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Hybridization is a common phenomenon, yet its evolutionary outcomes remain debated. Here, we ask whether hybridization can speed adaptive evolution using resynthesized hybrids between two species of Texas sunflowers (Helianthus annuus and H. debilis) that form a natural hybrid in the wild (H. annuus ssp. texanus). We established separate control and hybrid populations and allowed them to evolve naturally in a field evolutionary experiment. In a final common-garden, we measured fitness and a suite of key traits for these lineages. We show that hybrid fitness evolved in just seven generations, with fitness of the hybrid lines exceeding that of the controls by 14% and 51% by the end of the experiment, though only the latter represents a significant increase. More traits evolved significantly in hybrids relative to controls, and hybrid evolution was faster for most traits. Some traits in both hybrid and control lineages evolved in an adaptive manner consistent with the direction of phenotypic selection. These findings show a causal pathway from hybridization to rapid adaptation and suggest an explanation for the frequently noted association between hybridization and adaptive radiation, range expansion, and invasion.
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http://dx.doi.org/10.1038/s41598-019-43119-4 | DOI Listing |
Commun Biol
September 2025
Department of General and Applied Biology, São Paulo State University (UNESP), Institute of Bioscience, Rio Claro, SP, Brazil.
Symbiotic relationships shape the evolution of organisms. Fungi in the genus Escovopsis share an evolutionary history with the fungus-growing "attine" ant system and are only found in association with these social insects. Despite this close relationship, there are key aspects of Escovopsis evolution that remain poorly understood.
View Article and Find Full Text PDFSci Justice
September 2025
Centre international de criminologie comparée, Canada; École de criminologie, Université de Montréal, Canada.
Technology's rapid evolution has made digital traces a common part of our lives, holding significant value for investigations and legal cases across various national jurisdictions. However, law enforcement and judicial systems often struggle to adapt to these changes, resulting in possible misinterpretations of digital evidence in criminal trials. Drawing on insights from a qualitative analysis of a terrorism-related court case, this research aims to gain a deeper understanding of the fundamental challenges of decision-making in digital forensics and how they can impact a criminal case.
View Article and Find Full Text PDFNeurosci Biobehav Rev
September 2025
Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE 68182, USA. Electronic address:
The concept of optimality dominates contemporary human movement science, with researchers across biomechanics, motor control, and neuroscience routinely explaining observed behaviors as solutions that maximize or minimize objective functions. This paper critiques the pervasive application of optimality principles in human movement science. We argue that optimization frameworks mischaracterize biological systems for several reasons: (1) Evolution produces sufficient rather than optimal adaptations without foresight; (2) Biological systems serve multiple functions simultaneously with context-dependent prioritization; (3) Structure-function relationships co-evolve rather than optimize for fixed targets; (4) The fractal, multiscale nature of physiological signals makes traditional optimization mathematically meaningless-there are no well-defined minima or maxima in fractal landscapes; (5) Optimality models implicitly invoke a homunculus that selects optimization criteria; and (6) The concept is methodologically circular and unfalsifiable, as any behavior can be retroactively modeled as optimal for some function.
View Article and Find Full Text PDFJ Neurosci Methods
September 2025
Department of Computer Science and Engineering, IIT (ISM) Dhanbad, Dhanbad, 826004, Jharkhand, India. Electronic address:
Background: Interpretation of motor imagery (MI) in brain-computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.
New Methods: To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification.
Plant Sci
September 2025
State Key Laboratory of Tree Genetics and Breeding, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100091, China. Electronic address: yhailing77
Glutathione S-transferase (GST, EC 2.5.1.
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