Viktor Domazetoski, Holger Kreft, Helena Bestova, Philipp Wieder, Radoslav Koynov, Alireza Zarei, Patrick Weigelt
Premise
Functional plant ecology seeks to understand how functional traits govern species distributions, community assembly, and ecosystem functions. While global trait datasets have advanced the field, substantial gaps remain, and extracting trait information from text in books, research articles, and online sources via machine learning offers a valuable complement to costly field campaigns.
Methods
We propose a natural language processing pipeline that extracts traits from unstructured species descriptions by using classification models for categorical traits and question-answering models for numerical traits. The pipeline's performance is evaluated on two large databases with over 50,000 species descriptions, utilizing approaches ranging from a keyword search to large language models.
Results
Our final optimized pipeline used a transformer architecture and obtained a mean precision of 90.8% (range 81.6–97%) and a mean recall of 88.6% (77.4–97%) across five categorical traits, representing a 9.83% increase in precision and 42.35% increase in recall over a regular expression-based approach. The question-answering model yielded a normalized mean absolute error of 10.3% averaged across three numerical traits.
Discussion
The natural language processing pipeline we propose has the potential to facilitate the digitization and extraction of large amounts of plant functional trait information residing in scattered textual descriptions.
{"title":"Using large language models to extract plant functional traits from unstructured text","authors":"Viktor Domazetoski, Holger Kreft, Helena Bestova, Philipp Wieder, Radoslav Koynov, Alireza Zarei, Patrick Weigelt","doi":"10.1002/aps3.70011","DOIUrl":"10.1002/aps3.70011","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Functional plant ecology seeks to understand how functional traits govern species distributions, community assembly, and ecosystem functions. While global trait datasets have advanced the field, substantial gaps remain, and extracting trait information from text in books, research articles, and online sources via machine learning offers a valuable complement to costly field campaigns.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We propose a natural language processing pipeline that extracts traits from unstructured species descriptions by using classification models for categorical traits and question-answering models for numerical traits. The pipeline's performance is evaluated on two large databases with over 50,000 species descriptions, utilizing approaches ranging from a keyword search to large language models.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our final optimized pipeline used a transformer architecture and obtained a mean precision of 90.8% (range 81.6–97%) and a mean recall of 88.6% (77.4–97%) across five categorical traits, representing a 9.83% increase in precision and 42.35% increase in recall over a regular expression-based approach. The question-answering model yielded a normalized mean absolute error of 10.3% averaged across three numerical traits.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>The natural language processing pipeline we propose has the potential to facilitate the digitization and extraction of large amounts of plant functional trait information residing in scattered textual descriptions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diego Marcos, Robert van de Vlasakker, Ioannis N. Athanasiadis, Pierre Bonnet, Hervé Goëau, Alexis Joly, W. Daniel Kissling, César Leblanc, André S. J. van Proosdij, Konstantinos P. Panousis
Premise
Plant morphological traits, their observable characteristics, are fundamental to understanding the role played by each species within its ecosystem; however, compiling trait information for even a moderate number of species is a demanding task that may take experts years to accomplish. At the same time, online species descriptions contain massive amounts of information about morphological traits, but the lack of structure makes this source of data impossible to use at scale.
Methods
To overcome this, we propose to leverage recent advances in large language models and devise a mechanism for gathering and processing plant trait information in the form of unstructured textual descriptions, without manual curation.
Results
We evaluate our approach by automatically replicating three manually created species–trait matrices. Our method found values for over half of all species–trait pairs, with an F1 score of over 75%.
Discussion
Our results suggest that large-scale creation of structured trait databases from unstructured online text is now feasible due to the information extraction capabilities of large language models. However, the process is currently limited by the availability of textual descriptions that cover all traits of interest.
