taxonstand: An r package for species names standardisation in vegetation databases

Cayuela, Luis; Cerda, Íñigo Granzow‐de la; Albuquerque, Fábio Suzart de; Golicher, Duncan

doi:10.1111/j.2041-210x.2012.00232.x

Cited by 235 publications

(206 citation statements)

References 16 publications

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“…The taxonomic classification was obtained in two steps. First we used the Taxonstand (Cayuela et al 2012) library in R (R Development Core Team 2012) to validate species names and to assign genera to families. Subsequent assignment of families to orders followed APG III for angiosperms (Angiosperm Phylogeny Group 2009) and Christenhusz et al (2011) for gymnosperms.…”

Section: Data On Seed Dispersal Distances and Plant Traitsmentioning

confidence: 99%

Predicting species' maximum dispersal distances from simple plant traits

Tamme

Götzenberger

Zobel

et al. 2014

Ecology

223

288

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Abstract. Many studies have shown plant species' dispersal distances to be strongly related to life-history traits, but how well different traits can predict dispersal distances is not yet known. We used cross-validation techniques and a global data set (576 plant species) to measure the predictive power of simple plant traits to estimate species' maximum dispersal distances. Including dispersal syndrome (wind, animal, ant, ballistic, and no special syndrome), growth form (tree, shrub, herb), seed mass, seed release height, and terminal velocity in different combinations as explanatory variables we constructed models to explain variation in measured maximum dispersal distances and evaluated their power to predict maximum dispersal distances. Predictions are more accurate, but also limited to a particular set of species, if data on more specific traits, such as terminal velocity, are available. The best model (R 2 ¼ 0.60) included dispersal syndrome, growth form, and terminal velocity as fixed effects. Reasonable predictions of maximum dispersal distance (R 2 ¼ 0.53) are also possible when using only the simplest and most commonly measured traits; dispersal syndrome and growth form together with species taxonomy data. We provide a function (dispeRsal) to be run in the software package R. This enables researchers to estimate maximum dispersal distances with confidence intervals for plant species using measured traits as predictors. Easily obtainable trait data, such as dispersal syndrome (inferred from seed morphology) and growth form, enable predictions to be made for a large number of species.

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Section: Data On Seed Dispersal Distances and Plant Traitsmentioning

confidence: 99%

Predicting species' maximum dispersal distances from simple plant traits

Tamme

Götzenberger

Zobel

et al. 2014

Ecology

223

288

View full text Add to dashboard Cite

show abstract

“…To enable comparisons with other databases, the list of cultivated vegetables was standardized to accepted synonyms on the Plant List (http://www.theplantlist.org), which is a noble attempt toward a comprehensive online database of all plant species initiated by Royal Botanic Gardens, Kew, and the Missouri Botanical Garden [73]. The process of standardizing the synonyms was automated using the Taxonstand package in R [74]. Unresolved species were maintained that did not have other potential synonyms in the list.…”

Section: Methodsmentioning

confidence: 99%

Issues and Prospects for the Sustainable Use and Conservation of Cultivated Vegetable Diversity for More Nutrition-Sensitive Agriculture

et al. 2018

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Traditional vegetables are key assets for supporting more nutrition-sensitive agriculture under climate change as many have lower water requirements, adaptation to poor quality soils, higher resistance to pests and diseases, and higher nutritional values as compared to global vegetables. The effective use of traditional vegetables can be challenged however by lack of information and poor conservation status. This study reviewed the uses, growth forms and geographic origins of cultivated vegetables worldwide and the levels of research, ex situ conservation, and documentation they have received in order to identify gaps and priorities for supporting more effective use of global vegetable diversity. A total of 1097 vegetables were identified in a review of the Mansfeld Encyclopedia of Agricultural and Horticultural Plants, including species used for leaves (n = 495), multiple vegetative parts (n = 227), roots (n = 204), fruits or seeds (n = 90), and other parts like flowers, inflorescences, and stems (n = 81). Root vegetables have received significantly less research attention than other types of vegetable. Therophytes (annuals) have received significantly more attention from research and conservation efforts than vegetables with other growth forms, while vegetables originating in Africa (n = 406) and the Asian-Pacific region (n = 165) are notably neglected. Documentation for most vegetable species is poor and the conservation of many vegetables is largely realized on farm through continued use. Supportive policies are needed to advance research, conservation, and documentation of neglected vegetable species to protect and further their role in nutrition-sensitive agriculture.

