Applications in Plant Sciences

Abstract 1. Biomes are continental-scale vegetation units widely used in ecological and evolutionary research, often together with large-scale species-distribution data. Many conceptually distinct biome schemes exist, differing in the underlying definition, delineation methodology and spatial resolution, yet most studies using biomes default to a few readily available schemes rather than choosing the one best suited to the research question. 2. We present biomes, an R package distributing spatially explicit raster layers of 31 published global biome schemes in a harmonised format at a 10 x 10 km resolution, together with metadata and a concise description. To facilitate their use, biomes provides a user-friendly classification workflow to classify occurrence records into specific biomes and a data-driven ranking algorithm to help researchers to choose the most suitable biome scheme for their data and research question in a transparent, reproducible way. 3. The biomes workflow can be applied to any dataset of georeferenced terrestrial occurrences. It (1) accepts either user-provided occurrence records or records downloaded by the package, (2) ranks the schemes according to their fit to the occurrence data, (3) assigns the records to specific biomes, and (4) summarises the resulting taxon-biome patterns. We demonstrate this workflow using 17,030 occurrence records of 185 species of Bombacoideae (Malvaceae). The analysis identified the vegetation-based biome scheme of Ramankutty and Foley (1999) as the best fit and recovered the forest and savanna biome signal known in the group from the literature, with tropical evergreen woodland being the biome with the largest share of records (46.1%) and highest number of species (162 of 185 species; 87.6%). 4. biomes provides user-friendly access to technically standardised and spatially explicit layers of 31 biome schemes, and thereby enables the transparent, data-driven choice of biome schemes tailored to a specific research data and questions, improving the reproducibility and the specificity of biome use across ecological and evolutionary research.

Breedbase ( is a comprehensive, web-based, open-source breeding management and analysis software for plant breeding that was introduced to address gaps with existing software for plant breeding data. Breedbase is continually improved and developed, with new features being added as required by the breeding community. Since its inception, the capabilities of Breedbase were improved to better serve the diverse data modeling needs of additional crops beyond our original ensemble of cassava, banana, yam, sweet potato, tomato, rice, and wheat. We describe here major updates that have been made in the past 4 years, including (1) improved handling of repetitive measurements; (2) a redesigned experimental treatments interface; (3) a more fine-grained user permission system; (4) improved trial quality control workflows; (5) intercropping support; (6) the addition of a decision meeting feature that helps decision makers make the best selection with the available data; (7) transgenics and tracking tools and finally, (8) an improved field layout tool which consolidates several trials in one field. These new features integrate seamlessly with the previous feature set of Breedbase - making it one of the most feature rich breeding management softwares available.

At large scales, our ability to compare genetic diversity across species is often challenged by the difficulty of extracting range-wide sequence data from online databases. Using mitochondrial DNA from amphibians and reptiles spanning across six regions: Brazilian Atlantic Forest, Iberian Peninsula, northwestern Africa, Arabian Peninsula, Madagascar and Australian Monsoonal Tropics, we develop and implement a framework to overcome common limitations in macrogenetics. We compiled and manually curated a spatially explicit genetic dataset from public repositories, quantified the spatial representativeness of genetic sampling across species' ranges and developed a novel interpolation framework to estimate and map species-specific patterns of intraspecific genetic diversity (ISD) while accounting for heterogeneous spatial sampling. We estimated mean nucleotide diversity across species to produce regional maps for each genetic marker. Our final dataset comprised 182 amphibian and 239 reptile species, encompassing 184 genera from 54 families. Range representativeness of genetic sampling was generally high, despite some variation across study regions. Spatial patterns of ISD revealed geographic heterogeneity, and were consistent across the best-sampled mitochondrial markers. By combining automated and manual georeferencing, quantifying range representativeness, and accounting for bias in spatial interpolations, our pipeline is marker-agnostic and directly applicable to nuclear and genomic data as they become available.

