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

Check also: Niebuhr B. B, Panzacchi M, van Moorter B (2022). Oneimpact: Tools for the assessment of cumulative impacts of anthropogenic disturbance on ecological studies. https://github.com/NINAnor/oneimpact,

https://ninanor.github.io/oneimpact/. OneImpact R/GRASS GIS tools

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

ConScape - the docs - Welcome: https://conscape.github.io/site/

HOW TO USE IT

Install Julia
Install the ConScape library within Julia

Run ConScape

See the detailed description below.

CODE

- - Repository GitHub - ConScape/ConScape.jl: Repository for the ConScape package for landscape ecology
  - Documentation ConScape.jl · ConScape (juliahub.com)

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.