A pan-Himalayan test of predictions on plant species richness based on primary production and water-energy dynamics

Spatial variation in plant species diversity is well-documented but an overarching first-principles theory for diversity variation is lacking. Chemical energy expressed as Net Primary Production (NPP) is related to a monotonic increase in species richness at a macroscale and supports one of the leading energy-productivity hypotheses, the More individuals Hypothesis. Alternatively, water-energy dynamics (WED) hypothesizes enhanced species richness when water is freely available and energy supply is optimal. This theoretical model emphasises the amount and duration of photosynthesis across the year and therefore we include the length of the growing season and its interaction with precipitation. This seasonal-WED model assumes that biotemperature and available water represent the photosynthetically active period for the plants and hence, is directly related to NPP, especially in temperate and alpine regions. This study aims to evaluate the above-mentioned theoretical models using interpolated elevational species richness of woody and herbaceous flowering plants of the entire Himalayan range based on data compiled from databases. Generalized linear models (GLM) and generalized linear mixed models (GLMM) were used to analyse species richness (elevational gamma diversity) in the six geopolitical sectors of the Himalaya. NPP, annual precipitation, potential evapotranspiration (derived by the Holdridge formula), and length of growing season were treated as the explanatory variables and the models were evaluated using the Akaike Information Criterion (AIC) and explained deviance. Both precipitation plus potential evapotranspiration (PET), and NPP explain plant species richness in the Himalaya. The seasonal-WED model explains the species richness trends of both plant life-forms in all sectors of the Himalayan range better than the NPP-model. Despite the linear precipitation term failing to precisely capture the amount of water available to plants, the seasonal-WED model, which is based on the thermodynamical transition between water phases, is reasonably good and can forecast peaks in species richness under different climate and primary production conditions.publishedVersio

Behaviour of Cold-Formed Steel Semi Rigid Connections

Ductility and inelastic performance are important considerations in aseismic design of buildings. The dissipation of energy due to inelastic deformation is predominantly required in the connections like beam column joints. It is necessary to design these joints as semi rigid for its economic and structural benefits. Semi-rigid connections have highly nonlinear behaviour that makes the analysis and design of frames difficult and complicated. Steel structures are highly regarded for their seismic performance. It is required to understand and study the inelastic behavior of steel connections which would help in an economical and simpler design. This paper involves the modeling of deformational behaviour of a cold formed steel connection in a finite element software simulating the real time behavior. The ultimate moment and rotation is studied for different semi rigid connections after validation of the model

Nanotechnology for the detection of plant pathogens

abstract: Plant pathogens are the important yield-limiting factors, which significantly reduce crop productivity globally, posing serious problems for food security and continues to be the biggest agricultural concern in the world. Even though chemical treatment is still the primary strategy for reducing the incidence of plant disease, their repeated application can cause the pathogens to become less susceptible. Over spraying can also pollute the environment and significantly affect soil microbiota. Therefore, to ensure agricultural sustainability and food security, efficient diagnostic techniques for the rapid identification of plant pathogens in the early stages of infection become crucial. Many molecular approaches for rapid plant pathogen detection have been developed to achieve this goal. However, they are time-consuming, costly, require skilled operators, and are generally unsuitable for in-situ analysis. Plant protection is feasible when any of the nanotechnology tools like microneedle patches, nanopore sequencing, nano barcoding, nano biosensors, quantum dots, nano diagnostic kit equipment, metal nanoparticles, miRNA based nanodiagnosis, and array based nano sensors is used for plant pathogen diagnosis. As they emerge as a potential tool to improve the sensitivity, accuracy, and rapidness of plant pathogen identification, and facilitate high-throughput analysis. The current review focuses on the use of nanotechnology for more quick, inexpensive, and accurate plant pathogens diagnosis