Premise:: Conifers comprise one-sixth of global forests and landscape genomic studies are urgently needed to understand their biology, yet relatively few conifer species have reference genomes. Targeted exon-capture is a practical alternative to whole-genome sequencing, but selecting loci without a reference genome remains a barrier. We developed a multi-tissue reference transcriptome for pi non pine (Pinus edulis) to ask how multi-tissue sampling improves probe design, and what is lost under a conventional single-tissue approach. Methods: We assembled a reference transcriptome from four diploid tissues (needle, phloem, root, vegetative bud) and one haploid tissue (megagametophyte), using the latter as a pseudo-guide. We designed a target-capture probe-set by prioritizing highly expressed P. edulis transcripts and transcripts matching a conifer exon database. Results: We recovered 76% of conserved plant genes with few duplicated transcripts. Filtering yielded 15,664 transcripts totaling 31.4 Mb, ~0.1% of the genome. Tissue-specific expression was substantial: 27.4% of transcripts occurred exclusively in non-needle tissues. Functional annotation confirmed complementary contributions, including seed-desiccation and germination genes from megagametophytes, and defense- and stress-associated genes from roots and phloem. Discussion: We built a transcriptome and efficient probe set using a roadmap suitable for other non-model plants with large genomes; keys to success were prioritizing complementary tissues and using expression-based filtering.

Chlorophyll fluorescence imaging is a powerful tool used to characterize abiotic and biotic stress response of photosynthesis in plants. However, extracting metrics such as Fv/Fm (maximum quantum yield of photosystem II) from chlorophyll fluorescence images remains a substantial bottleneck in large measurement campaigns, often requiring significant manual effort. To resolve this issue, we developed fvfmPy, a user-friendly program which enables rapid analysis of chlorophyll fluorescence images of arrays of leaf discs. fvfmPy uses algorithmic image segmentation to identify regions of interest and extract fluorescence metrics from tens or hundreds of leaf samples per image with little to no manual input. fvfmPy is developed in Python and includes a graphical user interface enabling intuitive parameter adjustment and real-time quality control checking. We additionally developed fvfmR, an R wrapper package for fvfmPy. Fv/Fm values extracted with fvfmPy were indistinguishable from those obtained using manual methods (r2 > 0.99) but required 94% less time to complete (13 s vs. 200 s per 100 measurements). fvfmPy and fvfmR were built to directly integrate with existing fluorescence software packages and can greatly increase throughput of fluorescence measurements to help address key challenges in agriculture and ecology.

Speciation can be a slow process for long-lived organisms with large populations due to persistent incomplete lineage sorting, while hybridisation can further obscure species boundaries through admixture. In eucalypts, there is tremendous morphological and environmental variation with almost a thousand described species, subspecies, and hybrid taxa falling into multiple subgenera and intermediate sections. Multilocus molecular studies have generated concatenated species trees supporting deeper divergence between the Corymbia + Blakella + Angophora clade with the rest of eucalypts. However, the proportion of gene trees that recover the concatenated tree topology, including the taxonomic relationships among individual subgenera and sections, remains underappreciated. In this study, we assessed the predictive power of current eucalypt taxonomy - represented as a concatenated tree - in explaining topological variation across gene trees. To do this, we first extracted 1,187 BUSCO loci from short-read data of 701 samples representing roughly 500 described species using CAPTUS. We then inferred individual gene trees and a concatenated species tree using IQ-TREE2. Finally, we calculated the concordance factors for every described subgenus and section on the concatenated tree, and developed a taxonomic consistency test that tracks the taxonomic placement of each sample across gene trees. We show that, even though most taxonomic groups are monophyletic on the concatenated tree with 100% bootstrap support, several groups exhibit very low concordance, indicating that most gene trees do not recover their relationships as represented by the concatenated tree. These results highlight important considerations for current eucalypt taxonomic classification and provide a phylogenomic inference pipeline that can be applied not only to future eucalypt studies, but also to other evolutionarily complex groups of taxa.

Automated monitoring camera systems offer an efficient approach for quantifying pollinator biodiversity and plant-pollinator interactions, but financial and logistical constraints limit the number of flowering plant species that can be monitored. Using a large European database of plant-pollinator networks, we evaluated whether plant subsampling can capture key metrics of interest (i.e. pollinator richness and network structure). We compared abundance-based, flower-shape-informed, phylogenetically informed and random plant sampling. Abundance-based sampling consistently outperformed random sampling, while adding flower shape provided little additional benefit and phylogenetic selection performed similarly to random sampling. Overall, monitoring 12-15 flowering species was sufficient to characterize key network properties across varying community sizes. Performance of abundance-based plant sampling declined in species-rich communities and when rare but highly attractive plants were present in the community, while a specific subset of pollinator species was consistently missed even by the best sampling strategy. Based on their relative contribution to interactions within each network, only 12.6% of pollinator species accounted for 95% of interactions, suggesting that AI classifiers could prioritize a relatively small subset of species. Our results provide practical guidance for designing efficient camera-based pollinator monitoring schemes at large spatial scales.

