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  1. Hasegawa H, Miyata A, Yong HS
    J Parasitol, 1996 Jun;82(3):508-11.
    PMID: 8636863
    The synlophe of Batrachonema synaptospicula Yuen, 1965 collected from Rana limnocharis Boie, 1835 of peninsular Malaysia was found to be identical morphologically to that in the specimens from Rana narina Stejneger, 1901 of Okinawa, and R. limnocharis of Taiwan. In the midbody, 20-22 ridges are present, and the ridges increase gradually in size and are oriented from right to left in the dorsal and left ventral fields, whereas the right ventral ridges are small and almost perpendicular to the body wall. The orientation of ridges from right to left is considered to be a key characteristic of the genus Batrachonema. Because Amphibiophilus ranae Wang et al., 1978 and Amphibiophilus sp. from R. limnocharis of south China are regarded to be conspecific with B. synaptospicula, this nematode is surmised to be distributed widely in southeast and east Asia.
  2. Chang KY, Riley WJ, Knox SH, Jackson RB, McNicol G, Poulter B, et al.
    Nat Commun, 2021 Apr 15;12(1):2266.
    PMID: 33859182 DOI: 10.1038/s41467-021-22452-1
    Wetland methane (CH4) emissions ([Formula: see text]) are important in global carbon budgets and climate change assessments. Currently, [Formula: see text] projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent [Formula: see text] temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that [Formula: see text] are often controlled by factors beyond temperature. Here, we evaluate the relationship between [Formula: see text] and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between [Formula: see text] and temperature, suggesting larger [Formula: see text] sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.
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