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“…Kooyman et al (2012) pointed out that even after automated taxonomic cleaning, each taxon in an assembled dataset must be individually inspected to ensure all taxonomic inaccuracies have been dealt with. While there have been recent efforts to automate the most time-consuming process (step 5) of cleaning (Cayuela et al 2012, Boyle et al 2013, Pennell et al 2016, coordination of effort across a global or even more regional scales to provide combined automation of step 5 with steps 4 (new taxa), 6 (confirming occurrence) and 7 (verifying status) in particular will be much harder to achieve in the foreseeable future, and these steps will for some time require human vetting and expertise. This is especially so for step 6, as distributional records need to be inspected.…”

Section: Discussionmentioning

confidence: 99%

“…Recently-developed automated processes for various aspects cleaning (e.g. Cayuela et al 2012, Boyle et al 2013, Pennell et al 2016 can be implemented while following our protocol.…”

Section: Producing the Fd And CDmentioning

confidence: 99%

“…different ways of writing the same name), and misapplications (where an incorrect name has mistakenly been given to a taxon), with any corrected taxon names checked in case they are clones of taxa already in the working list (step 3), to ensure that clones are limited to single-name entries. In some cases, it might be helpful to make use of automated recognition and correction tools for plant taxonomy, such as TaxonStand (Cayuela et al 2012), the TNRS (Boyle et al 2013) and taxonlookup (Pennell et al 2016). If such tools are implemented, the version used must be carefully documented as these tools are also reliant on their underpinning sources of taxonomic information being maintained and kept up-to-date.…”

Section: Step 4: Identifying New Taxamentioning

confidence: 99%

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Taxonomic perils and pitfalls of dataset assembly in ecology: a case study of the naturalized Asteraceae in Australia

Murray¹,

Martin²,

Phillips³

et al. 2017

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The value of plant ecological datasets with hundreds or thousands of species is principally determined by the taxonomic accuracy of their plant names. However, combining existing lists of species to assemble a harmonized dataset that is clean of taxonomic errors can be a difficult task for non-taxonomists. Here, we describe the range of taxonomic difficulties likely to be encountered during dataset assembly and present an easy-to-use taxonomic cleaning protocol aimed at assisting researchers not familiar with the finer details of taxonomic cleaning. The protocol produces a final dataset (FD) linked to a companion dataset (CD), providing clear details of the path from existing lists to the FD taken by each cleaned taxon. Taxa are checked off against ten categories in the CD that succinctly summarize all taxonomic modifications required. Two older, publicly-available lists of naturalized Asteraceae in Australia were merged into a harmonized dataset as a case study to quantify the impacts of ignoring the critical process of taxonomic cleaning in invasion ecology. Our FD of naturalized Asteraceae contained 257 species and infra-species. Without implementation of the full cleaning protocol, the dataset would have contained 328 taxa, a 28% overestimate of taxon richness by 71 taxa. Our naturalized Asteraceae CD described the exclusion of 88 names due to nomenclatural issues (e.g. synonymy), the inclusion of 26 updated currently accepted names and four taxa newly naturalized since the production of the source datasets, and the exclusion of 13 taxa that were either found not to be in Australia or were in fact doubtfully naturalized. This study also supports the notion that automated processes alone will not be enough to ensure taxonomically clean datasets, and that manual scrutiny of data is essential. In the long term, this will best be supported by increased investment in taxonomy and botany in university curricula. NeoBiotaBrad R. Murray et al. / NeoBiota 34: 1-20 (2017) 2

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taxonstand: An r package for species names standardisation in vegetation databases

Cited by 235 publications

References 16 publications

Predicting species' maximum dispersal distances from simple plant traits

Predicting species' maximum dispersal distances from simple plant traits

Issues and Prospects for the Sustainable Use and Conservation of Cultivated Vegetable Diversity for More Nutrition-Sensitive Agriculture

Taxonomic perils and pitfalls of dataset assembly in ecology: a case study of the naturalized Asteraceae in Australia

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