phone-audio recordings, it reached sufficient infant-cry detection accuracy in 3 neonatal care environments (nursery—69%, sub-intensive—82%, intensive—77%) involving 20 infants subject to heterogeneous cry stimuli, and had substantial agreement with an expert's annotation. Our workflow is a cost-effective solution, particularly suited for a sub-intensive care environment, scalable to monitor from one to many infants. It allows a hospital to build and populate an extensive high-quality infant-cry database with a minimal investment. [ABSTRACT FROM AUTHOR]

Digital maps greatly support storytelling about territories, especially when enriched with data describing cultural, societal, and ecological aspects, conveying emotional messages that describe the territory as a whole. Story maps are interactive online digital narratives that can describe a territory beyond its map by enriching the map with text, pictures, videos, and other multimedia information. This paper presents a semi-automatic workflow to produce story maps from textual documents containing territory data. An expert first assembles one territory-contextual document containing text and images. Then, automatic processes use natural language processing and Wikidata services to (i) extract key concepts (entities) and geospatial coordinates associated with the territory, (ii) assemble a logically-ordered sequence of enriched story-map events, and (iii) openly publish online story maps and an interoperable Linked Open Data semantic knowledge base for event exploration and inter-story correlation analyses. Our workflow uses an Open Science-oriented methodology to publish all processes and data. Through our workflow, we produced story maps for the value chains and territories of 23 rural European areas of 16 countries. Through numerical evaluation, we demonstrated that territory experts considered the story maps effective in describing their territories, and appropriate for communicating with citizens and stakeholders. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic provides a major opportunity to study fishing effort dynamics and to assess the response of the industry to standard and remedial actions. Knowing a fishing fleet's capacity to compensate for effort reduction (i.e., its resilience) allows differentiating governmental regulations by fleet, i.e., imposing stronger restrictions on the more resilient and weaker restrictions on the less resilient. In the present research, the response of the main fishing fleets of the Adriatic Sea to fishing hour reduction from 2015 to 2020 was measured. Fleet activity per gear type was inferred from monthly Automatic Identification System data. Pattern recognition techniques were applied to study the fishing effort trends and barycentres by gear. The beneficial effects of the lockdowns on Adriatic endangered, threatened and protected (ETP) species were also estimated. Finally, fleet effort series were examined through a stock assessment model to demonstrate that every Adriatic fishing fleet generally behaves like a stock subject to significant stress, which was particularly highlighted by the pandemic. Our findings lend support to the notion that the Adriatic fleets can be compared to predators with medium-high resilience and a generally strong impact on ETP species. [ABSTRACT FROM AUTHOR]

Forecasting environmental parameters in the distant future requires complex modelling and large computational resources. Due to the sensitivity and complexity of forecast models, long-term parameter forecasts (e.g. up to 2100) are uncommon and only produced by a few organisations, in heterogeneous formats and based on different assumptions of greenhouse gases emissions. However, data mining techniques can be used to coerce the data to a uniform time and spatial representation, which facilitates their use in many applications. In this paper, streams of big data coming from AquaMaps and NASA collections of 126 long-term forecasts of nine types of environmental parameters are processed through a cloud computing platform in order to (i) standardise and harmonise the data representations, (ii) produce intermediate scenarios and new informative parameters, and (iii) align all sets on a common time and spatial resolution. Time series cross-correlation applied to these aligned datasets reveals patterns of climate change and similarities between parameter trends in 10 marine areas. Our results highlight that (i) the Mediterranean Sea may have a standalone 'response' to climate change with respect to other areas, (ii) the Poles are most representative of global forecasted change, and (iii) the trends are generally alarming for most oceans. [ABSTRACT FROM AUTHOR]

