scholarly journals Model-based ordination for species with unequal niche widths

2020 ◽  
Author(s):  
Bert van der Veen ◽  
Francis K.C. Hui ◽  
Knut A. Hovstad ◽  
Erik B. Solbu ◽  
Robert B. O’Hara
Keyword(s):  
Species Distribution Models ◽  
Common Species ◽  
Mixed Effects Models ◽  
List Type ◽  
Distribution Models ◽  
Ecological Gradient ◽  
Linear Mixed Effects ◽  
Quadratic Curve ◽  
Generalized Linear Mixed Effects ◽  
Better Than

SummaryIt is common practice for ecologists to examine species niches in the study of community composition. The response curve of a species in the fundamental niche is usually assumed to be quadratic. The center of a quadratic curve represents a species’ optimal environmental conditions, and the width its ability to tolerate deviations from the optimum.Most multivariate methods assume species respond linearly to the environment of the niche, or with a quadratic curve that is of equal width and height for all species. However, it is widely understood that some species are generalists who tolerate deviations from their optimal environment better than others. Rare species often tolerate a smaller range of environments than more common species, corresponding to a narrow niche.We propose a new method, for ordination and fitting Joint Species Distribution Models, based on Generalized Linear Mixed-Effects Models, which relaxes the assumptions of equal tolerances and equal maxima.By explicitly estimating species optima, tolerances, and maxima, per ecological gradient, we can better predict change in species communities, and understand how species relate to each other.

scholarly journals Linking musical metaphors and emotions evoked by the sound of classical music

2021 ◽  
pp. 030573562199123
Author(s):  
Simon Schaerlaeken ◽  
Donald Glowinski ◽  
Didier Grandjean
Keyword(s):  
Music Education ◽  
Classical Music ◽  
Empirical Method ◽  
Mixed Effects Models ◽  
Musical Meaning ◽  
Linear Mixed Effects ◽  
Generalized Linear Mixed Effects ◽  
Perceptual Characteristics ◽  
Valence And Arousal ◽  
Western Classical Music

Musical meaning is often described in terms of emotions and metaphors. While many theories encapsulate one or the other, very little empirical data is available to test a possible link between the two. In this article, we examined the metaphorical and emotional contents of Western classical music using the answers of 162 participants. We calculated generalized linear mixed-effects models, correlations, and multidimensional scaling to connect emotions and metaphors. It resulted in each metaphor being associated with different specific emotions, subjective levels of entrainment, and acoustic and perceptual characteristics. How these constructs relate to one another could be based on the embodied knowledge and the perception of movement in space. For instance, metaphors that rely on movement are related to emotions associated with movement. In addition, measures in this study could also be represented by underlying dimensions such as valence and arousal. Musical writing and music education could benefit greatly from these results. Finally, we suggest that music researchers consider musical metaphors in their work as we provide an empirical method for it.

scholarly journals The effects of data adequacy and calibration size on the accuracy of presence-only species distribution models

2019 ◽  
Author(s):  
Truly Santika ◽  
Michael F. Hutchinson ◽  
Kerrie A. Wilson
Keyword(s):  
Performance Measures ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Simulated Data ◽  
List Type ◽  
Model Accuracy ◽  
Distribution Models ◽  
Accuracy Measure ◽  
Data Coverage ◽  
True Distribution

ABSTRACTPresence-only data used to develop species distribution models are often biased towards areas that are frequently surveyed. Furthermore, the size of calibration area with respect to the area covered by the species occurrences has been shown to affect model accuracy. However, existing assessments of the effect of data inadequacy and calibration size on model accuracy have predominately been conducted using empirical studies. These studies can give ambiguous results, since the data used to train and test the model can both be biased.These limitations were addressed by applying simulated data to assess how inadequate data coverage and the size of calibration area affect the accuracy of species distribution models generated by MaxEnt and BIOCLIM. The validity of four presence-only performance measures, Contrast Validation Index (CVI), Boyce index, AUC and AUCratio, was also assessed.CVI, AUC and AUCratio ranked the accuracy of univariate models correctly according to the true importance of their defining environmental variable, a desirable property of an accuracy measure. Contrastingly, Boyce index failed to rank the accuracy of univariate models correctly and a high percentage of irrelevant variables produced models with a high Boyce index.Inadequate data coverage and increased calibration area reduced model accuracy by reducing the correct identification of the dominant environmental determinant. BIOCLIM outperformed MaxEnt models in predicting the true distribution of simulated species with a symmetric dominant response. However, MaxEnt outperformed BIOCLIM in predicting the true distribution of simulated species with skew and linear dominant responses. Despite this, the standard performance measures consistently overestimated the performance of MaxEnt models and showed them as always having higher model accuracy than the BIOCLIM models.It has been acknowledged that research should be directed towards testing and improving species distribution modelling tools, particularly how to handle the inevitable bias and scarcity of species occurrence data. Simulated data, as demonstrated here, provides a powerful approach to comprehensively test the performance of modelling tools and to disentangle the effects of data properties and modelling options on model accuracy. This may be impossible to achieve using real-world data.

scholarly journals Extensions to Bayesian generalized linear mixed effects models for household tuberculosis transmission

2017 ◽  
Vol 36 (16) ◽  
pp. 2522-2532 ◽  
Author(s):  
Avery I. McIntosh ◽  
Gheorghe Doros ◽  
Edward C. Jones-López ◽  
Mary Gaeddert ◽  
Helen E. Jenkins ◽  
...  
Keyword(s):  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Tuberculosis Transmission ◽  
Generalized Linear Mixed Effects

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 Construction of the Design Matrix for Generalized Linear Mixed-Effects Models in the Context of Clinical Trials of Treatment Sequences

2018 ◽  
Vol 41 (2) ◽  
pp. 191-233 ◽  
Author(s):  
Francisco J. Diaz
Keyword(s):  
Clinical Trials ◽  
Data Analysis ◽  
Full Rank ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Design Matrix ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Generalized Linear Mixed Effects ◽  
Treatment Sequences

The problem of constructing a design matrix of full rank for generalized linear mixed-effects models (GLMMs) has not been addressed in statistical literature in the context of clinical trials of treatment sequences. Solving this problem is important because the most popular estimation methods for GLMMs assume a design matrix of full rank, and GLMMs are useful tools in statistical practice. We propose new developments in GLMMs that address this problem. We present a new model for the design and analysis of clinical trials of treatment sequences, which utilizes some special sequences called skip sequences. We present a theorem showing that estimators computed through quasi-likelihood, maximum likelihood or generalized least squares, or through robust approaches, exist only if appropriate skip sequences are used. We prove theorems that establish methods for implementing skip sequences in practice. In particular, one of these theorems computes the necessary skip sequences explicitly. Our new approach allows building design matrices of full rank and facilitates the implementation of regression models in the experimental design and data analysis of clinical trials of treatment sequences. We also explain why the standard approach to constructing dummy variables is inappropriate in studies of treatment sequences. The methods are illustrated with a data analysis of the STAR*D study of sequences of treatments for depression.

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