taxize: taxonomic search and retrieval in R

Chamberlain, Scott; Szöcs, Eduard

doi:10.12688/f1000research.2-191.v1

Cited by 517 publications

(400 citation statements)

References 17 publications

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“…We welcome contributions and feedback from the community. We want to make metacoder functions and classes compatible with those from other bioinformatic R packages such as phyloseq , ape [20], seqinr [21], and taxize [22]. We might integrate more options for digital PCR and barcode gap analysis, perhaps using ecoPCR [23] or the R packages PrimerMiner [24] and Spider [25].…”

Section: Availability and Future Directionsmentioning

confidence: 99%

Metacoder: An R package for visualization and manipulation of community taxonomic diversity data

2017

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Community-level data, the type generated by an increasing number of metabarcoding studies, is often graphed as stacked bar charts or pie graphs that use color to represent taxa. These graph types do not convey the hierarchical structure of taxonomic classifications and are limited by the use of color for categories. As an alternative, we developed metacoder, an R package for easily parsing, manipulating, and graphing publication-ready plots of hierarchical data. Metacoder includes a dynamic and flexible function that can parse most text-based formats that contain taxonomic classifications, taxon names, taxon identifiers, or sequence identifiers. Metacoder can then subset, sample, and order this parsed data using a set of intuitive functions that take into account the hierarchical nature of the data. Finally, an extremely flexible plotting function enables quantitative representation of up to 4 arbitrary statistics simultaneously in a tree format by mapping statistics to the color and size of tree nodes and edges. Metacoder also allows exploration of barcode primer bias by integrating functions to run digital PCR. Although it has been designed for data from metabarcoding research, metacoder can easily be applied to any data that has a hierarchical component such as gene ontology or geographic location data. Our package complements currently available tools for community analysis and is provided open source with an extensive online user manual.

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Section: Availability and Future Directionsmentioning

confidence: 99%

Metacoder: An R package for visualization and manipulation of community taxonomic diversity data

2017

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“…The taxonomic classification used for the species was from gbif.org via the taxize R-package (Chamberlain & Szöcs, 2013). We excluded specimens without a taxon name (66 specimens) or without proper identification to species level (100 specimens), larvae (27 specimens) and specimens mounted with dorsal side downward (296 specimens) or missing either the head, pronotum, or elytra (504 specimens).…”

Section: Obtaining Imagesmentioning

confidence: 99%

Species‐level image classification with convolutional neural network enables insect identification from habitus images

Hansen

Svenning

Olsen

et al. 2019

Ecology and Evolution

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1. Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground-dwelling insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity.2. We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine-tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setting a confidence threshold, we trade-off between classification accuracy, precision, and recall and taxonomic resolution.3. The CNN classified 51.9% of 19,164 test images correctly to species level and 74.9% to genus level. Average classification recall on species level was 50.7%.Applying a threshold of 0.5 increased the average classification recall to 74.6% at the expense of taxonomic resolution. Higher top value from the output layer and larger sized species were more often classified correctly, as were images of species in genera with few species. 4. Fine-tuning enabled us to classify images with a high mean recall for the whole test dataset to species or higher taxonomic levels, however, with high variability.This indicates that some species are more difficult to identify because of properties such as their body size or the number of related species.5. Together, species-level image classification of arthropods from museum collections and ecological monitoring can substantially increase the amount of occurrence data that can feasibly be collected. These tools thus provide new opportunities in understanding and predicting ecological responses to environmental change. Jens-Christian Svenninghttps://orcid.

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“…These synonyms must be enumerated and included among search terms, or valuable data will be lost. In R, a package called taxize (Chamberlain et al, 2012) has some useful functions for this exercise; it is important to list all synonyms and maintain their links to accepted names so that data for species can be pooled in later analyses. Common names in multiple languages must also be used in searching SNSs, as citizen naturalists often refer to the organisms they observe only by common names; this custom is understandable, because communication via SNSs is more casual and informal than it is formal and scientific.…”

Section: Data Gathering and Organizationmentioning

confidence: 99%

Discovering and developing primary biodiversity data from social networking sites: A novel approach

Barve

2014

Ecological Informatics

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taxize: taxonomic search and retrieval in R

Cited by 517 publications

References 17 publications

Metacoder: An R package for visualization and manipulation of community taxonomic diversity data

Metacoder: An R package for visualization and manipulation of community taxonomic diversity data

Species‐level image classification with convolutional neural network enables insect identification from habitus images

Discovering and developing primary biodiversity data from social networking sites: A novel approach

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