Ecological and ecosystem modellers frequently need to interpolate spatiotemporal observations of geophysical and environmental parameters over an analysed area. However, particularly in marine science, modellers with low expertise in oceanography and hydrodynamics can hardly use interpolation methods optimally. This paper introduces an Open Science oriented, open-source, scalable and efficient workflow for 2D marine environmental parameters. It combines a fast, efficient interpolation method with a Bayesian hierarchical model embedding the stationary advection–diffusion equation as a constraint. Our workflow fills the usability gap between interpolation software providers and the users' communities. It can run entirely automatically without requiring expert parametrisation. It is also available on a cloud computing platform, with a Web Processing Service compliant interface, supporting collaboration, repeatability, reproducibility, and provenance tracking. We demonstrate that our workflow produces comparable results to a state-of-the-art model (frequently used in oceanography) in interpolating four environmental parameters at the global scale. • A Bayesian model for marine environmental parameters interpolation is presented. • It combines a standard interpolation method with a Bayesian model based on MCMCs. • It can run in a fully automatic way without requiring expert parametrisation. • It is Open Science-compliant and runs as a free standardised cloud-computing service. • Suitable for ecological/ecosystem modellers needing automatic interpolation methods. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract The hyporheic zone (HZ) is an area of interaction between surface and ground waters present in and around river beds. Bidirectional mixing within the HZ, termed hyporheic exchange flow (HEF), plays significant roles in nutrient transport, organic matter and biogeochemical processing in rivers. The functional importance of the HZ in river ecology and hydrology suggests that river managers should consider the HZ in their planning to help compromised systems recover. However, current river restoration planning tools do not take into account the HZ. This paper describes a novel multiscale, transferable method that combines existing environmental information at different spatial scales to identify areas with potentially significant HEF for use in restoration prioritization and planning. It uses a deductive approach that is suited for data-poor case studies, which is common for most rivers, given the very limited data on the spatial occurrence of areas of hyporheic exchange. Results on nine contrasting European rivers, demonstrate its potential to inform river management. Graphical abstract Image 1 Highlights • A multiscale statistical method is proposed to predict potentially suitable areas for HEF-focused restoration of rivers. • Existing hydrological, hydrogeological, topographic, anthropogenic and ecological data are analysed at 3 spatial scales. • Results on 9 rivers across Europe indicate reliable categorisations and general alignment with published empirical data. • The method can inform the selection of river restoration locations and measures, in the channel, valley and catchment. [ABSTRACT FROM AUTHOR]

Invasive species from the Suez Canal, also named “Lessepsian species”, often have an ecological and financial impact on marine life, fisheries, human well-being and health in the Mediterranean Sea. Among these, the silver-cheeked toad-fish Lagocephalus sceleratus (Gmelin, 1789) has rapidly colonised the eastern Mediterranean basin and is currently moving westwards. This pufferfish has a highly opportunistic behaviour, it attacks fish captured in nets and lines and seriously damages fishing gears and catch. It is a highly-toxic species with no immediate economic value for the Mediterranean market, although it currently represents 4% of the weight of the total artisanal catches. Consequently, the possible effects on Mediterranean fisheries and health require to enhance our understanding about the future geographical distribution of this pufferfish in the whole basin. In this paper, an overall habitat suitability map and an effective geographical spread map for L. sceleratus at Mediterranean scale are produced by using cloud computing-based algorithms to merge seven machine learning approaches. Further, the potential impact of the species is estimated for several Mediterranean Sea subdivisions: The major fishing areas of the Food and Agriculture Organization of the United Nations, the Economic Exclusive Zones, and the subdivisions of the General Fisheries Commission for the Mediterranean Sea. Our results suggest that without an intervention, L. sceleratus will continue its rapid spread and will likely have a high impact on fisheries. The presented method is generic and can be applied to other invasive species. It is based on an Open Science approach and all processes are freely available as Web services. [ABSTRACT FROM AUTHOR]

Catch and fishing effort time series are used by managers to safeguard the availability of resources in the future. Fisheries organizations estimate the status of a stock and the levels for sustainable harvest. Based on these indicators, regulations are developed to guarantee the availability of food and sustain economic growth. For example, tuna stocks are important not only in terms of nutrition, but also for the welfare, culture, revenue, and employment of the countries that rely upon them. However, fisheries time-series are the result of many natural and non-natural factors and are usually difficult to predict. Here, we propose a method to aggregate fisheries time series in space and time, to detect hidden periodicities, to predict the time series in the future and to identify the most stressed locations in the fishing area. We apply our method to tuna fisheries data in the Indian Ocean and compare it with respect to other methods. We demonstrate that the method is able to highlight periodic patterns in this high fishing activity area and to forecast the fisheries time series in the future. We use a research e- Infrastructure to comply with modern approaches supporting reproducibility, repeatability, and results sharing. [ABSTRACT FROM AUTHOR]

