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The dramatic Mass Mortality Event, MME, of Pinna nobilis populations initially detected in the western Mediterranean basin, has also spread rapidly to the central and eastern basin. Unfortunately, there is still a significant lack of information on the status and health of P. nobilis, since only a fragmentary picture of the mortality rate affecting these populations is available. Regarding the Italian coast, several surveys have given only localized or point-like views on the distribution of species and the effect of the MME. Therefore, for the first time, this study investigated P. nobilis density of individuals, distribution and mortality throughout 161 surveys along 800 km of coastline in the Apulia region (South-east of Italy). The geographical scale of this investigation made it the largest ever conducted in Italy, and this was achieved through a rapid and standardized protocol. During this monitoring campaign, 90 km of linear underwater transects were surveyed, along which no live individuals were observed. This result allowed to estimate that the P. nobilis populations had totally collapsed, with a mortality rate of 100% in Apulia. The distributional pattern of the species showed a strong overlap with seagrass meadows on meso- and macro-geographical scale, however this was not the case on a micro-scale. This result evidenced that relationships between P. nobilis and seagrass meadows are not limited to the habitat patch, but cross the boundaries of seagrass leading us to suggest that the distribution of P. nobilis hold a trophic link through the cross-boundary subsidy occurring from seagrass meadows to the nearby habitat, by means of the refractory detrital pathway.

The dramatic Mass Mortality Event, MME, of Pinna nobilis populations initially detected in 2016 in the western Mediterranean basin, has also spread rapidly to the central and eastern basin. Unfortunately, there is still a significant lack of information on the status and health of P. nobilis, since only a fragmentary picture of the mortality rate affecting these populations is available. Regarding the Italian coast, several surveys have given only localized or point-like views on the distribution of species and the effect of the MME. Therefore, for the first time, this study investigated P. nobilis densities, distribution and mortality in 164 surveys covering a total of 800 km along the southeast coast of Italy (Apulia region). The geographical scale of this investigation made it the largest ever conducted in Italy, and this was achieved through a rapid and standardized protocol. No live individuals were observed along the 92 km linear transects, allowing us to assess that the P. nobilis populations had totally collapsed, with a mortality rate of 100% in Apulia. The distributional pattern of the species showed a strong overlap with seagrass meadows on meso and macro geographical scale, however this was not the case on a micro scale. This result indicates that although there is a relationship between P. nobilis and seagrass meadows, it is not limited to the habitat patch but crosses the boundaries of seagrass. This observation led us to the conclusion that the distribution of P. nobilis shows a trophic link through the cross-boundary subsidy occurring from seagrass meadows to the nearby habitat, by means of the refractory detrital pathway.

Ecological systems can be regarded as natural capital that yields ecosystem services vital for human well-being. The provision of these services strictly depends on the protection of natural capital stocks generating them, highlighting the need for conservation and monitoring actions led by proper assessment methodologies. Among the available methods, the Environmental Accounting Model based on the emergy approach is rapidly gaining popularity in ecological applications. We used such method to assess the natural capital value of Posidonia oceanica meadows, widely recognized the most important ecosystems in the Mediterranean basin, at Italian national spatial scale. The natural capital value of P. oceanica was further weighed by the estimates provided by a Habitat Suitability Model. We observed that the estimated level of habitat suitability played an important role as modifier of the average biophysical value of P. oceanica. Our approach allowed to identify the meadows having the highest stability and over space and time, which we defined as the most valuable in biophysical terms, thus with highest natural capital value. The spatially explicit estimates we provided could support managers and policy-makers to ensure the long-term provision of ecosystem services generated by P. oceanica, enhancing ecosystem management and maritime spatial planning.

The emergy accounting method has been widely applied to terrestrial and marine ecosystems although there is a lack of emergy studies focusing on phytoplankton primary production. Phytoplankton production is a pivotal process since it is intimately coupled with oceanic food webs, energy fluxes, carbon cycle, and Earth's climate. In this study, we proposed a new methodology to perform a biophysical assessment of the global phytoplankton primary production combining Machine Learning (ML) techniques and an emergy-based accounting model. Firstly, we produced global phytoplankton production estimates using an Artificial Neural Network (ANN) model. Secondly, we assessed the main energy inputs supporting the global phytoplankton production. Finally, we converted these inputs into emergy units and analysed the results from an ecological perspective. Among the energy flows, tides showed the highest maximum emergy contribution to global phytoplankton production highlighting the importance of thise flow in the complex dynamics of marine ecosystems. In addition, an emergy/production ratio was calculated showing different global patterns in terms of emergy convergence into the primary production process. We believe that the proposed emergy-based assessment of phytoplankton production could be extremely valuable to improve our understanding of this key biological process at global scale adopting a systems perspective. This model can also provide a useful benchmark for future assessments of marine ecosystem services at global scale.

