Applications in Plant Sciences

Pangenome-level gene family identification often applies sequence similarity clustering without phylogenetic or synteny information, which risks biologically misleading evolutionary inferences. Using five transcription factor families (bHLH, MYB, NAC, WRKY, MADS-box) across 401 rice pangenome accessions, we compared clustering strategies: OrthoFinder alone, cd-hit alone, MMseqs2 alone, and OrthoFinder-informed refinement by cd-hit or MMseqs2. Methods solely based on sequence similarity merged distinct orthogroups and generated fewer orthogroups than approaches incorporating graph-based orthology. Conflicting cluster assignments, measured against OrthoFinder, varied strongly among families, from approximately 14% in MADS-box to approximately 57% in MYB, and were associated with protein length differences. Core, shell, and cloud gene classifications shifted substantially depending on the method, especially in MYB, NAC, and WRKY families. Critically, Ka/Ks distributions for core genes were highly method-sensitive, with orthology-aware methods yielding more convergent and less variable estimates of selective pressure, whereas noncore gene estimates remained robust. These findings demonstrate that neglecting graph-based orthogroup inference inflates methodological artifacts. We recommend a two-step strategy: initial graph-based orthogroup delineation followed by sequence similarity refinement to balance evolutionary accuracy and resolution in pangenome-scale gene family studies.

Forest trees pose numerous potential challenges to phylogenomic inference. Their large effective population sizes and relatively long generation times lead to deep allele coalescence and consequently incomplete lineage sorting (ILS), which biases inferences of divergence times toward older ages and introduces gene tree discordance. Deep phylogenetic divergences, reaching back into the Paleocene, introduce reference-mapping biases. Introgression--the movement of genes between lineages--may result in different phylogenies being inferred depending on which individuals are included in analysis, even if the plurality of the genome favors the divergence history unaffected by introgression. These factors influence phylogenetic inference across the Tree of Life but are particularly prevalent in forest trees. Oaks (Quercus) are notable for all three influences. In addition, our knowledge of the oak phylogeny is currently based strongly on restriction site associated DNA sequencing (RADseq) datasets published over the past decade, which may introduce additional sources of uncertainty. In this chapter, we analyze a 322-species RADseq dataset and genome resequencing data from across the genus to address sources of uncertainty in our understanding of the global oak phylogeny, which we hope will serve as a model for other research groups working on comparable woody plant groups.

Accurate quantification of leaf lesion severity is essential for plant disease research and phenotyping but is often limited by subjective visual scoring and time-intensive manual image analysis. We present LIME, a fully automated, open-source image analysis pipeline for high-throughput quantification of leaf lesions from disease assay images. LIME integrates zero-shot leaf segmentation using the Segment Anything Model with a convolutional neural network for lesion area estimation. Applied to Arabidopsis thaliana leaves infected with Sclerotinia sclerotiorum, the proposed approach achieved a mean absolute percentage error of 12.9%, comparable to observed intrarater variability in manual scoring. Stratified evaluation across lesion-size groups demonstrated consistent prediction accuracy for small, intermediate, and large lesions, and comparative analysis showed that the deep learning-based model substantially outperformed color-based baseline methods. Under GPU-accelerated execution, LIME processed complete assays containing approximately 200 leaves in 15 minutes, representing an approximate 13-fold reduction in processing time relative to manual annotation. Together, these results indicate that LIME enables objective, reproducible, and scalable quantification of leaf lesion severity in standardized plant pathology assays. The pipeline is released as an open-source tool to support quantitative phenotyping studies.

PremiseCentaurea melitensis (Asteraceae) is a problematic invader of grasslands globally, but little is known about its genetic makeup. Here we develop a reference genome to facilitate studies of its invasion history, genetic variation, and evolution. MethodsInbred offspring of a single individual of C. melitensis from its invasion of California, USA were used for flow cytometry to estimate genome size, and for genomic DNA extraction. DNA was sequenced with PacBio HiFi technology (yield = 85.7Gb). The genome was assembled with Hifiasm and annotated with BRAKER3. GENESPACE was used to compare gene order (synteny) with three other species within the subfamily Cichorioideae. ResultsWe estimated a mean genome size of 795.0 Mbp for C. melitensis, and our assembly totaled 696.6 Mbp in 48 contigs (N50 = 55.6 Mbp; BUSCO = 98%), with annotation of 25,157 protein-encoding genes. This included four telomere-to-telomere putative chromosomes, nine additional chromosome arms terminated by telomeric repeats, and a complete chloroplast genome. Synteny varied markedly across the genus and subfamily, suggesting a dynamic history of structural variation in the lineage of C. melitensis. DiscussionWe provide a highly complete and contiguous genome assembly to facilitate the further study of genomic variation in C. melitensis.

