Landscape connectivity, the extent to which a landscape facilitates the flow of ecological processes such as organism movement, has emerged as a central focus of landscape ecology and conservation science. Connectivity modelling now encompasses an enormous body of work across ecological theory and application. The dominant connectivity models in use today are based on the framework of ‘landscape resistance’, which is a way of measuring how landscape structure influences movement patterns. However, the simplistic assumptions and high degree of reductionism inherent to the landscape resistance paradigm severely limits the ability of connectivity algorithms to account for many fundamental aspects of animal movement, and thus greatly reduces the effectiveness and relevance of connectivity models for conservation theory and practice. In this paper, we first provide an overview of the development of connectivity modelling and resistance surfaces. We then discuss several key drivers of animal movement which are absent in resistance-based models, with a focus on spatiotemporal variation, human and interspecies interactions, and other context-dependent effects. We look at a range of empirical studies which highlight the strong impact these effects have on movement and connectivity predictions. But we also provide promising avenues of future research to address this: we discuss newly emerging technologies and interdisciplinary work, and look to developing methodologies, models and conversations which move beyond the limiting framework of landscape resistance, so that connectivity models can better reflect the complexities and richness of animal movement.

Landscape connectivity, the extent to which a landscape facilitates the flow of ecological processes such as organism movement, has grown to become a central focus of applied ecology and conservation science. Several computational algorithms have been developed to understand and map connectivity, and many studies have validated their predictions using empirical data. Yet at present, there is no published comparative analysis which uses a comprehensive simulation framework to measure the accuracy and performance of the dominant methods in connectivity modelling. Given the widespread usage of such models in spatial ecology and conservation science, a thorough evaluation of their predictive abilities using simulation techniques is essential for guiding their appropriate and effective application across different contexts. In this paper, we address this by using the individual-based movement model Pathwalker to simulate different connectivity scenarios generated from a wide range of possible movement behaviours and spatial complexities. With this simulated data, we test the predictive abilities of three major connectivity models: factorial least-cost paths, resistant kernels, and Circuitscape. Our study shows the latter two of these three models to consistently perform most accurately in nearly all cases, with their abilities varying substantially in different contexts. For the majority of conservation applications, we infer resistant kernels to be the most appropriate model, except for when the movement is strongly directed towards a known location. We conclude this paper with a review and interdisciplinary discussion of the current limitations and possible future developments of connectivity modelling.

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.

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