Renato De Giovanni

Bio: Renato De Giovanni is an academic researcher. The author has contributed to research in topics: Environmental niche modelling & Workflow. The author has an hindex of 13, co-authored 29 publications receiving 1351 citations.

Darwin Core: an evolving community-developed biodiversity data standard.

Abstract: Biodiversity data derive from myriad sources stored in various formats on many distinct hardware and software platforms. An essential step towards understanding global patterns of biodiversity is to provide a standardized view of these heterogeneous data sources to improve interoperability. Fundamental to this advance are definitions of common terms. This paper describes the evolution and development of Darwin Core, a data standard for publishing and integrating biodiversity information. We focus on the categories of terms that define the standard, differences between simple and relational Darwin Core, how the standard has been implemented, and the community processes that are essential for maintenance and growth of the standard. We present case-study extensions of the Darwin Core into new research communities, including metagenomics and genetic resources. We close by showing how Darwin Core records are integrated to create new knowledge products documenting species distributions and changes due to environmental perturbations.

openModeller: a generic approach to species' potential distribution modelling

Abstract: Species' potential distribution modelling is the process of building a representation of the fundamental ecological requirements for a species and extrapolating these requirements into a geographical region. The importance of being able to predict the distribution of species is currently highlighted by issues like global climate change, public health problems caused by disease vectors, anthropogenic impacts that can lead to massive species extinction, among other challenges. There are several computational approaches that can be used to generate potential distribution models, each achieving optimal results under different conditions. However, the existing software packages available for this purpose typically implement a single algorithm, and each software package presents a new learning curve to the user. Whenever new software is developed for species' potential distribution modelling, significant duplication of effort results because many feature requirements are shared between the different packages. Additionally, data preparation and comparison between algorithms becomes difficult when using separate software applications, since each application has different data input and output capabilities. This paper describes a generic approach for building a single computing framework capable of handling different data formats and multiple algorithms that can be used in potential distribution modelling. The ideas described in this paper have been implemented in a free and open source software package called openModeller. The main concepts of species' potential distribution modelling are also explained and an example use case illustrates potential distribution maps generated by the framework.

Comparing machine learning classifiers in potential distribution modelling

Abstract: Research highlights? We employ Machine Learning techniques in species' potential distribution modelling. ? The distribution of 35 species from Latin America was modelled by classifiers. ? The best performance in potential distribution modeling was achieved by random trees. Species' potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species' potential distribution.

Global biodiversity informatics: setting the scene for a "new world" of ecological modeling

Abstract: Recent developments in information and communication technology are allowing new experiences in the integration, analysis and visualization of biodiversity information, and are leading to development of a new field of research, biodiversity informatics. Although this field has great potential in diverse realms, including basic biology, human economics, and public health, much of this potential remains to be explored. The success of several concerted international efforts depends largely on broad deployment of biodiversity informatics information and products. Several global and regional efforts are organizing and providing data for conservation and sustainable development research, including the Global Biodiversity Information Facility, the European Biodiversity Information Network, and the Inter-American Biodiversity Information Network. Critical to development of this field is building a biodiversity information infrastructure, making primary biodiversity data freely and openly available over the Internet. In addition to specimen and taxonomic data, access to non-biological environmental data is critical to spatial analysis and modeling of biodiversity. Adoption of standards and protocols and development of tools for collection management, data- cleaning, georeferencing, and modeling tools, are allowing a quantum leap in the area. Open access to research data and open-source tools are leading to a new era of web services and computational frameworks for spatial biodiversity analysis, bringing new opportunities and dimensions to novel approaches in ecological analysis, predictive modeling, and synthesis and visualization of biodiversity information.