O_LIHerbivory is a major biotic stress for plants, triggering the induction and modulation of diverse specialized metabolites. Such induction responses are well studied for leaves and have been shown to depend on the herbivore feeding mode. Little is known about changes in flower metabolites and chemodiversity due to florivory type. Moreover, we lack an understanding of the intraspecific variation in such responses and whether these are spatially structured. C_LIO_LIThe aromatic plant Tanacetum vulgare, which shows high intraspecific chemodiversity in terpene profiles, was used to examine chemotype-specific metabolic responses of flower heads to infestation by the inflorescence-infesting aphid Macrosiphoniella tanacetaria or the flower-feeding beetle Olibrus spp. under field conditions. At peak flowering, each plant received both florivory treatments on separate stems, leaving one stem herbivore-free as a control. After four days, flower heads were harvested to analyze terpenes (GC-MS) and metabolic fingerprints (LC-MS). C_LIO_LIWe found stem-specific floral metabolic responses, with florivory altering specific chemical families and their chemodiversity. Levels of a few terpenes decreased following infestation, while none increased. Untargeted analyses revealed that aphid infestation had a lower effect on flower chemistry than beetle infestation, with aphid infestation mainly causing decreases and beetle infestation predominantly leading to increases in some metabolite intensities, but little overlap across treatments and chemotypes. C_LIO_LIOur results demonstrate that floral metabolic responses to florivory are spatially structured, florivore type-specific and shaped by plant chemotype. These findings highlight that the interplay between vascular organization, insect feeding mode, and intraspecific chemodiversity governs how flowers adjust their chemical defenses. C_LI One-sentence summaryTanacetum vulgare showed chemotype-specific responses to florivory by aphids (Macrosiphoniella tanacetaria) and beetles (Olibrus spp.), with aphids causing decreased and beetles increased levels of metabolic features within the same plant individuals, with little overlap in significant features across chemotypes.

Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) were systematically compared. The approach was validated using images of durum wheat flag leaves collected from a field experiment assessing eight-way varietal mixtures under natural fungal pressure. Cross-validation against manually annotated images demonstrated high segmentation accuracy across all symptom. Comparison among color spaces revealed only minor differences in performance. Overall, this workflow offers a cost-effective, annotation-efficient and reproducible alternative to deep learning approaches, leveraging open-source and actively maintained tools while requiring limited training data and enabling objective, reproducible and scalable disease phenotyping.

Quantitative pollen viability analysis is a critical but labor-intensive step in plant reproductive biology. Existing deep-learning Segment Anything Models (SAM) fail to reliably segment viable pollen in Alexander-stained anthers. To address this, we fine-tuned an existing Cellpose-SAM model for pollen segmentation. We integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application. PAT features instance segmentation with interactive quality control, an in-app model retraining module, and publication-ready statistical outputs. We deployed PAT in an EMS suppressor screen of semi-sterile Arabidopsis smg7-6 mutants, enabling efficient candidate prioritization for whole genome sequencing and mapping candidate mutation. This screen led to the identification of a point mutation in CAP-D2 (capd2-2), a Condensin I subunit, that rescues the smg7-6 meiotic phenotype. Notably, mutation in a Condensin II subunits (CAP-D3 and CAP-H2) does not confer rescue. Further characterization suggests the capd2-2 allele is hypomorphic, showing no defects in vegetative growth, chromocenter compaction, or transposable element silencing. Collectively, we demonstrate that accessible AI tools have the potential to bridge gaps in plant phenotyping and accelerate the pace of biological discovery. HighlightWe combined AI-powered image analysis with an easy-to-use desktop app to automate plant pollen counting, then used it to identify a new genetic suppressor of meiotic defects.

