Morphometric analysis of phytoliths: recommendations towards standardization from the International Committee for Phytolith Morphometrics

Ball, T. Blake; Davis, AnnaLisa; Evett, Rand R.; Ladwig, Jammi L.; Tromp, Monica; Out, Welmoed A.; Portillo, Marta

doi:10.1016/j.jas.2015.03.023

Cited by 66 publications

(40 citation statements)

References 15 publications

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“…This sample size fully satisfies statistical requirements (Ball et al . ), and the results of one‐way anova indicated that the parameters of the three morphotypes in herbaceous plants and woody plants differed significantly (Table S2). For blocky irregular types, the length and width values in herbaceous plants mainly ranged from 10–30 μm (Fig.…”

Section: Resultsmentioning

confidence: 99%

Phytolith reference study for identifying vegetation changes in the forest−grassland region of northeast China

Gao

Jie

Wang

et al. 2017

Boreas

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To provide a basis for tracing changes in vegetation and tree cover density, we studied the phytoliths of 129 common temperate plant species, and extracted the phytoliths from 75 surface soil samples from sites in grassland, forest−grassland ecotone and forest habitats in northeast China. From the analysis of shapes and morphological parameters of the plant samples, we developed a reference data set of herbaceous and woody phytoliths, and subsequently identified 21 herbaceous and 13 woody phytolith types in the surface soil samples. To test the reliability of soil phytolith analysis for distinguishing forest, grassland and the forest−grassland ecotone, we used principal components analysis (PCA) and discriminant analysis (DA) to summarize the soil phytolith assemblage characteristics of the different ecosystems. The results show that the grassland and forest samples are characterized by abundant herbaceous and woody phytoliths, respectively; and that forest−grassland ecotone habitats are characterized by low abundances of blocky polyhedral, multifaceted epidermal and sclereid phytoliths. In general, the surface soil phytolith assemblages can reliably differentiate samples from forest, grassland and the forest−grassland ecotone, with up to 92% of the samples classified correctly. We also tested the reliability of phytolith indices (W/G (1), W/G (2), W/G (3)) for discriminating different vegetation types in our study area, and found that W/G (2) was the most reliable index and corresponded well with the species inventory data. The W/G values for grassland ranged from 0 to 0.3, from 0.3 to 0.6 for the forest−grassland ecotone, and exceeded 0.6 for forest. We conclude that our study provides reliable analogues for phytolith assemblages from palaeoecological contexts, which can be used to reconstruct shifts in forest−grassland ecotones and vegetation succession in temperate areas.

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Section: Resultsmentioning

confidence: 99%

Phytolith reference study for identifying vegetation changes in the forest−grassland region of northeast China

Gao

Jie

Wang

et al. 2017

Boreas

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“…These advances include the development of computer‐assisted phytolith identification systems as well as a better integration of phytolith analysis with micromorphology and morphometric approaches to soil analysis (Ball et al. ; Evett and Cuthrell ).…”

Section: Collaborations In Archaeological Practicementioning

confidence: 99%

Anthropological Archaeology in 2016: Cooperation and Collaborations in Archaeological Research and Practice

Halperin

2017

American Anthropologist

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Ideas of collaboration and cooperation are often implicit, taken-for-granted elements in archaeological models and theorizing. Overshadowed by a growing archaeological preoccupation with warfare, violence, conflict, and crisis, cooperative acts and collaborative dispositions appear to lack the same emotional resonance and changestimulating properties that attract archaeological attention. Nonetheless, many archaeological publications in 2016 directly take on notions of collaboration in thinking about the past and our roles as archaeologists and as citizens of the future. This review underscores the significance of thinking about collaboration and cooperation as ambiguous, ambivalent, multiscalar, and dynamic. At issue is not just the creation of community but also of getting things done and, of course, celebrating, revising, or reworking the intended or unintended consequences of those actions over the generations. [archaeology, collaboration, cooperation] RESUMEN Las ideas de colaboración y cooperación son a menudo elementos implícitos, que se dan por sentados en los modelos y teorización arqueológicos. Opacados por una creciente preocupación arqueológica por la guerra, la violencia, el conflicto, y la crisis, a los actos cooperativos y disposiciones colaborativas parecen faltarles la misma resonancia emocional y propiedades estimulantes de cambio que atraen la atención arqueológica. No obstante, muchas publicaciones arqueológicas en 2016 empiezan directamente a tratar las nociones de colaboración, reflexionando acerca del pasado y nuestros roles como arqueólogos y como ciudadanos del futuro. Esta revisión enfatiza la importancia de considerar la colaboración y cooperación como ambiguas, ambivalentes, de múltiples escalas, y dinámicas. En cuestión no sólo está la creación de comunidad, sino también el lograr que se hagan las cosas, y por supuesto, celebrar, revisar, o rehacer las consecuencias planeadas o no planeadas de esas acciones sobre generaciones. [arqueología, colaboración, cooperación]