Challenges and perspectives for species distribution modelling in the neotropics

Abstract: The workshop ‘ Species distribution models: applications, challenges and perspectives ’ held at Belo Horizonte (Brazil), 29–30 August 2011, aimed to review the state-of-the-art in species distribution modelling (SDM) in the neotropical realm It brought together researchers in ecology, evolution, biogeography and conservation, with different backgrounds and research interests The application of SDM in the megadiverse neotropics—where data on species occurrences are scarce—presents several challenges, involving acknowledging the limitations imposed by data quality, including surveys as an integral part of SDM studies, and designing the analyses in accordance with the question investigated Specific solutions were discussed, and a code of good practice in SDM studies and related field surveys was drafted

Predicting species distributions for conservation decisions

Abstract: Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

Diversity of life

Abstract: It is clear that the above can lead to confusion when scientists of different countries are trying to communicate with each other. Another example is the burrowing rodent called a gopher found throughout the western United States. In the southeastern United States the term gopher refers to a burrowing turtle very similar to the desert tortoise found in the American southwest. One final example; two North American mammals known as the elk and the caribou are known in Europe as the reindeer and the elk. We never sing “Rudolph the Red-nosed elk”! Confused? This was the reason for creating an internationally recognized system of naming organisms. To avoid confusion, living organisms are assigned a scientific name based on Latin or Latinized words. The English sparrow is Passer domesticus or Passer domesticus (italics or underlining these two names is the official written representation of a scientific name). Using a uniform naming system allows scientists from all over the world to recognize exactly which life form a scientist is referring to. The naming process is called the binomial system of nomenclature. Passer is comparable to a surname and is called the genus, while domesticus is the specific or species name (like your given name) of the English sparrow. Now scientists can give all sparrow-like birds the genus Passer but the species name will vary. All similar genera (plural for genus) can be grouped into another, “higher” category (see below). Study the following for a more through understanding of taxonomy. Taxonomy Analogy Kingdom: Animalia Country

Darwin Core: an evolving community-developed biodiversity data standard.

Abstract: Biodiversity data derive from myriad sources stored in various formats on many distinct hardware and software platforms. An essential step towards understanding global patterns of biodiversity is to provide a standardized view of these heterogeneous data sources to improve interoperability. Fundamental to this advance are definitions of common terms. This paper describes the evolution and development of Darwin Core, a data standard for publishing and integrating biodiversity information. We focus on the categories of terms that define the standard, differences between simple and relational Darwin Core, how the standard has been implemented, and the community processes that are essential for maintenance and growth of the standard. We present case-study extensions of the Darwin Core into new research communities, including metagenomics and genetic resources. We close by showing how Darwin Core records are integrated to create new knowledge products documenting species distributions and changes due to environmental perturbations.

International Code of Zoological Nomenclature

Abstract: DURING this year I have so often been asked how this Code could be obtained that I hasten, with your permission, to announce that the Washington Biological Society has just published a reprint at the price of one dollar. Prof. C. W. Stiles, secretary to the Commission, says: “I would suggest that, if your colleagues wish copies, it would expedite matters to order a number at once”. The address of the Society is at the Bureau of Entomology, Washington, D.C., U.S.A.

Locating pleistocene refugia: comparing phylogeographic and ecological niche model predictions.

Abstract: Ecological niche models (ENMs) provide a means of characterizing the spatial distribution of suitable conditions for species, and have recently been applied to the challenge of locating potential distributional areas at the Last Glacial Maximum (LGM) when unfavorable climate conditions led to range contractions and fragmentation. Here, we compare and contrast ENM-based reconstructions of LGM refugial locations with those resulting from the more traditional molecular genetic and phylogeographic predictions. We examined 20 North American terrestrial vertebrate species from different regions and with different range sizes for which refugia have been identified based on phylogeographic analyses, using ENM tools to make parallel predictions. We then assessed the correspondence between the two approaches based on spatial overlap and areal extent of the predicted refugia. In 14 of the 20 species, the predictions from ENM and predictions based on phylogeographic studies were significantly spatially correlated, suggesting that the two approaches to development of refugial maps are converging on a similar result. Our results confirm that ENM scenario exploration can provide a useful complement to molecular studies, offering a less subjective, spatially explicit hypothesis of past geographic patterns of distribution.