scholarly journals The MIAmaxent R package: Variable transformation and model selection for species distribution models

2019 ◽  
Vol 9 (21) ◽  
pp. 12051-12068 ◽  
Author(s):  
Julien Vollering ◽  
Rune Halvorsen ◽  
Sabrina Mazzoni
Keyword(s):  
Model Selection ◽  
Species Distribution ◽  
Species Distribution Models ◽  
R Package ◽  
Distribution Models ◽  
Variable Transformation ◽  
Selection For

scholarly journals blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models

2018 ◽  
Author(s):  
Roozbeh Valavi ◽  
Jane Elith ◽  
José J. Lahoz-Monfort ◽  
Gurutzeta Guillera-Arroita
Keyword(s):  
Species Distribution ◽  
Cross Validation ◽  
Species Distribution Models ◽  
Predictive Performance ◽  
R Package ◽  
Species Distribution Modelling ◽  
List Type ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Evaluation Approaches

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.

scholarly journals phyr: An R package for phylogenetic species-distribution modelling in ecological communities

2020 ◽  
Author(s):  
Daijiang Li ◽  
Russell Dinnage ◽  
Lucas Nell ◽  
Matthew R. Helmus ◽  
Anthony Ives
Keyword(s):  
Community Composition ◽  
Species Distribution ◽  
Species Distribution Models ◽  
R Package ◽  
Bipartite Network ◽  
List Type ◽  
Ecological Communities ◽  
Phylogenetic Species ◽  
Distribution Models ◽  
Model Based

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.

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
Keyword(s):  
Species Richness ◽  
Species Distribution ◽  
Species Distribution Models ◽  
R Package ◽  
Distribution Models

scholarly journals Improving the interpretability of species distribution models by using local approximations

2018 ◽  
Author(s):  
Boyan Angelov
Keyword(s):  
Machine Learning ◽  
Species Distribution ◽  
Species Distribution Models ◽  
R Package ◽  
Ecological Niches ◽  
Distribution Models ◽  
Domain Experts ◽  
Applied Machine Learning ◽  
Black Boxes ◽  
Interpretable Model

ABSTRACTSpecies Distribution Models (SDMs) are used to generate maps of realised and potential ecological niches for a given species. As any other machine learning technique they can be seen as “black boxes”, due to a lack of interpretability. Advances in other areas of applied machine learning can be applied to remedy this problem. In this study we test a new tool relying on Local Interpretable Model-agnostic Explanations (LIME) by comparing its results of other known methods and ecological interpretations from domain experts. The findings confirm that LIME provides consistent and ecologically sound explanations of climate feature importance during the training of SDMs, and that the sdmexplain R package can be used with confidence.

scholarly journals SDMtune : An R package to tune and evaluate species distribution models

2020 ◽  
Vol 10 (20) ◽  
pp. 11488-11506
Author(s):  
Sergio Vignali ◽  
Arnaud G. Barras ◽  
Raphaël Arlettaz ◽  
Veronika Braunisch
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
R Package ◽  
Distribution Models

scholarly journals Trade‐offs in covariate selection for species distribution models: a methodological comparison

Ecography ◽  
2019 ◽  
Vol 43 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Stephanie J. Brodie ◽  
James T. Thorson ◽  
Gemma Carroll ◽  
Elliott L. Hazen ◽  
Steven Bograd ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Covariate Selection ◽  
Trade Offs ◽  
Selection For ◽  
Methodological Comparison
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