Open Accessecospat: an R package to support spatial analyses and modeling of species niches and distributions
Author(s) -
Di Cola Valeria,
Broennimann Olivier,
Petitpierre Blaise,
Breiner Frank T.,
D'Amen Manuela,
Randin Christophe,
Engler Robin,
Pottier Julien,
Pio Dorothea,
Dubuis Anne,
Pellissier Loic,
Mateo Rubén G.,
Hordijk Wim,
Salamin Nicolas,
Guisan Antoine
Publication year - 2017
Publication title -
ecography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.973
H-Index - 128
eISSN - 1600-0587
pISSN - 0906-7590
DOI - 10.1111/ecog.02671
Subject(s) - r package , ecological niche , workflow , environmental niche modelling , computer science , niche , species distribution , extrapolation , phylogenetic tree , spatial analysis , ecology , data mining , database , biology , statistics , mathematics , habitat , computational science , biochemistry , gene
The aim of the ecospat package is to make available novel tools and methods to support spatial analyses and modeling of species niches and distributions in a coherent workflow. The package is written in the R language (R Development Core Team) and contains several features, unique in their implementation, that are complementary to other existing R packages. Pre‐modeling analyses include species niche quantifications and comparisons between distinct ranges or time periods, measures of phylogenetic diversity, and other data exploration functionalities (e.g. extrapolation detection, ExDet). Core modeling brings together the new approach of ensemble of small models (ESM) and various implementations of the spatially‐explicit modeling of species assemblages (SESAM) framework. Post‐modeling analyses include evaluation of species predictions based on presence‐only data (Boyce index) and of community predictions, phylogenetic diversity and environmentally‐constrained species co‐occurrences analyses. The ecospat package also provides some functions to supplement the ‘biomod2’ package (e.g. data preparation, permutation tests and cross‐validation of model predictive power). With this novel package, we intend to stimulate the use of comprehensive approaches in spatial modelling of species and community distributions.