scholarly journals ssdm : An r package to predict distribution of species richness and composition based on stacked species distribution models

2017 ◽  
Vol 8 (12) ◽  
pp. 1795-1803 ◽  
Author(s):  
Sylvain Schmitt ◽  
Robin Pouteau ◽  
Dimitri Justeau ◽  
Florian Boissieu ◽  
Philippe Birnbaum
2018 ◽  
Author(s):  
Roozbeh Valavi ◽  
Jane Elith ◽  
José J. Lahoz-Monfort ◽  
Gurutzeta Guillera-Arroita

SummaryWhen applied to structured data, conventional random cross-validation techniques can lead to underestimation of prediction error, and may result in inappropriate model selection.We present the R package blockCV, a new toolbox for cross-validation of species distribution modelling.The package can generate spatially or environmentally separated folds. It includes tools to measure spatial autocorrelation ranges in candidate covariates, providing the user with insights into the spatial structure in these data. It also offers interactive graphical capabilities for creating spatial blocks and exploring data folds.Package blockCV enables modellers to more easily implement a range of evaluation approaches. It will help the modelling community learn more about the impacts of evaluation approaches on our understanding of predictive performance of species distribution models.


2020 ◽  
Author(s):  
Daijiang Li ◽  
Russell Dinnage ◽  
Lucas Nell ◽  
Matthew R. Helmus ◽  
Anthony Ives

SummaryModel-based approaches are increasingly popular in ecological studies. A good example of this trend is the use of joint species distribution models to ask questions about ecological communities. However, most current applications of model-based methods do not include phylogenies despite the well-known importance of phylogenetic relationships in shaping species distributions and community composition. In part, this is due to lack of accessible tools allowing ecologists to fit phylogenetic species distribution models easily.To fill this gap, the R package phyr (pronounced fire) implements a suite of metrics, comparative methods and mixed models that use phylogenies to understand and predict community composition and other ecological and evolutionary phenomena. The phyr workhorse functions are implemented in C++ making all calculations and model estimations fast.phyr can fit a variety of models such as phylogenetic joint-species distribution models, spatiotemporal-phylogenetic autocorrelation models, and phylogenetic trait-based bipartite network models. phyr also estimates phylogenetically independent trait correlations with measurement error to test for adaptive syndromes and performs fast calculations of common alpha and beta phylogenetic diversity metrics. All phyr methods are united under Brownian motion or Ornstein-Uhlenbeck models of evolution and phylogenetic terms are modelled as phylogenetic covariance matrices.The functions and model formula syntax we propose in phyr serves as a simple and unified framework that ignites the use of phylogenies to address a variety of ecological questions.


2013 ◽  
Vol 41 (4) ◽  
pp. 736-748 ◽  
Author(s):  
Anna F. Cord ◽  
Doris Klein ◽  
David S. Gernandt ◽  
Jorge A. Pérez de la Rosa ◽  
Stefan Dech

2015 ◽  
Vol 42 (7) ◽  
pp. 1255-1266 ◽  
Author(s):  
Manuela D'Amen ◽  
Anne Dubuis ◽  
Rui F. Fernandes ◽  
Julien Pottier ◽  
Loïc Pellissier ◽  
...  

2015 ◽  
Vol 21 (11) ◽  
pp. 1329-1338 ◽  
Author(s):  
Robin Pouteau ◽  
Élise Bayle ◽  
Élodie Blanchard ◽  
Philippe Birnbaum ◽  
Jean-Jérôme Cassan ◽  
...  

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