Geothermal data are published using different IT services, formats and content representations, and can refer to both regional and global scale information. Geothermal stakeholders search for information with different aims. E-Infrastructures are collaborative platforms that address this diversity of aims and data representations. In this paper, we present a prototype for a European Geothermal Information Platform that uses INSPIRE recommendations and an e-Infrastructure (D4Science) to collect, aggregate and share data sets from different European data contributors, thus enabling stakeholders to retrieve and process a large amount of data. Our system merges segmented and national realities into one common framework. We demonstrate our approach by describing a platform that collects data from Italian, French, Hungarian, Swiss and Icelandic geothermal data providers. [ABSTRACT FROM PUBLISHER]

Estimating absence locations of a species is important in conservation biology and conservation planning. For instance, using reliable absence as much as presence information, species distribution models can enhance their performance and produce more accurate predictions of the distribution of a species. Unfortunately, estimating reliable absence locations is difficult and often requires a deep knowledge of the species’ distribution and of its abiotic and biotic environmental preferences and tolerance. In this paper, we propose a methodology to reconstruct reliable absence information from presence-only information, and the conditions that those presence-only data have to meet to make this possible. Large species occurrence data collections (otherwise called occurrence datasets) contain high quality and expert-reviewed species observation records from scientific surveys. These surveys can be used to retrieve species presence locations, but they also record places where the species in their target list were not observed. Although these absences could be simply due to sampling variation, it is possible to intersect many of these reports to estimate true absence locations, i.e. those due to habitat unsuitability or geographical hindrances. In this paper, we present a method to generate reliable absence locations of this type for marine species, using scientific surveys reports contained in the Ocean Biogeographic Information System (OBIS), an authoritative species occurrence dataset. Our method spatially aggregates information from surveys focussing on the same target species. It detects absence locations for a given species as those locations in which repeated surveys (that included the species of interest in their target list) reported information only on other species. We qualitatively demonstrate the reliability of our method using distribution records of the Atlantic cod as a case study. Additionally, we quantitatively estimate its performance using another authoritative large species occurrence dataset, the Global Biodiversity Information Facility (GBIF). We also demonstrate that our approach has higher accuracy and presents complementary behaviour with respect to another method using environmental envelopes. Our process can support species distribution models (as well as other types of models, e.g. climate change models) by providing reliable data to presence/absence approaches. It can manage regional as well as global scale scenarios and runs within a collaborative e-Infrastructure (D4Science) that publishes it as-a-Service, allowing biologists to reproduce, repeat and share experimental results. [ABSTRACT FROM AUTHOR]

The appropriateness of three official fisheries management reference points used in the north-east Atlantic was investigated: (i) the smallest stock size that is still within safe biological limits ( SSB pa), (ii) the maximum sustainable rate of exploitation ( F msy) and (iii) the age at first capture. As for (i), in 45% of the examined stocks, the official value for SSB pa was below the consensus estimates determined from three different methods. With respect to (ii), the official estimates of F msy exceeded natural mortality M in 76% of the stocks, although M is widely regarded as natural upper limit for F msy. And regarding (iii), the age at first capture was below the age at maturity in 74% of the stocks. No official estimates of the stock size ( SSB msy) that can produce the maximum sustainable yield ( MSY) are available for the north-east Atlantic. An analysis of stocks from other areas confirmed that twice SSB pa provides a reasonable preliminary estimate. Comparing stock sizes in 2013 against this proxy showed that 88% were below the level that can produce MSY. Also, 52% of the stocks were outside of safe biological limits, and 12% were severely depleted. Fishing mortality in 2013 exceeded natural mortality in 73% of the stocks, including those that were severely depleted. These results point to the urgent need to re-assess fisheries reference points in the north-east Atlantic and to implement the regulations of the new European Common Fisheries Policy regarding sustainable fishing pressure, healthy stock sizes and adult age/size at first capture. [ABSTRACT FROM AUTHOR]