Sea cucumbers and sea urchins are promising candidates for aquaculture since they are high market value and low-trophic organisms. However, although they often co-exist in many marine habitats showing feeding interactions, there is currently a lack of investigations available regarding the co-culture of these organisms in Integrated Multi-Trophic Aquaculture (IMTA). The present study investigated, for the first time, the laboratory-scale feasibility of an integrated aquaculture between P. lividus and H. tubulosa, two of the most valuable Mediterranean echinoderms, through a four-month experiment. More specifically, three food sources with different fish meal concentrations were tested separately to sustain the integrated production in a land-based RAS (Recycling aquaculture system) of both co-cultured species: 1) completely vegetable diet (D-0), 2) vegetable diet with 20% of fish meal (D-20) and 3) vegetable diet with 40% of fish meal (D-40). Among these experimental diets D-20 (with 20% of fish meal supplement) was consumed more efficiently and sustained high growth rates for both co-cultured species. However, significant growth was detected with all experimental diets, indicating successful integrated aquaculture between sea urchins and sea cucumbers. The present study, therefore, suggested the existence of substantial benefits of an integrated aquaculture between these echinoderm species, that could promote the environmental and economic sustainability of their production on a large-scale. Our results showed, in fact, that less than 24% of the organic matter administered with the food remained as waste in our IMTA system after being ingested by the two trophic levels. The sea urchins ingested 87% of the food administered, absorbing 64% of the organic matter, whilst in the second step, the sea cucumbers consumed 54% of organic matter present in the sea urchin feces. Hence the aquaculture model investigated here was highly effective in reducing the total waste, at the same time providing added value in the form of sea cucumber biomass.

Posidonia oceanica meadows rank among the most important and most productive ecosystems in the Mediterranean basin, due to their ecological role and to the goods and services they provide. Estimations of crucial ecological process such as meadows productivity could play a major role in an environmental management perspective and in the assessment of P. oceanica ecosystem services. In this study, a Machine Learning approach, i.e. Random Forest, was aimed at modeling P. oceanica shoot density and rhizome primary production using as predictive variables only environmental factors retrieved from indirect measurements, such as maps. Our predictive models showed a good level of accuracy in modeling both shoot density and rhizome productivity (R2 = 0.761 and R2 = 0.736, respectively). Furthermore, as shoot density is an essential parameter in the estimation of P. oceanica productivity, we proposed a cascaded approach aimed at estimating the latter using predicted values of shoot density rather than observed measurements. In spite of the complexity of the problem, the cascaded Random Forest performed quite well (R2 = 0.637). While direct measurements will always play a fundamental role, our estimates could support large scale assessment of the expected condition of P. oceanica meadows, providing valuable information about the way this crucial ecosystem works.

Phytoplankton species can produce resting stages that persist in the sediment over long periods and accomplish important ecological functions. With the aim of investigating the phytoplankton assemblage structure in relation to environmental drivers and human pressures, we analyzed resting stages of diatoms and dinoflagellates, including harmful algal bloom taxa, in the surface sediments of 3 Mediterranean regional areas. Abundance of resting stages was determined by molecular quantitative polymerase chain reaction assay. Multivariate data analysis confirmed that the abundance of resting-stage assemblages seemed related to depth. Regional differences in the composition of the resting-stage assemblages were evident and environmental drivers were correlated with those regional differences. Three main groups of samples were defined according to SST and depth thresholds. Samples from the northern and central Adriatic Sea (average SST < 18°C) formed the richest assemblages, both in terms of abundance and species richness, while deep samples from all other basins (depth > 368 m) were poorer and less diverse than those from shallower sites (depth ≤ 368 m). Resting-stage taxa contributed differently to the 3 groups. Diatom spores and dinoflagellate cysts were the most abundant taxa, but Alexandrium minutum cysts and Ditylum brightwellii spores also accounted for a large share of the overall inter-group compositional distance. The structure of resting-stage assemblages can be regarded as a time- and space-integrated response of a subset of phytoplankton species to environmental conditions, including the physical oceanographic dynamics that favor or prevent sedimentation of resting stages.

