Tools

You will find here the tools our GIS lab and research group make available to the scientific community.

Miscellaneous methods and utilities for spatial ecology analysis, written by current and former members and collaborators of the ecospat group. The ecospat package offers the possibility to perform Pre-modelling Analysis, such as Spatial autocorrelation analysis, MESS (Multivariate Environmental Similarity Surfaces) analyses, Phylogenetic diversity Measures, Biotic Interactions. It also provides functions to complement biomod2 in preparing the data, calibrating and evaluating (e.g. boyce index) and projecting the models. Complementary analysis based on model predictions (e.g. co- occurrences analyses) are also provided. In addition, the ecospat package includes Niche Quantification and Overlap functions that were used in Broennimann et al. 2012 and Petitpierre et al. 2012 to quantify climatic niche shifts between the native and invaded ranges of invasive species.

You can install the ecospat package in R from CRAN or GitHub. CRAN provides the most stable version. GitHub provides the most updated functions but not fully tested yet. Check out the latest updates available on GitHub.

1) installation from CRAN in the R console:

> install.packages("ecospat")

> library(ecospat)

2) installation from GitHub in the R console:

> install.packages("devtools")

> library(devtools)

> install_github(repo="ecospat/ecospat/ecospat")

> library(ecospat)

 

Examples of how to use the functions:

Vignette ecospat package  (673 Ko)

Uniting species distribution modelling (SDM) techniques into one high-performance computing (HPC) pipeline, we developed N-SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N-SDM was built around a spatially-nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N-SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state-of-the-art SDM features embodied in N-SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithm-specific hyperparameter grid search and the ensemble of small-models approach. N-SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time.

You can install the NSDM package in R from GitHub.

The covsel R package is a ready-to-use, automated, covariate selection tool for species distribution modelling. It implements and streamlines the two steps of our novel “embedded” covariate selection procedure that combines (Step A) a collinearity-filtering algorithm and (Step B) three model-specific embedded regularization techniques, including generalized linear model with elastic net regularization, generalized additive model with null-space penalization, and guided regularized random forest.

All the information required for installing and running N-SDM is openly available on GitHub.

Adde A., Rey P.L., Fopp F., Petitpierre B., Schweiger A.K., Broennimann B., Lehmann A., Zimmermann N. E., Altermatt F., Pellissier L., Guisan A. 2023.  Too many candidates: Embedded covariate selection procedure for species distribution modelling with the covsel R package. Ecological Informatics. In press. doi:10.1016/j.ecoinf.2023.102080.

MigClim is an R package which allows simulating plant dispersal under climate change and landscape fragmentation scenarios. MigClim allows implementing various parameters, such as dispersal distance, increase in reproductive potential over time, landscape fragmentation or long-distance dispersal.

Link on CRAN

Reference :

Engler R. and Guisan A., 2009. MIGCLIM: Predicting plant distribution and dispersal in a changing climate. Diversity and Distribution, 15 (4), 590-601.

Engler R., Randin C.F., Vittoz P., Czáka T., Beniston M., Zimmermann N.E., Guisan A., 2009. Predicting future distributions of mountain plants under climate change: does dispersal capacity matter? Ecography, 32 (1), 34-45.

Engler R., Hordijk W., Guisan A., 2012. The MIGCLIM R package – seamless integration of dispersal constraints into projections of species distribution models. Ecography, 35 (10), 872–878.

...and don't miss out the presentation video of Migclim featuring Robin Engler at the Global Online Seminar in Biodiversity Informatics help by A. Townsend Peterson at the University of Kansas: Youtube link

 

The R package 'ecospat' R now includes the functions to perform measures of niche overlap and niche equivalency/similarity tests.

Short tutorial:

Create a folder with a R shortcut. Use this folder as workspace by setting the path in the proprieties of the shortcut (right click). In this folder also put your datasets of occurences data (delimited text file with column names x,y) and datesets of points representing the study areas with environmental values (column names should be x,y,X1,X2,...,Xn).

The user scripts allow setting the analyses for the calculations with an example data. Use user_script_2sp_2A.R if you want to compare niches of 2 species in different areas (e.g. invasive species). Use user_script_Nsp_1A.R if you want to compare niches of n species in the same area. The scripts use species occurrence online and climatic data from worldclim.org, but you can modify the code to import and use your own data.

If you encounter a problem during your analyses, please read this FAQ

user scripts:

user_script_2sp_2A.R , user_script_Nsp_1A.R

Reference:

Broennimann O. , Fitzpatrick M.C. , Pearman P.B. , Petitpierre B. , Pellissier L. , Yoccoz N.G. , Thuiller W. , Fortin M.J. , Randin C.R. , Zimmermann N.E. , Graham C.H. , Guisan A. 2012. Measuring ecological niche overlap from occurrence and spatial environmental data. Global Ecology and Biogeography 21(4): 481-497. DOI

Link to the BIOMAPPER software for predicting species occurrences developped by Alex Hirzel at UNIL.

Get the zip CANOGEN AML developped by Andrew D. Weiss and Stuart B. Weiss to predict plant distribution in ARCINFO directly from CANOCO outputs (see Guisan, A., Weiss, S.B. & Weiss, A.D. 1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecology 143(1) : 107-122)

Credits : Thanks to acknowledge Andy and Stu in any related publication ! Contact weiss.andrew (a) epamail.epa.gov.

canogen.zip  (4 Ko)