953 research outputs found
Marine Strategy, una sfida ed un'opportunità per la Biologia Marina italiana
The Marine Strategy Framework Directive, which came into force in 2008, can be
regarded as the environmental pillar for the Integrated European Maritime Policy. In the first phase of
its implementation EU member Countries carried out an initial assessment of the ecological status, set
environmental targets and defined the concept of Good Ecological Status. While marine biologists from
Italian Universities and other research Institutions actively participated in this process, new challenges
will be brought by its next phases, requiring a deeper involvement of the scientific community and a truly
holistic approach
Modeling Posidonia oceanica shoot density and rhizome primary production
Posidonia oceanicameadows 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. oceanicaecosystem 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 (R
2
= 0.761 and R
2
= 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
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
Applications of Self-Organizing Maps for Ecomorphological Investigations through Early Ontogeny of Fish
We propose a new graphical approach to the analysis of multi-temporal morphological and ecological data concerning the life history of fish, which can typically serves models in ecomorphological investigations because they often undergo significant ontogenetic changes. These changes can be very complex and difficult to describe, so that visualization, abstraction and interpretation of the underlying relationships are often impeded. Therefore, classic ecomorphological analyses of covariation between morphology and ecology, performed by means of multivariate techniques, may result in non-exhaustive models. The Self Organizing map (SOM) is a new, effective approach for pursuing this aim. In this paper, lateral outlines of larval stages of gilthead sea bream (Sparus aurata) and dusky grouper (Epinephelus marginatus) were recorded and broken down using by means of Elliptic Fourier Analysis (EFA). Gut contents of the same specimens were also collected and analyzed. Then, shape and trophic habits data were examined by SOM, which allows both a powerful visualization of shape changes and an easy comparison with trophic habit data, via their superimposition onto the trained SOM. Thus, the SOM provides a direct visual approach for matching morphological and ecological changes during fish ontogenesis. This method could be used as a tool to extract and investigate relationships between shape and other sinecological or environmental variables, which cannot be taken into account simultaneously using conventional statistical methods
The challenge of improving evidence-based therapy adherence in the secondary prevention of coronary artery disease: the next frontier of cardiac rehabilitation
Non-adherence to prescribed drug regimens is an increasing medical problem affecting physicians and patients and contribute to negative outcomes, such as the increased risk of subsequent cardiovascular events. Analysis of various patient populations shows that the choice of drug, its tolerability and the duration of treatment influence the non-adherence. Intervention is required toward patients and health-care providers to improve medication adherence. This review deals about the prevalence of non-adherence to therapy after medical and surgical cardiac event, the risk factors affecting non-adherence and the strategies to implement it. Interventions that may successfully improve adherence should include improved physician compliance with guidelines, patient education and patient reminders, frequent visits or telephone calls from staff, simplification of the patient's drug regimen by reducing the number of pills and daily doses. Since single interventions do not appear efficaceous, it is necessary to establish multiple interventions simultaneously addressing a number of barriers to adherence
Long-term changes and recurrent patterns in fisheries landings from Large Marine Ecosystems (1950–2004)
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 unsuper-vised 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
- …