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.

In heterogeneous landscapes, habitat preference constitutes a crucial link between landscape and population-level processes such as density and sex ratios. We conducted a non-invasive genetic study of white-tailed deer in southern Finland using fecal samples. We estimated deer density as a function of landcover type using a Spatial Capture-Recapture (SCR) model. Second-order habitat selection of white-tailed deer revealed particularly high densities in fields and mixed forest, and third-order selection was related to distance to fields and transitional woodlands (clearcuts). Including landscape heterogeneity improved model fit compared with assuming a homogenous landscape. Our findings underline the importance of including habitat covariates when estimating density.