Data from: samc: An R package for connectivity modeling with spatial absorbing Markov chains
Marx, Andrew et al. (2019), Data from: samc: An R package for connectivity modeling with spatial absorbing Markov chains, Dryad, Dataset, https://doi.org/10.5061/dryad.0k6djh9wk
Quantifying landscape connectivity is fundamental to better understand and predict how populations respond to environmental change. Currently, popular methods to quantify landscape connectivity emphasize how landscape features provide resistance to movement. While many tools are available to quantify landscape resistance, these do not discern between two fundamentally different sources of resistance: movement behavior and mortality. To address this issue, we developed the samc package that quantifies landscape connectivity using absorbing Markov chain theory. Within this mathematical framework, movements are represented as transient states in the Markov chain, while mortality is represented by transitions to absorbing states. Not only does this framework explicitly account for these different issues, it provides a probabilistic approach that can incorporate both short-term and long-term dynamics, as well as species distribution and abundance. The package includes functions to quantify life expectancy, long-term visitation rates, and various spatially and temporally explicit measures of mortality and movement at the local and landscape scale. These functions in samc have been optimized to find computationally practical solutions in landscapes of > 2 × 106 cells. Here, we illustrate the workflow of the samc package with publicly available movement and mortality data on the endangered Florida panther (Puma concolor coryi). This analysis showed that movement and mortality are generally correlated except for sites near roads that are within the dispersal range of source locations which had higher mortality risk. This pattern would have been undetectable with current methods that quantify movement resistance. Overall, the samc package provides a means for implementing spatial absorbing Markov chains that can distinguish between movement behavior and mortality resulting in more reliable landscape connectivity measures.