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“…Phytoliths are silicified plant tissues, where soluble silica from the groundwater has been deposited. They are an important source of microfossils because they can survive for a long time in sediment after the decay of plants (Piperno, ; Ball et al, ). The morphology of phytoliths is taxon‐specific in many plant groups (Rapp & Mulholland, ; Piperno, ) and has been increasingly used in studies of numerous disciplines, substantially extending our knowledge about these microfossils (reviewed by Hart, , and Zurro et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Second, taxon identification or discrimination is another critical step in phytolith studies and this practice relies heavily on the experience of the specialists who have obtained long‐term and professional training (Hart, ; Zurro et al, ). In this sense, artificial biases are most likely to be introduced in phytolith analyses such that quantitative discrimination with morphometric approaches have been strongly recommended (Ball et al, ; Evett & Cuthrell, ), which indeed have been used successfully in many cases (Whang et al, ; Zhao et al, ; Rovner & Gyulai, ). Nevertheless, how to make full use of morphometric traits in phytoliths and how to efficiently analyze the inherent information are still big challenges.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, it is difficult to compare results among studies with these terms, because different authors might have different understandings and treatments in their own studies. Second, taxon identification or discrimination is another critical step in phytolith studies and this practice relies heavily on the experience of the specialists who have obtained long-term and professional such that quantitative discrimination with morphometric approaches have been strongly recommended (Ball et al, 2016;Evett & Cuthrell, 2016), which indeed have been used successfully in many cases (Whang et al, 1998;Zhao et al, 1998;Rovner & Gyulai, 2007). Nevertheless, how to make full use of morphometric traits in phytoliths and how to efficiently analyze the inherent information are still big challenges.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae (Poaceae)

Cai

2017

J of Sytematics Evolution

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Phytoliths, as one of the important sources of microfossils, have been widely used in paleobotanyrelated studies, especially in the grass family (Poaceae) where abundant phytoliths are found. Despite great efforts, several challenges remain when phytoliths are used in various studies, including the accurate description of phytolith morphology and the effective utilization of phytolith traits in taxon identification or discrimination. In this study, we analyzed over 1000 phytolith samples from 18 taxa representing seven main genera in the tribe Oryzeae (subfamily Ehrhartoideae) and five taxa in the subfamilies Bambusoideae and Pooideae. By focusing on Oryzeae, which has been extensively investigated in terms of taxonomy and phylogeny, we were able to evaluate the discrimination power of phytoliths at lower taxonomic levels in grasses. With the help of morphometric analysis and by introducing several machine learning algorithms, we found that 87.7% of the phytolith samples could be classified correctly at the genus level. In spite of slightly different performances, all four machine learning algorithms significantly increased the resolving power of phytolith evidence in taxon identification and discrimination compared with the traditional phytolith analysis. Therefore, we propose a pipeline of phytolith analyses based on machine learning algorithms, including data collection, morphometric analysis, model building, and taxon discrimination. The methodology and pipeline presented here should be applied to various studies across different groups of plants. This study provides new insights into the utilization of phytoliths in evolutionary and ecology studies involving grasses and plants in general.

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Morphometric analysis of phytoliths: recommendations towards standardization from the International Committee for Phytolith Morphometrics

Cited by 66 publications

References 15 publications

Phytolith reference study for identifying vegetation changes in the forest−grassland region of northeast China

Phytolith reference study for identifying vegetation changes in the forest−grassland region of northeast China

Anthropological Archaeology in 2016: Cooperation and Collaborations in Archaeological Research and Practice

Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae (Poaceae)

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