Species commonness is often related to abundance and species conservation status. Intuitively, a “common species” is a species that is abundant in a certain area, widespread and at low risk of extinction. Analysing and classifying species commonness can help discovering indicators of ecosystem status and can prevent sudden changes in biodiversity. However, it is challenging to quantitatively define this concept. This paper presents a procedure to automatically characterize species commonness from biological surveys. Our approach uses clustering analysis techniques and is based on a number of numerical parameters extracted from an authoritative source of biodiversity data, i.e. the Ocean Biogeographic Information System. The analysis takes into account abundance, geographical and temporal aspects of species distributions. We apply our model to North Sea fish species and show that the classification agrees with independent expert opinion although sampling biases affect the data. Furthermore, we show that our approach is robust to noise in the data and is promising in classifying new species. Our method can be used in conservation biology, especially to reduce the effects of the sampling biases which affect large biodiversity collections. [ABSTRACT FROM AUTHOR]

The giant squid ( Architeuthis ) has been reported since even before the 16th century, and has recently been observed live in its habitat for the first time. Among the species belonging to this genus, Architeuthis dux has received special attention from biologists. The distribution of this species is poorly understood, as most of our information stems from stranded animals or stomach remains. Predicting the habitat and distribution of this species, and more in general of difficult to observe species, is important from a biological conservation perspective. In this paper, we present an approach to estimate the potential distribution of A. dux at global scale, with relative high resolution (1-degree). Our approach relies on a complex preparation phase, which improves the reliability of presence, absence and environmental data correlated to the species habitat. We compare our distribution with those produced by state-of-the-art approaches (MaxEnt and AquaMaps), and use an expert-drawn map as reference. We demonstrate that our model projection is in agreement with the expert's map and is also compliant with several biological assessments of the species habitat and with recent observations. Furthermore, we show that our approach can be generalized as a paradigm that is applicable to other rare species. [ABSTRACT FROM AUTHOR]

Highlights: [•] We numerically compare the niche distributions produced by a Neural Network and by an Expert System on Latimeria chalumnae. [•] We use information by the Expert System to train the Neural Network. [•] We assess the reliability of the maps by means of the Habitat Representativeness Score. [•] We compare the results with respect to maps produced with GARP and MaxEnt. [•] We show that the combination between the Expert System and the Neural Network is able to learn better from the environmental features in the case of L. chalumnae. [Copyright &y& Elsevier]

The article focuses on the Statistical Algorithms Importer (SAI) interface that allows the publishing of prototype R-scripts as-a-Service that runs on a cloud computing platform. Topics mentioned include the use of prototype scripting by scientist to share results with other scientists, the smart use of web services in direct integration of the scripts, and the creation of the cloud computing D4Science platform version to make SAI interface accessible for script providers.

The article focuses on the D4Science e-Infrastructure to bridge the communication gap of ocean acidification data from environmental data providers and SeaDataNet DIVA service. Topics include the benefits of using D4Science to the data and model providers of SeaDataNet, data sharing and publishing facilities of D4Science users, and the publication of the results as a GIS map.

Understanding the human spoken language recognition process is still a far scientific goal. Nowadays, commercial automatic speech recognisers (ASRs) achieve high performance at recognising clean speech, but their approaches are poorly related to human speech recognition. They commonly process the phonetic structure of speech while neglecting supra-segmental and syllabic tracts integral to human speech recognition. As a result, these ASRs achieve low performance on spontaneous speech and require enormous costs to build up phonetic and pronunciation models and catch the large variability of human speech. This paper presents a novel ASR that addresses these issues and questions conventional ASR approaches. It uses alternative acoustic models and an exhaustive decoding algorithm to process speech at a syllabic temporal scale (100–250 ms) through a multi-temporal approach inspired by psycho-acoustic studies. Performance comparison on the recognition of spoken Italian numbers (from 0 to 1 million) demonstrates that our approach is cost-effective, outperforms standard phonetic models, and reaches state-of-the-art performance. • We propose a novel Automatic Speech Recognizer inspired by psycho-acoustic studies. • It embeds multiple-time scale speech processing inspired to human speech processing. • Its optimal architecture uses an LSTM-based acoustic model and 1h training audio. • It has comparable performance to conventional ASRs on 1-million numbers recognition. [ABSTRACT FROM AUTHOR]