Myricaria Desv. (Tamaricaceae) is a genus of shrubs that play a keystone role in montane riparian ecosystems across Eurasia, shaping riverine habitats and supporting ecological function. The genus has a centre of origin in the Qinghai-Tibetan Plateau and exhibits a broad distribution, but many species boundaries are unresolved. Here, we present the first phylogenomic analysis of Myricaria using the Angiosperms353 target enrichment bait set, with sampling across its full geographic range. Off-target plastid loci were extracted from next-generation sequencing data using the CAPTUS bioinformatics pipeline. We infer species relationships using ASTRAL-IV and network analysis with SplitsTree, in conjunction with morphology. We also provide a taxonomic treatment of the genus, integrating our morphological observations and phylogenomic analyses for species delimitations. Based on our analyses, recognise two infrageneric sections: sect. Parallelantherae (containing only M. elegans) and sect. Myricaria (the remainder of the genus, with type species M. germanica). We also propose synonymising M. davurica, M. bracteata and M. squamosa with M. germanica. Although species boundaries in the Asian highlands remain unresolved, we conclude that the species M. elegans, M. germanica, M. laxiflora, M. longifolia, M. platyphylla, and M. pulcherrima are well-supported both phylogenomically and morphologically.

Abstract Background Many ecologically important small invertebrates are preserved in ethanol, which compromises DNA integrity and severely limits their potential to be utilized in further genomic research. The lack of soil arthropod representation ultimately results in limited availability of reference genomes and population genomic inference from pooled sequencing (Pool-seq). Results We introduce PoLoCo (Pooled Low-Coverage), a reproducible workflow integrating pooled specimen draft genome assembly from ethanol-preserved specimens with population-level allele-frequency analysis. We assembled a 411 Mb draft genome of Entomobrya nivalis from pooled short reads, achieving 97.7% BUSCO completeness despite notable fragmentation. Comparison with the published reference genome showed partial genomic correspondence while also revealing reference-dependent differences in the retained SNP datasets. The application of PoLoCo to 82 retained pooled populations produced a final distance-thinned allele-frequency dataset suitable for population-scale genomic inference. Case-study execution documented the computational requirements of the major workflow components on standard HPC infrastructure, with the workflow, documentation, and software environments openly available in Zenodo. Conclusions PoLoCo demonstrates that draft genome assemblies and reproducible Pool-seq allele-frequency analyses can be performed with long-preserved sample material from the field. PoLoCo integrates reference construction, sync-based allele-frequency analysis, and benchmarking into a free and open workflow. It establishes a reproducibility benchmark for biodiversity genomics and yields a transferable workflow for ecological and evolutionary initiatives where high-quality DNA or long-read sequencing is not feasible.

Premise of the study: Doryopteris J.Sm. is a genus of dry-adapted ferns found widely across the southern hemisphere and the tropics. In the Hawaiian Islands, the genus is represented by four endemic species and one putative hybrid. Two of the endemic species (D. decora and D. decipiens) are widespread across all major islands, while two (D. angelica and D. takeuchii) are federally and state-listed endangered single-island endemics.We aimed to resolve the evolutionary relationships and biogeographic history of Hawaiian Doryopteris. Methods: We used a target-capture approach and the GoFlag 408 flagellate land plant probe set to sequence more than 400 nuclear loci for 40 Hawaiian and 29 non-Hawaiian individuals in Doryopteris and the related genera Lytoneuron and Ormopteris. After processing the sequences into alignments and single nucleotide polymorphism sites, we performed phylogenetic inference and population-level principal component analyses. Key results: We infer that Hawaiian Doryopteris are monophyletic and are nested within the pantropical species D. concolor. Within Hawai'i, clades and clusters of individuals correspond to geographical, geological, and climatological factors, more so than species identifications, with a few exceptions. We posit that Hawaiian Doryopteris represents an incipient radiation at the very earliest stages of divergence, and that budding speciation has led to non-monophyly of several taxa. Conclusions Our inference using genome-wide sequencing data sheds light on the complex evolutionary history of Hawaiian Doryopteris. Overall, the phylogeny of Hawaiian Doryopteris shows strong links with geographical, geological, and climatological processes and little with morphological evolution.