Widespread arthropod declines pose risks to ecosystem functioning and agriculture. Assessing this decline or potential remediation implies the need for standardized and scalable population monitoring. Image-based methods, including camera traps and citizen science programs, are increasingly used, but the volume of data collected requires automated analysis. Robust arthropod detection is essential for individual counting or fine-grained classification, yet current datasets and algorithms do not address the vast morphological diversity across arthropod species and often overlook the variety of photographic contexts, such as differences in background, lighting, and image composition, in which arthropods are captured. To address this gap, we developed an arthropod detection dataset, covering all terrestrial families present in France with available validated images on the iNaturalist platform (749 families). To achieve this, we employed an iterative workflow in which a YOLOv11 model pre-annotated images -- using one representative species per family-- followed by manual correction and model retraining. Repeating this process progressively reduced annotation effort and improved model accuracy. The final outcome consists of a publicly available curated detection dataset and a robust arthropod detector for natural background scenes. The detector achieves an F1-score of 0.91, demonstrating strong performance despite substantial interspecific morphological variation and heterogeneity in photographic contexts. We further demonstrated the taxonomical universality of the model showing high F1-score and IoU averaged at the class (0.79, 0.85) and order level (0.82, 0.86) and also a good detection generalizability (F1-score>0.90, IoU>0.83) on species, genera and families never encountered by the model during training. Finally, we show how this model can be improved to generalize to new datasets using data augmentation, complementary training data or fine-tuning and increase detection of small objects. In particular, we report performance of the improved models on three use cases largely used in non lethal insect monitoring: (i) diurnal pollinator monitoring through citizen science or (ii) flower and nocturnal insects monitoring through smartphone time-lapse of a UV-illuminated white panel. These results mark an important step toward automated analysis of arthropod images in natural contexts, from both large-scale automated monitoring approaches or from citizen science monitoring programs.

Pennycress (Thlaspi arvense L.) is an intermediate winter oilseed crop that has only recently been domesticated for agronomic use. Improving agronomic traits requires sources of genetic variation, and mutagenesis is frequently used to help overcome the limitations of natural populations. We investigate the impact of Ethyl methanesulfonate (EMS) on genetically effective cells (GECs) to characterize the intra-individual genetic variation of EMS mutagenesis in pennycress. We identified that pennycress contains at least 4 GECs which, when treated with EMS, create unique mutations across different branches within the same individual plant. We then propagated the M2 plants for whole genome sequencing, providing extensive characterization of the EMS mutation profile and developing a gene index as a resource for future reverse genetic screenings. Article SummaryPennycress is an emerging winter oil seed crop in the American Midwest. Domestication efforts have advanced rapidly through a combination of genetic techniques. One of the most successful methods has been the use of a mutant gene index, a large collection of pennycress seed where new genetic variation has been created through Ethyl methanesulfonate (EMS). EMS mutations are not uniform however, and a single treated seed can have wide genetic variation within the resulting plant. We investigate the role of genetically effective cells on EMS variation, and present the full EMS population as a resource for further pennycress domestication efforts.

Skin-tone labels are absent from public dermoscopy benchmarks such as the International Skin Imaging Collaboration (ISIC), making it impossible to audit whether clinical AI performs equitably across skin tones. While several recent works estimate skin tone automatically from clinical photography and selfies, we ask whether this approach is feasible on dermoscopy, the primary imaging modality of these benchmarks. To answer this, we make three main contributions. First, we release MST-Derm, a dual-rater Monk Skin Tone (MST) annotation benchmark on 500 ISIC 2018 images. Raters were given an explicit unrateable option for crops where the skin surrounding the lesion was too occluded to label confidently. We find that 60% of images were marked unrateable, yielding a 193-image consensus subset (quadratic-weighted Cohen's Kappa = 0.82). Second, we conduct a systematic feasibility study of three pixel-based MST annotation pipelines spanning the principal families in prior work: palette matching in perceptual colour space, robust colour statistics, and projection to a 1D colorimetric scalar. All three pipelines produce ordinal signal above chance (95% confidence intervals on quadratic-weighted Kappa exclude zero). However, ISIC 2018's extreme light-skin bias leaves 82% of the evaluation set at MST 2, giving a constant "always predict MST 2" baseline an accuracy floor the methods cannot overcome. To separate algorithmic signal from dataset bias, we evaluate on a class-balanced subset. The best method reaches quadratic-weighted Kappa = 0.43 against the trivial baseline of Kappa = 0.00, confirming the signal is genuine. Third, we diagnose this performance ceiling. We trace the bottleneck to two causes: dermoscopy's specialised illumination physically compresses the colour range on which lighter skin tones differ, and ISIC's dataset skew makes standard absolute-accuracy metrics uninformative. We conclude that while pixel-based colour features carry real MST signal on dermoscopy, current performance is insufficient for autonomous annotation. We release the benchmark, annotation protocol, all prediction runs, and analysis code to facilitate the development of robust skin-tone estimators, a vital prerequisite for accurately auditing fairness and mitigating bias in dermatological machine learning.

