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Specialization, contextualized in a resource axis of an organism niche, is a core concept in ecology. In biotic interactions, specialization can be determined by the range of interacting partners. Evolutionary and ecological factors, in combination with the surveyed scale (spatial, temporal, biological, and/or taxonomic), influence the conception of specialization. This study aimed to assess the specialization patterns and drivers in the lichen symbiosis, considering the interaction between the principal fungus (mycobiont) and the associated (cyanobiont), from a community perspective considering different spatial scales. Thus, we determined phylogroup richness and composition of lichen communities in 11 forests across a wide latitudinal gradient in Chile. To measure specialization, cyanobiont richness, Simpson's and ' indices were estimated for 37 mycobiont species in these communities. Potential drivers that might shape composition and specialization measures along the environmental gradient were analysed. Limitations in lichen distributional ranges due to the availability of their cyanobionts were studied. Turnover patterns of cyanobionts were identified at multiple spatial scales. The results showed that environmental factors shaped the composition of these communities, thus limiting cyanobiont availability to establish the symbiotic association. Besides, specialization changed with the spatial scale and with the metric considered. Cyanolichens were more specialized than cephalolichens when considering partner richness and Simpson's index, whereas the ' index was mostly explained by mycobiont identity. Little evidence of lichen distributional ranges due to the distribution of their cyanobionts was found. Thus, lichens with broad distributional ranges either associated with several cyanobionts or with widely distributed cyanobionts. Comparisons between local and regional scales showed a decreasing degree of specialization at larger scales due to an increase in cyanobiont richness. The results support the context dependency of specialization and how its consideration changes with the metric and the spatial scale considered. Subsequently, we suggest considering the entire community and widening the spatial scale studied as it is crucial to understand factors determining specialization.
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http://dx.doi.org/10.1002/ece3.10296 | DOI Listing |
ACS Chem Neurosci
September 2025
Chemical and Biomolecular Engineering Dept, University of California, Los Angeles, Los Angeles, California 90095, United States.
Simulations in three dimensions and time provide guidance on implantable, electroenzymatic glutamate sensor design; relative placement in planar sensor arrays; feasibility of sensing synaptic release events; and interpretation of sensor data. Electroenzymatic sensors based on the immobilization of oxidases on microelectrodes have proven valuable for the monitoring of neurotransmitter signaling in deep brain structures; however, the complex extracellular milieu featuring slow diffusive mass transport makes rational sensor design and data interpretation challenging. Simulations show that miniaturization of the disk-shaped device size below a radius of ∼25 μm improves sensitivity, spatial resolution, and the accuracy of glutamate concentration measurements based on calibration factors determined .
View Article and Find Full Text PDFBrief Bioinform
August 2025
School of Information and Artificial Intelligence, Anhui Agricultural University, 130 Changjiang Road, Shushan District, Hefei, Anhui 230036, China.
Protein-nucleic acid binding sites play a crucial role in biological processes such as gene expression, signal transduction, replication, and transcription. In recent years, with the development of artificial intelligence, protein language models, graph neural networks, and transformer architectures have been adopted to develop both structure-based and sequence-based predictive models. Structure-based methods benefit from the spatial relationship between residues and have shown promising performance.
View Article and Find Full Text PDFNew Phytol
September 2025
Department of Ecology & Evolution, University of Chicago, Chicago, IL, 60637, USA.
Understanding the rate and nature of adaptation is crucial for managing biodiversity across our changing landscapes. This perspective synthesizes insights from resistance evolution - a case of rapid, repeated adaptation to extreme human-mediated selection - to reveal how adaptive genetic architectures determine and feedback with evolutionary dynamics. Recent population genomic and quantitative genetic approaches have demonstrated that the extent of genetic parallelism and reliance on de novo vs standing genetic variation can vary with the complexity of genetic architectures.
View Article and Find Full Text PDFMar Life Sci Technol
August 2025
Key Laboratory of Mariculture of Ministry of Education, Fisheries College, Ocean University of China, Qingdao, 266003 China.
Unlabelled: Microhabitat heterogeneity results in significant variations in the thermal environment on a small spatial scale, leading to different intensities of cold stress during extreme low-temperature events. Investigating variations in body temperature and metabolomic responses of organisms inhabiting different microhabitats emerges as an important task for understanding how organisms respond to more frequent extreme low-temperature events in the face of climate change. In the present study, we measured substrate temperature, air temperature, wind speed, light intensity, and body temperature to evaluate the relative importance of drivers that affect body temperature in different microhabitats, and determined the metabolomic responses of intertidal snails and limpets from different microhabitats (snail: exposed vs.
View Article and Find Full Text PDFFront Plant Sci
August 2025
School of Computer Science, Yangtze University, Jingzhou, China.
Thrips can damage over 200 species across 62 plant families, causing significant economic losses worldwide. Their tiny size, rapid reproduction, and wide host range make them prone to outbreaks, necessitating precise and efficient population monitoring methods. Existing intelligent counting methods lack effective solutions for tiny pests like thrips.
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