A Machine learning approach to the study of a red coral <i>Corallium rubrum</i> (l.) population = Un'Applicazione del machine learning per lo studio di una popolazione di corallo rosso <i>Corallium rubrum</i> (L.)

This study deals with the application of a machine learning algorithm (a classification tree) to assess the weight of Corallium rubrum (Cnidaria, Octocorallia) ramifications on the basis of the number of apices. Our approach can be easily applied to obtain in situ estimates of weight and basal diameter of colonies. Future developments include the integration with image acquisition and processing hardware

A machine learning approach to the assessment of the vulnerability of Posidonia oceanica meadows

In this study, we adopted a modelling approach to assess the vulnerability of Posidonia oceanica meadows, the most widespread seagrass in Mediterranean Sea. P. oceanica has a crucial ecological role all over the basin. In fact, this seagrass is a habitat-forming species that can extend from the surface to 45 m depth, forming meadows. These meadows rank among the most valuable ecosystem in the Mediterranean Sea, in term of the services they provide. However, in areas where alterations of environmental conditions happened, regression of the meadows may occur. Despite it is one of the main targets of conservation actions all over the basin, P. oceanica is declining at alarming rate, especially due to the anthropogenic impacts. Thereby, there is a urgent need to study the effects of environmental factors that could affect its ecological status. We used a Random Forest for developing a Habitat Suitability Model (HSM) for P. oceanica in the Italian seas. The use of HSMs has been especially promoted to support ecosystem assessment and conservation planning, since they allow to better understand both the habitat requirements and the potential distribution of species. Since the spatial distribution of meadows in Italian seas is already known, we used the HSM predictions to evaluate the suitability of the habitat for P. oceanica at large spatial scale and, consequently, we assessed the vulnerability of the meadows. Particularly, our occurrence data included both areas were P. oceanica was known as living and regressed meadows. After the RF training, we validated the model using an independent test set and we evaluated the performance using both ROC curve and K statistic. The results showed that the HSM presented a quite good level of accuracy. Thus, we carried out a spatial analysis of the HSM predictions in relation to the actual ecological status of P. oceanica. The results showed that in areas where living meadows actually occurred, high habitat suitability predictions were significantly more frequent. On the contrary, where regressed meadows were actually observed, predictions indicated low habitat suitability for P. oceanica. This study stressed that modeling can effectively support the assessment of ecosystem status as well as conservation actions

A constrained depth-resolved artificial neural network model of marine phytoplankton primary production

Marine phytoplankton primary production is a process of paramount importance not only in biological oceanography, but also in a wider perspective, due to its relationship 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 solution. We developed a depth-resolved model of marine phytoplankton primary production using an Artificial Neural Network, namely a three-layer perceptron trained with the Error Back-Propagation algorithm. Despite numerous variables could be useful to estimate primary production, we chose to use predictive variables that can be acquired by remote sensing in order to enhance the practical value of the model. Indeed, using exclusively this type of predictors in combination with a depth-resolved approach allows to expand the two-dimensional view from satellite images to the estimated three-dimensional distribution of phytoplankton primary production. Since the vertically integrated values of this process are the basis for any connection to other levels of the pelagic food web, it is worth noting that, once integrated, the primary production estimates of this depth-resolved model are more accurate than those obtained from a similar vertically integrated approach. We also tried to improve the accuracy of the primary production estimates using constraints during the training procedure. Those constraints were based on theoretical knowledge of the marine photosynthesis process. Accordingly, the training phase has been modified in order to add penalty terms to the solutions which were not compliant with the constraints. For instance, one of the constraints acts as a selection tool for the shape of the modelled production profile. The above-mentioned approach not only enhanced the ecological soundness of the artificial neural network predictions. In fact, the constrained version of the model also explained a larger share of variance than the original one

Phytoplankton RNA/DNA and 18S rRNA/rDNA ratios in a coastal marine ecosystem

The RNA/DNA ratio is used as indicator of growth in various marine organisms and to assess physiological status at species or community level. To evaluate the utility of the RNA/DNA ratio as a proxy of phytoplankton primary production, the relationships between phytoplankton RNA/DNA, taxon-specific diatom and dinoflagellate 18S rRNA/rDNA ratios and autotrophic phytoplankton biomass were investigated as a first step. Significant correlations between all phytoplankton ratios and total phytoplankton, diatom and dinoflagellate biomass as chlorophyll a (chl a) and carbon content were found. Diatoms showed higher correlation than dinoflagellates (18S rRNA/rDNA vs. chl a, rs =0.74 and 0.64, P <0.001; 18S rRNA/rDNA vs. carbon, rs =0.66 and 0.53, P <0.001, respectively), because they represented the most abundant and frequent group within sampled assemblages. Further, phytoplankton biomass production is known to be linked to protein biosynthesis and significant relationships between RNA/DNA ratios and protein content of phytoplankton assemblage were found (rs =0.62 and 0.52, P <0.001 for diatom and dinoflagellates, respectively). As taxon-specific RNA/DNA ratios were correlated with biomass and protein content, our results can be regarded as the first step toward further studies on the applicability of RNA/DNA ratios as indicators of growth rate and primary production in phytoplankton assemblages

Cascaded neural networks improving fish species prediction accuracy: the role of the biotic information