QuestionsEcological monitoring, repeated collection of ecological data, is essential to document how ecosystems respond to change. In grasslands, different vegetation monitoring protocols are used across disciplines, making it difficult to address multiple management objectives or research questions. We asked four questions about how three common vegetation monitoring protocols compare. (1) How do the protocols differ in how they collect data? (2) How do the protocols differ in their utility? (3) In what ways do vegetation measurements quantitatively differ across protocols? (4) What are each protocols strengths? LocationThis study was conducted on working ranches in the Southern Great Plains with vegetation consisting mainly of native forbs and grasses. MethodsWe implemented three protocols at each site: (1) the Rangeland Analysis Platform (RAP), (2) the Grassland Effectiveness Monitoring (GEM) protocol, and (3) a typical pollinator ecology survey protocol. We qualitatively compared each protocols utility and quantitatively compared cover measurements that each produced. ResultsAll three protocols displayed positive associations within cover categories, but differed in actual cover measurements. The RAP protocol, which uses remote sensing, measured the highest total vegetation cover. The GEM protocol, a line-point intercept method, had more capability to capture fine-scale cover patterns. The GEM protocol measured the most bare ground while the Pollinator protocol measured more forb coverage. ConclusionFine-scale methods like the GEM protocol are most appropriate to address objectives that require capturing small patterns that would otherwise be overlooked with methods like quadrats or remote sensing. Remote sensing is advantageous when monitoring large areas or inaccessible land, but may over-estimate cover. The Pollinator protocol is best equipped to address questions regarding flower abundance and richness. Similarities among protocols can facilitate synergy across disciplines for more effective monitoring. We emphasize the importance of denoting a clear scale and scope of monitoring objectives before selecting methods.

AO_SCPLOWBSTRACTC_SCPLOWO_LIAirborne imaging spectroscopy enables species-level classification in hyperdiverse tropical forests, but accuracy varies enormously among species. We asked which ecological and evolutionary attributes make a tropical tree species spectrally separable. C_LIO_LIUsing 3,256 field-verified crowns spanning 169 species in a hyperdiverse moist forest in French Guiana, we tested seven hypothesised determinants of classification accuracy at species, pairwise, and individual-crown scales using random forest, beta regression, elastic net, and binomial GLMM analyses. C_LIO_LIPhenological regularity - the strength and consistency of seasonal leaf-cycling - was the single strongest predictor of separability, emerging as the top-ranked variable across all analyses. The presence of congeneric species in the classification pool also reduced accuracy, while broader phylogenetic isolation contributed in multivariate models. At the crown level, crown area was the strongest predictor of correct classification, while liana infestation reduced odds of correct identification by 38%. Leaf chemical traits did not predict separability. C_LIO_LIIt is the consistency of a species ecological signal - its phenological rhythm, spatial sampling, and freedom from canopy contamination - rather than any single functional trait, that determines whether it can be reliably mapped from imaging spectroscopy. C_LI

Recent studies have demonstrated the presence of kynurenine (KYN) and kynurenic acid (KYNA) in several plant species, but the metabolic function of these metabolites remains undefined. We hypothesized that KYN and KYNA are metabolites of auxin and play a role in plant morphogenesis. To test our hypothesis, we developed a plant tissue-culture-based bioassay using Hypericum perforatum (St. Johns wort; SJW), a model system for auxin and indoleamine metabolism and pharmacological inhibitors (PF-04859989, RO-61-8048, and KMO inhibitor II, JM6) of human kynurenine pathways enzymes. SJW is an interesting model system because explants root in the absence of plant growth regulators but supplementation of the culture media with 10 M IAA induces a callus response without de novo root organogenesis. Supplementation of the culture media with 10 M KYN increased root number and internodal length relative to basal media. We used a previously validated high-resolution mass spectrometry analytical method to quantify KYN, KYNA, and 3-hydroxyanthranilic acid (3-HAA). KYN, KYNA and 3-HAA were quantified in roots and shoots of SJW grown on basal media. Supplementation of the culture media with 10 M KYN increased the concentration of KYN, KYNA and 3-HAA in roots and shoots. Treatment with 10 M IAA increased KYN and 3-HAA concentration in shoots. Three pharmaceutical candidates that are kynurenine pathway inhibitors in humans were taken up into the tissues from the culture media and increased KYN content as compared to basal control. Together, these data propose a role for KYN in IAA metabolism, shoot and root organogenesis. HighlightsO_LIKynurenine metabolites are detected and accumulate in H. perforatum tissue culture C_LIO_LIIAA redirects metabolism towards accumulation of KYN and 3-HAA in shoots C_LIO_LIExogenous KYN promotes KYNA accumulation C_LIO_LIPharmacological inhibition alters kynurenine pathway metabolite profiles in a tissue-specific manner C_LIO_LIKynurenine and IAA differentially regulate root development C_LI

