ConScape Library
CONnected landSCAPE
ConScape is an open-source Julia library to compute landscape network-based metrics of habitat funcionality and movement flow. The tool can be used to estimate functional connectivity, quantify the importance of different areas for landscape suitability and connectivity, and predict movement corridors and paths. ConScape builds upon and bridges the gap between the libraries CONefor & CircuitSCAPE. ConScape allows to simultaneously compute connectivity indices (Functional habitat indices, such as the integral index of connectivity and the probability of connectivity from Conefor, and other extensions) and predict connectivity and movement flow (such as Circuitscape). Furthermore, by modeling connectivity using a Randomized Shortest Path algorithm, it can bridge a continuum of connectivity representations, from least-cost paths to random walks.
PLEASE VISIT: www.conscape.org
KEY PUBLICATIONS
Library: Van Moorter M, Kivimaki I, Noack A, Devooght R, Panzacchi M, Hall K, Leleux P, Saerens M. (2023) Accelerating advances in landscape connectivity modeling with the ConScape library. Methods in Ecology and Evolution, 14, 133– 145. https://doi.org/10.1111/2041-210X.13850 [download pdf]
Approach: Van Moorter B, Kivimäki I, Panzacchi M, Saura S, Niebuhr BB, Strand O, Særens M (2023) Habitat functionality: Integrating environmental and geographic space in niche modeling for conservation planning. Ecology. https://doi.org/10.1002/ecy.4105 [download pdf]
Conceptual basis: Van Moorter B, Kivimäki I, Panzacchi M, Særens M (2021). Defining and quantifying effective connectivity of landscapes for species' movements. Ecography 44, 6: 870-884. [download pdf]
RSP algorithm:
Kivimäki I, Shimbo M, Særens M (2014) Developments in the theory of randomized shortest paths with a comparison of graph node distances. Physica A: Statistical Mechanics and its Applications 393: 600-616
Kivimäki I, van Moorter B, Panzacchi M, Jari Saramäki, Marco Saerens. (2020) Maximum likelihood estimation for randomized shortest paths with trajectory data. Journal of Complex Networks (8),4
INFO
HOW TO USE IT
Install Julia
Install the ConScape library within Julia
Run ConScape
See the detailed description below.
CODE
Install and get started with Julia
Why Julia? It combines performance, generality, and productivity: the code is nearly as readable as R or Python code, but the performance is like C. This makes it possible to compute connectivity metrics fast, at high resolution on large extent landscapes.
Download julia: Download Julia (julialang.org)
Get started with Julia:
Some selected video tutorials for Julia: Video tutorials with Notebooks (julialang.org)
A broad range of tutorials for Julia: Tutorials (julialang.org)
Use Julia with a friendly IDE:
Using an integrated development environment might help a lot (even though this is a user preference):
Install and use ConScape
Once you have a Julia session up and running, you can proceed as following:
Install the ConScape library within Julia
It is as simple as that:
using Pkg
Pkg.add("ConScape")
You only need to do it in the first time you use the library, or when you want to update it to a new version.
Run ConScape
Check the notebooks here: ConScape tutorial