In semi-arid environments, the marked contrast in temperature and precipitation over the year strongly shapes ecological communities. The composition of species and their ecological interactions within a community may vary greatly over time. Although intra-annual variations are often studied, empirical information on how plant-bird relationships are structured within and among years, and how their drivers may change over time are still limited. In this study, we analyzed the temporal dynamics of the structure of plant-hummingbird interaction networks by evaluating changes in species richness, diversity of interactions, modularity, network specialization, nestedness, and β-diversity of interactions throughout four years in a Mexican xeric shrubland landscape. We also evaluated if the relative importance of abundance, phenology, morphology, and nectar sugar content consistently explains the frequency of pairwise interactions between plants and hummingbirds across different years. We found that species richness, diversity of interactions, nestedness, and network specialization did vary within and among years. We also observed that the β-diversity of interactions was high among years and was mostly associated with species turnover (i.e., changes in species composition), with a minor contribution of interaction rewiring (i.e., shifting partner species at different times). Finally, the temporal co-occurrence of hummingbird and plant species among months was the best predictor of the frequency of pairwise interactions, and this pattern was consistent within and among years. Our study underscores the importance of considering the temporal scale to understand how changes in species phenologies, and the resulting temporal co-occurrences influence the structure of interaction networks.
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http://dx.doi.org/10.1007/s00442-020-04727-4 | DOI Listing |
Hum Genomics
December 2024
Department of Stomatology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Background: Oral squamous cell carcinoma (OSCC) is an aggressive malignancy with poor prognosis. Neutrophil infiltration has been associated with unfavorable outcomes in OSCC, but the underlying molecular mechanisms remain unclear.
Methods: This study integrated single-cell transcriptomics (scRNA-seq) with bulk RNA-seq data to analyze neutrophil infiltration patterns in OSCC and identify key gene modules using weighted gene co-expression network analysis (hdWGCNA).
BMC Bioinformatics
December 2024
College of Computer Science and Technology, Inner Mongolia Minzu University, Tongliao, 028000, China.
As a heterogeneous disease, prostate cancer (PCa) exhibits diverse clinical and biological features, which pose significant challenges for early diagnosis and treatment. Metabolomics offers promising new approaches for early diagnosis, treatment, and prognosis of PCa. However, metabolomics data are characterized by high dimensionality, noise, variability, and small sample sizes, presenting substantial challenges for classification.
View Article and Find Full Text PDFBMC Pregnancy Childbirth
December 2024
Ministry of Education-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background: Delivery mode has been linked to child health, e.g., allergic disease.
View Article and Find Full Text PDFMol Med
December 2024
Department of Nephrology, First Affiliated Hospital, Kunming Medical University, Kunming, Yunnan Province, China.
Background: Diabetes often causes diabetic nephropathy (DN), a serious long-term complication. It is characterized by chronic proteinuria, hypertension, and kidney function decline, can progress to end-stage renal disease, lowering patients' quality of life and lifespan. Inflammation and apoptosis are key to DN development.
View Article and Find Full Text PDFSports Med
December 2024
Research Centre in Physical Activity, Health and Leisure, and Laboratory for Integrative and Translational Research in Population Health, University of Porto, Porto, Portugal.
Motor competence is related to a large number of correlates of different natures, forming together a system with flexible parts that are synergically and cooperatively connected to produce a wide range of motor outcomes that cannot be explained from a predetermined linear view or a unique mechanism. The diversity of interacting correlates, the various connections between them, and the fast changes between assessments at different time points are clear barriers to the study of motor competence. In this manuscript, we present a multilayer framework that accounts for the theoretical background and the potential mathematical procedures necessary to represent the non-linear, complex, and dynamic relationships between several underlying correlates that emerge as a motor competence network.
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