Mapping connectivity and conflict risk to identify safe corridors for the Persian leopard

Landscape Ecology - Many large carnivores depend on habitat patches outside protected areas, as well as safe corridors between them. However, corridor assessments typically ignore potential...     

Effect of tillage and fertilizer levels on wheat yield, nitrogen uptake and their correlation with carbon isotope discrimination under rainfed conditions in north-west Pakistan

Appropriate cultural practices need to be determined for enhancing crop yields with low inputs under rainfed conditions. A field experiment was conducted to study the effect of tillage practices and fertilizer levels on yield, nitrogen (N) uptake and carbon (C) isotope discrimination in wheat (Triticum aestivum L.) grown under semi-arid conditions at three sites in north-west Pakistan: NIFA, Urmar and Jalozai. Two fertilizer levels, 60 kg N ha⁻¹+30 kg P ha⁻¹ (L1) and 60 kg N ha⁻¹+60 kg P ha⁻¹ (L2), were applied to wheat grown under conventional tillage (T1) and no-tillage (T0) practices. Labeled urea having 1% ¹⁵N atom excess at 60 kg N ha⁻¹ was applied as aqueous solution in microplots within each treatment plot. A pre-sowing irrigation of 60 mm was applied and during the growing season, the crop relied entirely on rainfall (268 mm). Biomass yield, N uptake and stable C isotope composition (δ¹³C) of plants were determined at maturity. Yield of wheat was improved by tillage at two sites (Sites 1 and 2), while at the third site yield was reduced by tillage as compared with the no-tillage treatment. At Sites 1 and 2, nutrient addition (L2, 60 kg N ha⁻¹+60 kg P ha⁻¹) increased the yield of all plant parts (straw, grain and root) in contrast to Site 3 where only grain yield was increased significantly. Maximum grain yield of wheat was observed with tillage under nutrient level L2 at all sites. Generally, the tillage treatment did not affect the N content in plant parts compared with no-tillage (T0) treatment at all three sites. However, fertilizer N uptake by wheat was variable under different fertilizer levels and tillage practices. Nitrogen derived from fertilizer (Ndff) for grain at Site 2 was higher in tilled plots but was not affected by tillage practice at the other sites. The C isotope (δ¹³C) values varied from −28.96 to −26.03‰ under different treatments at the three sites. The δ¹³C values were less negative indicating more effective water use at Sites 2 and 3 compared to Site 1. The C isotope discrimination (Δ) values were positively correlated with yield of wheat straw (r=0.578^*), grain (r=0.951^**) and root (r=0.583^*). Further, the Δ in grain had significant negative relationship (r=0.912^**) with Ndff (%). The tillage practice exerted a positive effect on yield, N uptake and plant N derived from fertilizer by wheat compared to no-tillage. The positive correlation of Δ with grain, straw and root yields and negative correlation with the Ndff (%) by wheat suggest that this value (Δ) could be used to predict these parameters. However, further studies on different crops under varied environmental conditions are necessary.     

Post-translational modifications in effectors and plant proteins involved in host–pathogen conflicts

Molecular interplay between two species is largely driven by protein–protein interactions and protein modifications that set the pace of co-evolution in these species. During host–pathogen interactions, proteins involved in virulence and defence impart tempospatial dynamic post-translational modifications (PTMs) to gain advantage for the causative species. Pathogens mainly cause disease in plant hosts by secreting elicitors (peptides and small molecules) or proteins in the inter- and intracellular space of host cells. These pathogen proteins have evolved a wide array of sophisticated mechanisms to manipulate host responses, including resistance. Through a set of diverse events ranging from PTMs to post-translational oligomerization, these proteins are able to enhance virulence and suppress the otherwise elaborate plant immune system. Similarly, PTMs adapted by host proteins often lead to the activation of a robust defence response. Insights into the PTMs of pathogen and host proteins are therefore germane to the understanding of the co-evolutionary arms race. This review summarizes the characterization of PTMs in pathogen effectors and their target host proteins. Based on this, a metaphorical view of host–pathogen conflicts is proposed, where PTMs act as molecular pivots in a 3D combinatorial game model – a novel abstraction of the arms race, where these molecular pivots restore the balance of competition between the two organisms.     

Improving nodulation,growth and yield of Cicer arietinum L. through bacterial ACC-deaminase induced changes in root architecture

Bacteria containing ACC-deaminase in the vicinity of roots may influence plant growth by modifying root architecture through their potential to regulate ethylene synthesis in plant roots. Approximately 138 isolates capable of utilizing ACC as the sole source of N were isolated from the rhizosphere soil of chickpea (Cicer arietinum L.) plants. Under axenic conditions, some rhizobacterial isolates were highly effective in increasing root length (up to 2.08 fold), number (up to 3.7 fold) and length (up to 3.9 fold) of lateral roots, and root biomass (up to 83%) of chickpea as compared to uninoculated control. Serratia proteamaculans strain J119 was found to be the most effective plant growth promoting rhizobacterium (PGPR) in improving root and shoot growth, nodulation and grain yield of chickpea as compared to respective controls in growth pouches, pot and field trials. A highly significant direct correlation (r = 0.99) was observed between number of lateral roots under axenic conditions (jar trial) and number of nodules per plant in pot and field trials. Interestingly, S. proteamaculans J119 also exhibited highest ACC-deaminase activity in addition to root colonization compared to other tested strains. The results of this study demonstrated that changes in root growth and architecture (particularly lateral roots) as a result of inoculation with PGPR containing ACC-deaminase are crucial for improving growth, yield and nodulation of chickpea under field conditions.     

