Author: ovcre.upvisayas

Tropical mangrove ecosystems are known for high productivity and extensive ecosystem services. While there has been extensive research on floristics and faunistics in mangroves, there has been limited information in community structure and carbohydrate analysis of flower nectars, which can be explored for possible apiculture. The study was conducted to assess community structure, species diversity, and total carbohydrate (TC) analysis of floral nectars in 15 mangrove sites in Maqueda Bay, Matarinao Bay, and Leyte Gulf in Samar, Eastern Samar, and Leyte, respectively. A total of 15 sites were assessed using the transect line-plot method. A total of 23 mangrove species were identified from the 15 sites (2 dominant, 17 minor, and 2 associated species) belonging to 12 families, including 21 true mangroves and 2 mangrove associates. Leyte Gulf and Maqueda Bay have 17 species, while Matarinao Bay has 13 species. In general, the mangrove sites are dominated by Rhizophora apiculata and Sonneratia alba. Rehabilitated sites are dominantly planted with Rhizophora. In contrast, natural mangrove stands are composed of mixed species of Aegiceras, Avicennia, Brugueira, Rhizophora, Sonneratia, and Xylocarpus. Across the three habitats, S. alba showed much higher nectar TC (mg) per flower than R. apiculate ranging from 7.45 – 0.02 mg/ flower. These data suggest that sites dominated with S. alba has good prospects for apiculture, considering the total carbohydrate analysis, over the popularly planted R. apiculata.

An essential element for describing and predicting the dynamic behavior of interacting components within a biochemical system is the formulation of mathematical models. One of the widely used mathematical models is the power-law formalism-based S-systems. However, parameter estimation in S-systems continues to be a difficult challenge because of the nonlinearity and high dimensionality of the underlying coupled systems of ordinary differential equations. Hence it is important to provide efficient and effective novel methods to tackle parameter estimation problem. In this paper, we use decoupling technique on the S-system and then Xin- She Yang’s Firefly Algorithm (FA), a metaheuristic algorithm based on the bioluminescence process which characterizes fireflies, as optimization algorithm in the parameter estimation. FA’s automatic subdivision of the whole population into subgroups and natural capability of dealing with multi-modal optimization are the two major advantages of FA over other algorithms, making FA a good algorithm choice. Using three S-systems of increasing complexity from the MADMan Benchmarking Framework, we assess the performance of the method. Simulation results show that FA-decoupling method is applicable in estimating parameters of the 3 S-system models using noise-free data with concentration error lower than 10-3 and better approximations were recorded for kinetic orders than rate constants. Since FA-decoupling method worked on parameter estimation of S-system models using noise-free data, it is now reasonable to implement the method using datasets with different noise levels to check how the method is affected by the presence of noise.