The Role of Plant Evolutionary History in Shaping the Variation in Specific Leaf Area Across China

Specific leaf area (SLA, leaf area per unit leaf dry mass) occupies a central position in both community assembly and ecosystem functioning. Although SLA has significant phylogenetic signals, how and to what extent the evolutionary history influences the variation in SLA remain poorly understood. In this study, based on a dataset containing 1264 plant species belonging to 549 genera and 141 families in gymnosperms, monocots, and eudicots across China, we analyzed the influences of climatic conditions and soil properties on SLA, calculated the phylogenetic signals of SLA, and quantified the relative contributions of evolutionary history (represented by interspecific relatedness and intraspecific variation) to the variation in SLA. The results showed that the interspecific relatedness accounts for 50.46% of the total variance in SLA, followed by the intraspecific variation (36.12%), climatic conditions (30.68%), and soil properties (24.74%). Along the phylogenetic tree, the split between angiosperms and gymnosperms had the largest contribution to the variation in SLA. Other detailed splits (e.g., the split between monocots and eudicots, the splits within Rosidae, and etc.) had significant but much smaller contributions. The relationship between SLA and environmental variables (climatic conditions and soil properties) was different between angiosperms and gymnosperms, with the climatic conditions having larger influences on SLA than the soil properties, implying interactive effects between environment and evolutionary history on SLA. Within the woody angiosperms, deciduous and evergreen species exhibited differential responses of SLA to climatic and soil factors, suggesting a non-negligible role of leaf longevity in explaining the variation in SLA. Our results highlighted a much more important role of evolutionary history in the variation in SLA than previous studies. Neglecting such a great contribution could lead to biased conclusions if the evolutionary rate does not keep pace with the rapidly changing environments in the future.

EcoNicheS: enhancing ecological niche modeling, niche overlap and connectivity analysis using the shiny dashboard and R package

EcoNicheS (https://github.com/armandosunny/EcoNicheS) is a comprehensive R package built on a Shiny dashboard that offers an intuitive and streamlined workflow for creating ecological niche models (ENMs) and landscape connectivity models. It incorporates tools for niche modeling, overlap analysis, and connectivity modeling, leveraging robust algorithms from the biomod2 suite. EcoNicheS is designed to simplify the technical complexities of ENMs, bridging the gap between advanced modeling techniques and user accessibility. The package offers an interactive interface for streamlined data input, model parameterization, and result visualization. Its comprehensive toolset includes occurrence data processing, pseudoabsence point generation, urbanization filters, and ecological connectivity modeling, distinguishing it from other platforms. EcoNicheS integrates innovative workflows with dynamic output visualizations while emphasizing reproducibility and comparability across statistical methods. Its practical applications span diverse research fields, including biogeography, epidemiology, evolutionary studies, climate change impacts, landscape connectivity, and biodiversity conservation. This versatility makes EcoNicheS a valuable resource for advancing in ecological and conservation science.

Phylogenomics and phylogeographic model testing using convolutional neural networks reveal a history of recent admixture in the Canarian Kleinia neriifolia

Multiple-island endemics (MIE) are considered ideal natural subjects to study patterns of island colonization that involve recent population-level genetic processes. Kleinia neriifolia is a Canarian MIE widespread across the archipelago, which exhibits a close phylogenetic relationship with species in northwest Africa and at the other side of the Sahara Desert. Here, we used target sequencing with plastid skimming (Hyb-Seq), a dense population-level sampling of K. neriifolia, and representatives of its African-southern Arabian relatives to infer phylogenetic relationships and divergence times at the species and population levels. Using population genetic techniques and machine learning (convolutional neural networks [CNNs]), we reconstructed phylogeographic relationships and patterns of genetic admixture based on a multilocus SNP nuclear dataset. Phylogenomic analysis based on the nuclear dataset identifies the northwestern African Kleinia anteuphorbium as the sister species of K. neriifolia, with divergence starting in the early Pliocene. Divergence from its sister clade, comprising species from the Horn of Africa and southern Arabia, is dated to the arid Messinian period, lending support to the climatic vicariance origin of the Rand Flora. Phylogeographic model testing with CNNs supports an initial colonization of the central island of Tenerife followed by eastward and westward migration across the archipelago, which resulted in the observed east/west phylogeographic split. Subsequent population extinctions linked to aridification events, and recolonization from Tenerife, are proposed to explain the patterns of genetic admixture in the eastern Canary Islands. We demonstrate that CNNs based on SNPs can be used to discriminate among complex scenarios of island migration and colonization.