{"title":"Fully automatic extraction of morphological traits from the web: Utopia or reality?","authors":"Diego Marcos, Robert van de Vlasakker, Ioannis N. Athanasiadis, Pierre Bonnet, Hervé Goëau, Alexis Joly, W. Daniel Kissling, César Leblanc, André S. J. van Proosdij, Konstantinos P. Panousis","doi":"10.1002/aps3.70005","DOIUrl":"10.1002/aps3.70005","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Plant morphological traits, their observable characteristics, are fundamental to understanding the role played by each species within its ecosystem; however, compiling trait information for even a moderate number of species is a demanding task that may take experts years to accomplish. At the same time, online species descriptions contain massive amounts of information about morphological traits, but the lack of structure makes this source of data impossible to use at scale.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To overcome this, we propose to leverage recent advances in large language models and devise a mechanism for gathering and processing plant trait information in the form of unstructured textual descriptions, without manual curation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We evaluate our approach by automatically replicating three manually created species–trait matrices. Our method found values for over half of all species–trait pairs, with an F1 score of over 75%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>Our results suggest that large-scale creation of structured trait databases from unstructured online text is now feasible due to the information extraction capabilities of large language models. However, the process is currently limited by the availability of textual descriptions that cover all traits of interest.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Poppy C. Northing, Jessie A. Pelosi, D. Lawrence Venable, Katrina M. Dlugosch
Premise
Pectocarya recurvata (Boraginaceae, subfamily Cynoglossoideae), a species native to the Sonoran Desert (North America), has served as a model system for a suite of ecological and evolutionary studies. However, no reference genomes are currently available in Cynoglossoideae. A high-quality reference genome for P. recurvata would be valuable for addressing questions in this system and across broader taxonomic scales.
Methods
Using PacBio HiFi sequencing, we assembled a reference genome for P. recurvata and annotated coding regions with full-length transcripts from an Iso-Seq library. We assessed genome completeness with BUSCO and k-mer analysis, and estimated the genome size of six individuals using flow cytometry.
Results
The chromosome-scale genome assembly for P. recurvata was 216.0 Mbp long (N50 = 12.1 Mbp). Previous observations indicated P. recurvata is 2n = 24. Our assembly included 12 primary contigs (158.3 Mbp) containing 30,655 genes with telomeres at 23 out of 24 ends. Flow cytometry measurements from the same population included two plants with 1C = 196.9 Mbp, the smallest measured for Boraginaceae, and four with 1C = 385.8 Mbp, which is consistent with tetraploidy in this population.
Discussion
The P. recurvata genome assembly and annotation provide a high-quality genomic resource in a sparsely represented area of the angiosperm phylogeny. This new reference genome will facilitate answering open questions in ecophysiology, biogeography, and systematics.
{"title":"Chromosome-scale reference genome of Pectocarya recurvata, the species with the smallest reported genome size in Boraginaceae","authors":"Poppy C. Northing, Jessie A. Pelosi, D. Lawrence Venable, Katrina M. Dlugosch","doi":"10.1002/aps3.70008","DOIUrl":"10.1002/aps3.70008","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p><i>Pectocarya recurvata</i> (Boraginaceae, subfamily Cynoglossoideae), a species native to the Sonoran Desert (North America), has served as a model system for a suite of ecological and evolutionary studies. However, no reference genomes are currently available in Cynoglossoideae. A high-quality reference genome for <i>P. recurvata</i> would be valuable for addressing questions in this system and across broader taxonomic scales.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using PacBio HiFi sequencing, we assembled a reference genome for <i>P. recurvata</i> and annotated coding regions with full-length transcripts from an Iso-Seq library. We assessed genome completeness with BUSCO and <i>k</i>-mer analysis, and estimated the genome size of six individuals using flow cytometry.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The chromosome-scale genome assembly for <i>P. recurvata</i> was 216.0 Mbp long (N50 = 12.1 Mbp). Previous observations indicated <i>P. recurvata</i> is 2<i>n</i> = 24. Our assembly included 12 primary contigs (158.3 Mbp) containing 30,655 genes with telomeres at 23 out of 24 ends. Flow cytometry measurements from the same population included two plants with 1C = 196.9 Mbp, the smallest measured for Boraginaceae, and four with 1C = 385.8 Mbp, which is consistent with tetraploidy in this population.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>The <i>P. recurvata</i> genome assembly and annotation provide a high-quality genomic resource in a sparsely represented area of the angiosperm phylogeny. This new reference genome will facilitate answering open questions in ecophysiology, biogeography, and systematics.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, plant science has seen transformative advances in scalable data collection for sequence and chemical data. These large datasets, combined with machine learning, have demonstrated that conducting plant metabolic research on large scales yields remarkable insights. A key next step in increasing scale has been revealed with the advent of accessible large language models, which, even in their early stages, can distill structured data from the literature. This brings us closer to creating specialized databases that consolidate virtually all published knowledge on a topic.
