Expanding the scope of connectivity modeling with Markov chains: perspectives and applications in R
April 12, 2021
Overview
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 the failure of movement (e.g., mortality). To address these issues, we developed the samc package in R that quantifies landscape connectivity using absorbing Markov chain theory. Not only does this framework explicitly account for these different issues, it provides a probabilistic approach that can incorporate short-term dynamics, time-explicit predictions, long-term dynamics, variation in species distribution and abundance, and directionality in movement and gene flow.
In this workshop, we will provide an overview on potential applications of this framework, along with guidance on implementing the framework with the samc package. This workshop will include illustrating how the package can be used for different types of data, such as GPS telemetry and genetic data, and different problems in connectivity modeling.