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Abstract Carbon starvation posits that defoliation‐ and drought‐induced mortality results from drawing down stored non‐structural carbohydrates (NSCs), but evidence is mixed, and few studies evaluate mortality directly. We tested the relationships among defoliation severity, NSC drawdown and tree mortality by measuring NSCs in mature oak trees defoliated by an invasive insect,Lymantria dispar, across a natural gradient of defoliation severity.We collected stem and root samples from mature oaks (Quercus rubraandQ.alba) in interior forests (n = 34) and forest edges (n = 47) in central Massachusetts, USA. Total NSC (TNC; sugar + starch) stores were analysed with respect to tree size, species and defoliation severity, which ranged between 5% and 100%.TNC stores declined significantly with increasingly severe defoliation. Forest edge trees had higher TNC stores that were less sensitive to defoliation than interior forest trees, although this may be a result of differing defoliation history. Furthermore, we observed a mortality threshold of 1.5% dry weight TNC.Our study draws a direct link between insect defoliation and TNC reserves and defines a TNC threshold below which mortality is highly likely. These findings advance understanding and improve model parametrization of tree response to insect outbreaks, an increasing threat with globalization and climate change. A freePlain Language Summarycan be found within the Supporting Information of this article.
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StomataCounter: a neural network for automatic stomata identification and counting
Summary Stomata regulate important physiological processes in plants and are often phenotyped by researchers in diverse fields of plant biology. Currently, there are no user‐friendly, fully automated methods to perform the task of identifying and counting stomata, and stomata density is generally estimated by manually counting stomata.We introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify stomata in a variety of different microscopic images. We use a human‐in‐the‐loop approach to train and refine a neural network on a taxonomically diverse collection of microscopic images.Our network achieves 98.1% identification accuracy onGinkgoscanning electron microscropy micrographs, and 94.2% transfer accuracy when tested on untrained species.To facilitate adoption of the method, we provide the method in a publicly available website athttp://www.stomata.science/.
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- Award ID(s):
- 1461868
- PAR ID:
- 10443420
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 223
- Issue:
- 3
- ISSN:
- 0028-646X
- Page Range / eLocation ID:
- p. 1671-1681
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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