The only known population of Conolophus marthae (Reptilia, Iguanidae) and a population of C. subcristatus are syntopic on Wolf Volcano (Isabela Island, Galápagos). No gene flow occurs suggesting that effective reproductive isolating mechanisms exist between these two species. Chemical signature of femoral pore secretions is important for intra- and inter-specific chemical communication in squamates. As a first step towards testing the hypothesis that chemical signals could mediate reproductive isolation between C. marthae and C. subcristatus, we compared the chemical profiles of femoral gland exudate from adults caught on Wolf Volcano. We compared data from three different years and focused on two years in particular when femoral gland exudate was collected from adults during the reproductive season. Samples were processed using Gas Chromatography coupled with Mass Spectrometry (GC–MS). We identified over 100 different chemical compounds. Non-Metric Multidimensional Scaling (nMDS) was used to graphically represent the similarity among individuals based on their chemical profiles. Results from non-parametric statistical tests indicate that the separation between the two species is significant, suggesting that the chemical profile signatures of the two species may help prevent hybridization between C. marthae and C. subcristatus. Further investigation is needed to better resolve environmental influence and temporal reproductive patterns in determining the variation of biochemical profiles in both species.

Enhancing the understanding of marine phytoplankton primary production is paramount due to the relationships with oceanic food webs, energy fluxes, carbon cycle and Earth's climate. As field measurements of this process are both expensive and time consuming, indirect approaches, which can estimate primary production from remotely sensed imagery, are the only viable large-scale solution. We boosted the quality of phytoplankton primary production estimates, with respect to a previously developed model, by embedding ecological knowledge into the training of an artificial neural network. In order to achieve this goal, we drove the training procedure on the basis of both theoretical and data-derived ecological knowledge about phytoplankton primary production. A “single peak” constraint exploits the theoretical knowledge about the vertical shape of the production profile; a “depth-weighted error” procedure was based on available information about the production magnitude along the water column; a variable learning rate and momentum approach allowed to better exploit the available data to train our artificial neural network. Thanks to this customized procedure, we improved the quality of the primary production estimates from both a theoretical and a numerical point of view. Accordingly, the new artificial neural networks not only provided ecologically sounder estimates, but also explained up to 4% more variance with respect to the traditional error back-propagation solution. This result was achieved exclusively through an ecologically-driven customization of the basic algorithm, since the dataset and predictive variables were the same utilized in the conventional counterpart, trained with a classic error back-propagation algorithm. We suggest that the proposed rationale could lead to improved performances in similar modelling applications.

The understanding of the first movements of the ancestral populations within the African continent is still unclear, particularly in West Africa, due to several factors that have shaped the African genetic pool across time. To improve the genetic representativeness of the Beninese population and to better understand the patterns of human settlement inside West Africa and the dynamics of peopling of the Democratic Republic of Benin, we analyzed the maternal genetic variation of 193 Beninese individuals belonging to Bariba, Berba, Dendi, and Fon populations. Results support the oral traditions indicating that the western neighbouring populations have been the ancestors of the first Beninese populations, and the extant genetic structure of the Beninese populations is most likely the result of admixture between populations from neighbouring countries and native people. The present findings highlight how the Beninese populations contributed to the gene pool of the extant populations of some American populations of African ancestry. This strengthens the hypothesis that the Bight of Benin was not only an assembly point for the slave trade during the Trans-Atlantic Slave Trade but also an important slave trapping area.

