Sensitivity of Functional Traits to Environmental factors in a Karst Plant Community

In this study, the plant communities at five succession stages (herbage, herbage-shrub, shrub, tree-shrub, and tree) in the Zhenning Karst Plateau area of Guizhou were examined. The changes of plant functional characteristics in different succession stages were analyzed, as was the relationship between functional traits and environmental factors. The main results include the following. (1) During the succes-sion process, plant height, leaf dry matter mass, leaf area, leaf nitrogen content, and leaf phosphorus content gradually increased, whereas leaf thickness and specific leaf area decreased, and leaf C:P ratio and leaf N:P ratios did not change significantly. (2) Soil organic matter, soil total nitrogen, soil total phosphorus, soil C:N, soil C:P, and soil C:K increased at first and then decreased, reaching a peak at the tree-shrub stage. Soil total potassium fluctuated and soil bulk density gradually decreased and reached the lowest value at the tree-shrub stage. (3) Redundancy analysis (RDA) showed that the plant community shifted from a nutri-ent-poor soil environment to a nutrient-rich environment. Soil total phosphorus, soil C:K, soil organic mat-ter, soil C:N, and soil bulk density were the key environmental factors affecting the change of functional traits. (4) Structural equation modeling suggests that that specific leaf area and leaf nitrogen content had more sensitive responses to soil nutrient resources and environmental factors, respectively.

Combining Spectral and Texture Features of UAS-Based Multispectral Images for Maize Leaf Area Index Estimation

The leaf area index (LAI) is of great significance for crop growth monitoring. Recently, unmanned aerial systems (UASs) have experienced rapid development and can provide critical data support for crop LAI monitoring. This study investigates the effects of combining spectral and texture features extracted from UAS multispectral imagery on maize LAI estimation. Multispectral images and in situ maize LAI were collected from test sites in Tongshan, Xuzhou, Jiangsu Province, China. The spectral and texture features of UAS multispectral remote sensing images are extracted using the vegetation indices (VIs) and the gray-level co-occurrence matrix (GLCM), respectively. Normalized texture indices (NDTIs), ratio texture indices (RTIs), and difference texture indices (DTIs) are calculated using two GLCM-based textures to express the influence of two different texture features on LAI monitoring at the same time. The remote sensing features are prescreened through correlation analysis. Different data dimensionality reduction or feature selection methods, including stepwise selection (ST), principal component analysis (PCA), and ST combined with PCA (ST_PCA), are coupled with support vector regression (SVR), random forest (RF), and multiple linear regression (MLR) to build the maize LAI estimation models. The results reveal that ST_PCA coupled with SVR has better performance, in terms of the VIs + DTIs (R2 = 0.876, RMSE = 0.239) and VIs + NDTIs (R2 = 0.877, RMSE = 0.236). This study introduces the potential of different texture indices for maize LAI monitoring and demonstrates the promising solution of using ST_PCA to realize the combining of spectral and texture features for improving the estimation accuracy of maize LAI.

Consistent Grime’s CSR Strategy of Daisy Fleabane Erigeron annuus (L.) Pers. despite Its High Morphological Variability—A Case Study from Zagreb and Medvednica Mt., Croatia

Alongside the direct destruction of natural habitats and changes in land use, invasive species are considered one of the greatest threats to global biodiversity. Daisy fleabane Erigeron annuus (L.) Pers. is among the most widespread invasive plants in Croatia. Invasions of E. annuus may be aided by morphological variability, which this study investigates. The variability of life traits (stem height, fresh and dry leaf mass, length, width and leaf area, specific leaf area, and leaf dry matter content) was examined among 18 locations throughout Zagreb and Medvednica Mt. Overall, 87 plant specimens and 435 leaves were measured and analysed using univariate and multivariate statistics. Viable populations were recorded in diverse habitat types, mostly with marked human impact. We determined Grime’s CR plant life strategies for all, except for two localities with C/CR plant strategies. Two populations with a more pronounced competitive strategy had high leaf dry matter content, with smaller leaves and medium height stems. Significant differences between the localities were found, with the specific leaf area (SLA) and plant height being the most diverse. Despite its high morphological variability, daisy fleabane had a consistent CSR strategy, which likely enables its widespread invasions across variable habitats.

