Biology subjects

Lima, R. A. F.

Publications and source records attributed to Lima, R. A. F..

4 recordsLinked to original sources

bioRxiv2021

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

O_LISpecies 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 standardization 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. C_LIO_LITo help collection curators and final users perform those tasks, we introduce plantR, an open-source package that provides a comprehensive tool-box 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 data sets 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. C_LIO_LIThe plantR workflow includes tools to (1) download records from different data repositories, (2) standardize typical fields associated with species records, (3) validate the locality, geographical coordinates, taxonomic nomenclature, and species identifications, including the retrieval of duplicates across collections, and (4) summarize and export records, including the construction of species checklists with vouchers. C_LIO_LIOther R packages provide tools to tackle some of the workflow steps described above. But in addition to the new features and resources related to the data editing 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. C_LI

bioinformatics↗

bioRxiv2020

The erosion of biodiversity and biomass in the Atlantic Forest biodiversity hotspot

Tropical forests are being deforested worldwide, and the remaining fragments are suffering from biomass and biodiversity erosion. Quantifying this erosion is challenging because ground data on tropical biodiversity and biomass are often sparse. Here, we use an unprecedented dataset of 1,819 field surveys covering the entire Atlantic Forest biodiversity hotspot. We show that 83-85% of the surveys presented losses in forest biomass and tree species richness, functional traits and conservation value. On average, forest fragments had 25-32% less biomass, 23-31% fewer species, and 33%, 36% and 42% fewer individuals of late-successional, large-seeded and endemic species, respectively. Biodiversity and biomass erosion were both lower inside strictly protected conservation units, particularly in large ones. We estimate that biomass erosion across the Atlantic Forest remnants was equivalent to the loss of 55-70 thousand km2 of forests or US$2.3-2.6 billion in carbon credits. These figures have direct implications on mechanisms of climate change mitigation.

ecology↗

bioRxiv2020

Defining endemism levels for biodiversity conservation: tree species in the Atlantic Forest hotspot

Endemic species are important for biodiversity conservation. Yet, quantifying endemism remains challenging because endemism concepts can be too strict (i.e., pure endemism) or too subjective (i.e., near endemism). We propose a data-driven approach to objectively estimate the proportion of records inside a given the target area (i.e., endemism level) that optimizes the separation of near-endemics from non-endemic species. We apply this approach to the Atlantic Forest tree flora using millions of herbarium records retrieved from multiple sources. We first report an updated checklist of 5044 species for the Atlantic Forest tree flora and then we compare how species-specific endemism levels obtained from herbarium data match species-specific endemism accepted by taxonomists. We show that an endemism level of 90% separates well pure and near-endemic from non-endemic species, which in the Atlantic Forest revealed an overall endemism ratio of 45% for its tree flora. We also found that the diversity of pure and near endemics and of endemics and overall species was congruent in space. Our results for the Atlantic Forest reinforce that pure and near endemic species can be combined to quantify regional endemism and therefore to set conservation priorities taking into account endemic species distribution. We provided general guidelines on how the proposed approach can be used to assess endemism levels of regional biotas in other parts of the world.

ecology↗

bioRxiv2020

Disentangling the effects of sampling scale and size on the shape of species abundance distributions

Many authors have tried to explain the shape of the species abundance distribution (SAD). Some of them have suggested that sampling scale is an important factor shaping SADs. These suggestions, however, did not consider the indirect and well-known effect of sample size, which increases as samples are combined to generate SADs at larger scales. Here, we separate the effects of sample size and sampling scale on the shape of the SAD for three groups of organisms (trees, beetles and birds) sampled in the Brazilian Atlantic Forest. We compared the observed SADs at different sampling scales with simulated SADs having the same richness, relative abundances but comparable sample sizes, to show that the main effect shaping SADs is sample size and not sampling scale. The effect of scale was minor and deviations between observed and simulated SADs were present only for beetles. For trees, the match between observed and simulated SADs was improved at all scales when we accounted for conspecific aggregation, which was even more important than the sampling scale effect. We build on these results to propose a conceptual framework where observed SADs are shaped by three main factors, in decreasing order of importance: sample size, conspecific aggregation and beta diversity. Therefore, studies comparing SADs across sites or scales should use sampling and/or statistical approaches capable of disentangling these three effects on the shape of SADs.

ecology↗