Interspecific interactions and movement are key factors that drive the coexistence of metapopulations in heterogenous landscapes. Yet, it is challenging to understand these factors because separating movement from local population processes relied on capture-based data that are difficult to collect. Recent development of spatially explicit dynamic N-mixture models (SEDNMs) made it possible to draw inference on local population growth and movement using count data of unmarked populations. Here, we further developed SEDNMs to account for interspecific interactions and both false positive and false negative observation errors. Simulations showed that the models provide unbiased parameter estimates regardless of the ecological system (competition, predator-prey), observation process (binomial, Poisson), and sampling situation. Case studies further demonstrated the capabilities of these models in revealing important ecological processes including competition, predator-prey interactions, movement patterns, and differential habitat preferences. The flexible structures of these models make them highly adaptive and relevant in population and community ecology.