  Ecological studies involving counts of abundance, presence-absence or
occupancy rates often produce data having a substantial proportion of zeros.
Furthermore, these types of processes are typically multivariate and only
adequately described by complex nonlinear relationships involving externally
measured covariates. Ignoring these aspects of the data and implementing
standard approaches can lead to models that fail to provide adequate scientific
understanding of the underlying ecological processes, possibly resulting in a
loss of inferential power. One method of dealing with data having excess zeros
is to consider the class of univariate zero-inflated generalized linear models.
However, this class of models fails to address the multivariate and nonlinear
aspects associated with the data usually encountered in practice. Therefore, we
propose a semiparametric bivariate zero-inflated Poisson model that takes into
account both of these data attributes. The general modeling framework is
hierarchical Bayes and is suitable for a broad range of applications. We
demonstrate the effectiveness of our model through a motivating example on
modeling catch per unit area for multiple species using data from the Missouri
River benthic fish study, implemented by the United States Geological Survey.