BackgroundSecondary haplotigs retained in a primary assembly inflate genome size and duplicate gene content, and their removal remains a necessary curation step for heterozygous diploid genomes. HapSolo (Solares et al. 2021) treats this step as an optimization problem: it searches three pairwise alignment thresholds (percent identity, query coverage, and the ratio of aligned query to aligned reference length) to minimize a cost defined over single-copy, duplicated, and missing conserved orthologs. The original implementation depended on a separate BUSCO v3 run for every contig, on Python 2.7 and pandas in its inner loop, on a single randomized forward-walk optimizer, and on OrthoDB v9 lineage datasets that are no longer distributed. ResultsHere we report HapSolo2, a reimplementation that replaces the per-contig BUSCO step with miniprot-based ortholog classification against OrthoDB v9, v10, or v12.2 lineage datasets, adds steepest descent and simulated annealing to the original random forward walk, and evaluates all walkers in batched CuPy array operations on a single GPU. On an avocado assembly of 5,122 contigs scored against 1,440 orthologs, 100,000 iterations of random walk and steepest descent ran 5.6- and 7.6-fold faster on one NVIDIA RTX A6000 than on 10 CPU processes. Across 45 runs on four diploid assemblies (Anopheles funestus, Persea americana, Vitis vinifera, and Amblyraja radi-ata; 212 Mb to 3.2 Gb) and three OrthoDB releases, the three optimizers reached best costs within 1.8% of one another. Independent assessment with BUSCO v6.1 showed that HapSolo2 reduced duplicated orthologs from 52.0% to 5.6% in P. americana, from 17.9% to 10.4% in V. vinifera, and from 7.8% to 2.8% in A. funestus, with completeness decreasing by at most 1.3 percentage points. On the avocado assembly, HapSolo2 reduced the cost reached by the original HapSolo from 0.2200 to 0.1036 and raised contig N50 from 3,366 kb to 4,608 kb. ConclusionsHapSolo2 makes ortholog-guided haplotig removal practical in minutes on a single workstation, independent of a BUSCO installation, and reproducible across OrthoDB releases. It is distributed as an installable Python package with a unified command-line pipeline, an assembly statistics and plotting tool, an Apptainer container, and archived copies of the legacy ortholog datasets.

Understanding floral visitor dynamics along elevational gradients is essential for assessing potential implications for pollen limitation and population genetic processes in fragmented populations of cliff-dwelling alpine endemics. Physoplexis comosa (Campanulaceae) is a chasmophytic paleoendemic restricted to vertical rock faces on dolomitic and limestone bedrock from 200 to 2,800 m a.s.l. The species exhibits a specific floral morphology with fused corolla apices, basal openings for nectar access, and secondary pollen presentation, yet its functional floral visitor assemblage has remained poorly characterized across the species' elevational range. In this pilot study, we tested a two-tier camera-monitoring setup combining action cameras for diurnal monitoring with Raspberry Pi-based systems equipped with IR illumination for combined diurnal and nocturnal recording. We conducted monitoring across five elevational sites (1,480 to 2,400 m a.s.l.) in the Eastern Alps. Recorded video material was downsampled and annotated using the Addax platform alongside manual cross-validation to record physical contact events. Preliminary results confirm the technical feasibility of continuous monitoring in steep terrain and suggest possible elevational variation in diurnal visitors, alongside nocturnal activity by Geometridae moths. Camera-based observations provided higher functional resolution and revealed a different visitor assemblage than previous eDNA metabarcoding trials. This proof-of-concept establishes a methodological foundation for forthcoming multi-day monitoring campaigns and complements ongoing studies of the mating system and population-genetic analyses.

