Using multivariate generalized linear latent variable models to measure the difference in event count for stranded marine animals

Abstract

BACKGROUND AND OBJECTIVES: The classification of marine animals as protected species makes data and information on them to be very important. Therefore, this led to the need to retrieve and understand the data on the event counts for stranded marine animals based on location emergence, number of individuals, behavior, and threats to their presence. Whales are generally often stranded in very shallow areas with sloping sea floors and sand. Data were collected in this study on the incidence of stranded marine animals in 20 provinces of Indonesia from 2015 to 2019 with the focus on animals such as Balaenopteridae, Delphinidae, Lamnidae, Physeteridae and Rhincodontidae. METHODS:Multivariate latent generalized linear model was used to compare several distributions to analyze the diversity of event counts. Two optimization models including Laplace and Variational approximations were also applied. RESULTS: The best theta parameter in the latent multivariate latent generalized linear latent variable model was found in the Akaike Information Criterion, Akaike Information Criterion Corrected and Bayesian Information Criterion values, andthe information obtained was used to create a spatial cluster. Moreover, there was a comprehensive discussion on ocean-atmosphere interaction and the reasons the animals were stranded. CONCLUSION: The changes in marine ecosystems due to climate change, pollution, overexploitation, changes in sea use, and the existence of invasive alien species deserve serious attention.