Seasonal differences in leaf-level physiology give lianas a competitive advantage over trees in a tropical seasonal forest

Lianas are an important component of most tropical forests, where they vary in abundance from high in seasonal forests to low in aseasonal forests. We tested the hypothesis that the physiological ability of lianas to fix carbon (and thus grow) during seasonal drought may confer a distinct advantage in seasonal tropical forests, which may explain pan-tropical liana distributions. We compared a range of leaf-level physiological attributes of 18 co-occurring liana and 16 tree species during the wet and dry seasons in a tropical seasonal forest in Xishuangbanna, China. We found that, during the wet season, lianas had significantly higher CO2 assimilation per unit mass (Amass), nitrogen concentration (Nmass), and δ13C values, and lower leaf mass per unit area (LMA) than trees, indicating that lianas have higher assimilation rates per unit leaf mass and higher integrated water-use efficiency (WUE), but lower leaf structural investments. Seasonal variation in CO2 assimilation per unit area (Aarea), phosphorus concentration per unit mass (Pmass), and photosynthetic N-use efficiency (PNUE), however, was significantly lower in lianas than in trees. For instance, mean tree Aarea decreased by 30.1% from wet to dry season, compared with only 12.8% for lianas. In contrast, from the wet to dry season mean liana δ13C increased four times more than tree δ13C, with no reduction in PNUE, whereas trees had a significant reduction in PNUE. Lianas had higher Amass than trees throughout the year, regardless of season. Collectively, our findings indicate that lianas fix more carbon and use water and nitrogen more efficiently than trees, particularly during seasonal drought, which may confer a competitive advantage to lianas during the dry season, and thus may explain their high relative abundance in seasonal tropical forests

Machine learning plastic deformation of crystals

Plastic deformation of micron-scale crystalline solids exhibits stress-strain curves with significant sample-to-sample variations. It is a pertinent question if this variability is purely random or to some extent predictable. Here we show, by employing machine learning techniques such as regression neural networks and support vector machines that deformation predictability evolves with strain and crystal size. Using data from discrete dislocations dynamics simulations, the machine learning models are trained to infer the mapping from features of the pre-existing dislocation configuration to the stress-strain curves. The predictability vs strain relation is non-monotonic and exhibits a system size effect: larger systems are more predictable. Stochastic deformation avalanches give rise to fundamental limits of deformation predictability for intermediate strains. However, the large-strain deformation dynamics of the samples can be predicted surprisingly well.Peer reviewe