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 [Final version available] Explainable Artificial Intelligence enhances the ecological interpretability of black-box species distribution models

2020 ◽  
Author(s):  
Masahiro Ryo ◽  
Boyan Angelov ◽  
Stefano Mammola ◽  
Jamie M. Kass ◽  
Blas M. Benito ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Complex Model ◽  
Boosted Regression Trees ◽  
Learning Approaches ◽  
Distribution Models ◽  
Interpretable Model ◽  
Scale Behavior

Species distribution models (SDMs) are widely used in ecology, biogeography and conservation biology to estimate relationships between environmental variables and species occurrence data and make predictions of how their distributions vary in space and time. During the past two decades, the field has increasingly made use of machine learning approaches for constructing and validating SDMs. Model accuracy has steadily increased as a result, but the interpretability of the fitted models, for example the relative importance of predictor variables or their causal effects on focal species, has not always kept pace. Here we draw attention to an emerging subdiscipline of artificial intelligence, explainable AI (xAI), as a toolbox for better interpreting SDMs. xAI aims at deciphering the behavior of complex statistical or machine learning models (e.g. neural networks, random forests, boosted regression trees), and can produce more transparent and understandable SDM predictions. We describe the rationale behind xAI and provide a list of tools that can be used to help ecological modelers better understand complex model behavior at different scales. As an example, we perform a reproducible SDM analysis in R on the African elephant and showcase some xAI tools such as local interpretable model-agnostic explanation (LIME) to help interpret local-scale behavior of the model. We conclude with what we see as the benefits and caveats of these techniques and advocate for their use to improve the interpretability of machine learning SDMs.

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.

Understanding the ecological niche to elucidate spatial strategies of the southernmost Tupinambis lizards

2013 ◽  
Vol 34 (4) ◽  
pp. 551-565 ◽  
Author(s):  
Sofía Lanfri ◽  
Valeria Di Cola ◽  
Sergio Naretto ◽  
Margarita Chiaraviglio ◽  
Gabriela Cardozo
Keyword(s):  
Species Distribution ◽  
Evolutionary Biology ◽  
Species Distribution Models ◽  
Regional Scale ◽  
Distribution Patterns ◽  
Niche Differentiation ◽  
Ecological Niches ◽  
Distribution Models ◽  
Interspecific Differences ◽  
Spatial Strategies

Understanding factors that shape ranges of species is central in evolutionary biology. Species distribution models have become important tools to test biogeographical, ecological and evolutionary hypotheses. Moreover, from an ecological and evolutionary perspective, these models help to elucidate the spatial strategies of species at a regional scale. We modelled species distributions of two phylogenetically, geographically and ecologically close Tupinambis species (Teiidae) that occupy the southernmost area of the genus distribution in South America. We hypothesized that similarities between these species might have induced spatial strategies at the species level, such as niche differentiation and divergence of distribution patterns at a regional scale. Using logistic regression and MaxEnt we obtained species distribution models that revealed interspecific differences in habitat requirements, such as environmental temperature, precipitation and altitude. Moreover, the models obtained suggest that although the ecological niches of Tupinambis merianae and T. rufescens are different, these species might co-occur in a large contact zone. We propose that niche plasticity could be the mechanism enabling their co-occurrence. Therefore, the approach used here allowed us to understand the spatial strategies of two Tupinambis lizards at a regional scale.

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
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