Plants meet machines: Prospects in machine learning for plant biology

Soltis, Pamela S.; Nelson, Gil; Zare, Alina; Meineke, Emily K.

doi:10.1002/aps3.11371

Cited by 44 publications

(29 citation statements)

References 54 publications

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“…Given the lack of three‐dimensional information, the predictive power obtained here with both random forests and the inference of convergence with evolutionary models is noteworthy, particularly given that the two‐dimensional shapes were extracted from pressed specimens in herbaria. This highlights the potential of natural history collections as a source for extracting large amounts of functional trait information (Holmes et al, 2016; Chen et al, 2018; Soltis et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Judge it by its shape: a pollinator‐blind approach reveals convergence in petal shape and infers pollination modes in the genus Erythrina

Bilbao

Bruneau

Joly

2021

American J of Botany

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Premise Pollinators are thought to exert selective pressures on plants, mediating the evolution of convergent floral shape often recognized as pollination syndromes. However, little is known about the accuracy of using petal shape for inferring convergence in pollination mode without a priori pollination information. Here we studied the genus Erythrina L. as a test case to assess whether ornithophyllous pollination modes (hummingbirds, passerines, sunbirds, or mixed pollination) can be inferred based on the evolutionary analysis of petal shape. Methods We characterized the two‐dimensional dissected shape of standard, keel, and wing petals from 106 Erythrina species using geometric morphometrics and reconstructed a phylogenetic tree of 83 Erythrina species based on plastid trnL‐F and nuclear ribosomal ITS sequences. We then used two phylogenetic comparative methods based on Ornstein–Uhlenbeck models, SURFACE and l1OU, to infer distinct morphological groups using petal shape and identify instances of convergent evolution. The effectiveness of these methods was evaluated by comparing the groups inferred to known pollinators. Results We found significant petal shape differences between hummingbird‐ and passerine‐pollinated Erythrina species. Our analyses also revealed that petal combinations generally provided better inferences of pollinator types than individual petals and that the method and optimization criterion can affect the results. Conclusions We show that model‐based approaches using petal shape can detect convergent evolution of floral shape and relatively accurately infer pollination modes in Erythrina. The inference power of the keel petals argues for a deeper investigation of their role in the pollination biology of Erythrina and other bird‐pollinated legumes.

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Section: Discussionmentioning

confidence: 99%

Judge it by its shape: a pollinator‐blind approach reveals convergence in petal shape and infers pollination modes in the genus Erythrina

Bilbao

Bruneau

Joly

2021

American J of Botany

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“…Tansley review New Phytologist review show that digitized specimen data are finding rapidly increasing applications in many fields pertinent to stemming the loss of species through extinction, for example, genomics, conservation assessment, ecology, phenology, and taxonomic revisions (Franklin et al, 2017;Soltis et al, 2018Soltis et al, , 2020. Thanks to online data aggregators such as GBIF, iDigBio (Integrated Digitized Biocollections; https://www.idigbio.org/), GPI (Global Plant Initiatives; http://gpi.myspecies.info/), and JACQ (https://www.jacq.org/), which allow for access to millions of digitized specimens together with a significant amount of metadata (annotations, label content, GPS coordinates etc.…”

Section: Reviewmentioning

confidence: 99%

Reversing extinction trends: new uses of (old) herbarium specimens to accelerate conservation action on threatened species

et al. 2021

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Summary Although often not collected specifically for the purposes of conservation, herbarium specimens offer sufficient information to reconstruct parameters that are needed to designate a species as ‘at‐risk’ of extinction. While such designations should prompt quick and efficient legal action towards species recovery, such action often lags far behind and is mired in bureaucratic procedure. The increase in online digitization of natural history collections has now led to a surge in the number new studies on the uses of machine learning. These repositories of species occurrences are now equipped with advances that allow for the identification of rare species. The increase in attention devoted to estimating the scope and severity of the threats that lead to the decline of such species will increase our ability to mitigate these threats and reverse the declines, overcoming a current barrier to the recovery of many threatened plant species. Thus far, collected specimens have been used to fill gaps in systematics, range extent, and past genetic diversity. We find that they also offer material with which it is possible to foster species recovery, ecosystem restoration, and de‐extinction, and these elements should be used in conjunction with machine learning and citizen science initiatives to mobilize as large a force as possible to counter current extinction trends.

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“…1A-E). One of the most exciting new areas is the automated derivation of data from herbarium specimens, previously laborious to obtain manually but increasingly achievable with greater throughput using machine learning (reviewed by Soltis et al, 2020;Rocchetti et al, 2021). Data types achieved through such high throughput methods include leaf morphometric parameters REVIEW of various flavors (Corney et al, 2012;MacLeod and Steart, 2015;Weaver et al, 2020;White et al, 2020; see an example in Fig.…”

Section: Revisiting Classic Hypothesesmentioning

confidence: 99%

Biodiversity at the global scale: the synthesis continues

Folk

Siniscalchi

2021

American J of Botany

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Traditionally, the generation and use of biodiversity data and their associated specimen objects have been primarily the purview of individuals and small research groups. While deposition of data and specimens in herbaria and other repositories has long been the norm, throughout most of their history, these resources have been accessible only to a small community of specialists. Through recent concerted efforts, primarily at the level of national and international governmental agencies over the last two decades, the pace of biodiversity data accumulation has accelerated, and a wider array of biodiversity scientists has gained access to this massive accumulation of resources, applying them to an ever‐widening compass of research pursuits. We review how these new resources and increasing access to them are affecting the landscape of biodiversity research in plants today, focusing on new applications across evolution, ecology, and other fields that have been enabled specifically by the availability of these data and the global scope that was previously beyond the reach of individual investigators. We give an overview of recent advances organized along three lines: broad‐scale analyses of distributional data and spatial information, phylogenetic research circumscribing large clades with comprehensive taxon sampling, and data sets derived from improved accessibility of biodiversity literature. We also review synergies between large data resources and more traditional data collection paradigms, describe shortfalls and how to overcome them, and reflect on the future of plant biodiversity analyses in light of increasing linkages between data types and scientists in our field.

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Plants meet machines: Prospects in machine learning for plant biology

Cited by 44 publications

References 54 publications

Judge it by its shape: a pollinator‐blind approach reveals convergence in petal shape and infers pollination modes in the genus Erythrina

Judge it by its shape: a pollinator‐blind approach reveals convergence in petal shape and infers pollination modes in the genus Erythrina

Reversing extinction trends: new uses of (old) herbarium specimens to accelerate conservation action on threatened species

Biodiversity at the global scale: the synthesis continues

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