Package: bioLEC
bioLEC -Landscape Elevational Connectivity PackagebioLECdocumentation is found atbiolec.readthedocs.iobioLECis a parallel python package built to calculate theLandscape elevational connectivity(LEC).LECquantifies the closeness of a site to all others withsimilar elevation. It measures how easily aspecies living in a given patch can spread and colonise other patches. It is assumed to beelevation-dependentand the metric depends on how often a species adapted to a given elevationneeds to travel outside its optimal elevation rangewhen moving from its patch to any other in the landscape [Bertuzzo et al., 2016].InstallationDependenciesYou will needPython 2.7 or 3.5+.
Also, the following packages are required:numpyscipypandasmpi4pyscikit-imagerasterioInstalling using pipYou can installbioLECusing thepip package managerwith either version of Python:python2-mpipinstallbioLEC
python3-mpipinstallbioLECInstalling using DockerA more straightforward installation which does not depend on specific compilers relies on thedockervirtualisation system.To install the docker image and test it is working:dockerpullgeodels/biolec:latestdockerrun--rmgeodels/biolec:latesthelpTo build the dockerfile locally, we provide a script. First ensure you have checked out the source code from github and then run the script in the Docker directory. If you modify the dockerfile and want to push the image to make it publicly available, it will need to be retagged to upload somewhere other than the GEodels repository.gitcheckouthttps://github.com/Geodels/bioLEC.gitcdbioLECsourceDocker/build-dockerfile.shUsageBinder & docker containerLaunch the demonstration atmybinder.orgNotebooks environmentwill not be the best option forlarge landscape modelsand we will recommend the use of the python script:runLEC.pyin HPC environment. the code will need to bempirun-np400pythonrunLEC.pyOr using the Docker container available through Kitematicgeodels/biolec.CollaborationsHow to contribute?We welcome all kinds of contributions!Please get in touch if you would like to help out.Everything fromcodetonotebookstoexamplesanddocumentationare all equally valuable so please don't feel you can't contribute.To contribute pleasefork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.If you found a bug, have questions, or are just having trouble withbioLEC, you can:join thebioLEC User Group on Slackby sending an email request to:tristan.salles@sydney.edu.auopen an issue in ourissue-trackerand we'll try to help resolve the concern.Where to find support?Please feel free to submit new issues to theissue-logto request new features, document new bugs, or ask questions.LicenseThis program is free software: you can redistribute it and/or modify it under the terms of theGNU Lesser General Public Licenseas published by theFree Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with this program.  If not, seehttp://www.gnu.org/licenses/lgpl-3.0.en.html.ReferencesE. Bertuzzo, F. Carrara, L. Mari, F. Altermatt, I. Rodriguez-Iturbe & A. Rinaldo - Geomorphic controls on species richness.PNAS, 113(7) 1737-1742,DOI: 10.1073/pnas.1518922113, 2016.T.R. Etherington - Least-cost modelling and landscape ecology: concepts, applications, and opportunities. Current Landscape Ecology Reports 1:40-53,DOI: 10.1007/s40823-016-0006-9, 2016.S. van der Walt , J.L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J.D. Warner, N. Yager, E. Gouillart & T. Yu - Scikit Image Contributors - scikit-image: image processing in Python,PeerJ 2:e453, 2014.T.R. Etherington - Least-cost modelling with Python using scikit-image,Blog, 2017.