MULTIVARIATE MULTIVARIATE ANALYSIS OF WEEDS OF CHICKPEA, MUSTARD AND WHEAT CROP FIELDS OF TEHSIL ISAKHEL, DISTRICT MIANWALI (PUNJAB), PAKISTAN

Multivariate analysis through Two Way Indicator Species Analysis (TWINSPAN) was conducted to study the phytosociological attributes of weeds of some selected crop fields of chickpea, mustard and wheat of Tehsil Isa Khel, District Mianwali, Punjab. Forty one (41) weed species were collected from the study area belonging to twenty one (21) different families. Twenty four weed species found in chickpea, twenty five in mustard and twenty nine in wheat crop fields. Sixteen weed species were common in three crops. Family Poaceae and Astraceae had maximum weed species i.e. 7 and 6 species respectively followed by Euphorbiaceae, Fabaceae, Chenopodiaceae, Papaveraceae, Zygophyllaceae and so on. Asphodelus tenuifolius, Medicago monantha and Carthamus oxycantha are frequently occurring weeds relative to others. Two-Way Indicator Species Analysis (TWINSPAN) was performed on the percentage cover basis which divided the weed species into groups, sub groups, associations and sub associations.

Two way indicator species analysis of weed species of potato and wheat in crop fields of Sharqpur Tehsil, Pakistan

This study was carried out to determine the distribution of the weeds in two important cash crops of Pakistan along with their distribution pattern in research area. Total of 56 weed species was recorded belonging to 23 plant families by the quadrat method with random sampling in wheat and potato fields of seven different villages from Tehsil Sharqpur Sharif, Punjab, Pakistan. A multivariate technique, Two Way Indicator Species Analysis (TWINSPAN), using PC-ORD (Version-6.22) classified the weeds into groups and associations. The dominant weed communities were Cichorium-Euphorbia- Cyperus community, Chenopodium-Digera-Fumaria weed community, Poa- Chenopodium-Coronopus weed community, Parthenium-Euphorbia-Veronica weed community, Euphorbia-Achyranthes-Brassica community and Setaria-Polypogon- Solanum community. Phytodiversity of weeds found in this study, remarkably indicated the variable distribution pattern of weeds in both the crop fields. Moreover, communities of weeds emerged can be helpful in better planning of the weed management in crop fields. Bangladesh J. Plant Taxon. 28(1): 233-240, 2021 (June)

Diversity and degradation of the vegetation of mountain belt forests of central Adjara (the Lesser Caucasus), Georgia

The Colchis forests contribute to the biodiversity hotspot in the Caucasus eco-region. We investigated the plant diversity of these forests in the central part of Adjara (W Georgia). The aims of our study were (i) to differentiate the forest vegetation diversity in the mountain belt forests by means of phytosociology, (ii) to associate endemic taxa with the revealed forest types, and (iii) to assess degradation of the forest vegetation diversity by means of environmental abiotic and biotic factors. We sampled the forest vegetation on 135 plots with the size of 10 × 10 m2 and 237 plant taxa were recorded. Principal Component Analysis (PCA) was used to reduce environmental variables to a few orthogonal composed variables. The derived factors (PC1, PC2) were used in ordination analysis to group the plot measured forest vegetation diversity. One-way ANOVA was used for the comparison of means between the separated clusters in PCA. Two-Way Indicator Species Analysis (TWINSPAN) and Indicator Species Analysis (ISA) were applied for the association of the plant taxa with the vegetation cluster groups separated by PCA. Our analysis revealed two general ecologically distinct forest types which were characterized as dry and humid forests. Endemic species had the main occurrence in dry forests of the studied territory which are heavily impacted by the local land use. The results indicated that the vegetation diversity of dry forests is under higher threat of degradation than that of humid ones because these forests are not protected and are subjected to non-sustainable forest exploitation. Additionally, many rural and invasive plant species change the native plant assemblages. Based on our findings, we recommend to the organization which manages the local forests to find a balance between the use of forest resources and protection of the unique floristic diversity of local forests in order to avoid their degradation.

Indicator Species Analysis: A Useful Tool for Plant Disease Studies

Indicator species analysis (ISA) uses indices of an organism’s relative abundance and occurrence to estimate the strength of its associations with a priori groups of interest and a simple randomization test to evaluate the probability of association. Because ISA values tend to be greatest when a species is both relatively more abundant than other species in a particular group and it occurs more frequently in that same group (the expectations of a causal agent in diseased plants), ISA should be useful for identifying and narrowing the list of potential causal agents from a pool of pathogens in both emerging plant diseases and when the causal agent is unclear. Recent ISA plant disease applications suggests it may either directly identify a single causal agent from a pool of potential pathogens or narrow the pool of pathogens as candidates for pathogenicity tests in the process of fulfilling Koch’s postulates. In this letter, we explain the underpinnings of ISA, summarize the known applications to plant pathosystems, offer caveats about the analysis, and suggest scenarios where ISA may be broadly applicable for plant disease studies.