Methods
Here, we first test different combinations of prompt engineering techniques and language models in the identification of validated enzyme–product pairs. Next, we evaluate the application of automated prompt engineering and retrieval-augmented generation to identify compound–species associations. Finally, we build and determine the accuracy of a multimodal language model–based pipeline that transcribes images of tables into machine-readable formats.
Results
When tuned for each specific task, these methods perform with high (80–90%) or modest (50%) accuracies for enzyme–product pair identification and table image transcription, but with lower false-negative rates than previous methods (decreasing from 55% to 40%) for compound–species pair identification.
Discussion
We enumerate several suggestions for researchers working with language models, among which is the importance of the user's domain-specific expertise and knowledge.
{"title":"Advancing plant metabolic research by using large language models to expand databases and extract labeled data","authors":"Rachel Knapp, Braidon Johnson, Lucas Busta","doi":"10.1002/aps3.70007","DOIUrl":"10.1002/aps3.70007","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Recently, plant science has seen transformative advances in scalable data collection for sequence and chemical data. These large datasets, combined with machine learning, have demonstrated that conducting plant metabolic research on large scales yields remarkable insights. A key next step in increasing scale has been revealed with the advent of accessible large language models, which, even in their early stages, can distill structured data from the literature. This brings us closer to creating specialized databases that consolidate virtually all published knowledge on a topic.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Here, we first test different combinations of prompt engineering techniques and language models in the identification of validated enzyme–product pairs. Next, we evaluate the application of automated prompt engineering and retrieval-augmented generation to identify compound–species associations. Finally, we build and determine the accuracy of a multimodal language model–based pipeline that transcribes images of tables into machine-readable formats.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>When tuned for each specific task, these methods perform with high (80–90%) or modest (50%) accuracies for enzyme–product pair identification and table image transcription, but with lower false-negative rates than previous methods (decreasing from 55% to 40%) for compound–species pair identification.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>We enumerate several suggestions for researchers working with language models, among which is the importance of the user's domain-specific expertise and knowledge.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Makenzie Gibson, William Thives Santos, Alan R. Oyler, Lucas Busta, Craig A. Schenck
Premise
Specialized metabolites serve various roles for plants and humans. Unlike core metabolites, specialized metabolites are restricted to certain plant lineages; thus, in addition to their ecological functions, specialized metabolites can serve as diagnostic markers of plant lineages.
Methods
We investigated the phylogenetic distribution of plant metabolites using non-proteogenic amino acids (NPAA). Species–NPAA associations for eight NPAAs were identified from the existing literature and placed within a phylogenetic context using R packages and the Interactive Tree of Life. To confirm and extend the literature-based NPAA distribution, we selected azetidine-2-carboxylic acid (Aze) and screened over 70 diverse plants using gas chromatography–mass spectrometry (GC-MS).
Results
Literature searches identified 163 NPAA-relevant articles, which were manually inspected to identify 822 species–NPAA associations. NPAAs were mapped at the order and genus level, revealing that some NPAAs are restricted to single orders, whereas others are present across divergent taxa. The observed distribution of Aze across plants and ancestral state reconstruction suggests a convergent evolutionary history.
Discussion
Although reliance on chemotaxonomy has decreased in recent years, there is still value in placing metabolites within a phylogenetic context to understand the evolutionary processes of plant chemical diversification. This approach can be applied to metabolites present in any organism and compared at a range of taxonomic levels.
