Science Enabled by Specimen Data

Zhao, J., J.-G. Wang, Y.-P. Hu, C.-J. Huang, S.-L. Fang, Z.-Y. Wan, R.-J. Li, et al. 2025. Phylogenetic Inferences and Historical Biogeography of Onocleaceae. Plants 14: 510. https://doi.org/10.3390/plants14040510

The family Onocleaceae represents a small family of terrestrial ferns, with four genera and around five species. It has a circumboreal to north temperate distribution, and exhibits a disjunct distribution between Eurasia and North America, including Mexico. Historically, the taxonomy and classification of this family has been subject to debate and contention among scholars, leading to contradictory classifications and disagreements on the number of genera and species within the family. Furthermore, due to this disjunct intercontinental distribution and the lack of detailed study across its wide range, this family merits further study to clarify its distributional pattern. Maximum likelihood and Bayesian phylogenetic reconstructions were based on a concatenated sequence dataset for 17 plastid loci and one nuclear locus, which were generated from 106 ingroup and six outgroup taxa from three families. Phylogenetic analyses support that Onocleaceae is composed of four main clades, and Pentarhizidium was recovered as the first branching lineages in Onocleaceae. Molecular dating and ancestral area reconstruction analyses suggest that the stem group of Onocleaceae originated in Late Cretaceous, with subsequent diversification and establishment of the genera Matteuccia, Onoclea, Onocleopsis, and Pentarhizidium during the Paleogene and Neogene. The ancestors of Matteuccia, Onoclea, and Onocleopsis could have migrated to North America via the Beringian land bridge or North Atlantic land bridge which suggests that the diversification of Matteuccia + Onoclea + Onocleopsis closely aligns with the Paleocene-Eocene Thermal Maximum (PETM). In addition, these results suggest that Onocleaceae species diversity peaks during the late Neogene to Quaternary. Studies such as this enhance our understanding of the mechanisms and climatic conditions shaping disjunct distribution in ferns and lycophytes of eastern Asia, North America, and Mexico and contribute to a growing body of evidence from other taxa, to advance our understanding of the origins and migration of plants across continents.

Zhang, H., W. Guo, and W. Wang. 2023. The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models. Ecology and Evolution 13. https://doi.org/10.1002/ece3.10747

How to effectively obtain species‐related low‐dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high‐dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes.

Campbell, C., G. Granath, and H. Rydin. 2021. Climatic drivers of Sphagnum species distributions. Frontiers of Biogeography 13. https://doi.org/10.21425/f5fbg51146

Peatmosses(genus Sphagnum) dominate most Northern mires and show distinct distributional limits in Europe despite having efficient dispersal and few dispersal barriers. This pattern indicates that Sphagnum species distributions are strongly linked to climate. Sphagnumdominated mires have been the la…