Motivation: Tree sets appear naturally when performing model based phylogenetic analyses. When tracking host-parasite co-evolution, testing sensitivity of different model assumptions, or even just checking convergence of individual runs of the same analysis, the question arises: are a given pair of tree sets the same? And if they are not the same, how do they differ? Results: We provide DensiTree 3, an interactive tool for inspecting pairs of sets of trees that allow visualising both tree sets using a single panel. Incorporation of CCDs provide better summary trees, taxon orderings and extra functionality like rogue detection, making it easier to interpret tree samples. Furthermore, a clade comparison graph allows inspection of differences in clade support and clade heights linked to the double-DensiTree panel. Together, these visualisations of tree sets make it easy to identify potential differences in the tree sets and interactively inspect these differences. Furthermore, recent advances in conditional clade distributions (CCDs) to represent tree distributions have been incorporated in DensiTree 3, and the most visible part of the user interface has been updated for a more modern look. Availability: DensiTree 3 is freely available from https://cs.auckland.ac.nz/~remco/DensiTree/. The program is licensed under GPL and source code is available via the link above.

Multiple sequence alignments (MSAs) are often treated as a solved problem. However, ambiguously or incorrectly aligned regions are the source of much error in phylogenetics and comparative genomics. CRAIC is a cross-platform desktop workbench that treats an alignment as a set of hypotheses to be stress-tested. CRAIC uses the posterior match probabilities of a three-state pair hidden Markov model (pair-HMM) to derive complementary views of alignment confidence. The five views are (i) a reliability overlay with live column and residue masking; (ii) a multi-aligner disagreement map, (iii) a local realignment sandbox, (iv) an interactive posterior explorer, and, where the true alignment is known, (v) a per-residue view of correctly predicted homologies. When present, MAFFT, MUSCLE, ProbCons, Clustal Omega, ClustalW and PRANK are integrated in the tool. In benchmarks the reliability score can locate misaligned columns, but while CRAIC can implement automatic filtering, it is primarily built for inspection and curation.

Ubiquitous in phylogenomic and population-genomic introgression work, the D-statistic has fallen victim to its own success. Its reliability is compromised by two major confounding factors: among-lineage substitution rate heterogeneity, which generates spurious signals of introgression, and unsampled "ghost" lineages, which mislead the characterization of introgression by erroneously attributing ghost ancestry to extant species. Here, we developed D-plus, a maximum-likelihood tool that explicitly accounts for both confounding factors to unpack the sources of significant D-statistic signals. Simulations demonstrate that D-plus effectively mitigates false introgression calls under among-lineage rate variation. Meanwhile, it retains high sensitivity for genuine introgression signals and correctly assigns detected signals to introgression involving ghost lineages, or solely between extant non-sister lineages, or a combination of the two. Our D-plus re-analysis of phylogenomic data from Jaltomata (Solanaceae) overturns prior interpretations of significant D-statistic signals, which were conventionally regarded as evidence for recurrent non-sister introgression. Instead, our results favor alternative explanations invoking artefacts driven by lineage-specific rate heterogeneity as well as two ghost introgression events. Collectively, D-plus addresses major methodological limitations inherent to traditional D-statistic analyses and enables accurate detection and characterization of introgression events.

Research in bioinformatics demands that many workflows and operations be consistently repeated across different tools. These operations include managing a taxdump, dealing with accession mappings, and performing analysis on the taxonomic tree. We introduce taxutils to address and alleviate the strain of these common workflows. To exhibit the extent of the package's capabilities, we introduce a strategy for assessing quality of an assembly via its potential impact on and perturbation of its mapping to a lowest-common-ancestor index. We illustrate the steps required for such an analysis, and show how the tool makes data management easy and otherwise-complex workflows simple.

Species delimitation increasingly relies on multiple sources of evidence, but most frameworks are still limited to genetics and morphology or analyze data types separately and compare results qualitatively. We present delimSOM 2.0, an R package that leverages unsupervised machine learning through multilayer self-organizing maps. Any kind of input data, including genomic, phenotypic, ecological, and spatial, is analyzed jointly, retaining each data type as a separate layer and balancing their contributions through layer-specific weighting. Our package provides functions for preprocessing, replicate training, multivariate clustering, hyperparameter optimization, model-quality assessment, and variable- and layer-importance analyses. We evaluated our approach using two simulation sets and eight empirical studies spanning diverse taxa and data types. Overall, performance was strongest with larger sample sizes (>10 individuals per lineage) and modest numbers of lineages (K < 5), but unlike most species delimitation methods, it can reliably infer K = 1. Across two-population demographic simulations of genetic data, our method performed similarly to the well-established DAPC across gene-flow and sampling regimes, was more conservative than STRUCTURE and sNMF in inferring K = 2 and showed mixed support for K = 1 and K = 2 at intermediate differentiation levels and with gene flow. Crucially, our method is robust to missing data and variation in hyperparameters. Empirical examples showed that genetic data were typically most influential, but phenotypic, ecological, and spatial layers often contributed substantial complementary information. By combining diverse data types into a single unsupervised analysis, delimSOM 2.0 enables fully integrative species delimitation, reduces over-splitting, and advances efforts toward more objective and standardized taxonomy and speciation biology.

