Long-branch attraction is a systematic artifact that results in erroneous groupings of fast-evolving taxa. The combination of short, deep internodes in tandem with long-branch attraction artifacts has produced empirically intractable parts of the Tree of Life. One such group is the arthropod subphylum Chelicerata, whose backbone phylogeny has remained unstable despite improvements in phylogenetic methods and genome-scale data sets. Pseudoscorpion placement is particularly variable across data sets and analytical frameworks, with this group either clustering with other long-branch orders or with Arachnopulmonata (scorpions and tetrapulmonates). To surmount long-branch attraction, we investigated the effect of taxonomic sampling via sequential deletion of basally branching pseudoscorpion superfamilies, as well as varying gene occupancy thresholds in supermatrices. We show that concatenated supermatrices and coalescent-based summary species tree approaches support a sister group relationship of pseudoscorpions and scorpions, when more of the basally branching taxa are sampled. Matrix completeness had demonstrably less influence on tree topology. As an external arbiter of phylogenetic placement, we leveraged the recent discovery of an ancient genome duplication in the common ancestor of Arachnopulmonata as a litmus test for competing hypotheses of pseudoscorpion relationships. We generated a high-quality developmental transcriptome and the first genome for pseudoscorpions to assess the incidence of arachnopulmonate-specific duplications (e.g., homeobox genes and miRNAs). Our results support the inclusion of pseudoscorpions in Arachnopulmonata (new definition), as the sister group of scorpions. Panscorpiones (new name) is proposed for the clade uniting Scorpiones and Pseudoscorpiones.

Cereals grains are the prime component of the human diet worldwide. To promote food security and sustainability, new approaches to non-chemical weed control are needed. Early vigor cultivars with enhanced weed-competitiveness ability are a potential tool, nonetheless, the introduction of such trait in breeding may be a long and labor-intensive process. Here, two image-driven plant phenotyping methods were evaluated to facilitate effective and accurate selection for early vigor in cereals. For that purpose, two triticale genotypes differentiating in vigor and growth rate early in the season were selected as model plants: X-1010 (high) and Triticale1 (low). Two modeling approaches, 2-D and 3-D, were applied on the plants offering an evaluation of various morphological growth parameters for the triticale canopy development, under controlled and field conditions. The morphological advantage of X-1010 was observed only at the initial growth stages, which was reflected by significantly higher growth parameter values compared to the Triticale1 genotype. Both modeling approaches were sensitive enough to detect phenotypic differences in growth as early as 21 days after sowing. All growth parameters indicated a faster early growth of X-1010. However, the 2-D related parameter [projected shoot area (PSA)] is the most available one that can be extracted via end user-friendly imaging equipment. PSA provided adequate indication for the triticale early growth under weed-competition conditions and for the improved weed-competition ability. The adequate phenotyping ability for early growth and competition was robust under controlled and field conditions. PSA can be extracted from close and remote sensing platforms, thus, facilitate high throughput screening. Overall, the results of this study may improve cereal breeding for early vigor and weed-competitiveness.

BACKGROUND: Crop rotation is a central component of integrated weed management (IWM) under real‐world conditions, yet its impact on herbicide use remains unclear. To address this challenge, we developed an ecoinformatics‐driven analysis approach based on farmer‐reported data from 11 farms, spanning approximately 400 fields and 3303 crop‐year records across diverse arable zones. The workflow analysis was divided into three main stages: (i) trend‐screening, examining temporal changes in herbicide intensity across major summer crops and rotational patterns of crops and herbicides; (ii) identification of key drivers associated with variability in herbicide intensity, and (iii) mechanistic clue finding, revealing explanations for patterns observed in the first two stages. RESULTS: A significant positive association was found between herbicide intensity and years in maize ( P < 0.001). The frequency of cotton in crop rotation was identified as the most consistent predictor of herbicide intensity in this crop (estimate = −0.25, P < 0.001). Maize fields in which cotton was prevalent in the rotation received approximately one fewer herbicide application than fields without cotton in the rotation. Principal component analysis (PCA) highlighted tillage as the primary driver of the differentiation between maize fields with and without a history of cotton. Temporal testing showed that the cotton effect persisted for 2 years. CONCLUSION: Cotton‐based rotations reduced herbicide inputs in maize, with cotton‐specific tillage practices providing a mechanistic explanation for the weed‐suppressive effect. These results highlight the advantage of IWM and demonstrate the utility of ecoinformatics for detecting rotation effects in large‐scale farm data. © 2026 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.