Linear regression is widely used in applied sciences and, in particular, in satellite optical oceanography, to relate dependent to independent variables. It is often adopted to establish empirical algorithms based on a finite set of measurements, which are later applied to observations on a larger scale from platforms such as autonomous profiling floats equipped with optical instruments (e.g., Biogeochemical Argo floats; BGC-Argo floats) and satellite ocean colour sensors (e.g., SeaWiFS, VIIRS, OLCI). However, different methods can be applied to a given pair of variables to determine the coefficients of the linear equation fitting the data, which are therefore not unique. In this work, we quantify the impact of the choice of “regression method” (i.e., either type-I or type-II) to derive bio-optical relationships, both from theoretical perspectives and by using specific examples. We have applied usual regression methods to an in situ data set of particulate organic carbon (POC), total chlorophyll-a (TChla), optical particulate backscattering coefficient (bbp), and 19 years of monthly TChla and bbp ocean colour data. Results of the regression analysis have been used to calculate phytoplankton carbon biomass (Cphyto) and POC from: i) BGC-Argo float observations; ii) oceanographic cruises, and iii) satellite data. These applications enable highlighting the differences in Cphyto and POC estimates relative to the choice of the method. An analysis of the statistical properties of the dataset and a detailed description of the hypothesis of the work drive the selection of the linear regression method.

Holothuria polii (Delle Chiaje, 1823) is a common Mediterranean sea cucumber and has recently become a highly exploited target species destined mainly to the Asian markets. Unregulated harvesting is putting the natural stocks at risk, with potential consequences for the benthic communities to which these organisms belong. In response aquaculture, sea ranching and restocking could become solutions to this issue in the Mediterranean region. In this study, we present the first results on artificial breeding of H. polii, describing the spawning induction, larval development and early juvenile growth in hatchery cultures, our goal is to develop a rearing protocol for this species. The trials were conducted between July and September 2016. Holothuria polii was induced to spawn by testing two different methods. The thermal shock proved a simple and effective method for obtaining healthy gametes from this species. Larval development in H. polii progressed through five stages, reaching the pentactula stage in 10 days. Three different microalgal feeding regimens were tested for larval breeding; the higher feeding concentration 20,000–40,000 cells mL−1 proved to be the best, with 14% of the larvae metamorphosed into settled juveniles by adhering to the artificial substrates at 15 days. Our results indicate that H. polii performs very well in the hatchery rearing during the larval and juvenile phases; this indicates that this species could be a valuable new candidate for aquaculture in the Mediterranean region both for production and for restocking purposes.

Sensitivity analysis applied to Artificial Neural Networks (ANNs) as well as to other types of empirical ecological models allows assessing the importance of environmental predictive variables in affecting species distribution or other target variables. However, approaches that only consider values of the environmental variables that are likely to be observed in real-world conditions, given the underlying ecological relationships with other variables, have not yet been proposed. Here, a constrained sensitivity analysis procedure is presented, which evaluates the importance of the environmental variables considering only their plausible changes, thereby exploring only ecological meaningful scenarios. To demonstrate the procedure, we applied it to an ANN model predicting fish species richness, as identifying relationships between environmental variables and fish species occurrence in river ecosystems is a recurring topic in freshwater ecology. Results showed that several environmental variables played a less relevant role in driving the model output when that sensitivity analysis allowed them to vary only within an ecologically meaningful range of values, i.e. avoiding values that the model would never handle in its practical applications. By comparing percent changes in MSE between constrained and unconstrained sensitivity analysis, the relative importance of environmental variables was found to be different, with habitat descriptors and urbanization factors that played a more relevant role according to the constrained procedure. The ecologically constrained procedure can be applied to any sensitivity analysis method for ANNs, but obviously it can also be applied to other types of empirical ecological models.

The rationale applied for monitoring and managing fisheries is based on the implicit assumption that yield and stocks status is essentially determined by fisheries. Moreover, the fisheries yield is quantified and analyzed in terms of landings with respect to official management area of registration of vessels. In this way, the real area of activity of each fleet is not considered and this prevent an effective spatial analysis of the factors affecting fisheries yield and stocks status. This paper firstly presents a VMS-based reconstruction of the fishing effort and of the area of activity of the Italian trawlers in the Mediterranean Sea. The fishing area of each fleet is then used as a spatial reference to estimate primary productivity rate and gross primary production and to investigate, by using General Additive Models, the effects of trawling effort, primary production and time on fisheries yield, fisheries productivity and overexploitation rate for some key demersal species. The results evidence that the usage of satellite-based information of fishing activities and of primary production, when combined at the real spatial scale of fishing activities, could effectively improve our ability to analyze the response of the ecosystems to these driving forces and allow capturing the main trends of yield, productivity and overexploitation rate of demersal stocks.