A large and increasing number of countries use geothermal energy as power source for domestic and industrial applications. Geothermal power plants produce energy out of this natural and renewable source in a sustainable way and contribute to reduce global warming. However, power plants effectiveness depends on the suitability of an area to geothermal energy production, which is a complex and unknown combination of many environmental factors. Nowadays, geothermal suitability assessments require invasive inspections, high costs, and legal permissions. Thus, having a global suitability map of geothermal sites as reference would be useful prior knowledge during assessments, and would help saving time and money. In this paper, the first suitability map of potential geothermal sites at global scale is presented. The map is the result of the application of data collection and preparation processes, and a Maximum Entropy model, to geospatial data potentially correlated with geothermal site suitability and geothermal plants operation. The reliability of our map is assessed against currently active and planned geothermal power plants. Our approach follows the Open Science paradigm that guarantees results reproduction and transparency, and allows stakeholders to reuse the produced standardised data, services, and Web interfaces in other experiments or to generate new maps at regional scale. Overall, our results can help scientists, industry operators, and policy makers in geothermal sites assessments. Also, our approach supports communication with citizens whose territories are involved in probing and assessments, in order to transparently inform them about the reasons driving the selection of their territory and the potential future benefits. • The first global-scale map of the suitability of an area to geothermal energy production and plant installation is presented. • Geospatial data correlated with geothermal site suitability are processed through DIVA and MaxEnt. • The reliability of the map is tested against currently active and planned geothermal power plants. • An Open Science-compliant tool is proposed to allow stakeholders to increase the map resolution and revise parameters. • This research aims at increasing the transparency of the assessment of geothermal sites. [ABSTRACT FROM AUTHOR]

• A Maximum-Entropy Ecological Niche Model is used to estimate a global-scale probability distribution of COVID-19 high infection rate. • Environmental parameters (surface air temperature, precipitation, and elevation) and humanrelated parameters (CO 2 emission and population density) are used in the model. • The model is trained only with data of Italian provinces with high infection rate, but predicts known actual infection focuses, e.g. the Hubei province in China. • A risk index is proposed, which correctly classifies most World countries, which have reported high COVID-19 spread rate, as zones with high-risk of infection rate increase. • The methodology follows an Open-science approach where the model is published as a standardized Web service that maximises re-usability on new data and new diseases, and guarantees the transparency of the approach and the results. COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions. In this paper, infection rate of COVID-19 is modelled globally at a 0.5∘ resolution, using a Maximum Entropy-based Ecological Niche Model that identifies geographical areas potentially subject to a high infection rate. The model identifies locations that could favour infection rate due to their particular geophysical (surface air temperature, precipitation, and elevation) and human-related characteristics (CO 2 and population density). It was trained by facilitating data from Italian provinces that have reported a high infection rate and subsequently tested using datasets from World countries' reports. Based on this model, a risk index was calculated to identify the potential World countries and regions that have a high risk of disease increment. The distribution outputs foresee a high infection rate in many locations where real-world disease outbreaks have occurred, e.g. the Hubei province in China, and reports a high risk of disease increment in most World countries which have reported significant outbreaks (e.g. Western U.S.A.). Overall, the results suggest that a complex combination of the selected parameters might be of integral importance to understand the propagation of COVID-19 among human populations, particularly in Europe. The model and the data were distributed through Open-science Web services to maximise opportunities for re-usability regarding new data and new diseases, and also to enhance the transparency of the approach and results. [ABSTRACT FROM AUTHOR]