{"title":"A new spin on chemotaxonomy: Using non-proteogenic amino acids as a test case","authors":"Makenzie Gibson, William Thives Santos, Alan R. Oyler, Lucas Busta, Craig A. Schenck","doi":"10.1002/aps3.70006","DOIUrl":"10.1002/aps3.70006","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Specialized metabolites serve various roles for plants and humans. Unlike core metabolites, specialized metabolites are restricted to certain plant lineages; thus, in addition to their ecological functions, specialized metabolites can serve as diagnostic markers of plant lineages.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We investigated the phylogenetic distribution of plant metabolites using non-proteogenic amino acids (NPAA). Species–NPAA associations for eight NPAAs were identified from the existing literature and placed within a phylogenetic context using R packages and the Interactive Tree of Life. To confirm and extend the literature-based NPAA distribution, we selected azetidine-2-carboxylic acid (Aze) and screened over 70 diverse plants using gas chromatography–mass spectrometry (GC-MS).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Literature searches identified 163 NPAA-relevant articles, which were manually inspected to identify 822 species–NPAA associations. NPAAs were mapped at the order and genus level, revealing that some NPAAs are restricted to single orders, whereas others are present across divergent taxa. The observed distribution of Aze across plants and ancestral state reconstruction suggests a convergent evolutionary history.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>Although reliance on chemotaxonomy has decreased in recent years, there is still value in placing metabolites within a phylogenetic context to understand the evolutionary processes of plant chemical diversification. This approach can be applied to metabolites present in any organism and compared at a range of taxonomic levels.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Georgina González-Rebeles, Miguel Ángel Alonso-Arevalo, Eulogio López, Rodrigo Méndez-Alonzo
Premise
The quantification of plant drought resistance, particularly embolism formation, within and across species, is critical for ecosystem management and agriculture. We developed a cost-effective protocol to measure the water potential at which 50% of hydraulic conductivity (P50) is lost in stems, using affordable and accessible materials in comparison to the traditional optical method.
Methods and Results
Our protocol uses inexpensive USB microscopes, which are secured along with the plants to a pegboard base to avoid movement. A Python program automatized the image acquisition. This method was applied to quantify P50 in an exotic species (Nicotiana glauca) and native species (Rhus integrifolia) of the Mediterranean vegetation in Baja California, Mexico.
Conclusions
The intra- and interspecific patterns of variation in stem P50 of N. glauca and R. integrifolia were obtained using the low-cost optical method with widely available and affordable materials that can be easily replicated for other species.
{"title":"A low-cost protocol for the optical method of vulnerability curves to calculate P50","authors":"Georgina González-Rebeles, Miguel Ángel Alonso-Arevalo, Eulogio López, Rodrigo Méndez-Alonzo","doi":"10.1002/aps3.70004","DOIUrl":"10.1002/aps3.70004","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>The quantification of plant drought resistance, particularly embolism formation, within and across species, is critical for ecosystem management and agriculture. We developed a cost-effective protocol to measure the water potential at which 50% of hydraulic conductivity (<i>P</i><sub>50</sub>) is lost in stems, using affordable and accessible materials in comparison to the traditional optical method.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>Our protocol uses inexpensive USB microscopes, which are secured along with the plants to a pegboard base to avoid movement. A Python program automatized the image acquisition. This method was applied to quantify <i>P</i><sub>50</sub> in an exotic species (<i>Nicotiana glauca</i>) and native species (<i>Rhus integrifolia</i>) of the Mediterranean vegetation in Baja California, Mexico.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The intra- and interspecific patterns of variation in stem <i>P</i><sub>50</sub> of <i>N. glauca</i> and <i>R. integrifolia</i> were obtained using the low-cost optical method with widely available and affordable materials that can be easily replicated for other species.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carl J. Rothfels, Jaemin Lee, Michael A. Sundue, Alan R. Smith, Amy Kasameyer, Joyce Gross, Garth Holman, Shusheng Hu, Matt von Konrat, Emily B. Sessa, Kimberly Watson, Alan Weakley, Libing Zhang, Patricia Gensel, Michael Hassler, Katelin D. Pearson, Ed Gilbert, Robyn J. Burnham, Richard K. Rabeler, Patrick Sweeney, Alejandra Vasco, Weston Testo, David E. Giblin, Stefanie M. Ickert-Bond, Margaret Landis, Melanie Link-Perez, Tatyana Livshultz, Ian Miller, Christopher Neefus, Kathleen Pigg, Mitchell Power, Alan Prather, Tiana Rehman, Lena Struwe, Michael Vincent, George Weiblen, Timothy Whitfeld, Michael D. Windham, George Yatskievych, Aaron Liston, Elizabeth Makings, Kathleen M. Pryer, Caroline Strömberg, Eve Atri, Jason Best, Ian Glasspool, Layne Huiet, Elizabeth Johnson, Megan R. King, Az Klymiuk, Richard Lupia, Lucas C. Majure, Carol Ann McCormick, Richard McCourt, Shanna Oberreiter, Kent D. Perkins, Yarency Rodriguez, Chelsea Smith, James Solomon, Jordan Teisher, Donna Ford-Werntz, Petra Fuehrding-Potschkat, Holly Little, Tom A. Ranker, Eric Schuettpelz, Carrie M. Tribble, Diane M. Erwin, Cindy V. Looy
Premise
Pteridophytes—vascular land plants that disperse by spores—are a powerful system for studying plant evolution, particularly with respect to the impact of abiotic factors on evolutionary trajectories through deep time. However, our ability to use pteridophytes to investigate such questions—or to capitalize on the ecological and conservation-related applications of the group—has been impaired by the relative isolation of the neo- and paleobotanical research communities and by the absence of large-scale biodiversity data sources.
