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.

Seleção de fragmentos prioritários para a criação de unidades de conservação do cerrado no estado de São Paulo

Abstract: Dois workshops foram realizados nos últimos anos para a indicação de áreas de Cerrado prioritárias para a conservação deste bioma, um deles exclusivo para o Estado de São Paulo. Cada área indicada por esses workshops, porém, geralmente contém um grande número de fragmentos e, naturalmente, não é possível transformar todos eles em unidades de conservação. O objetivo do presente estudo foi estabelecer critérios e indicadores, aplicando-os para a seleção de fragmentos remanescentes com alta prioridade para a conservação do Cerrado no Estado de São Paulo. Considerando que São Paulo já possui uma rede de áreas protegidas e que novas unidades de conservação têm sido criadas individualmente, desenvolveu-se um algoritmo cuja aplicação resultou em uma classificação hierárquica dos principais fragmentos segundo seu valor biológico para a conservação, como ferramenta de suporte à tomada de decisão. Oitenta e seis áreas foram avaliadas, segundo quinze indicadores agrupados em: atributos biofísicos, integridade dos recursos naturais e influências externas, dados estes obtidos em expedições de campo e a partir de interpretação de imagens de satélite, analisados mediante a rede já existente de unidades de conservação. São apresentados e descritos os fragmentos considerados prioritários pelo seu alto valor biológico e também alguns que se destacam por estarem sob forte ameaça.

The Real Task of Selecting Records for Ecological Niche Modelling

Abstract: Biological collections evoke contrasting feelings for being such a vast source of biodiversity data which is prone to all sorts of errors and uncertainties. The situation is not different for Brazilian herbaria, currently sharing more than two million easily accessible records on the Web. Properly dealing with this reality is a crucial task when using this kind of data for ecological niche modelling (ENM), so that errors and uncertainties do not generate misleading results in conservation. Here we investigate some of the issues that can be found in herbarium specimen data, describing a set of automatic procedures that can be used for a prior selection of records for ENM. In total, 11531 records for 135 species of Passifloraceae that natively occur in Brazil were analyzed considering different spatial resolutions, ranging from 30 arc-seconds to 10 arc-minutes. After applying the procedures, the proportion of spatially unique records was 9.3% for the highest resolution considering all species, with an average number of 8 records selected per species. These numbers increased to 17% and 16, respectively, for all other resolutions. This scenario highlights the importance of using data quality filters and further developing ENM presence-only methods that can work with a low number of records per species. Automatic procedures still cannot discard expert review, but they can greatly facilitate it by drawing attention to a much smaller number of records potentially useful for ENM. Most of the data quality procedures described here can also be applied to other taxonomic groups, regions and specimen data sources.

Model-R: A Framework for Scalable and Reproducible Ecological Niche Modeling

Abstract: Spatial analysis tools and synthesis of results are key to identifying the best solutions in biodiversity conservation. The importance of process automation is associated with increased efficiency and performance both in the data pre-processing phase and in the post-analysis of the results generated by the packages and modeling programs. The Model-R framework was developed with the main objective of unifying pre-existing ecological niche modeling tools into a common framework and building a web interface that automates steps of the modeling process and occurrence data retrieval. The web interface includes RJabot, a functionality that allows for searching and retrieving occurrence data from Jabot, the main reference on botanical collections management system in Brazil. It returns data in a suitable format to be consumed by other components of the framework. Currently, the tools are multi-projection, they can thus be applied to different sets of temporal and spatial data. Model-R is also multi-algorithm, with seven algorithms available for modeling: BIOCLIM, Mahalanobis distance, Maxent, GLM, RandomForest, SVM, and DOMAIN. The algorithms as well as the entire modeling process may be parametrized using command-line tools or through the web interface. We hope that the use of this application, not only by modeling specialists but also as a tool for policy makers, will be a significant contribution to the continuous development of biodiversity conservation analysis. The Model-R web interface can be installed locally or on a server. A software container is provided to automate the installation.

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.