Jada-Simone S. White

Bio: Jada-Simone S. White is an academic researcher from University of Florida. The author has contributed to research in topics: Statistical inference & Generalized linear mixed model. The author has an hindex of 2, co-authored 3 publications receiving 6410 citations. Previous affiliations of Jada-Simone S. White include Centre national de la recherche scientifique & California State University, Chico.

Generalized linear mixed models: a practical guide for ecology and evolution

Abstract: How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.

Indirect effects of a key ecosystem engineer alter survival and growth of foundation coral species

Abstract: Stegastes nigricans, a "farmerfish" that cultivates algal turf and defends territories from grazers and other intruders, can affect coral indirectly due to increased competition with farmed algal turf and/or reduced predation resulting from territorial aggression directed at corallivores. To investigate the indirect effects of this key ecosystem engineer on coral mortality and growth, we transplanted caged and exposed fragments of four coral species to patch reefs in French Polynesia on which we manipulated the presence of S. nigricans and turf, and to reefs naturally devoid of S. nigricans. Reef access was two to four times higher for herbivorous fishes, and two times higher for corallivorous fishes, when S. nigricans was removed, indicating that reef access is reduced for two important guilds of fishes when S. nigricans is present. Stegastes' territoriality indirectly benefited delicate acroporids (Montipora floweri and Acropora striata), yielding a twofold to fivefold reduction in skeletal loss due to lower predation frequencies in the presence of S. nigricans. Three corals, A. striata, M. floweri, and especially Porites australiensis, suffered mortality due to overgrowth significantly more frequently in the presence of farmed turf, but Pocillopora verrucosa did not. Algal abundance predicted the frequency of overgrowth for only A. striata and P. australiensis. M. floweri were more likely to be overgrown when exposed (uncaged) in the presence of S. nigricans, suggesting an interaction modification, in this case that initial predation increased susceptibility to competition with turf. In this community, the presence of S. nigricans may increase algal overgrowth of massive Porites by facilitating its turf competitors and simultaneously reduce predation of branching corals through territorial exclusion of corallivores. These indirect interactions may underlie previously documented community transitions from disturbance-resistant massive coral to recovering branching corals within S. nigricans territories.

High mortality in a surgeonfish following an exceptional settlement event

Abstract: Marine organisms occasionally settle at exceptional densities, whereby thousands of individuals arrive concurrently. High levels of mortality, which has historically been attributed to predation or competition, often follow this episodic settlement of reef fishes. Here, however, we observed large numbers of newly settled surgeonfish (Ctenochaetus striatus) with white lesions lying dead on the sand amongst patch reefs following separate episodic settlement events in 2006 and 2009 in Moorea, French Polynesia. Pathogens have been identified as an important driver of population dynamics in other marine organisms but less so for reef fishes. Our observations suggest that disease outbreaks may play an underappreciated role as a mechanism of mortality following episodic settlement events in reef fishes.

A general and simple method for obtaining R2 from generalized linear mixed-effects models

Abstract: Summary The use of both linear and generalized linear mixed-effects models (LMMs and GLMMs) has become popular not only in social and medical sciences, but also in biological sciences, especially in the field of ecology and evolution. Information criteria, such as Akaike Information Criterion (AIC), are usually presented as model comparison tools for mixed-effects models. The presentation of ‘variance explained’ (R2) as a relevant summarizing statistic of mixed-effects models, however, is rare, even though R2 is routinely reported for linear models (LMs) and also generalized linear models (GLMs). R2 has the extremely useful property of providing an absolute value for the goodness-of-fit of a model, which cannot be given by the information criteria. As a summary statistic that describes the amount of variance explained, R2 can also be a quantity of biological interest. One reason for the under-appreciation of R2 for mixed-effects models lies in the fact that R2 can be defined in a number of ways. Furthermore, most definitions of R2 for mixed-effects have theoretical problems (e.g. decreased or negative R2 values in larger models) and/or their use is hindered by practical difficulties (e.g. implementation). Here, we make a case for the importance of reporting R2 for mixed-effects models. We first provide the common definitions of R2 for LMs and GLMs and discuss the key problems associated with calculating R2 for mixed-effects models. We then recommend a general and simple method for calculating two types of R2 (marginal and conditional R2) for both LMMs and GLMMs, which are less susceptible to common problems. This method is illustrated by examples and can be widely employed by researchers in any fields of research, regardless of software packages used for fitting mixed-effects models. The proposed method has the potential to facilitate the presentation of R2 for a wide range of circumstances.

glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling

Abstract: Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions We present a new package, glmmTMB, and compare it to other R packages that fit zero-inflated mixed models The glmmTMB package fits many types of GLMMs and extensions, including models with continuously distributed responses, but here we focus on count responses glmmTMB is faster than glmmADMB, MCMCglmm, and brms, and more flexible than INLA and mgcv for zero-inflated modeling One unique feature of glmmTMB (among packages that fit zero-inflated mixed models) is its ability to estimate the Conway-Maxwell-Poisson distribution parameterized by the mean Overall, its most appealing features for new users may be the combination of speed, flexibility, and its interface’s similarity to lme4

Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists.

Abstract: Repeatability (more precisely the common measure of repeatability, the intra-class correlation coefficient, ICC) is an important index for quantifying the accuracy of measurements and the constancy of phenotypes. It is the proportion of phenotypic variation that can be attributed to between-subject (or between-group) variation. As a consequence, the non-repeatable fraction of phenotypic variation is the sum of measurement error and phenotypic flexibility. There are several ways to estimate repeatability for Gaussian data, but there are no formal agreements on how repeatability should be calculated for non-Gaussian data (e.g. binary, proportion and count data). In addition to point estimates, appropriate uncertainty estimates (standard errors and confidence intervals) and statistical significance for repeatability estimates are required regardless of the types of data. We review the methods for calculating repeatability and the associated statistics for Gaussian and non-Gaussian data. For Gaussian data, we present three common approaches for estimating repeatability: correlation-based, analysis of variance (ANOVA)-based and linear mixed-effects model (LMM)-based methods, while for non-Gaussian data, we focus on generalised linear mixed-effects models (GLMM) that allow the estimation of repeatability on the original and on the underlying latent scale. We also address a number of methods for calculating standard errors, confidence intervals and statistical significance; the most accurate and recommended methods are parametric bootstrapping, randomisation tests and Bayesian approaches. We advocate the use of LMM- and GLMM-based approaches mainly because of the ease with which confounding variables can be controlled for. Furthermore, we compare two types of repeatability (ordinary repeatability and extrapolated repeatability) in relation to narrow-sense heritability. This review serves as a collection of guidelines and recommendations for biologists to calculate repeatability and heritability from both Gaussian and non-Gaussian data.

Simple means to improve the interpretability of regression coefficients

Abstract: Summary 1. Linear regression models are an important statistical tool in evolutionary and ecological studies. Unfortunately, these models often yield some uninterpretable estimates and hypothesis tests, especially when models contain interactions or polynomial terms. Furthermore, the standard errors for treatment groups, although often of interest for including in a publication, are not directly available in a standard linear model. 2. Centring and standardization of input variables are simple means to improve the interpretability of regression coefficients. Further, refitting the model with a slightly modified model structure allows extracting the appropriate standard errors for treatment groups directly from the model. 3. Centring will make main effects biologically interpretable even when involved in interactions and thus avoids the potential misinterpretation of main effects. This also applies to the estimation of linear effects in the presence of polynomials. Categorical input variables can also be centred and this sometimes assists interpretation. 4. Standardization (z-transformation) of input variables results in the estimation of standardized slopes or standardized partial regression coefficients. Standardized slopes are comparable in magnitude within models as well as between studies. They have some advantages over partial correlation coefficients and are often the more interesting standardized effect size. 5. The thoughtful removal of intercepts or main effects allows extracting treatment means or treatment slopes and their appropriate standard errors directly from a linear model. This provides a simple alternative to the more complicated calculation of standard errors from contrasts and main effects. 6. The simple methods presented here put the focus on parameter estimation (point estimates as well as confidence intervals) rather than on significance thresholds. They allow fitting complex, but meaningful models that can be concisely presented and interpreted. The presented methods can also be applied to generalised linear models (GLM) and linear mixed models.