Methods
Here we present the Pteridophyte Collections Consortium (PCC), an interdisciplinary community uniting neo- and paleobotanists, and the associated PteridoPortal, a publicly accessible online portal that serves over three million pteridophyte records, including herbarium specimens, paleontological museum specimens, and iNaturalist observations. We demonstrate the utility of the PteridoPortal through discussion of three example PteridoPortal-enabled research projects.
Results
The data within the PteridoPortal are global in scope and are queryable in a flexible manner. The PteridoPortal contains a taxonomic thesaurus (a digital version of a Linnaean classification) that includes both extant and extinct pteridophytes in a common phylogenetic framework. The PteridoPortal allows applications such as greatly accelerated classic floristics, entirely new “next-generation” floristic approaches, and the study of environmentally mediated evolution of functional morphology across deep time.
Discussion
The PCC and PteridoPortal provide a comprehensive resource enabling novel research into plant evolution, ecology, and conservation across deep time, facilitating rapid floristic analyses and other biodiversity-related investigations, and providing new opportunities for education and community engagement.
{"title":"The PteridoPortal: A publicly accessible collection of over three million records of extant and extinct pteridophytes","authors":"Carl J. Rothfels, Jaemin Lee, Michael A. Sundue, Alan R. Smith, Amy Kasameyer, Joyce Gross, Garth Holman, Shusheng Hu, Matt von Konrat, Emily B. Sessa, Kimberly Watson, Alan Weakley, Libing Zhang, Patricia Gensel, Michael Hassler, Katelin D. Pearson, Ed Gilbert, Robyn J. Burnham, Richard K. Rabeler, Patrick Sweeney, Alejandra Vasco, Weston Testo, David E. Giblin, Stefanie M. Ickert-Bond, Margaret Landis, Melanie Link-Perez, Tatyana Livshultz, Ian Miller, Christopher Neefus, Kathleen Pigg, Mitchell Power, Alan Prather, Tiana Rehman, Lena Struwe, Michael Vincent, George Weiblen, Timothy Whitfeld, Michael D. Windham, George Yatskievych, Aaron Liston, Elizabeth Makings, Kathleen M. Pryer, Caroline Strömberg, Eve Atri, Jason Best, Ian Glasspool, Layne Huiet, Elizabeth Johnson, Megan R. King, Az Klymiuk, Richard Lupia, Lucas C. Majure, Carol Ann McCormick, Richard McCourt, Shanna Oberreiter, Kent D. Perkins, Yarency Rodriguez, Chelsea Smith, James Solomon, Jordan Teisher, Donna Ford-Werntz, Petra Fuehrding-Potschkat, Holly Little, Tom A. Ranker, Eric Schuettpelz, Carrie M. Tribble, Diane M. Erwin, Cindy V. Looy","doi":"10.1002/aps3.70003","DOIUrl":"10.1002/aps3.70003","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Pteridophytes—vascular land plants that disperse by spores—are a powerful system for studying plant evolution, particularly with respect to the impact of abiotic factors on evolutionary trajectories through deep time. However, our ability to use pteridophytes to investigate such questions—or to capitalize on the ecological and conservation-related applications of the group—has been impaired by the relative isolation of the neo- and paleobotanical research communities and by the absence of large-scale biodiversity data sources.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Here we present the Pteridophyte Collections Consortium (PCC), an interdisciplinary community uniting neo- and paleobotanists, and the associated PteridoPortal, a publicly accessible online portal that serves over three million pteridophyte records, including herbarium specimens, paleontological museum specimens, and iNaturalist observations. We demonstrate the utility of the PteridoPortal through discussion of three example PteridoPortal-enabled research projects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The data within the PteridoPortal are global in scope and are queryable in a flexible manner. The PteridoPortal contains a taxonomic thesaurus (a digital version of a Linnaean classification) that includes both extant and extinct pteridophytes in a common phylogenetic framework. The PteridoPortal allows applications such as greatly accelerated classic floristics, entirely new “next-generation” floristic approaches, and the study of environmentally mediated evolution of functional morphology across deep time.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>The PCC and PteridoPortal provide a comprehensive resource enabling novel research into plant evolution, ecology, and conservation across deep time, facilitating rapid floristic analyses and other biodiversity-related investigations, and providing new opportunities for education and community engagement.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Isabella Niewiadomski, Monica Antonio, Luiza Maria T. Aparecido, Mickey Boakye, Sonoma Carlos, Andrea Echevarria, Adrian Fontao, Joseph Mann, Ilaíne Silveira Matos, Norma Salinas, Bradley Vu, Benjamin Wong Blonder
