plantR: An R package and workflow for managing species records from biological collections

Species records from biological collections are becoming increasingly available online. This unprecedented availability of records has largely supported recent studies in taxonomy, biogeography, macroecology and biodiversity conservation. Biological collections vary in their documentation and notation standards, which have changed through time. For different reasons, neither collections nor data repositories perform the editing, formatting and standardisation of the data, leaving these tasks to the final users of the species records (e.g. taxonomists, ecologists and conservationists). These tasks are challenging, particularly when working with millions of records from hundreds of biological collections. To help collection curators and final users perform those tasks, we introduce plantR, an open-source package that provides a comprehensive toolbox to manage species records from biological collections. The package is accompanied by the proposal of a reproducible workflow to manage this type of data in taxonomy, ecology and biodiversity conservation. It is implemented in R and designed to handle relatively large datasets as fast as possible. Initially designed to handle plant species records, many of the plantR features also apply to other groups of organisms, given that the data structure is similar. The plantR workflow includes tools to (a) download records from different data repositories, (b) standardise typical fields associated with species records, (c) validate the locality, geographical coordinates, taxonomic nomenclature and species identifications, including the retrieval of duplicates across collections, and (d) summarise and export records, including the construction of species lists with vouchers. Other R packages provide tools to tackle some of the workflow steps described above. But in addition to the new tools and resources related to data standardisation and validation, the greatest strength of plantR is to provide a comprehensive and user-friendly workflow in one single environment, performing all tasks from data retrieval to export. Thus, plantR can help researchers better assess data quality and avoid data leakage in a wide variety of studies using species records

Comparing machine learning classifiers in potential distribution modelling

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. (C) 2010 Elsevier Ltd. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo)CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Comprehensive conservation assessments reveal high extinction risks across Atlantic Forest trees

International audienceBiodiversity is declining globally, yet many biodiversity hotspots still lack comprehensive species conservation assessments. Using multiple International Union for Conservation of Nature (IUCN) Red List criteria to evaluate extinction risks and millions of herbarium and forest inventory records, we present automated conservation assessments for all tree species of the Atlantic Forest biodiversity hotspot, including ~1100 heretofore unassessed species. About 65% of all species and 82% of endemic species are classified as threatened. We rediscovered five species classified as Extinct on the IUCN Red List and identified 13 endemics as possibly extinct. Uncertainties in species information had little influence on the assessments, but using fewer Red List criteria severely underestimated threat levels. We suggest that the conservation status of tropical forests worldwide is worse than previously reported

Challenges and perspectives for species distribution modelling in the neotropics

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

Biodiversity conservation : uncertainty in predictions of extinction risk/ effects of changes in climate and land use/ climate change and extinction risk (reply)

Thomas et al. reply — We reconsider our estimates of climate-related extinction in the light of three questions raised by Thuiller et al., Buckley and Roughgarden and Harte et al. We are able to confirm our original conclusion that climate change represents a major threat to terrestrial species.2 page(s

Extinction risk from climate change

Climate change over the past 30 years has produced numerous shifts in the distributions and abundances of species and has been implicated in one species-level extinction. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15–37% of species in our sample of regions and taxa will be 'committed to extinction'. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction (18%) than mid-range (24%) and maximum-change (35%) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration