Q-NET – a new scholarly network on quantitative wood anatomy

Georg von Arx, Marco Carrer, Alan Crivellaro, Veronica De Micco, Patrick Fonti, Frederic Lens, Angela Luisa Prendin, Sabine Rosner, Ute Sass-Klaassen
2021 Dendrochronologia  
Quantitative wood anatomy (QWA) is a dynamic research approach of increasing interest that can provide answers to a wide range of research questions across different disciplines. However, the lack of common protocols and knowledge gaps hinder the realisation of the full potential of QWA. Therefore, we established the new community-based network Q-NET to provide an open interdisciplinary platform for exchange and research around QWA. Q-NET (https://qwa-net.com) combines an online knowledge and
more » ... change base with virtual workshops. The first two workshops each attracted more than 125 participants from around the world, demonstrating the community's interest in QWA and this virtual way of networking and collaborating. Indeed, virtual networks such as Q-NET could increase the inclusiveness, efficiency and sustainability of scientific collaboration while providing additional training and teaching opportunities for early career scientists, both of which complement in-person conferences and workshops. Quantitative wood anatomy (QWA) is the numeric analysis of xylem anatomical traits of trees, shrubs, and herbaceous species and their relationship to plant functioning, growth, environment, wood quality and species identification (De Micco et al., 2019; Lens et al., 2020; von Arx et al., 2016). The xylem anatomical traits include measurable and countable anatomical variables of cells (lumen and cell wall dimensions, counts, position and spatial arrangement; e.g., IAWA Committee, 1989 Scholz et al., 2013), tissues (area, abundance and counts; e.g., von Arx et al. (2015); Ziemińska et al., 2013) , pits (dimensions of aperture, pit border, pit membrane thickness, torus and margo, and counts; e.g., Bouche et al. (2014 ), Li et al. (2016 ), Plavcová et al. (2013) ), discrete anatomical features such as intra-annual density fluctuations (IADFs; e.
doi:10.1016/j.dendro.2021.125890 fatcat:mt3rcgtpozef3ey4lildcl5qca