Karyotypes and chromosomal characteristics of two species of bullhead catfish of the genus Liobagrus, namely L. marginatus and L. styani were examined by means of conventional (Giemsa and CMA₃ staining) and molecular (FISH with telomeric, 5S and 18S rDNA probes, respectively) cytogenetic techniques. The diploid numbers of L. marginatus and L. styani were 2n = 24 and 30 respectively. The karyotypes were: L. marginatus, 20m+2sm+2st in females and 19m+2sm+2st+1a in males; L. styani, 16m+ 10sm+4st in both sexes. The karyotypes of the two species and the delayed-pairing of synaptonemal complexes in meiosis of L. marginatus suggested the presence of heteromorphic XX/XY sex chromosome systems. The results of (TTAGGG)_nFISH and CMA₃-staining suggested that Robertsonian fusion might play an important role in chromosome differentiation among these species of Liobagrus. The 5S rDNA situated on the sex chromosomes of L. marginatus and 18S rDNA on the sex chromosomes of L. styani were located by FISH, and the origins of heteromorphic sex chromosomes via chromosomal reversion or unequal crossing over of heterochromatin were hypothesized.

Knowledge about global patterns of the decomposition kinetics of distinct soil organic matter (SOM) pools is crucial to robust estimates of land-atmosphere carbon fluxes under climate change. However, the current Earth system models often adopt globally-consistent reference SOM decomposition rates (kref), ignoring effects from edaphic-climate heterogeneity. Here, we compile a comprehensive set of edaphic-climatic and SOM decomposition data from published incubation experiments and employ machine-learning techniques to develop models capable of predicting the expected sizes and kref of multiple SOM pools (fast, slow, and passive). We show that soil texture dominates the turnover of the fast pools, whereas pH predominantly regulates passive SOM decomposition. This suggests that pH-sensitive bacterial decomposers might have larger effects on stable SOM decomposition than previously believed. Using these predictive models, we provide a 1-km resolution global-scale dataset of the sizes and kref of these SOM pools, which may improve global biogeochemical model parameterization and predictions.