Applications in Plant Sciences最新文献

Premise: Despite the availability of several plant trait databases, significant traits remain undocumented for a substantial number of species. To address this issue, the current study explored digital text from the Global Biodiversity Information Facility (GBIF) as an innovative source of plant trait information and evaluated its capacity to reduce existing deficiencies.

Methods: Data were gathered on lifespan (LS), growth form (GF), flower color (FC), and maximum plant height (PH _max ) for vascular plants from GBIF and four common plant trait databases. The resulting dataset was used to evaluate the novelty and consistency of trait values derived from GBIF's digital text relative to those from other databases.

Results: Incorporation of GBIF-extracted trait data led to increases of 1.2-, 1.2-, 5.5-, and 2.7-fold in the number of species with documented LS, GF, FC, and PH _max traits, respectively. GBIF's trait data demonstrated high congruence with other databases, except in the case of FC. Additionally, a comprehensive dataset compiling these trait values from GBIF and the four databases was created, offering a valuable resource for future plant trait research.

Discussion: Our findings suggest that digital text in GBIF represents a significant source of plant trait information and can effectively help bridge information gaps.

Premise: This study investigates the value of scale-space representations of spatial features for the identification and mapping of invasive plants using high-resolution multispectral imagery. Scale-space representations combine the two-dimensional spatial domain with a third dimension representing spatial scale, such that features can be represented across both space and image spatial resolution.

Methods: Gaussian pyramids were constructed to create representations of the features across five image resolution levels (between 3 and 46 cm) within the scale space. A case study was employed to evaluate the importance of features across the scale space for the identification and mapping of spotted knapweed in a grassland ecosystem using multispectral imagery acquired using a remotely piloted aircraft system (RPAS). Classification was performed using a random forest classifier that was constructed using a process that included feature and hyperparameter optimization.

Results: The results of this case study show that classifiers constructed with only high spatial resolution features were associated with better classification performance than those constructed with only lower spatial resolution features; however, the best classification is achieved by leveraging features from throughout the feature scale spaces.

Discussion: These findings suggest that optimization of the spatial scale at which features are computed can substantially improve remote sensing image analysis and potentially inform RPAS flight planning to improve mission efficiency.

Premise: Herbarium specimens represent critical historical records of plant phenology, yet automating annotation of reproductive structures remains challenging given the diversity of floral morphologies, specimen age and quality, and image quality.

Methods: Here, we present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community. After testing multiple strategies for generating training data, we found that in-house expert-curated annotations were essential for producing reliable results.

Results: Expert validation found that the modeling pipeline detected present floral structures with relatively strong accuracy but had moderately high false negative rates. Applying the ensemble to our filtered final image dataset of 22 million records resulted in 11.1 million records labeled with flowers present. However, only 2.9 million of these contained the complete metadata necessary for downstream phenology research, highlighting the need for full label digitization efforts.

Discussion: The dataset presented here represents a large compilation of historical herbarium-derived phenology records available as a resource for the phenology community. We end by demonstrating how integrating these machine-labeled records into Phenobase, a publicly available phenology database, expands taxonomic and temporal coverage for large-scale phenological analyses.

Premise: Advances in the digitization of herbarium collections are enabling open access to specimen data for research and conservation. In Brazil, the Herbário Virtual Reflora (HVR) hosts over 4.8 million high-resolution images of botanical specimens and their associated data from 86 national and international herbaria.

Methods and results: To facilitate access and use of HVR data in biodiversity research, we developed the R package refloraR. This tool interacts with the HVR Integrated Publishing Toolkit (IPT) to download, parse, summarize, and filter herbarium records in Darwin Core Archive (DwC-A) and dataframes. It also retrieves occurrence records and indeterminate specimens for any hierarchical level. We provide a working example that combines the refloraR functions for quantifying indeterminate specimens as an indicator of taxonomic gaps within and among collections.

Conclusions: The refloraR package supports reproducible workflows in systematics, conservation, and ecology. By enabling the efficient retrieval of specimen data, it integrates the HVR into modern biodiversity informatics and democratizes access to natural history collections.

Premise: Herbarium specimens are increasingly used to extract morphological traits for ecological and evolutionary studies, yet the effects of tissue desiccation on trait measurements remain poorly understood. Here, we tested two hypotheses: (H1) whether higher tissue water content leads to greater measurement changes after herborization and (H2) whether fresh trait values can be reliably predicted from measurements of herborized specimens.

Methods: We evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species. Phylogenetic least squares models and machine learning regressions were used to test H1 and H2.

Results: Leaves and flowers generally shrank after herborization, fruits size metrics tended to increase, and seeds were largely unaffected. Water content was significantly associated with the magnitude of herborization effects in flowers and some leaf and seed traits. Fresh trait values were accurately predicted from measurements of herborized specimens. Prediction errors were lowest for leaf traits, followed by fruits, flowers, and seeds.

Discussion: These results partially support H1 and support H2, indicating that herborized specimens can be reliably used for trait analyses when organ-specific responses are considered, providing a practical framework to account for potential desiccation bias in functional trait research.

Premise: Road verges function as refuges for semi-natural species, but they can also facilitate the spread of non-native plants such as Lupinus polyphyllus. Unmanned aerial vehicle (UAV)-based remote sensing is a promising tool for mapping these species; however, its application in roadside contexts remains limited.

Methods: We developed and evaluated a UAV-based workflow to detect and map lupine presence and density along roads. Red-green-blue (RGB) imagery was processed into orthomosaics and classified using object-based and pixel-based approaches with support vector machines and random forest algorithms under multiple classification schemes.

Results: The highest accuracy (88.8%) was achieved using pixel-based random forest classification and a simplified scheme. Flowering purple lupine showed strong classification performance (F1 score = 96.3%), while early-stage greenish individuals were harder to detect (74.5%). Spatial heatmap comparison revealed low overestimation (1.79%) and underestimation (1.47%).

Discussion: Assuming optimal phenological conditions, UAV-based mapping provides a scalable and cost-efficient complement to ground surveys for the management of L. polyphyllus within infrastructure areas.

Premise: Ex situ spore banks are essential for fern conservation and research, yet traditional germination tests for assessing spore quality are time consuming and labor intensive. We explored whether electrical conductivity (EC), a rapid proxy for seed vigor, could offer an efficient alternative for evaluating fern spore quality.

Methods: Using chlorophyllous spores of Plenasium banksiifolium, we developed a microscale EC protocol (0.01-g spores, 1-h immersion) to track EC and germination over eight weeks of storage. Spore longevity (p ₅₀) was estimated via probit analysis, and correlations among EC, viability, and longevity were assessed.

Results: Fresh spores from different individual plants showed varied initial EC (EC₀) values, with lower EC₀ values associated with longer longevity (p ₅₀). During storage, EC values increased over time, while germination percentage declined. The relative EC ratio (EC_i/EC₀) showed significant negative correlations with germination percentage, with a ratio of ~2 indicating 50% viability loss and ~3 signaling complete viability loss.

Discussion: The EC test presents a promising approach for rapidly assessing spore viability and longevity. Lower initial EC (EC₀) values predicted greater initial spore viability and longer longevity, likely reflecting differences in spore maturity. The relative EC_i/EC₀ ratio effectively tracked viability loss, supporting its use in improving spore bank management.

[This corrects the article DOI: 10.1002/aps3.11496.].