BackgroundAs pangenomes approach saturation, identifying additional genomes that contribute novel sequence information becomes increasingly difficult. Current sample-selection strategies often rely on global diversity metrics or variant counts and do not explicitly account for the composition of an existing pangenome, a limitation that becomes increasingly relevant as pangenomes mature. Here, we present SelHap, a haplotype-based pipeline that uses whole-genome sequencing (WGS) data to prioritize accessions based on their contribution of novel haplotypes relative to a defined background, enabling targeted and iterative pangenome expansion. ResultsWe applied SelHap to the barley pangenome, using 76 assembled genomes as a background to select new accessions from a large WGS panel. Using this approach, we generated chromosome-scale genome assemblies from 19 accessions selected with SelHap and from 17 elite lines selected based on their relevance in historical barley breeding. Across multiple benchmarking scenarios, SelHap-based selection consistently resulted in a greater increase in non-redundant (single-copy) pangenome sequence, demonstrating that prioritizing haplotype novelty relative to an existing background maximizes unrepresented sequence content. ConclusionsBy transforming complex haplotype-clustering outputs into interpretable summaries and ranked candidate lists, SelHap provides a practical framework for targeted pangenome expansion. Beyond sample selection, SelHap can facilitate ancestry and germplasm comparisons across diverse panels. As WGS data become more accessible, SelHap offers a scalable and interpretable solution for extending mature pangenomes by explicitly targeting previously unrepresented sequence space.

Understanding plant growth dynamics requires imaging across day-and-night cycles to quantify growth, movement and development in the aerial plant body and to capture the rhythmic nature of these processes. This requires imaging in light during the day and in darkness at night without perturbing plant physiology. Nighttime imaging has typically depended on infrared (IR) illumination, producing monochrome datasets that require specialised hardware and separate analysis pipelines when combined with daytime RGB imaging. Here, we evaluated very low-intensity green (dimG) illumination from standard LEDs as a practical alternative for colour-consistent nighttime imaging and assessed its physiological impact in Arabidopsis thaliana and Lactuca sativa (lettuce). We show that high resolution colour images can be obtained under dimG using low- cost cameras, with sufficient consistency between full-spectrum and dimG images to allow direct comparison and unified image analysis. We show that very low-fluence green light (<0.5 mol m-2 s-1) does not sustain circadian oscillations of gene activity under continuous exposure and does not perturb rhythms when applied during the dark phase of diel cycles. DimG imaging enabled accurate detection of diel leaf movement profiles in Arabidopsis circadian mutants, revealing genotype-specific phase differences under varying photoperiods. In lettuce, dimG pulses and continuous dimG enabled accurate quantification of diel leaf movement without affecting growth, stomatal opening, electron transport rate or chlorophyll content. Motion profiles under continuous dimG mirrored those under darkness. Our findings establish dim green illumination as a cost-effective solution for night-time imaging, simplifying phenotyping workflows with minimal impact on physiology.

