Network Analysis Using Markov Chain Applied to Wildlife Habitat Selection

Abstract

In the present study, behavioral states for habitat selection are examined using a discrete-time Markov chain (DTMC) combined with a network model with wildlife movement data. Four male boars (Sus scrofa Linnaeus) at the Bukhansan National Park in South Korea were continuously tracked with an interval of approximately 2 h to 313 days from June 2018 to May 2019. The time-series movement positions were matched with covariates of environmental factors (leaf types and water) in field conditions. Stationary probabilities were used to quantify the habitat selection preference of wild boars, including maximum probability (0.714) with the “broadleaf without water habitat” where in-degree centrality was at its maximum (0.54), but out-degree centrality was low and even (0.17) for all states. Betweenness was the maximum for the “needleleaf without water habitat”, suggesting its role as a bridging habitat between other habitats. Out-closeness scores presented the highest values in the “broadleaf without water habitat” (0.26). Similarly, the first hitting time to the habitat was shortest at the “broadleaf without water habitat” (3.64–5.16 h) and slightly longer than one day in other examined habitats, including “broadleaf with water,” “needleleaf without water,” and “no-leaf without water”. The network model using the Markov chain provided information on both local movement behavior and general resource-use patterns of wild boars in field conditions.