Spatial dynamic N‐mixture models with interspecific interactions

Abstract

1. 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.2. 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 predatorprey system (bobcat Lynx rufus, wild turkey Meleagris gallopavo) with Poisson observations.3. 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.4. 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

A review of N‐mixture models

“…The future of N‐mixture models includes expanded application and technical developments focused on increasing complexity of model components, such as spatial and temporal dynamics as well as increasing ecological sophistication, such as their application to multiple species systems [e.g., Zhao et al (2022)] to model predator–prey dynamics or competition. We also envision increased application of N‐mixture models to problems of “data integration,” in which observations obtained by multiple monitoring programs can be analyzed jointly using individual component models having shared parameters (Schaub & Kery, 2021).…”

“…Howell et al (2020) devised a similar spatially‐explicit model allowing distance‐based movement of frogs among ponds in a metapopulation system, but with a simpler model of net population growth without distinguishing survival from recruitment. Zhao et al (2022) developed a multi‐species extension of this “net growth” model.…”

“…Howell et al (2020) devised a similar spatially-explicit model allowing distance-based movement of frogs among ponds in a metapopulation system, but with a simpler model of net population growth without distinguishing survival from recruitment. Zhao et al (2022) developed a multi-species extension of this "net growth" model. Brintz et al (2021) adapted the asymptotic approximation to model the number of cases N i,t of a communicable disease in site i at time t, where observed counts n i,t may be much too large to approximate (6) using finite sums.…”

Designing count‐based studies in a world of hierarchical models

“…with additional demands on the data (Royle and Link 2006, Clare et al 2021, Zhao et al 2022. Additional assumptions are also required for each approach ( and that distances are measured accurately.…”

“…Researchers using these models must constrain their survey protocols to exclude false detections. Where necessary, researchers can add sub‐models to account for false positive errors (e.g., from species misidentification) with additional demands on the data (Royle and Link 2006, Clare et al 2021, Zhao et al 2022). Additional assumptions are also required for each approach (Table 3; Matsuoka et al 2014, Duarte et al 2018, Louvrier et al 2018, Marques et al 2017, Fogarty and Fleishman 2021).…”