Speeding up ecological and evolutionary computations in R; essentials of high performance computing for biologists

Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and quality. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (aprof) that helps to identify computational bottlenecks in R code and determine whether optimization can be effective. Our review is complemented by a practical set of examples and detailed Supporting Information material (S1–S3 Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research

Spatiotemporal effects of Hurricane Ivan on an endemic epiphytic orchid: 10 years of follow-up

Background: Hurricanes have a strong influence on the ecological dynamics and structure of tropical forests. Orchid populations are especially vulnerable to these perturbations due to their canopy exposure and lack of underground storage organs and seed banks. Aims: We evaluated the effects of Hurricane Ivan on the population of the endemic epiphytic orchid Encyclia bocourtii to propose a management strategy. Methods: Using a pre- and post-hurricane dataset (2003–2013), we assessed the population asymptotic and transient dynamics. We also identified the individual size-stages that maximise population inertia and E. bocourtii’s spatial arrangement relative to phorophytes and other epiphytes. Results: Hurricane Ivan severely affected the survival and growth of individuals of E. bocourtii, and caused an immediate decline of the population growth rate from λ = 1.05 to λ = 0.32, which was buffered by a population reactivity of ρ1 = 1.42. Our stochastic model predicted an annual population decrease of 14%. We found an aggregated spatial pattern between E. bocourtii and its host trees, and a random pattern relative to other epiphytes. Conclusion: Our findings suggest that E. bocourtii is not safe from local extinction. We propose the propagation and reintroduction of reproductive specimens, the relocation of surviving individuals, and the establishment of new plantations of phorophytes.This work was supported by the Inter-ministerial Commission for Science and Technology under Grant [CICYT-Spain, Project CGL2015-69985-R]; and the Havana Project of the University of Alicante

occCite: Tools for querying and managing large biodiversity occurrence datasets

The amount of observational and specimen-based biodiversity data available to researchers is increasing exponentially, yet the ability to manage and cite large, complex biodiversity datasets lags behind. This management and citation gap impedes reproducibility for data users and the ability for data publishers to track use and accumulate use citations, ultimately harming the longer-term sustainability of the still-emerging enterprise of research data-sharing. Here we present an R package, occCite (v. 0.4.7), to aid researchers in querying large species occurrence data aggregators (specifically, the Global Biodiversity Information Facility, GBIF, and the Botanical Information and Ecology Network, BIEN), and store metadata such as primary data providers, database accession dates, DOIs, and the taxonomic source used for search terms. occCite also includes tools to summarize and visualize query results and generate citation lists of all data providers and software packages used during the query process. We provide examples of a basic occurrence search and citation workflow as well as an advanced workflow using features for custom optimized searches, visualization, and summary procedures. occCite improves upon existing R packages by uniting data from powerful API-based query packages (rgbif and BIEN) into a unified object-based framework, while maintaining metadata vital to best-practice recommendations for documenting biodiversity analysis workflows. occCite aims to efficiently close the gap in the citation cycle between primary data providers and final research products, allowing researchers to meet dataset documentation standards without sacrificing time and resources to the demands of providing increasing levels of detail on their datasets

changeRangeR: An R package for reproducible biodiversity change metrics from species distribution estimates

Conservation planning and decision-making rely on evaluations of biodiversity status and threats that are based upon species' distribution estimates. However, gaps exist regarding automated tools to delineate species' current ranges from distribution estimates and use those estimates to calculate both species- and community-level biodiversity metrics. Here, we introduce changeRangeR, an R package that facilitates workflows to reproducibly transform estimates of species' distributions into metrics relevant for conservation. For example, by combining predictions from species distribution models (SDMs) with other maps of environmental data (e.g., suitable forest cover), researchers can characterize the proportion of a species' range that is under protection, metrics used under the IUCN Criteria A and B guidelines (Area of Occupancy and Extent of Occurrence), and other more general metrics such as taxonomic and phylogenetic diversity and endemism. Further, changeRangeR facilitates temporal comparisons among biodiversity metrics to inform efforts toward complementarity and consideration of future scenarios in conservation decisions. changeRangeR also provides tools to determine the effects of modeling decisions through sensitivity tests. Transparent and repeatable workflows for calculating biodiversity change metrics from SDMs such as those provided by changeRangeR are essential to inform conservation decision-making efforts and represent key extensions for SDM methodology and associated metadata documentation.journal articl

wallace 2: a shiny app for modeling species niches and distributions redesigned to facilitate expansion via module contributions

Released 4 years ago, the Wallace EcoMod application (R package wallace) provided an open-source and interactive platform for modeling species niches and distributions that served as a reproducible toolbox and educational resource. wallace harnesses R package tools documented in the literature and makes them available via a graphical user interface that runs analyses and returns code to document and reproduce them. Since its release, feedback from users and partners helped identify key areas for advancement, leading to the development of wallace 2. Following the vision of growth by community expansion, the core development team engaged with collaborators and undertook a major restructuring of the application to enable: simplified addition of custom modules to expand methodological options, analyses for multiple species in the same session, improved metadata features, new database connections, and saving/loading sessions. wallace 2 features nine new modules and added functionalities that facilitate data acquisition from climate-simulation, botanical and paleontological databases; custom data inputs; model metadata tracking; and citations for R packages used (to promote documentation and give credit to developers). Three of these modules compose a new component for environmental space analyses (e.g., niche overlap). This expansion was paired with outreach to the biogeography and biodiversity communities, including international presentations and workshops that take advantage of the software's extensive guidance text. Additionally, the advances extend accessibility with a cloud-computing implementation and include a suite of comprehensive unit tests. The features in wallace 2 greatly improve its expandability, breadth of analyses, and reproducibility options, including the use of emerging metadata standards. The new architecture serves as an example for other modular software, especially those developed using the rapidly proliferating R package shiny, by showcasing straightforward module ingestion and unit testing. Importantly, wallace 2 sets the stage for future expansions, including those enabling biodiversity estimation and threat assessments for conservation.journal articl

The bien r package: A tool to access the Botanical Information and Ecology Network (BIEN) database

There is an urgent need for largeâ scale botanical data to improve our understanding of community assembly, coexistence, biogeography, evolution, and many other fundamental biological processes. Understanding these processes is critical for predicting and handling humanâ biodiversity interactions and global change dynamics such as food and energy security, ecosystem services, climate change, and species invasions.The Botanical Information and Ecology Network (BIEN) database comprises an unprecedented wealth of cleaned and standardised botanical data, containing roughly 81 million occurrence records from c. 375,000 species, c. 915,000 trait observations across 28 traits from c. 93,000 species, and coâ occurrence records from 110,000 ecological plots globally, as well as 100,000 range maps and 100 replicated phylogenies (each containing 81,274 species) for New World species. Here, we describe an r package that provides easy access to these data.The bien r package allows users to access the multiple types of data in the BIEN database. Functions in this package query the BIEN database by turning user inputs into optimised PostgreSQL functions. Function names follow a convention designed to make it easy to understand what each function does. We have also developed a protocol for providing customised citations and herbarium acknowledgements for data downloaded through the bien r package.The development of the BIEN database represents a significant achievement in biological data integration, cleaning and standardization. Likewise, the bien r package represents an important tool for open science that makes the BIEN database freely and easily accessible to everyone.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142458/1/mee312861_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142458/2/mee312861.pd

A standard protocol for reporting species distribution models

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready-to-use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically-based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta-analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web-based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community

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