Bayesian Hierarchical Models can Infer Interpretable Predictions of Leaf Area Index From Heterogeneous Datasets

Environmental scientists often face the challenge of predicting a complex phenomenon from a heterogeneous collection of datasets that exhibit systematic differences. Accounting for these differences usually requires including additional parameters in the predictive models, which increases the probability of overfitting, particularly on small datasets. We investigate how Bayesian hierarchical models can help mitigate this problem by allowing the practitioner to incorporate information about the structure of the dataset explicitly. To this end, we look at a typical application in remote sensing: the estimation of leaf area index of white winter wheat, an important indicator for agronomical modeling, using measurements of reflectance spectra collected at different locations and growth stages. Since the insights gained from such a model could be used to inform policy or business decisions, the interpretability of the model is a primary concern. We, therefore, focus on models that capture the association between leaf area index and the spectral reflectance at various wavelengths by spline-based kernel functions, which can be visually inspected and analyzed. We compare models with three different levels of hierarchy: a non-hierarchical baseline model, a model with hierarchical bias parameter, and a model in which bias and kernel parameters are hierarchically structured. We analyze them using Markov Chain Monte Carlo sampling diagnostics and an intervention-based measure of feature importance. The improved robustness and interpretability of this approach show that Bayesian hierarchical models are a versatile tool for the prediction of leaf area index, particularly in scenarios where the available data sources are heterogeneous.

Commercially available organic manures on growth, yield and nutrient content of Burmese Grapes (Baccaurea ramiflora)

A pot experiment was carried out in the net house of the Department of Soil, Water and Environment, University of Dhaka to evaluate the effectS of different organic manures on the growth performance, biomass production, NPKS content in leaf, stem and root of Burmese grapes (Baccaurea ramiflora) and some chemical properties of post- harvest soil. Two-month old Burmese grapes seedlings were planted in different organic manure-treated soils (viz. ACI, BGF, Bhola, Green Life, Kazi, Modern, Mazim, Payel, Payel Earth, and Shebok). A significant variation was observed among the treatments. Compared to control treatment, application of organic manures exhibited a significant influence on the physiological growth, viz. plant height (30.4 - 34.06 cm), leaf area (64.2 - 71.30 cm2/plant), leaf area index (0.17 - 0.19), fresh weight (32.2 - 53.06 g/plant), and dry weight (22.09 - 41.24 g/plant) after 120 days of the experiment. The highest plant height, leaf area, leaf area index, fresh weight, and dry weight were recorded in Kazi, BGF, Payel and Payel Earth, respectively. Similarly, the concentrations of N, P, K, and S in leaf, stem and root, and their uptakes were found significantly higher than the control. The highest N, P, K and S uptake by leaf, stem and root ranged from 37.66 - 41.69 kg/ha, 0.70 - 0.92 g/ha, 66.19 - 84.926 kg/ha and 6.82 - 10.11 kg/ha, respectively. Protein content was the highest in Payel in leaf (8%) and root (5%), and stem (7%) in Modern manure treatment. The overall results revealed that Kazi and Payel manures might be better for raising strong and healthy Burmese grape seedlings. J. Biodivers. Conserv. Bioresour. Manag. 2021, 7(1): 63-72

Seedling growth and fall armyworm feeding preference influenced by dhurrin production in sorghum

Cyanogenic glucosides (CGs) play a key role in host-plant defense to insect feeding; however, the metabolic tradeoffs between synthesis of CGs and plant growth are not well understood. In this study, genetic mutants coupled with nondestructive phenotyping techniques were used to study the impact of the CG dhurrin on fall armyworm [Spodoptera frugiperda (J.E. Smith)] (FAW) feeding and plant growth in sorghum [Sorghum bicolor (L.) Moench]. A genetic mutation in CYP79A1 gene that disrupts dhurrin biosynthesis was used to develop sets of near-isogenic lines (NILs) with contrasting dhurrin contents in the Tx623 bmr6 genetic background. The NILs were evaluated for differences in plant growth and FAW feeding damage in replicated greenhouse and field trials. Greenhouse studies showed that dhurrin-free Tx623 bmr6 cyp79a1 plants grew more quickly than wild-type plants but were more susceptible to insect feeding based on changes in green plant area (GPA), total leaf area, and total dry weight over time. The NILs exhibited similar patterns of growth in field trials with significant differences in leaf area and dry weight of dhurrin-free plants between the infested and non-infested treatments. Taken together, these studies reveal a significant metabolic tradeoff between CG biosynthesis and plant growth in sorghum seedlings. Disruption of dhurrin biosynthesis produces plants with higher growth rates than wild-type plants but these plants have greater susceptibility to FAW feeding.