Posidonia oceanica is an endemic Mediterranean seagrass that ranks among the most important and valuable species, with regard to both its ecological role and the services it provides. Despite this species is one of the main targets of conservation actions, the current regression trend of P. oceanica is alarming, underlying the urgent need for reliable methods capable of assessing meadows vulnerability. To address this need, we developed a Habitat Suitability Model (HSM) aimed at assessing the vulnerability of P. oceanica meadows in the Italian marine coastal waters using the Random Forest (RF) Machine Learning technique. Building on the current knowledge on both spatial distribution and condition of meadows in the Italian seas, the RF was used as a classifier aimed at modeling the habitat suitability for P. oceanica, rather than for predictive purposes. The assessment of the potentially most vulnerable P. oceanica meadows at increasing risk of regression was performed through the analysis of the RF output. The HSM showed a good level of accuracy, i.e. Cohen’s K = 0.685. The proposed approach provided valuable information regarding the vulnerability of P. oceanica meadows over the Italian marine coastal waters. In addition, an evaluation of the relative importance of the predictors was carried out using the permutation measure. The developed HSM can support conservation and monitoring programs regarding this species playing a crucial role in the marine ecosystems of the Mediterranean Sea.

Marine litter has significant ecological, social and economic impacts, ultimately raising welfare and conservation concerns. Assessing marine litter hotspots or inferring potential areas of accumulation are challenging topics of marine research. Nevertheless, models able to predict the distribution of marine litter on the seabed are still limited. In this work, a set of Artificial Neural Networks were trained to both model the effect of environmental descriptors on litter distribution and estimate the amount of marine litter in the Central Mediterranean Sea. The first goal involved the use of self-organizing maps in order to highlight the importance of environmental descriptors in affecting marine litter density. The second goal was achieved by developing a multilayer perceptron model, which proved to be an efficient method to estimate the regional quantity of seabed marine litter. Results demonstrated that machine learning could be a suitable approach in the assessment of the marine litter issues.

Marine phytoplankton primary production is an extremely important process and its estimates play a major role not only in biological oceanography, but also in a broader context, due to its relationship with oceanic food webs, energy fluxes, carbon cycle and Earth’s climate. The measurement of this process is both expensive and time consuming. Therefore, indirect methods, which can estimate phytoplankton primary production using only remotely sensed predictive information, have many advantages. We describe the development of a depth-resolved model based on an Artificial Neural Network for estimating global phytoplankton primary production. Furthermore, we applied two different approaches, based on input perturbation analysis and on connection weights, to assess the relative importance of the predictive variables. Finally, we compared the results of our depth-resolved model with a previous depth-integrated solution, showing that through the depth-resolution we gained not only useful information on the vertical distribution of the estimated primary production, but also an enhanced accuracy in its depth-integrated estimates.

Knowledge of the vertical structure of the bio-chemical properties of the ocean is crucial for the estimation of primary production, phytoplankton distribution, and biological modelling. The vertical profiles of chlorophyll-a (Chla) are available via in situ measurements that are usually quite rare and not uniformly distributed in space and time. Therefore, obtaining estimates of the vertical profile of the Chla field from surface observations is a new challenge. In this study, we employed an Artificial Neural Network (ANN) to reconstruct the 3-Dimensional (3D) Chla field in the Mediterranean Sea from surface satellite estimates. This technique is able to reproduce the highly nonlinear nature of the relationship between different input variables. A large in situ dataset of temperature and Chla calibrated fluorescence profiles, covering almost all Mediterranean Sea seasonal conditions, was used for the training and test of the network. To separate sources of errors due to surface Chla and temperature satellite estimates, from errors due to the ANN itself, the method was first applied using in situ surface data and then using satellite data. In both cases, the validation against in situ observations shows comparable statistical results with respect to the training, highlighting the feasibility of applying an ANN to infer the vertical Chla field from surface in situ and satellite estimates. We also analyzed the usefulness of our approach to resolve the Chla prediction at small temporal scales (e.g., day) by comparing it with the most widely used Mediterranean climatology (MEDATLAS). The results demonstrated that, generally, our method is able to reproduce the most reliable profile of Chla from synoptical satellite observations, thus resolving finer spatial and temporal scales with respect to climatology, which can be crucial for several marine applications. We demonstrated that our 3D reconstructed Chla field could represent a valid alternative to overcome the absence or discontinuity of in situ sampling. © 2018 by the authors.