Sensor data fusion to predict multiple soil properties

The accuracy of a single sensor is often low because all proximal soil sensors respond to more than one soil property of interest. Sensor data fusion can potentially overcome this inability of a single sensor and can best extract useful and complementary information from multiple sensors or sources. In this study, a data fusion was performed of a Vis?CNIR spectrometer and an EM38 sensor for multiple soil properties. Stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and principal components analysis combined with stepwise multiple linear regression (PCA?+?SMLR) methods were used in three different fields. Soil properties investigated for data fusion included soil texture (clay, silt and sand), EC, pH, total organic carbon (TOC), total nitrogen (TN) and carbon to nitrogen ratio (CN). It was found that soil property models based on fusion methods significantly improved the accuracy of predictions of soil properties measureable by both sensors, such as clay, silt, sand, EC and pH from those based on either of the individual sensors. The accuracy of predictions of TOC, TN and CN was also improved in some cases, but was not consistent in all fields. Among data fusion methods, PLSR outperformed both SMLR and PCA?+?SMLR methods because it proved to have a better ability to deal with the multi-collinearity among the predictor variables of both sensors. The best data fusion results were found in a clayey field and the worst in a sandy field. It is concluded that sensor data fusion can enhance the quality of soil sensing in precision agriculture once a proper set of sensors has been selected for fusion to estimate desired soil properties. More efficient statistical data analysis methods are needed to handle a large volume of data effectively from multiple sensors for sensor data fusion.     

New flavane gallates isolated from the leaves of Plicosepalus curviflorus and their hypoglycemic activity

Two new flavane gallates were isolated from the leaves of Plicosepalus curviflorus. The structure of the new compounds was established as 2S,3R-3,3′,4′,5,7-pentahydroxyflavane-5-O-gallate (1) and 2S,3R-3,3′,4′,5,5′,7-hexahydroxyflavane-4′,5-di-O-gallate (2), respectively. In addition, seven known compounds (−)-catechin (3), quercetin (4), lupeol (5), β-sitosterol (6), pomolic acid (7), β-sitosterol 3-O-β-d-glucopyranoside (8) and 4-methoxycinnamic acid (9) were reported for the first time from the genus Plicosepalus. Compounds 1, 2 and 3 were investigated for their hypoglycemic activity and showed significant hypoglycemic activity in Swiss Albino mice.     

Allometric relationship for estimating above-ground biomass of Aegialitis rotundifolia Roxb. of Sundarbans mangrove forest, in Bangladesh

Tree biomass plays a key role in sustainable management by providing different aspects of ecosystem. Estimation of above ground biomass by non-destructive means requires the development of allometric equations. Most researchers used DBH (diameter at breast height) and TH (total height) to develop allometric equation for a tree. Very few species-specific allometric equations are currently available for shrubs to estimate of biomass from measured plant attributes. Therefore, we used some of readily measurable variables to develop allometric equations such as girth at collar-height (GCH) and height of girth measuring point (GMH) with total height (TH) for A. rotundifolia, a mangrove species of Sundarbans of Bangladesh, as it is too dwarf to take DBH and too irregular in base to take Girth at a fixed height. Linear, non-linear and logarithmic regression techniques were tried to determine the best regression model to estimate the above-ground biomass of stem, branch and leaf. A total of 186 regression equations were generated from the combination of independent variables. Best fit regression equations were determined by examining co-efficient of determination (R2), co-efficient of variation (CV), mean-square of the error (MSerror), residual mean error (Rsme), and F-value. Multiple linear regression models showed more efficient over other types of regression equation. The performance of regression equations was increased by inclusion of GMH as an independent variable along with total height and GCH.     

In vitro callogenesis and detection of somaclonal variations in Plantago ovata L.

Planago ovata L. is an economically important species in the monotypic genus Plantago. It is a short-stemmed annual herb. The seed husk of this plant is commonly called psyllium or isabgol which is important in pharmaceutical formulation and food industry. In this study, callus induction was optimized using different explants of Plantago ovata. Callus DNA was utilized to access the somaclonal variations using the Random Amplification of Polymorphic DNA (RAPD) markers. The maximum callus growth was observed in Murashige and Skoog (MS) medium containing 4 mg L^?1 2,4-D concentration for shoots, 0.5 mg L^?1 for seeds and 2 mg L^?1 for roots. Moreover, the effect of culture age was considered in assessing genetic variability. Maximum genetic variability was observed in the DNA samples of callus at the concentration of 2 mg L^?1 2,4-D for all explants (roots, shoots, and seeds). Cluster analysis was performed based on 1) similarity coefficient between samples and 2) molecular data using the Numerical Taxonomy and Multivariate Analysis System (NTSYS) PC version 2.01; similarity index was generated by similarity for Quantitative Data (SIMQUAL). Our study indicated that Random Amplified Polymorphic DNAs can successfully be used to explore polymorphism among callus samples at different hormonal concentrations. This study can be useful for the production of callus from Plantago ovata and estimation of genetic variations due to tissue culture conditions. Evaluation of genetic variations can display novel features and manipulate genetic bottlenecks in Plantago ovata. New genetic variations in somaclones can bring vital insight for plant improvement.