Premise
Leaf venation network architecture can provide insights into plant evolution, ecology, and physiology. Venation networks are typically assessed through histological methods, but existing protocols provide limited guidance on processing large or challenging leaves.
Methods and results
We present an illustrated protocol for visualizing whole leaf venation networks, including sample preparation, clearing, staining, mounting, imaging, and archiving steps. The protocol also includes supply lists, troubleshooting procedures, safety considerations, and examples of successful and unsuccessful outcomes. The protocol is suitable for a wide range of leaf sizes and morphologies and has been used with all major plant groups.
Conclusion
We provide a workflow for obtaining high-quality mounts and images of venation networks of a wide range of species, using readily available materials.
{"title":"A comprehensive illustrated protocol for clearing, mounting, and imaging leaf venation networks","authors":"Isabella Niewiadomski, Monica Antonio, Luiza Maria T. Aparecido, Mickey Boakye, Sonoma Carlos, Andrea Echevarria, Adrian Fontao, Joseph Mann, Ilaíne Silveira Matos, Norma Salinas, Bradley Vu, Benjamin Wong Blonder","doi":"10.1002/aps3.70002","DOIUrl":"10.1002/aps3.70002","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Leaf venation network architecture can provide insights into plant evolution, ecology, and physiology. Venation networks are typically assessed through histological methods, but existing protocols provide limited guidance on processing large or challenging leaves.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and results</h3>\u0000 \u0000 <p>We present an illustrated protocol for visualizing whole leaf venation networks, including sample preparation, clearing, staining, mounting, imaging, and archiving steps. The protocol also includes supply lists, troubleshooting procedures, safety considerations, and examples of successful and unsuccessful outcomes. The protocol is suitable for a wide range of leaf sizes and morphologies and has been used with all major plant groups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We provide a workflow for obtaining high-quality mounts and images of venation networks of a wide range of species, using readily available materials.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xinyu Yuan, Nathaniel S. S. Smith, Gaurav D. Moghe
Plant metabolomes are structurally diverse. One of the most popular techniques for sampling this diversity is liquid chromatography–mass spectrometry (LC-MS), which typically detects thousands of peaks from single organ extracts, many representing true metabolites. These peaks are usually annotated using in-house retention time or spectral libraries, in silico fragmentation libraries, and increasingly through computational techniques such as machine learning. Despite these advances, over 85% of LC-MS peaks remain unidentified, posing a major challenge for data analysis and biological interpretation. This bottleneck limits our ability to fully understand the diversity, functions, and evolution of plant metabolites. In this review, we first summarize current approaches for metabolite identification, highlighting their challenges and limitations. We further focus on alternative strategies that bypass the need for metabolite identification, allowing researchers to interpret global metabolic patterns and pinpoint key metabolite signals. These methods include molecular networking, distance-based approaches, information theory–based metrics, and discriminant analysis. Additionally, we explore their practical applications in plant science and highlight a set of useful tools to support researchers in analyzing complex plant metabolomics data. By adopting these approaches, researchers can enhance their ability to uncover new insights into plant metabolism.