O_LIAnimal-mediated seed dispersal is key for the maintenance and functioning of tropical ecosystems. Specifically, in the Cerrado, the largest Neotropical savanna and a global biodiversity hotspot, nearly 60% of plant species rely on animals for dispersal. C_LIO_LIClimate change threatens these interactions by affecting species distributions, reshaping communities, and potentially decoupling plants from their dispersers. Anticipating how such disruptions may alter seed dispersal networks is particularly relevant for understanding the resilience of future tropical ecosystems. C_LIO_LIHere, we combined empirical data on 139 pairwise plant-frugivore interactions with species distribution forecasts to build probabilistic interaction matrices under present and future climate scenarios, which were then used to construct 6,221 local seed dispersal networks. Using ecological niche modelling, we tested how climate change influences species range size and centroid displacement. Then, we evaluated whether such changes translate into losses of pairwise plant-frugivore co-occurrence. Finally, we investigated how these changes in occurrence overlap may affect key structural properties of future local seed dispersal networks. C_LIO_LIWe forecast that by the 2070s, under a business-as-usual climate scenario, species are likely to contract their ranges by 56 {+/-} 33% and shift their distribution centroids by 88 {+/-} 57 km within the Cerrado, leading to a 27 {+/-} 29% loss in plant-frugivore co-occurrence mainly driven by reductions in plant species distributions. At the community level, these losses will lead to smaller and more nested networks and specialized, indicating a structural simplification of seed dispersal systems in the Cerrado. C_LIO_LISynthesis: By combining empirical data on animal-mediated seed dispersal with forecasts of species distributions, we found that climate change may simplify frugivore-plant interaction networks in the Cerrado by decreasing species ranges and co-occurrence of partners. Our study demonstrates that future climate may pose a threat not only to species distributions but also to ecological interactions, such as seed dispersal, that are key to enabling climate-tracking by plants. Thus, preventing the simplification of interaction networks will be essential to conserve biodiversity in species-rich regions. C_LI

Chlamydomonas reinhardtii is a model green alga extensively used to study photosynthesis and cilia using molecular biology and genetics. Electroporation is a very common technique to transform DNA into the nuclear genome, which is essential to generate mutant collections and express transgenes. Here, we describe a simple, fast, and efficient protocol to transform strains with an intact cell wall. It achieves a good transformation efficiency without cell wall digestion or use of commercial kits and is compatible with the widely available Gene Pulser electroporation system. Key featuresO_LIHigh transformation efficiency of Chlamydomonas reinhardtii strains with an intact cell wall. C_LIO_LIFaster than currently available electroporation protocols. C_LI

Fungal genomics has expanded rapidly over the past 30 years, and recently the pace and breath has further quickened for many taxa, although many taxonomic gaps persist. With three decades of rapid growth, fungal genomics now merits a re-examination of its history, progress, and unresolved taxonomic gaps. Here, we review the development of fungal genomics from early efforts such as the Fungal Genome Initiative to current progress driven by third-generation long-read sequencing. We have compiled and summarised publicly available fungal genomes to highlight trends in assembly quality, adoption of long-read technologies, and taxonomic representation. Notably, substantial phylogenetic gaps remain, particularly outside Dikarya, and significant challenges persist for unculturable taxa. This review identifies priorities for the fungal community, including: (1) coordinated efforts to close major taxonomic gaps across the fungal tree of life; (2) improved repository metrics to facilitate identification of high-quality assemblies; and (3) improved and standardised genome annotation which is lacking for most assemblies. Together, these steps will support the development of reliable genomic resources that capture the full breadth of diversity across the fungal kingdom, generating foundational data for comparative genomics, evolutionary biology, functional studies, genetic studies and applied research.

Longitudinal data on disease susceptibility in forest trees are rare but essential for understanding host-pathogen dynamics and genetic variation in susceptibility traits. We present a long-term multisite common garden dataset quantifying susceptibility of Scots pine (Pinus sylvestris) to Dothistroma needle blight. The dataset comprises annual disease assessments collected from the same trees across 11 years, spanning 168 families and 21 Scottish provenances. This design enables partitioning of genetic and environmental sources of variation, evaluation of temporal stability in host response, and estimation of variance components and narrow-sense heritability of susceptibility. The data support analyses of phenotypic plasticity, provenance-level responses, and interactions between disease susceptibility and other adaptive traits. This resource will facilitate predictive modelling of host susceptibility under current and future environmental conditions.

Belemia belongs to Nyctaginaceae and comprises two species of delicate vines. Both species are endemic to Brazil. Belemia fucsioides, the type species, described in 1981, occurs in a restricted area of the Atlantic Forest in southeastern Brazil. Belemia cordata, described in 2020, is known from only two records from the same area in the Cerrado of northern Brazil. Here, we describe the taxonomic history of Belemia and provide the first synopsis for the genus. We include species description, distribution map, identification key, and anatomical data. We used field observations over the past decade and modeled anthropogenic changes in the species range to conduct a conservation assessment in accordance with the IUCN Red List criteria. Conservation assessments indicate significant concerns for Belemia, classified as either endangered (B. fucsioides) or critically endangered (B. cordata). The species are threatened primarily by habitat loss to land used for agriculture, forestry, and livestock production. This study contributes to ongoing initiatives exploring plant diversity in the Neotropics and supports efforts to identify threats to biodiversity.