Successional and Intraspecific Variations in Leaf Traits, Spectral Reflectance Indices and Herbivory in a Brazilian Tropical Dry Forest

Leaf traits are good indicators of ecosystem functioning and can affect herbivory and leaf reflectance patterns, allowing a better understanding of changes in environmental conditions, such those observed during forest natural regeneration. The aim of this study was to evaluate the intraspecific variation in leaf traits and their influence on the pattern of herbivory and leaf reflectance in three species distributed along a successional gradient (early, intermediate and late stages) in a tropical dry forest (TDF) in northern Minas Gerais, Brazil. We sampled individuals of the following abundant tree species that occurred in multiple successional stages: Cenostigma pluviosum, Handroanthus ochraceus, and Tabebuia reticulata. We collected 10 leaves from each tree to determine the contents of chlorophyll a, b, and total, carotenoids and water, as well as the percentage of leaf area removed by herbivores and leaf specific mass (LSM). We also measured five spectral reflectance indices (Normalized Difference Vegetation Index-NDVI, Simple Ratio-SR, modified Normalized Difference-nND, modified SR-mSR and Water Index-WI) using a portable spectrometer. Our results showed intraspecific differences in most leaf traits along the successional gradient, suggesting that local adaptation may play an important role in plant community assembly. However, herbivory only differed for H. ochraceus in early and intermediate stages, but it was not affected by the leaf traits considered here. Spectral reflectance indices also differed among successional stage for all species together and for each species separately, except for T. reticulata in intermediate and late stages. Thus, leaf spectral signatures may be an important tool to the remote detection of different successional stages in TDFs, with implications for forest management.

Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks

Monitoring and managing water quality parameters (WQPs) in water bodies (e.g., lakes) on a large scale using sampling-point techniques is tedious, laborious, and not highly representative. Hyperspectral and data-driven technology have provided a potentially valuable tool for the precise measurement of WQPs. Therefore, the objective of this work was to integrate WQPs, derived spectral reflectance indices (published spectral reflectance indices (PSRIs)), newly two-band spectral reflectance indices (NSRIs-2b) and newly three-band spectral indices (NSRIs-3b), and artificial neural networks (ANNs) for estimating WQPs in Lake Qaroun. Shipboard cruises were conducted to collect surface water samples at 16 different sites throughout Lake Qaroun throughout a two-year study (2018 and 2019). Different WQPs, such as total nitrogen (TN), ammonium (NH4+), orthophosphate (PO43−), and chemical oxygen demand (COD), were evaluated for aquatic use. The results showed that the highest determination coefficients were recorded with the NSRIs-3b, followed by the NSRIs-2b, and then followed by the PSRIs, which produced lower R2 with all tested WQPs. The majority of NSRIs-3bs demonstrated strong significant relationships with three WQPs (TN, NH4+, and PO43−) with (R2 = 0.70 to 0.77), and a moderate relationship with COD (R2 = 0.52 to 0.64). The SRIs integrated with ANNs would be an efficient tool for estimating the investigated four WQPs in both calibration and validation datasets with acceptable accuracy. For examples, the five features of the SRIs involved in this model are of great significance for predicting TN. Its outputs showed high R2 values of 0.92 and 0.84 for calibration and validation, respectively. The ANN-PO43−VI-17 was the highest accuracy model for predicting PO43− with R2 = 0.98 and 0.89 for calibration and validation, respectively. In conclusion, this research study demonstrated that NSRIs-3b, alongside a combined approach of ANNs models and SRIs, would be an effective tool for assessing WQPs of Lake Qaroun.

Integration of Spectral Reflectance Indices and Adaptive Neuro-Fuzzy Inference System for Assessing the Growth Performance and Yield of Potato under Different Drip Irrigation Regimes

Simultaneous and timely assessment of growth and water status-related plant traits is critical for precision irrigation management in arid regions. Here, we used proximal hyperspectral sensing tools to estimate biomass fresh weight (BFW), biomass dry weight (BDW), canopy water content (CWC), and total tuber yield (TTY) of two potato varieties irrigated with 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Plant traits were assessed remotely using published and newly constructed vegetation and water spectral reflectance indices (SRIs). We integrated genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) models to predict the measured traits based on all SRIs. The different plant traits and SRIs varied significantly (p < 0.05) between the three irrigation regimes for the two varieties. The values of plant traits and majority SRIs showed a continuous decrease from the 100% ETc to the 50% ETc. Water-SRIs performed better than vegetation-SRIs for estimating the four plant traits. Almost all indices of the two SRI types had a weak relationship with the four plant traits (R2 = 0.00–0.37) under each irrigation regime. However, the majority of vegetation-SRIs and all water-SRIs showed strong relationships with BFW, CWC, and TTY (R2 ≥ 0.65) and moderate relationships with BDW (R2 ≥ 0.40) when the data of all irrigation regimes and varieties were analyzed together for each growing season or the data of all irrigation regimes, varieties, and seasons were combined together. The ANFIS-GA model predicted plant traits with satisfactory accuracy in both calibration (R2 = 1.0) and testing (R2 = 0.72–0.97) modes. The results indicate that SRI-based ANFIS models can improve plant trait estimation. This analysis also confirmed the benefits of applying GA to ANFIS to estimate plant responses to different growth conditions.

Combining Genetic Analysis and Multivariate Modeling to Evaluate Spectral Reflectance Indices as Indirect Selection Tools in Wheat Breeding under Water Deficit Stress Conditions

Progress in high-throughput tools has enabled plant breeders to increase the rate of genetic gain through multidimensional assessment of previously intractable traits in a fast and nondestructive manner. This study investigates the potential use of spectral reflectance indices (SRIs; 15 vegetation-SRIs; 15 water-SRIs) as alternative selection tools for destructively measured traits in wheat breeding programs. The genetic variability, heritability (h2), genetic gain (GG), and expected genetic advances (GA) of these indices were compared with those of destructively measured traits in 43 F7-8 recombinant inbred lines (RILs) grown under limited water conditions. The performance of SRIs to estimate the destructively measured traits directly was also evaluated using the partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) models. Most vegetation-SRIs exhibited high genotypic variation, similar to the measured traits, and phenotypic correlations with these traits, compared with the water-SRIs. Most vegetation-SRIs presented comparable values for h2 (>60%) and GG (>20%) as intermediate traits, while about half of water-SRIs exhibited a high h2 (>60%), but low GG (<20%). Principle component analysis revealed that most vegetation-SRIs and seven of 15 water-SRIs were grouped together in a positive direction, had a moderate to strong relationship with measured traits, and could identify the drought-tolerant parent Sakha 93 and several RILs. The PLSR model based on all SRIs as a single index showed moderate to high R2 in calibration (0.53–0.75) and validation (0.46–0.72) datasets, with strong relationships between observed and predicted values of measured traits. The SMLR models identified four and three SRIs from vegetation-SRIs and water-SRIs, respectively, to explain 63–86% of the total variability in measured traits among genotypes. These results demonstrated that vegetation-SRIs can be used individually or combined with water-SRIs as alternative breeding tools to increase genetic gains and selection accuracy in spring wheat breeding.