Climate affects biological processes and organisms. It is extensively recorded, and its effects are frequently studied. Climatic variables are recorded as time series of measurements, but these are often summarized as long-term averages, throwing away biologically relevant information. Subsequent analyses can therefore miss important climate effects or suffer from low predictive power. Here, we propose a new method, StatClim, inferring the most informative climatic summary from the data. Instead of taking a fixed arbitrary summary of a time series, StatClim automatically estimates the beginning and width of a climatic window and simultaneously calculates data aggregation, which best predicts a response variable. We implemented the proposed approach as a Bayesian hierarchical model in Stan. We illustrated the potential of StatClim on published data on mast seeding of Fagus sylvatica. StatClim correctly identified known climatic windows relevant for this tree species, but also uncovered that mast seeding is triggered by high temperatures within these windows. We demonstrated flexibility of the model to account for complex data structures, we ran power analysis showing the ability of the model to identify climatic effects even with modest datasets, and we evaluated its false positive rate, which is lower than 0.01. We discuss the advantages and limitations of StatClim. Compared to existing alternatives, it is suitable for hypothesis testing and offers important advances, such as estimation of the type of aggregation. In combination with existing methods, StatClim will facilitate data-driven use of climatic and microclimatic data and therefore discovering so far unknown climatic effects on biological processes and ecosystem functioning.

1. Thermal performance curves (TPCs) are widely used to quantify temperature dependence of biological traits, but estimating them requires experiments across multiple temperatures. Because these experiments can be costly, researchers must balance the number and placement of temperature treatments against replication within treatments. Evenly spaced temperatures are a common default, but may allocate effort inefficiently when thermal responses are nonlinear or strongly asymmetric. 2. We developed TPCdesign, an R package for planning temperature-trait experiments. TPCdesign uses a pseudo-Bayesian (robust) D-optimal experimental design to identify sets of temperatures expected to provide the most information about a TPC, while allowing users to incorporate prior knowledge of thermal limits and curve shape, uncertainty in that knowledge, available numbers of treatments, and practical restrictions on temperature spacing. 3. Using simulation-based worked examples, we show that optimized designs can recover TPCs more accurately than evenly spaced designs, particularly for asymmetric curves and when the number of available temperature treatments is modest. Optimization primarily improves resolution of regions where performance changes rapidly and can substantially improve estimation of upper thermal limits while retaining information across the rest of the curve. 4. TPCdesign provides a practical framework for allocating limited experimental resources according to the biological response researchers seek to estimate. Rather than prescribing a universal set of temperatures, it allows experimental designs to reflect existing biological knowledge, uncertainty, and logistical constraints, improving the information gained from costly thermal performance experiments.

The evolutionary processes underlying successful invasions are often difficult to disentangle. Historical collections provide a temporal perspective, offering great potential to reconstruct invasion histories and identify the processes driving invasions. Japanese stiltgrass (Microstegium vimineum) is an invasive C4 grass introduced to eastern North America over a century ago and now established in 31 USA states. To reconstruct its invasion history, we leveraged low-coverage whole-genome sequencing of 142 herbarium and 330 contemporary samples spanning the temporal and spatial extent of the North American invasion and the native range in Asia. We identified three independent introductions in the USA: an awnless form in the South and two independent introductions of awned forms in the North, with limited admixture among introductions across space and time. In both regions, we observed high genetic diversity in the earliest available collections despite demographic bottlenecks and identified several putative selective sweeps in or near flowering-time genes, suggesting rapid evolution in the introduced range. Our analyses support separate invasions in the USA, likely facilitated by standing genetic variation and selection, but provide little support for admixture playing a major role. Our study highlights the power of historical collections to uncover the processes and mechanisms underlying successful biological invasions.