Abstract
Model-based learning of organism dynamics is challenging, particularly when modeling count correlated data. In this paper, we adapt the multivariate Poisson distribution to model nematode dynamics. This distribution relaxes the mean-equal-variance property of the univariate Poisson distribution and allows recovery of the correlation among nematode genera. An observational dataset with 68 soil samples, 11 nematode genera, and 12 soil parameters is analyzed. The Spike and Slab Variable Selection procedure is adapted to obtain parsimonious models for the nematode occurrence. Nematode genus to genus interaction is assessed through the correlation matrix of the model. A simulation study validated the model's implementation. As a result, the model determined the most important covariates for each nematode and classified pairs of nematodes as: sympathetic, antagonistic or neutral, based on their estimated correlations. The model is useful for researchers and practitioners interested in studying population dynamics. In particular, the current results are important inputs when planning strategies for improving or managing soil health regarding nematodes.