Research on rainfall is critically important due to its significant impact on climate change and natural disasters in Indonesia. Various factors influence rainfall variability. Consequently, when examining spatial aspects, it is likely that spatial dependency exists not only in the response variable but also in the exogenous variables. Hence, a model that accounts for spatial dependencies between these variables is required. The integration of the Spatial Durbin Model (SDM) with Casetti’s expansion approach can be utilized to predict spatial patterns of rainfall influenced by exogenous variables. By incorporating spatial effects and relevant independent variables, this model can provide more precise estimates of rainfall distribution across different regions. This modeling technique is particularly effective for accurate rainfall prediction, considering exogenous factors such as air temperature, humidity, solar irradiation, and surface pressure. The SDM with Casetti’s expansion approach was employed to predict rainfall patterns in the Java Island region, utilizing data from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) big data website. The application of this model in the context of rainfall prediction highlights its importance in enhancing the understanding of weather dynamics and aiding disaster risk mitigation in Java Island, a highly populated region characterized by a Monsoon rainfall pattern. The rainfall prediction follows a Knowledge Discovery in Databases (KDD) methodology. The results of this study are expected to be valuable to relevant agencies, such as the Meteorology, Climatology, and Geophysics Agency (BMKG), and agribusiness companies, improving agricultural planning and planting seasons. Additionally, the general public can benefit from more accurate climate information, particularly regarding rainfall. The computational framework is developed within an RShiny web application, and the performance of the proposed technique is measured by the Mean Absolute Percentage Error (MAPE), achieving a very accurate prediction rate of 2.78%.

This paper derives the bivariate version of the extended standard U-quadratic (eSU) distribution using the method of compounding or pseudo family of distributions. The joint probability and cumulative distribution functions of the derived distribution are obtained and it is observed that the said distribution can generate bivariate shape with the following properties: $(i)$ $X$ and $Y$ have bathtub shapes; $(ii)$ $X$ has constant and $Y$ has bathtub shapes; and $(iii)$ $X$ has inverted bathtub and $Y$ has bathtub shapes. Moreover, some properties of the derived distribution such as the marginal distribution, conditional distributions, conditional moments, product and ratio moments are derived. Further, the maximum likelihood estimation is performed to estimate the parameters of the derived distribution. Also, a simulation study is carried out to evaluate the behavior of the estimates of the parameters. Moreover, we derive a new bivariate Kumaraswamy distribution and use it to simulate bivariate data with $X$ and $Y$ having bathtub shapes. Furthermore, A new bivariate version of the Cubic Transmuted Uniform (CTU) distribution is also derived. Finally, the proposed Bivariate eSU distribution is applied to simulated data and compared with the said Bivarite Cubic Transmuted Uniform distribution. The result shows that the proposed Bivariate eSU distribution provides a better fit for the said simulated dataset as compared with the Bivariate CTU distribution.

The Philippines is a top exporter of marine ornamental fish in the world. Angelfishes (Pomacanthidae) are among the most valuable in the marine ornamental fish trade, particularly the species of Centropyge. Assessment of their conservation status is vital to regulate harvesting, prevent extinction, and sustain the ornamental industry. Critical information for their assessment is limited. Thus, this study used local knowledge to determine the distribution, exploitation, and threats to Centropyge bispinosa, C. heraldi, and C. bicolor. A total of 157 ornamental fish divers, 8 exporters, and 18 middlemen were interviewed from 12 municipalities covering eight provinces identified as major producers of marine ornamental fish in the Philippines. Results indicate a wide distribution of these species in the Philippines. These species were utilized for aquarium trading only. Zambales fisherfolk has the highest catch number for all three Centropyge species, with an average weekly catch of 72 pieces of C. bispinosa, 60 pieces of C. heraldi, and 36 species of C. bicolor. Their conservation status is Least Concern based on 2009-2010 IUCN assessments which are outdated. Illegal fishing, water pollution, natural disasters, and crown-of-thorns starfish infestation could drive them toward the threatened category. Information from this study will be used to re-assess the current conservation status using IUCN Red List Categories and Criteria. The result of this study will provide crucial information for advancing the conservation of marine biodiversity, sustainable management resources, and evidence-based policy formulation.

This paper introduces the Generalized extended Standard U-quadratic (GeSU) distribution, an extension of the extended Standard U-quadratic (eSU) distribution, developed by incorporating an additional parameter. The GeSU distribution effectively models asymmetric data exhibiting bathtub and inverted bathtub shapes over the unit interval [0,1]. Several special cases of the proposed distribution are also derived. Key statistical properties, including moments, the moment-generating function, mean, variance, median, mode, skewness, and kurtosis, are established, along with a random number generation algorithm. The performance of maximum likelihood estimation for the parameters of the GeSU distribution is assessed through simulation studies. Furthermore, the distribution is applied to a real dataset, demonstrating a superior fit compared to the eSU, Unit-Rayleigh, and Unit-Burr XII distributions. These results highlight the flexibility and applicability of the proposed GeSU distribution in modeling unit-interval data.

Herbito Jr. LC, Guihawan JQ, Casal CMV, Polestico DLL, Torres AG. 2024. Unveiling the prevalence of invasive alien plant species in multiple-use zone of Initao-Libertad Protected Landscape and Seascape, Misamis Oriental, Philippines. Biodiversitas 25: 3286-3294. Invasive Alien Plant Species (IAPS) threaten Protected Areas (PAs) globally, causing habitat degradation, biodiversity reduction, and ecosystem disruption. In the Philippines, data on invasive species in PAs is limited. This study aimed to investigate the prevalence, abundance, and diversity of IAPS in multiple-use zone of Initao-Libertad Protected Landscape and Seascape (ILPLS), Misamis Oriental, Philippines. Using belt-transect method, a total of 15 nested plots (20×20 m for trees, 5×5 m for herbs and shrubs, and 1×1 m for grasses and lianas) were established. Twenty-nine plant species from 18 families were identified, comprising 18 Native Species (NS), seven Non-Invasive Alien Species (NIAS), and four IAPS. The IAPS identified were Gmelina arborea, Leucaena leucocephala, Lantana camara, and Swietenia macrophylla which had the highest relative abundance of 96.83%. The Shannon-Weiner diversity indices showed very low to low diversity of 0.30 to 2.17, and an unbalanced to semi-balanced species evenness of 0.17 to 0.70. The Wilcoxon signed-rank test showed a significant difference in relative abundance between NS and IAPS (p = 0.01576). The Wilcoxon rank-sum test indicated significant differences in species diversity and evenness of p = 0.03359 and 0.03343, respectively. These findings are crucial for developing IAPS control strategies in the ILPLS in the future.