Environmental and microbial controls on microbial necromass recycling, an important precursor for soil carbon stabilization

There is an emerging consensus that microbial necromass carbon is the primary constituent of stable soil carbon, yet the controls on the stabilization process are unknown. Prior to stabilization, microbial necromass may be recycled by the microbial community. We propose that the efficiency of this recycling is a critical determinant of soil carbon stabilization rates. Here we explore the controls on necromass recycling efficiency in 27 UK grassland soils using stable isotope tracing and indicator species analysis. We found that recycling efficiency was unaffected by land management. Instead, recycling efficiency increased with microbial growth rate on necromass, and was highest in soils with low historical precipitation. We identified bacterial and fungal indicators of necromass recycling efficiency, which could be used to clarify soil carbon stabilization mechanisms. We conclude that environmental and microbial controls have a strong influence on necromass recycling, and suggest that this, in turn, influences soil carbon stabilization.

Community composition, beta diversity and structure of high altitude grasslands along an altitudinal gradient in southeastern Brazil

Introduction: Studies on how the altitudinal gradient determines community composition and structure in tropical high altitude grasslands are limited. Objective: To evaluate the plant community composition and structure and their relationship with altitude and soil properties along an altitudinal gradient of three granitic rocky outcrops at the Serra do Brigadeiro State Park, Minas Gerais, in southeastern Brazil. Methods: In each selected site, 100 plots of 1 × 1 m were established, totalizing 300 plots in the study area. We compared floristic composition, relative coverage and abundance among sites. We performed beta diversity analysis. We also performed an indicator species analysis and a canonical correlation analysis to investigate possible relations between abiotic (soil and altitude) and biotic (indicator species abundances) variables. Results: We sampled a total of 9 276 individuals belonging to 39 families and 102 species. Significant differences were observed among sites regarding composition, abundance and coverage. The indicator species analysis revealed that 57 species (55.88 %) were indicators. The distribution of indicator species abundances was correlated with environmental variables. Conclusions: It was observed that altitude and soil play an important role in controlling community composition and structure, beta diversity and species distribution in the highland grasslands studied.

Potential for the Pathogenicity of Plant-Parasitic Nematodes Associated with Blueberries in Georgia and North Carolina

Ring (Mesocriconema ornatum) nematode in Georgia (GA) has been associated with blueberry replant disease. A survey of plant-parasitic nematodes (PPNs) from production blueberry fields in GA and North Carolina (NC) was previously presented and evaluated from an abundance perspective. However, PPNs are often patchily distributed, occurring in close physical association with infected plant roots. Soil cores may or may not sample the areas of high nematode abundance, and nematode counts tend to be highly variable because of this patchiness. To evaluate the survey data for potentially unrecognized blueberry PPNs, we reanalyzed nematode survey data from NC and GA using multivariate analyses that integrate both patterns of occurrence and patterns in relative abundance. Indicator species analysis identified ring nematode in GA as a potentially pathogenic nematode, consistent with previous confirmation of pathogenicity through a container study. Indicator species analysis also identified two potentially pathogenic nematodes in NC blueberries: awl (Dolichodorus spp.) and sheath (Hemicycliophora spp.) nematodes. Of the two nematodes, awl shared a similar position in the NC blueberry nematode community when compared with ring nematode in GA. However, awl nematode relative abundance was low enough, when compared with ring nematode in GA, to suggest that although it is likely parasitic on NC blueberries, it may not be pathogenic. Our analysis from a previous survey suggests that PPNs are unlikely to be a threat to NC blueberry production. However, if blueberry replant disease emerges in NC, then ring, awl, and sheath nematodes should be considered as potential causal agents.

Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition

Microbial communities are ubiquitous and often influence macroscopic properties of the ecosystems they inhabit. However, deciphering the functional relationship between specific microbes and ecosystem properties is an ongoing challenge owing to the complexity of the communities. This challenge can be addressed, in part, by integrating the advances in DNA sequencing technology with computational approaches like machine learning. Although machine learning techniques have been applied to microbiome data, use of these techniques remains rare, and user-friendly platforms to implement such techniques are not widely available. We developed a tool that implements neural network and random forest models to perform regression and feature selection tasks on microbiome data. In this study, we applied the tool to analyze soil microbiome (16S rRNA gene profiles) and dissolved organic carbon (DOC) data from a 44-day plant litter decomposition experiment. The microbiome data includes 1709 total bacterial operational taxonomic units (OTU) from 300+ microcosms. Regression analysis of predicted and actual DOC for a held-out test set of 51 samples yield Pearson’s correlation coefficients of .636 and .676 for neural network and random forest approaches, respectively. Important taxa identified by the machine learning techniques are compared to results from a standard tool (indicator species analysis) widely used by microbial ecologists. Of 1709 bacterial taxa, indicator species analysis identified 285 taxa as significant determinants of DOC concentration. Of the top 285 ranked features determined by machine learning methods, a subset of 86 taxa are common to all feature selection techniques. Using this subset of features, prediction results for random permutations of the data set are at least equally accurate compared to predictions determined using the entire feature set. Our results suggest that integration of multiple methods can aid identification of a robust subset of taxa within complex communities that may drive specific functional outcomes of interest.