Pathwalker: A New Individual-Based Movement Model for Conservation Science and Connectivity Modelling

Abstract

Understanding organism movement is at the heart of many ecological disciplines. The study of landscape connectivity—the extent to which a landscape facilitates organism movement—has grown to become a central focus of spatial ecology and conservation science. Several computational algorithms have been developed to model connectivity; however, the major models in use today are limited by their lack of flexibility and simplistic assumptions of movement behaviour. In this paper, we introduce a new spatially-explicit, individual- and process-based model called Pathwalker, which simulates organism movement and connectivity through heterogeneous landscapes as a function of landscape resistance, the energetic cost of movement, mortality risk, autocorrelation, and directional bias towards a destination, all at multiple spatial scales. We describe the model’s structure and parameters and present statistical evaluations to demonstrate the influence of these parameters on the resulting movement patterns. Written in Python 3, Pathwalker works for any version of Python 3 and is freely available to download online. Pathwalker models movement and connectivity with greater flexibility compared with the dominant connectivity algorithms currently available in conservation science, thereby, enabling more detailed predictions for conservation practice and management. Moreover, Pathwalker provides a highly capable simulation framework for exploring theoretical and methodological questions that cannot be addressed with empirical data alone.

Connectivity at home: A data-driven connectivity modeling framework for home range movements in heterogeneous landscapes

“…They have been instrumental in creating conservation networks, assessing population fragmentation, and designing urban green spaces for both wildlife and humans (Koen et al, 2014;Mimet et al, 2016Mimet et al, , 2020. Over the past two decades, numerous connectivity modeling approaches have emerged (Fletcher et al, 2019;McRae et al, 2008;Unnithan Kumar, Kaszta, et al, 2022;Urban et al, 2009;Van Moorter et al, 2023). These approaches encompass a spectrum of complexity, ranging from simple distance analyses of habitat patches (Calabrese & Fagan, 2004;Foltête et al, 2014) to intermediately complex methodologies utilizing graph and circuit theory with habitat patches, links, and resistance surfaces (McRae et al, 2008;Urban et al, 2009), and advanced approaches simulating individual movement (Unnithan Kumar, Kaszta, et al, 2022).…”

“…Over the past two decades, numerous connectivity modeling approaches have emerged (Fletcher et al, 2019;McRae et al, 2008;Unnithan Kumar, Kaszta, et al, 2022;Urban et al, 2009;Van Moorter et al, 2023). These approaches encompass a spectrum of complexity, ranging from simple distance analyses of habitat patches (Calabrese & Fagan, 2004;Foltête et al, 2014) to intermediately complex methodologies utilizing graph and circuit theory with habitat patches, links, and resistance surfaces (McRae et al, 2008;Urban et al, 2009), and advanced approaches simulating individual movement (Unnithan Kumar, Kaszta, et al, 2022). Among these, graph and circuit modeling tools strike a balance between data requirements and the generation of sufficiently detailed model results (Calabrese & Fagan, 2004;Martensen et al, 2017), rendering them prevalent at the research-landscape planning interface (Foltête et al, 2014;Koen et al, 2014;Molné et al, 2023).…”