Reconstructing past human impact on vegetation using pollen data DOI

Marie-José Gaillard,

Ralph Fyfe

Elsevier eBooks, Год журнала: 2023, Номер unknown, С. 326 - 355

Опубликована: Ноя. 28, 2023

Язык: Английский

High‐throughput assessment of anemophilous pollen size and variability using imaging cytometry DOI Creative Commons
Thomas Hornick, W. Stanley Harpole, Susanne Dunker

и другие.

New Phytologist, Год журнала: 2025, Номер unknown

Опубликована: Март 28, 2025

Summary Pollen grain size relates to plant community structure via pollen dispersal, resource allocation into regenerative processes, phylogeny and genetics (ploidy), or it can be used as a decisive trait for species distinction. However, the availability of data is limited because labor‐ time‐consuming methodological constraints classically based on fewer than 50 measured grains per species, thus restricting our knowledge temporal spatial variability in response biotic abiotic conditions. We addressed this gap by using imaging flow cytometry (IFC), which allows high‐throughput assessment > 500 000 single from 100 anemophilous that were sampled between 2018 2022. present workflow analysis, show agreement IFC estimates with literature assess context phylogeny. Our approach us make statistically robust measurements are not sampling effort sample throughput answer broad ecological questions at large scales.

Язык: Английский

Процитировано

1

Palynology, landscape and land use: retrospect, prospect and research agendas DOI Creative Commons

Ralph Fyfe,

Kevin J. Edwards, Laura Scoble

и другие.

Journal of Archaeological Science, Год журнала: 2025, Номер 179, С. 106233 - 106233

Опубликована: Апрель 18, 2025

Язык: Английский

Процитировано

0

A user‐friendly method to get automated pollen analysis from environmental samples DOI Creative Commons
Betty Gimenez, Sébastien Joannin, Jérôme Pasquet

и другие.

New Phytologist, Год журнала: 2024, Номер 243(2), С. 797 - 810

Опубликована: Май 28, 2024

Automated pollen analysis is not yet efficient on environmental samples containing many taxa and debris, which are typical in most pollen-based studies. Contrary to classification, detection remains overlooked although it the first step from errors can propagate. Here, we investigated a simple but method automate for samples, optimizing workload performance. We applied YOLOv5 algorithm debris c. 40 Mediterranean plant taxa, designed tested several strategies annotation, analyzed variation errors. About 5% of grains were left undetected, while falsely detected as pollen. Undetected was mainly poor-quality images, or rare irregular morphology. Pollen remained effective when never seen by algorithm, improved spending time provide taxonomic details. single model taxon reduced annotation workload, only morphologically differentiated taxa. offer guidelines scientists analyze automatically any sample, providing sound criteria apply using common user-friendly tools. Our contributes enhance efficiency replicability

Язык: Английский

Процитировано

2

PollenNet: A Novel Deep Learning Architecture for High Precision Pollen Grain Classification through Deep Learning and Explainable AI DOI Creative Commons

F M Javed Mehedi Shamrat,

Mohd Yamani Idna Idris, Xujuan Zhou

и другие.

Heliyon, Год журнала: 2024, Номер 10(19), С. e38596 - e38596

Опубликована: Сен. 28, 2024

Язык: Английский

Процитировано

1

Application of confocal laser microscopy for identification of modern and fossil pollen grains, an example in palm Mauritiinae DOI
Rosane G. Collevatti,

Marcela Castañeda,

Silane Aparecida Ferreira da Silva-Caminha

и другие.

Review of Palaeobotany and Palynology, Год журнала: 2024, Номер 327, С. 105140 - 105140

Опубликована: Июнь 6, 2024

Язык: Английский

Процитировано

0

An Analysis of Longan Honey from Taiwan and Thailand Using Flow Cytometry and Physicochemical Analysis DOI Creative Commons
Lekhnath Kafle,

Tandzisile Zine Mabuza

Foods, Год журнала: 2024, Номер 13(23), С. 3772 - 3772

Опубликована: Ноя. 25, 2024

The increase in honey fraud the global market has highlighted importance of pollen analysis determining or confirming botanical and geographical origins honey. Numerous studies are currently underway to develop efficient rapid methods for determination quality, botanical, origin Typically, physicochemical is used evaluate its quality source. In this study, flow cytometry, a technique extensively employed immunology hematology, was first applied analyze characterize from longan honeys Taiwan Thailand. cytometry forward scatter (FSC), side (SSC), Y610-A, NUV450 samples Taiwan's were rich pollens; however, based upon FSC SSC analyses, pollens Thai larger more granular. Y610/20 emission area greatest pollens. measured near UV laser also greater pollen. Additionally, analysed physiochemical properties including moisture content, pH, ash viscosity, hydroxymethylfurfural (HMF) both countries. content varied between 20.90% 23.40%, whereas Thailand ranged 19.50% 23.50%. A total 60% found have dark amber color, only 20% exhibited color. Furthermore, pH range be 4.00 4.16, 4.01 4.12. 0.05% 0.23%, had 0.01% 0.9%. All negative Fiehe's test, indicating absence HMF. This lays groundwork identification honey, applying conjunction with assess quality.

Язык: Английский

Процитировано

0

Reconstructing past human impact on vegetation using pollen data DOI

Marie-José Gaillard,

Ralph Fyfe

Elsevier eBooks, Год журнала: 2023, Номер unknown, С. 326 - 355

Опубликована: Ноя. 28, 2023

Язык: Английский

Процитировано

0