If you are confused and intimidated by the sheer number of tools to analyze connectivity related questions, don’t worry. We feel you.
We’ve all been there – we have an interesting research question, we collected data, but we come to a screeching halt when we are faced with the numerous tools in the field of connectivity science. Every paper we read points us in a different direction, and at the end we are left wondering which one we should use and why.
In a recent review of connectivity tools, we tried to make things easier by sorting this mess and streamlining the process. We focused on a specific and popular approach that uses resistance surfaces, defined as the spatial representation of how difficult it is for a species to move across a landscape. Resistance surfaces are often the foundation for connectivity analyses but working with them can be daunting due to the diversity and complexity of relevant software available for creating and using them.
We organized the resistance-surface based workflow for connectivity analyses into three steps: (1) preparing the data, (2) constructing and optimizing resistance surfaces, and (3) using resistance surfaces. We reviewed 43 connectivity tools currently available and associated each with one of the three steps. We found that some tools had functions that could be applied to multiple steps in the workflow. To the best possible extent, we identified functions that would enable users to perform specific tasks relevant to this workflow. Finally, we made a decision-tree to help users decide which tools to select for resistance-surface based connectivity analyses, and provided an R-script with example data for beginners to get familiar with the steps.
In parallel, we conducted a survey to assess which tools are most widely used, the experience people had in using these tools, and what kind of functions they would like tools to have in the future. After collecting basic information on their level of experience, we asked people to rank tools on a three-point scale across five criteria: ease of data formatting, speed, stability, customization, and getting help (Thank you to everyone who participated in this survey!). Based on the survey responses, we identified the tools that ranked highly across all 5 criteria. Important point here is that there appears to be some trade-offs between the different criteria.
We also compiled suggestions for programmers that include developing tools that are independent of propriety software, maintaining trouble-shooting help forums, and accounting for dynamic and uncertainty in modelling outputs.
We hope this review will help beginners have a smooth entry into resistance-based connectivity research, highlight the variety of available options to experienced researchers, and provide developers with ideas to improve the performance and usefulness of their tools. Ultimately, the diversity of methods, algorithms, and tools should help facilitate a better understanding of drivers of connectivity in fragmented landscapes and contribute to the conservation of biodiversity on Earth.
Dutta, T., Sandeep, S., Meyer, N.F.V, Larroque, J., and Balkenhol, N. 2022. An overview of computational tools for preparing, constructing and using resistance surfaces in connectivity research. Landscape Ecology 37: 2195 – 2224.
This blogpost has been published on Conservation Corridor on 14 September 2022: A decision guide for choosing the right connectivity tools – Conservation Corridor
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