Wallace: A flexible platform for reproducible modeling of species niches and distributions built for community expansion

Abtract 1.Scientific research increasingly calls for open-source software that is flexible, interactive, and expandable, while providing methodological guidance and reproducibility. Currently, many analyses in ecology are implemented with “black box” graphical user interfaces that lack flexibility or command-line interfaces that are infrequently used by non-specialists. 2.To help remedy this situation in the context of species distribution modeling, we created Wallace, an open and modular application with a richly documented graphical user interface to underlying R scripts that is flexible and highly interactive. 3.Wallace guides users from acquiring and processing data to building models and examining predictions. Additionally, it is designed to grow via community contributions of new modules to expand functionality. All results are downloadable, along with code to reproduce the analysis. 4.Wallace provides an example of an innovative platform to increase access to cutting-edge methods and encourage plurality in science and collaboration in software development. This article is protected by copyright. All rights reserved.

[1]  Robert P. Anderson,et al.  Ecological Niches and Geographic Distributions , 2011 .

[2]  Matthew E. Aiello-Lammens,et al.  spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models , 2015 .

[3]  K. A. S. Mislan,et al.  Elevating the status of code in ecology , 2015, bioRxiv.

[4]  Alberto Jiménez-Valverde,et al.  Not as good as they seem: the importance of concepts in species distribution modelling , 2008 .

[5]  Antoine Guisan,et al.  SESAM – a new framework integrating macroecological and species distribution models for predicting spatio‐temporal patterns of species assemblages , 2011 .

[6]  L. Knowles,et al.  DOES NICHE DIVERGENCE ACCOMPANY ALLOPATRIC DIVERGENCE IN APHELOCOMA JAYS AS PREDICTED UNDER ECOLOGICAL SPECIATION?: INSIGHTS FROM TESTS WITH NICHE MODELS , 2009, Evolution; international journal of organic evolution.

[7]  Gerhard Weis,et al.  The Biodiversity and Climate Change Virtual Laboratory: Where ecology meets big data , 2016, Environ. Model. Softw..

[8]  D. Alvarado-Serrano,et al.  Ecological niche models in phylogeographic studies: applications, advances and precautions , 2014, Molecular ecology resources.

[9]  J. Brashares,et al.  The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models , 2012 .

[10]  Dan L Warren,et al.  Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. , 2011, Ecological applications : a publication of the Ecological Society of America.

[11]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[12]  J. Franklin.  Moving beyond static species distribution models in support of conservation biogeography , 2010 .

[13]  David J. Gavaghan,et al.  The zoon r package for reproducible and shareable species distribution modelling , 2017 .

[14]  Kenton O'Hara,et al.  Troubling Trends in Scientific Software Use , 2013, Science.

[15]  L. Prior,et al.  Ecological Models and Data in R , 2011 .

[16]  Alan R. Williams,et al.  ENM Components: a new set of web service-based workflow components for ecological niche modelling , 2016 .

[17]  Harris David,et al.  A statistical explanation of MaxEnt for ecologists , 2013 .

[18]  Jennifer A. Miller,et al.  Mapping Species Distributions: Spatial Inference and Prediction , 2010 .

[19]  Matthew J. Smith,et al.  Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates , 2013, bioRxiv.

[20]  Peter Brewer,et al.  openModeller: a generic approach to species’ potential distribution modelling , 2011, GeoInformatica.

[21]  Jj Allaire,et al.  Web Application Framework for R , 2016 .

[22]  W. Christopher Lenhardt,et al.  The Tao of open science for ecology , 2015 .

[23]  Robert P. Anderson,et al.  Making better Maxent models of species distributions: complexity, overfitting and evaluation , 2014 .

[24]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[25]  J. Andrew Royle,et al.  Presence‐only modelling using MAXENT: when can we trust the inferences? , 2013 .

[26]  C. Graham,et al.  Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know? , 2009 .

[27]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[28]  Roger Pradel,et al.  Statistical ecology comes of age , 2014, Biology Letters.

[29]  Steven J. Phillips,et al.  The art of modelling range‐shifting species , 2010 .

[30]  Robert A. Boria,et al.  ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models , 2014 .

[31]  Graziano Pesole,et al.  UvA-DARE ( Digital Academic Repository ) BioVeL : a virtual laboratory for data analysis and modelling in biodiversity science and ecology , 2016 .

[32]  Miguel B. Araújo,et al.  sdm: a reproducible and extensible R platform for species distribution modelling , 2016 .

[33]  Trevor H. Booth,et al.  bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies , 2014 .