Leaf architecture and functional traits for 122 species at the University of California Botanical Garden at Berkeley

The dataset contains leaf venation architecture and functional traits for a phylogenetically diverse set of 122 plant species (including ferns, basal angiosperms, monocots, basal eudicots, asterids, and rosids) collected from the living collections of the University of California Botanical Garden at Berkeley (37.87° N, 122.23° W; CA, USA) from February to September 2021. The sampled species originated from all continents, except Antarctica, and are distributed in different growth forms (aquatic, herb, climbing, tree, shrub). The functional dataset comprises 31 traits (mechanical, hydraulic, anatomical, physiological, economical, and chemical) and describes six main leaf functional axes (hydraulic conductance, resistance and resilience to damages caused by drought and herbivory, mechanical support, and construction cost). It also describes how architecture features vary across venation networks. Our trait dataset is suitable for (1) functional and architectural characterization of plant species; (2) identification of venation architecture-function trade-offs; (3) investigation of evolutionary trends in leaf venation networks; and (4) mechanistic modeling of leaf function. Data are made available under the Open Data Commons Attribution License.

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Publication Details

Journal
Ecology
Published
2024-10-25
DOI
https://doi.org/10.1002/ecy.4436
Citations
2
Primary Topic
Tree Root and Stability Studies
Type
article
Field-Weighted Citation Impact
0.43
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article

Leaf architecture and functional traits for 122 species at the University of California Botanical Garden at Berkeley

Samantha J. McDonough, Mickey Boakye, Mark D. Fricker, Hailey Jiyoon Park et al.
2 citations
Ecology
Tree Root and Stability Studies
0.43
article

Leaf architecture and functional traits for 122 species at the University of California Botanical Garden at Berkeley

Samantha J. McDonough, Mickey Boakye, Mark D. Fricker, Hailey Jiyoon Park, Benjamin Blonder, Nicole Yokota, L. M. T. Aparecido, H. D. Forbes, Ashley Chu, Ilaíne Silveira Matos, Liang Huang, Miguel A Duarte, Adrian Fontao, Jason To, Diana S. Kalantar, Sumedh Joshi, Nam P. Nguyen, Breanna Carrillo Johnson, James Rohde, Roshni Sahu, Meg Scudder, Bradley Vu, Izzi Niewiadomski, Monica Antonio, Andrea Echevarria, Lisa Garcia, Sonoma Carlos, Srinivasan Madhavan, Satvik Sharma, Caroline Pechuzal, LeeDar Sneor, Natalie Vuong
article en
2 citations

Abstract

The dataset contains leaf venation architecture and functional traits for a phylogenetically diverse set of 122 plant species (including ferns, basal angiosperms, monocots, basal eudicots, asterids, and rosids) collected from the living collections of the University of California Botanical Garden at Berkeley (37.87° N, 122.23° W; CA, USA) from February to September 2021. The sampled species originated from all continents, except Antarctica, and are distributed in different growth forms (aquatic, herb, climbing, tree, shrub). The functional dataset comprises 31 traits (mechanical, hydraulic, anatomical, physiological, economical, and chemical) and describes six main leaf functional axes (hydraulic conductance, resistance and resilience to damages caused by drought and herbivory, mechanical support, and construction cost). It also describes how architecture features vary across venation networks. Our trait dataset is suitable for (1) functional and architectural characterization of plant species; (2) identification of venation architecture-function trade-offs; (3) investigation of evolutionary trends in leaf venation networks; and (4) mechanistic modeling of leaf function. Data are made available under the Open Data Commons Attribution License.

EcologyVol. 105(12)
University of Waterloo (CA), University of New Mexico (US), University of Utah (US), University of Oxford (GB), Arizona State University (US), The University of Adelaide (AU), University of California, Berkeley (US)
Openalex Percentile: Top 41%
Tree Root and Stability Studies
0.43
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