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 Artifcial Neural Networks (ANN) approach is proposed in order to increase the accuracy of fsh species occurrence estimates and a case study for Leucos aulain 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. aulamodel based on environmental variables only was improved by the addition of occurrence data for A. arborellaand S. erythrophthalmus. While biotic information was needed to train the ANNs, the fnal 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

A model predicting the {PSP} toxic dinoflagellate Alexandrium minutum occurrence in the coastal waters of the {NW} Adriatic Sea

Increased anthropic pressure on the coastal zones of the Mediterranean Sea caused an enrichment in nutrients, promoting microalgal proliferation. Among those organisms, some species, such as the dinofagellate Alexandrium minutum, can produce neurotoxins. Toxic blooms can cause serious impacts to human health, marine environment and economic maritime activities at coastal sites. A mathematical model predicting the presence of A. minutumin coastal waters of the NW Adriatic Sea was developed using a Random Forest (RF), which is a Machine Learning technique, trained with molecular data of A. minutumoccurrence obtained by molecular PCR assay. The model is able to correctly predict more than 80% of the instances in the test data set. Our results showed that predictive models may play a useful role in the study of Harmful Algal Blooms (HAB)

Population status, distribution and trophic implications of Pinna nobilis along the South-eastern Italian coast

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

A thirty-nine-year survival with the Starr-Edwards mitral valve prosthesis

We report a case of a 57 year-old woman with Starr- Edwards model 6120 mitral valve replacement and Kay- Shiley bioprosthetic tricuspid valve replacement in 1968 at Niguarda Hospital in Milan. The mitral caged-ball has proved its excellent durability and its good hemodynamic performance in many patients, even if subject to high tendency to thrombosis. In literature there is no evidence of durability of this prosthesis longer than 35 years. Our patient after 39 years from mitral valve replacement lives a happy and fulfilling life (NYHA II), with no evidence of hemolysis, ball variance, symptomatic embolization or major bleeding

Composition of Arthropod Species Assemblages in Bt-expressing and Near Isogenic Eggplants in Experimental Fields

The environmental impact of genetically modified (GM) plants in experimental fields has been examined in several ways, in particular with respect to the dynamics of specific nontarget organisms. The approach of sampling for biodiversity in agroecosystems to compare complex patterns could also be useful in studying potential disruptions caused by GM crops. In this study, we set up replicated field plots of Bt-expressing eggplants and near isogenic untransformed eggplants as a control. We monitored the presence and abundance of herbivore and predator arthropods in weekly visual samplings of the plant canopy for three growing seasons (2001-2003). Insect species were pooled in organismal taxonomic units (OTUs); three multivariate methods were used to compare species assemblage as an estimate of insect biodiversity. This multistep statistical approach proved to be efficient in recognizing association patterns, as evidenced by the data for the target species Leptinotarsa decemlineata Say (Coleoptera: Chrysomelidae) clearly showing a significant association with the control plots. All the analyses indicate a comparable species assemblage between transgenic and near isogenic eggplant areas. Our results suggest that some taxa may warrant more specific study. For example, Alticinae beetles (Coleoptera: Chrysomelidae) were alternatively more abundant in either of the two treatments, and their overall abundance was significantly higher on transgenic eggplants. In light of these results and because of their taxonomic proximity to the target species, these herbivores may represent an important nontarget group to be further studied. Moreover, some sap feeders (e.g., Homoptera: Cicadellidae) were more abundant on Bt-expressing plants in some samples in all 3 y

Trophic Requirements of the Sea Urchin Paracentrotus lividus Varies at Different Life Stages: Comprehension of Species Ecology and Implications for Effective Feeding Formulations

Investigations on trophic requirements of different life cycle stages of Paracentrotus lividus are crucial for the comprehension of species ecology and for its artificial rearing. The future success of echinoculture depends heavily on the development of suitable and cost-effective diets that are specifically designed to maximize somatic growth during the early life stages and gonadal production in the later stages. In this context, a considerable number of studies have recommended animal sources as supplements in sea urchin diets. However, with the exception of Fernandez and Boudouresque (2000), no studies have investigated the dietary requirements over the different life stages of the sea urchin. In the present study, the growth and nutrition of three life stages of P. lividus (juveniles: 15-25 mm; subadults: 25-35 mm; adults: 45-55 mm) were analyzed over a 4-month rearing experiment. Three experimental diets, with 0%, 20% and 40% of animal sourced enrichments, were tested in parallel in sea urchin three size classes. The food conversion ratio, somatic and gonadal growth were assessed in each condition in order to evaluate the optimal level of animal-sourced supplements for each life stage. A general growth model covering the full post-metamorphic P. lividus life cycle was defined for each condition. During the juvenile stage P. lividus requires higher animal supply (40%), while a feeding requirement shift takes place toward lower animal supply (20%) in sub-adult and adult stages. Our results evidenced that the progressive increase in size after the metamorphosis led to a consequent variation of trophic requirements and food energy allocation in the sea urchin P. lividus. Macronutrient requirements varied widely during the different life stages, in response to changes in the energy allocation from somatic growth to reproductive investment. This study sheds light on P. lividus trophic ecology, broadening our basic knowledge of the dietary requirements of juveniles, sub-adults and adults as a function of their behavior also in the natural environment