Species distribution is the result of complex interactions that involve environmental parameters as well as biotic factors. However, methodological approaches that consider the use of biotic variables during the prediction process are still largely lacking. Here, a cascaded Artificial Neural Networks (ANN) approach is proposed in order to increase the accuracy of fish species occurrence estimates and a case study for Leucos aula in NE Italy is presented as a demonstration case. Potentially useful biotic information (i.e. occurrence of other species) was selected by means of tetrachoric correlation analysis and on the basis of the improvements it allowed to obtain relative to models based on environmental variables only. The prediction accuracy of the L. aula model based on environmental variables only was improved by the addition of occurrence data for A. arborella and S. erythrophthalmus. While biotic information was needed to train the ANNs, the final cascaded ANN model was able to predict L. aula better than a conventional ANN using environmental variables only as inputs. Results highlighted that biotic information provided by occurrence estimates for non-target species whose distribution can be more easily and accurately modeled may play a very useful role, providing additional predictive variables to target species distribution models.

Posidonia oceanica meadows host a huge number of shoots and their dynamics is strictly related to the spatial distribution patterns of those shoots. To investigate the structure of P. oceanica meadows at very small spatial scale (i.e. in the 100–102 cm range), point patterns of shoot micro-distribution were analyzed. Spatial distribution of shoots was recorded by cutting all the leaves and by digitizing shoot location from pictures of square frames (1 m2) that were randomly positioned in seemingly uniformly dense stands. Ten frames were sampled, all from Southern Italian meadows, and the position of 7828 shoots was recorded. Nearest neighbour distance (NNd) statistics revealed recurring patterns at the different spatial scales: regular patterns were recognized among shoots at smaller spatial scale (100–101 cm), while aggregated shoot distribution emerged in the 101–102 cm range and an important stochastic component was observed at larger spatial scales. Reasons underpinning different spatial point patterns in P. oceanica meadows were discussed by relating the observed patterns to ecological processes (i.e. competition among shoots, role of “species-specific” drivers or “site-specific” features), also including relationships between shoot NNd and shoot density counts. The raw data, provided as supplementary material, are currently the first and the only source of information available about shoot spatial micro-distribution. In this regard, although our data set cannot represent the whole spectrum of variability in P. oceanica meadows, it can be regarded as a first step towards a better knowledge of small scale shoot point patterns in P. oceanica meadows.

Studying taxonomic and ecological diversity of phytoplankton assemblages is often difficult because morphological analysis cannot provide a complete description of their composition. Therefore, more robust and feasible approaches have to be chosen to elucidate the interactions between environmental and human pressures and phytoplankton assemblages. The Ocean Sampling Day (OSD) allowed collecting seawater samples from a wide range of oceanic regions including the Mediterranean Sea. In this study, a total of 754,167 V4-18S ribosomal DNA (rDNA) metabarcodes derived from 20 plankton samples collected at 19 sampling sites across the coastal areas of the Mediterranean Sea were analyzed to explore the relationships between phytoplankton assemblages' composition, sub-regional environmental features and human pressures. We reduced the whole set of autotroph plankton (1398 OTUs) to a smaller number of ecologically relevant entities (205 taxa) and used the latter for analysing the structure of phytoplankton assemblages. Chaetoceros was the only genus occurring in all the samples, while the number of taxa was maximum in the W Mediterranean. Based on the assigned OTUs, the structure of E Mediterranean phytoplankton was the most homogeneous. Further, phytoplankton assemblages from the three Mediterranean sub-regions (Western, Adriatic and Eastern) were significantly different (R=0.25, p=0.0136) based on Jaccard similarity. We also observed that phytoplankton diversity and human impact on marine ecosystems were not significantly related to each other based on Mantel's test.