{"title":"Analysis of plant metabolomics data using identification-free approaches","authors":"Xinyu Yuan, Nathaniel S. S. Smith, Gaurav D. Moghe","doi":"10.1002/aps3.70001","DOIUrl":"10.1002/aps3.70001","url":null,"abstract":"<p>Plant metabolomes are structurally diverse. One of the most popular techniques for sampling this diversity is liquid chromatography–mass spectrometry (LC-MS), which typically detects thousands of peaks from single organ extracts, many representing true metabolites. These peaks are usually annotated using in-house retention time or spectral libraries, in silico fragmentation libraries, and increasingly through computational techniques such as machine learning. Despite these advances, over 85% of LC-MS peaks remain unidentified, posing a major challenge for data analysis and biological interpretation. This bottleneck limits our ability to fully understand the diversity, functions, and evolution of plant metabolites. In this review, we first summarize current approaches for metabolite identification, highlighting their challenges and limitations. We further focus on alternative strategies that bypass the need for metabolite identification, allowing researchers to interpret global metabolic patterns and pinpoint key metabolite signals. These methods include molecular networking, distance-based approaches, information theory–based metrics, and discriminant analysis. Additionally, we explore their practical applications in plant science and highlight a set of useful tools to support researchers in analyzing complex plant metabolomics data. By adopting these approaches, researchers can enhance their ability to uncover new insights into plant metabolism.</p>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hanane Ariouat, Youcef Sklab, Edi Prifti, Jean-Daniel Zucker, Eric Chenin
Premise
Deep learning has become increasingly important in the analysis of digitized herbarium collections, which comprise millions of scans that provide valuable resources for studying plant evolution and biodiversity. However, leveraging deep learning algorithms to analyze these scans presents significant challenges, partly due to the heterogeneous nature of the non-plant material that forms the background of the scans. We hypothesize that removing such backgrounds can improve the performance of these algorithms.
Methods
We propose a novel method based on deep learning to segment and generate plant masks from herbarium scans and subsequently remove the non-plant backgrounds. The semi-automatic preprocessing stages involve the identification and removal of non-plant elements, substantially reducing the manual effort required to prepare the training dataset.
Results
The results highlight the importance of effective image segmentation, which achieved an F1 score of up to 96.6%. Moreover, when used in classification models for plant morphological trait identification, the images resulting from segmentation improved classification accuracy by up to 3% and F1 score by up to 7% compared to non-segmented images.
Discussion
Our approach isolates plant elements in herbarium scans by removing background elements to improve classification tasks. We demonstrate that image segmentation significantly enhances the performance of plant morphological trait identification models.
{"title":"Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentation","authors":"Hanane Ariouat, Youcef Sklab, Edi Prifti, Jean-Daniel Zucker, Eric Chenin","doi":"10.1002/aps3.70000","DOIUrl":"10.1002/aps3.70000","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Deep learning has become increasingly important in the analysis of digitized herbarium collections, which comprise millions of scans that provide valuable resources for studying plant evolution and biodiversity. However, leveraging deep learning algorithms to analyze these scans presents significant challenges, partly due to the heterogeneous nature of the non-plant material that forms the background of the scans. We hypothesize that removing such backgrounds can improve the performance of these algorithms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We propose a novel method based on deep learning to segment and generate plant masks from herbarium scans and subsequently remove the non-plant backgrounds. The semi-automatic preprocessing stages involve the identification and removal of non-plant elements, substantially reducing the manual effort required to prepare the training dataset.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The results highlight the importance of effective image segmentation, which achieved an F1 score of up to 96.6%. Moreover, when used in classification models for plant morphological trait identification, the images resulting from segmentation improved classification accuracy by up to 3% and F1 score by up to 7% compared to non-segmented images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>Our approach isolates plant elements in herbarium scans by removing background elements to improve classification tasks. We demonstrate that image segmentation significantly enhances the performance of plant morphological trait identification models.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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