Garryanalyzer: A morphometric workflow and open-source ImageJ plug-in for quantitative morphological analysis of Pacific Northwest Quercus leaves

IF 3.5 3区生物学 Q2 PLANT SCIENCES

Applications in Plant Sciences Pub Date : 2026-08-21 Epub Date: 2026-07-23 DOI:10.1002/aps3.70069

Ziqi Xie, Tan Bahar, Keith Karoly

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引用次数: 0

Abstract

Premise

Accurate species identification is crucial for ecological restoration and can be especially challenging for understudied non-model species. Quercus garryana is the only native oak species in the Pacific Northwest and is an important component of the endangered oak savanna ecosystem. Quercus robur is an imported ornamental species from Europe and has been found to be mistakenly planted as Q. garryana in habitat restoration projects.

Methods

We measured leaf morphological traits sampled from herbarium collections in their native ranges using the digital morphometric tools MorphoLeaf and Tomato Analyzer. We then used Lasso logistic analysis to generate a predictive model and tested it on leaves from Portland, Oregon. To streamline this species detection process, we developed Garryanalyzer, an ImageJ plug-in that automatically measures leaf traits and outputs species predictions.

Results

Garryanalyzer demonstrated 95% accuracy in predicting the species identity of herbarium specimens of oaks. Garryanalyzer correctly identified all Q. robur individuals sampled in Portland but showed lower accuracy for Q. garryana.

Discussion

Many existing morphometric software are not open source, which makes them unable to be customized to specific study systems. Garryanalyzer is built upon the widely used open-source ImageJ platform. This study also demonstrates a viable workflow for developing similar tools for other ecologically important non-model plant species.

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Garryanalyzer：一个形态测量工作流和开源ImageJ插件，用于太平洋西北栎叶的定量形态分析

准确的物种识别对生态恢复至关重要，对未充分研究的非模式物种尤其具有挑战性。garryana栎是太平洋西北地区唯一的本土栎树种，是濒临灭绝的栎树草原生态系统的重要组成部分。栎是一种从欧洲进口的观赏树种，在生境恢复工程中被误认为是黑栎。方法利用数字形态测量工具MorphoLeaf and Tomato Analyzer，从植物标本馆采集植物叶片的形态特征进行测量。然后，我们使用Lasso逻辑分析生成预测模型，并在俄勒冈州波特兰的叶子上进行测试。为了简化这个物种检测过程，我们开发了Garryanalyzer，这是一个ImageJ插件，可以自动测量叶片特征并输出物种预测。结果Garryanalyzer对栎树标本馆标本的物种鉴别准确率达95%。Garryanalyzer正确识别了波特兰采样的所有Q. robur个体，但对Q. garryana的准确性较低。许多现有的形态测量软件都不是开源的，这使得它们无法针对特定的研究系统进行定制。Garryanalyzer建立在广泛使用的开源ImageJ平台上。该研究还为其他生态上重要的非模式植物物种开发类似工具提供了可行的工作流程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applications in Plant Sciences PLANT SCIENCES-

CiteScore

7.30

自引率

0.00%

发文量

审稿时长

12 weeks

期刊介绍： Applications in Plant Sciences (APPS) is a monthly, peer-reviewed, open access journal promoting the rapid dissemination of newly developed, innovative tools and protocols in all areas of the plant sciences, including genetics, structure, function, development, evolution, systematics, and ecology. Given the rapid progress today in technology and its application in the plant sciences, the goal of APPS is to foster communication within the plant science community to advance scientific research. APPS is a publication of the Botanical Society of America, originating in 2009 as the American Journal of Botany''s online-only section, AJB Primer Notes & Protocols in the Plant Sciences. APPS publishes the following types of articles: (1) Protocol Notes describe new methods and technological advancements; (2) Genomic Resources Articles characterize the development and demonstrate the usefulness of newly developed genomic resources, including transcriptomes; (3) Software Notes detail new software applications; (4) Application Articles illustrate the application of a new protocol, method, or software application within the context of a larger study; (5) Review Articles evaluate available techniques, methods, or protocols; (6) Primer Notes report novel genetic markers with evidence of wide applicability.