Automatic speech recognition systems based on end-to-end models (E2E-ASRs) can achieve comparable performance to conventional ASR systems while reproducing all their essential parts automatically, from speech units to the language model. However, they hide the underlying perceptual processes modelled, if any, and they have lower adaptability to multiple application contexts, and, furthermore, they require powerful hardware and an extensive amount of training data. Model-explainability techniques can explore the internal dynamics of these ASR systems and possibly understand and explain the processes conducting to their decisions and outputs. Understanding these processes can help enhance ASR performance and reduce the required training data and hardware significantly. In this paper, we probe the internal dynamics of three E2E-ASRs pre-trained for English by building an acoustic-syllable boundary detector for Italian and Spanish based on the E2E-ASRs' internal encoding layer outputs. We demonstrate that the shallower E2E-ASR layers spontaneously form a rhythmic component correlated with prominent syllables, central in human speech processing. This finding highlights a parallel between the analysed E2E-ASRs and human speech recognition. Our results contribute to the body of knowledge by providing a human-explainable insight into behaviours encoded in popular E2E-ASR systems. [ABSTRACT FROM AUTHOR]

The Law of the Sea and regional and national laws and agreements require exploited populations or stocks to be managed so that they can produce maximum sustainable yields. However, exploitation level and stock status are unknown for most stocks because the data required for full stock assessments are missing. This study presents a new method [abundance maximum sustainable yields (AMSY)] that estimates relative population size when no catch data are available using time series of catch-per-unit-effort or other relative abundance indices as the main input. AMSY predictions for relative stock size were not significantly different from the "true" values when compared with simulated data. Also, they were not significantly different from relative stock size estimated by data-rich models in 88% of the comparisons within 140 real stocks. Application of AMSY to 38 data-poor stocks showed the suitability of the method and led to the first assessments for 23 species. Given the lack of catch data as input, AMSY estimates of exploitation come with wide margins of uncertainty, which may not be suitable for management. However, AMSY seems to be well suited for estimating productivity as well as relative stock size and may, therefore, aid in the management of data-poor stocks. [ABSTRACT FROM AUTHOR]

This study presents a new method (LBB) for the analysis of length frequency data from commercial catches. LBB works for species that grow throughout their lives, such as most commercially-important fish and invertebrates, and requires no input in addition to length frequency data. It estimates asymptotic length, length at first capture, relative natural mortality, and relative fishing mortality. Standard fisheries equations can then be used to approximate current exploited biomass relative to unexploited biomass. In addition, these parameters allow the estimation of length at first capture that would maximize catch and biomass for a given fishing effort, and estimation of a proxy for the relative biomass capable of producing maximum sustainable yields. Relative biomass estimates of LBB were not significantly different from the "true" values in simulated data and were similar to independent estimates from full stock assessments. LBB also presents a new indicator for assessing whether an observed size structure is indicative of a healthy stock. LBB results will obviously be misleading if the length frequency data do not represent the size composition of the exploited size range of the stock or if length frequencies resulting from the interplay of growth and mortality are masked by strong recruitment pulses. [ABSTRACT FROM AUTHOR]

This study presents a Monte Carlo method ( CMSY) for estimating fisheries reference points from catch, resilience and qualitative stock status information on data-limited stocks. It also presents a Bayesian state-space implementation of the Schaefer production model ( BSM), fitted to catch and biomass or catch-per-unit-of-effort ( CPUE) data. Special emphasis was given to derive informative priors for productivity, unexploited stock size, catchability and biomass from population dynamics theory. Both models gave good predictions of the maximum intrinsic rate of population increase r, unexploited stock size k and maximum sustainable yield MSY when validated against simulated data with known parameter values. CMSY provided, in addition, reasonable predictions of relative biomass and exploitation rate. Both models were evaluated against 128 real stocks, where estimates of biomass were available from full stock assessments. BSM estimates of r, k and MSY were used as benchmarks for the respective CMSY estimates and were not significantly different in 76% of the stocks. A similar test against 28 data-limited stocks, where CPUE instead of biomass was available, showed that BSM and CMSY estimates of r, k and MSY were not significantly different in 89% of the stocks. Both CMSY and BSM combine the production model with a simple stock-recruitment model, accounting for reduced recruitment at severely depleted stock sizes. [ABSTRACT FROM AUTHOR]