The understanding of the first movements of the ancestral populations within the African continent is still unclear, particularly in West Africa, due to several factors that have shaped the African genetic pool across time. To improve the genetic representativeness of the Beninese population and to better understand the patterns of human settlement inside West Africa and the dynamics of peopling of the Democratic Republic of Benin, we analyzed the maternal genetic variation of 193 Beninese individuals belonging to Bariba, Berba, Dendi, and Fon populations. Results support the oral traditions indicating that the western neighbouring populations have been the ancestors of the first Beninese populations, and the extant genetic structure of the Beninese populations is most likely the result of admixture between populations from neighbouring countries and native people. The present findings highlight how the Beninese populations contributed to the gene pool of the extant populations of some American populations of African ancestry. This strengthens the hypothesis that the Bight of Benin was not only an assembly point for the slave trade during the Trans-Atlantic Slave Trade but also an important slave trapping area. ispartof: Annals of Human Genetics vol:81 issue:2 pages:78-90 ispartof: location:England status: published

Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments.

Logbook data constitute a key element within the electronic recording and reporting system of the European Fisheries Control Technologies Framework and are used to record, report, process, store and send information about fishing operations, including landings and fishing gear. A relevant application of logbook data is to account for the heterogeneity of fishing practices (e.g., by gear or metier), which is a key aspect of the Common Fishery Policy. However, despite their importance, few published studies have explored the potential and pitfalls of logbook data, even in combination with other powerful data sources such as the Vessel Monitoring System (VMS). Here, a new approach to characterizing the composition of landings for the different types of gear based on the use of Self-Organizing Maps (SOMs − a particular type of Artificial Neural Network) is applied to the Italian fleet logbook dataset. The SOM is trained on the landings composition and the resulting patterns are interpreted using some measures obtained from the analysis of the corresponding VMS data. Namely, the mean sea bottom depth and the area of activity are obtained for each fishing trip. Moreover, the ability of the trained SOM to predict gear from landings is tested using a new dataset. The trained SOM classifies logbook records according to the ecological, taxonomical, and trophic characteristics of the species caught, and the depth of fishing activities plays an important role in diversifying the landings associated with certain widely used fishing gear such as the bottom otter trawl. The clustering of SOM units allows the identification of a set of 12 groups, which are strongly related to the types of gear used by the Italian fleet. Furthermore, the trained SOM shows a high ability to recognize gear from logbook data, thus confirming the robustness of the landings profiles detected.

The estimation and quantification of potentially toxic cyanobacteria in lakes and reservoirs are often used as a proxy of risk for water intended for human consumption and recreational activities. Here, we present data sets collected from three volcanic Italian lakes (Albano, Vico, Nemi) that present filamentous cyanobacteria strains at different environments. Presented data sets were used to estimate abundance and morphometric characteristics of potentially toxic cyanobacteria comparing manual Vs. automated estimation performed by ACQUA (“ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning” (Gandola et al., 2016) [1]). This strategy was used to assess the algorithm performance and to set up the denoising algorithm. Abundance and total length estimations were used for software development, to this aim we evaluated the efficiency of statistical tools and mathematical algorithms, here described. The image convolution with the Sobel filter has been chosen to denoise input images from background signals, then spline curves and least square method were used to parameterize detected filaments and to recombine crossing and interrupted sections aimed at performing precise abundances estimations and morphometric measurements. Keywords: Comparing data, Filamentous cyanobacteria, Algorithm, Deoising, Natural sample

The regional dynamics of industrial fisheries within Large Marine Ecosystems (LMEs) boundaries were investigated by means of a historical-descriptive approach. Landings data from the Sea Around Us Project database were used to detect trends in total yields and variations in landings composition by functional groups over time. The temporal and spatial scales covered by this study allowed general issues to be addressed such as the detection of recurrent patterns and synchronies in fisheries landings. An unsupervised artificial neural network, Self Organizing Map (SOM), is used as a tool to analyze fisheries landings composition variation over five decades in 51 LMEs all over the world. From the historical analysis of “fishing behaviors” within LMEs a broad distinction between two main types of fisheries emerged: (1) small and medium pelagics fisheries, with stable compositions or cyclic behaviors, occurred in LMEs which share common productive features, despite different geographical locations and (2) demersal fisheries, which are more affected by economic drivers and tend to concentrate in LMEs in the Northern Hemisphere. Our analysis can be regarded as a first step towards the challenging scope of describing the relative influence of environmental and economic drivers on exploited ecosystems.