98%
921
2 minutes
20
Understanding when and how habitat transitions occurred is essential for a comprehensive insight into the succession of marine ecosystem and biodiversity. Here we investigated the evolutionary process of an ancient, widespread and ecologically diversified lineage of marine benthic fauna, the ghost and mud shrimps (Decapoda: Axiidea). To reconstruct a robust, time-calibrated phylogeny of this intractable group, we sampled more comprehensively than in previous studies and utilized three types of sequencing data: Sanger, genome-skimming and ultra-conserved elements (UCEs). The UCEs tree supports a monophyletic Axiidea sister to the 'Gebiidea + (Brachyura + Anomura)' clade. Our findings reveal the monophyletic status of Callianideidae and Micheleidae, whereas Axiidae and Strahlaxiidae as presently understood are shown to be non-monophyletic. Axiidae s.s. is now restricted to four genera, Strahlaxiidae to one genus, with most former "axiid" genera reclassified under Calocarididae. We determine that crown axiidean shrimps diverged in the Middle Triassic, with a significant habitat transition from epibenthic to endobenthic during the Middle to Late Jurassic, possibly in response to environmental changes and available ecological niche. We hypothesize that the extreme morphological and behavioural adaptations to the obligate/subsurface burrowing life facilitated the radiation and diversification of ghost shrimps, despite some instances of adaptive convergence.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/cla.70000 | DOI Listing |
Environ Monit Assess
September 2025
School of Civil Engineering, Putian University, Putian City, 351100, China.
Land degradation (LD) is a critical environmental challenge caused by human activities and climate change. Reversing degraded land requires effective LD monitoring. The UN Sustainable Development Goal (SDG) indicator 15.
View Article and Find Full Text PDFFood Res Int
November 2025
College of Food Science and Technology, Northwest University, 229 North TaiBai Road, Xi'an 710069, China. Electronic address:
Food combinations featuring specific functional components represent one of the effective intervention strategies for alleviating functional gastrointestinal disorders induced by dietary and environmental factors. Honey and aloe vera have both been recognized as natural agents with laxative effects, yet the synergistic effects of their combination in alleviating constipation and the underlying regulatory mechanism remain to be elucidated. This study formulated a honey-aloe paste by employing honey as the primary ingredient compounded with aloe vera gel and investigated its preventive effects on loperamide-induced slow-transit constipation through a comprehensive analysis of gastrointestinal function and intestinal microenvironment.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
September 2025
Chinese Academy of Sciences Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China.
Vegetation phenology, i.e., seasonal biological events such as leaf-out and leaf-fall, regulates local climate through biophysical processes like evapotranspiration (ET) and albedo.
View Article and Find Full Text PDFHealth Equity
August 2025
Alumni Endowed Professor of Medicine, Division of Nephrology, Washington University School of Medicine, St. Louis, Missouri, USA.
Importance: The U.S. medical education system attracts and trains the next generation of physicians to advance the health care needs of a growing and increasingly diverse nation.
View Article and Find Full Text PDFBidens macroptera symbolizes the change of a season, marking the transition from the rainy season to autumn, heralding the new year for Ethiopians. Despite a general understanding of its geographic regions, significant gaps remain in identifying the habitat distribution and key predictor variables of Bidens macroptera through species distribution modeling (SDM) in the context of climate change. We developed an ensemble species distribution model using 2 statistical and 3 machine learning algorithms.
View Article and Find Full Text PDF