The current scientific context is characterized by intensive digitization of the research outcomes and by the creation of data infrastructures for the systematic publication of datasets and data services. Several relationships can exist among these outcomes. Some of them are explicit, e.g. the relationships of spatial or temporal similarity, whereas others are hidden, e.g. the relationship of causality. By materializing these hidden relationships through a linking mechanism, several patterns can be established. These knowledge patterns may lead to the discovery of information previously unknown. A new approach to knowledge production can emerge by following these patterns. This new approach is exploratory because by following these patterns, a researcher can get new insights into a research problem. In the paper, we report our effort to depict this new exploratory approach using Linked Data and Semantic Web technologies (RDF, OWL). As a use case, we apply our approach to the archaeological domain. [ABSTRACT FROM AUTHOR]

The deep demersal snapper-grouper fishery in Indonesia is a data-poor fisheries resource that provides food security and a source of income to millions globally. Owing to an ongoing crew-operated data recording system implemented in Indonesia since 2015, the stocks of this fishery can now be assessed using length-frequency data and updated life-history parameters. Here, we use two length-based methods, one that is fishery-specific and another that is more generalized, to assess the status of Indonesian stocks. Specifically, we develop a literature-based assessment method based on a patchwork of conventional approaches but tailored to the studied stocks, and compare it with a newly established and broadly applicable length-based Bayesian biomass estimation method (LBB). The methods were applied to 16 stocks from 4 Indonesian Fisheries Management Areas and were compared based on simulations, as well as the convergence of the resulting stock status classification and uncertainty of the results. Analyzing the effect of using the literature-based species/family-specific life-history parameter values for asymptotic length (L inf) and relative natural mortality (M/K) in LBB showed that different values do affect the estimated biomass indicator. Nevertheless, in more than half the cases, the stock status classification did not differ between the two methods, while LBB results became more reliable with narrower confidence limits. Simulations, as well as similar status indicators between the two models support the value of the literature-based approach as an assessment methodology for the Indonesian deep demersal fisheries. Narrower confidence ranges highlight the importance of using fishery-specific information when applying generalized stock assessment methods. While most catches had few immature fish, half of the assessed stocks were consistently shown to have low biomass, indicating that important Indonesian stocks are at high risk of overfishing. [ABSTRACT FROM AUTHOR]

Recreational diving is known to have both direct and indirect impacts on coral habitats. Direct impacts include increasing sedimentation, breaks and diseases that lead to a decrease in the richness and abundances of hard corals. Indirect impacts include urban development, land management and sewage disposal. The ecological effects of scuba diving on the spatial composition metrics of reef benthic communities are less well studied, and they have not been investigated at seascape scale. In this study, we combine orthomosaics derived from Structure from Motion (SfM) photogrammetry and data-mining techniques to study the spatial composition of reef benthic communities of recreational diving sites at seascape scale (>25 m 2 ). The study focuses on the case study area of Ponta do Ouro Partial Marine Reserve (Mozambique). Results showed that scuba-diving resistant taxa (i.e., sponges and algae) were abundant at small (>850 m 2 ) and highly dived sites (>3000 dives yr − 1 ), characterized by low diversity and density, and big organisms with complex shapes. Fragile taxa (i.e., Acropora spp.) were abundant at low (365 dives yr − 1 ) and moderately dived sites (1000–3000 dives yr − 1 ) where the greater depth and wider coral reef surfaces attenuate the abrasive effect of waves and re-suspended sediments. Highest taxa diversity and density, and lowest abundance of resistant taxa were recorded at large (>2000 m 2 ) and rarely dived sites. This study highlights the potential applications for a photogrammetric approach to support monitoring programs at Ponta do Ouro Partial Marine Reserve (Mozambique), and provides some insight to understand the influence of scuba diving on benthic communities. [ABSTRACT FROM AUTHOR]