Marinez Ferreira de Siqueira

Bio: Marinez Ferreira de Siqueira is an academic researcher from University of York. The author has contributed to research in topics: Environmental niche modelling & Biodiversity. The author has an hindex of 17, co-authored 48 publications receiving 8068 citations.

Taxonomy, conservation, geographic and potential distribution of Macrotoru s Perkins (Mollinedioideae, Monimiaceae), and a key to the Neotropical genera of Monimiaceae

Abstract: Macrotorus is a dioecious, monotypic genus that is endemic to the Brazilian Atlantic rainforest, where it occurs in lowland, submontane and montane forests; different from most other Monimiaceae in Atlantic rainforest, which are restricted to dense, humid, montane forests. Here, an updated description of Macrotorus utriculatus is provided, including the first description of the pistillate flowers, together with the lectotypification of the species, comparison with related genera, its conservation status, geographic distribution, and modeling of potential distribution. We also provide an identification key to the Neotropical genera of Monimiaceae.

Potential Distribution Modelling Using Machine Learning

Abstract: Potential distribution modelling has been widely used to predict and to understand the geographical distribution of species. These models are generally produced by retrieving the environmental conditions where the species is known to be present or absent and feeding this data into a modelling algorithm. This paper investigates the use of Machine Learning techniques in the potential distribution modelling of plant species Stryphnodendron obovatumBenth (MIMOSACEAE). Three techniques were used: Support Vector Machines, Genetic Algorithms and Decision Trees. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species being considered.

A Survey of e-Biodiversity: Concepts, Practices, and Challenges.

Abstract: The unprecedented size of the human population, along with its associated economic activities, have an ever increasing impact on global environments. Across the world, countries are concerned about the growing resource consumption and the capacity of ecosystems to provide them. To effectively conserve biodiversity, it is essential to make indicators and knowledge openly available to decision-makers in ways that they can effectively use them. The development and deployment of mechanisms to produce these indicators depend on having access to trustworthy data from field surveys and automated sensors, biological collections, molecular data, and historic academic literature. The transformation of this raw data into synthesized information that is fit for use requires going through many refinement steps. The methodologies and techniques used to manage and analyze this data comprise an area often called biodiversity informatics (or e-Biodiversity). Biodiversity data follows a life cycle consisting of planning, collection, certification, description, preservation, discovery, integration, and analysis. Researchers, whether producers or consumers of biodiversity data, will likely perform activities related to at least one of these steps. This article explores each stage of the life cycle of biodiversity data, discussing its methodologies, tools, and challenges.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Novel methods improve prediction of species' distributions from occurrence data

Abstract: Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

Predicting species distribution: offering more than simple habitat models.

Abstract: In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.