Spatial dynamic N‐mixture models with interspecific interactions

Abstract

Abstract 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 spatial dynamic N‐mixture models (SDNMs) made it possible to draw inferences on local population growth and movement using count data of unmarked populations. However, no SDNMs have been developed to account for interspecific interactions and double‐counting observation errors. In this study we further developed SDNMs to account for interspecific interactions and both false‐negative and double‐counting observation errors. We conducted simulation studies to evaluate the inferential performance of these models under different ecological systems (competition, predator–prey), observation processes (binomial, Poisson) and sampling situations including the number of surveyed sites, detection and the adoption of robust sampling design. We then illustrated the applications of these models with two case studies, one representing a competition system (mallard Anas platyrhynchos, northern pintail Anas acuta) with binomial observations and the other representing a predator–prey system (bobcat Lynx rufus, wild turkey Meleagris gallopavo) with Poisson observations. The results of the simulation studies showed that the models provide unbiased parameter estimates regardless of the ecological system, observation process and sampling situation. Case studies further demonstrated the capabilities of these models in revealing important ecological processes. More specifically, the first case study revealed a negative effect of the superior competitor, Mallard, on the population growth of the inferior competitor, pintail as well as different movement patterns between these species, whereas the second case study reveal a top‐down effect of bobcat abundance on wild turkey population growth and their differential habitat preferences. The models developed in this study could be used by themselves on count data or serve as population sub‐models in integrated modelling frameworks to provide insights about metapopulation dynamics of interacting species in heterogeneous landscapes. The flexible structures of these models made them highly adaptive and relevant in population and community ecology.