Steven C. Walker

Bio: Steven C. Walker is an academic researcher from National Research Council. The author has contributed to research in topics: Species richness & Species evenness. The author has an hindex of 17, co-authored 27 publications receiving 49210 citations. Previous affiliations of Steven C. Walker include McMaster University & Université de Montréal.

Extending Joint Models in Community Ecology: A Response to Beissinger et al.

Abstract: The joint modelling of many variables in community ecology is a new and technically challenging area with many opportunities for future developments. The possibility of extending joint models to deal with imperfect detection has been highlighted by Beissinger et al. as an important problem worthy of further investigation [1]. We agree, and previously pointed to this potential extension as an outstanding question [2], alongside models that can estimate phylogenetic repulsion or attraction, nonlinearity in the response to latent variables, and spatial or temporal correlation, because further developments in all these directions are needed.

Random-effects ordination: describing and predicting multivariate correlations and co-occurrences

Abstract: Ecology is inherently multivariate, but high-dimensional data are difficult to understand. Dimension reduction with ordination analysis helps with both data exploration and clarification of the meaning of inferences (e.g., randomization tests, variation partitioning) about a statistical population. Most such inferences are asymmetric, in that variables are classified as either response or explanatory (e.g., factors, predictors). But this asymmetric approach has limitations (e.g., abiotic variables may not entirely explain correlations between interacting species). We study symmetric population-level inferences by modeling correlations and co-occurrences, using these models for out-of-sample prediction. Such modeling requires a novel treatment of ordination axes as random effects, because fixed effects only allow within-sample predictions. We advocate an iterative methodology for random-effects ordination: (1) fit a set of candidate models differing in complexity (e.g., number of axes); (2) use information...

Assessing size-dependent cytotoxicity of boron nitride nanotubes using a novel cardiomyocyte AFM assay

Abstract: As boron nitride nanotubes (BNNTs) find increased use in numerous applications, potential adverse health effects of BNNT exposure are a growing concern. Current in vitro cytotoxicity studies on BNNTs are inconsistent and even contradictory, likely due to the lack of reference materials, standardized characterization methods and measurement protocols. New approaches, particularly with the potential to reliably relate in vitro to in vivo studies, are critically needed. This work introduces a novel atomic force microscopy (AFM)-based cardiomyocyte assay that reliably assesses the cytotoxicity of a well-characterized boron nitride nanotube reference material, code named BNNT-1. High energy probe sonication was used to modify and control the length of BNNT-1. The polymer polyethylenimine (PEI) was used concurrently with sonication to produce stable, aqueous dispersions of BNNT-1. These dispersions were used to perform a systematic analysis on both the length and height of BNNT-1 via a correlated characterization approach of dynamic light scattering (DLS) and AFM. Cytotoxicity studies using the novel cardiomyocyte AFM model were in agreement with traditional colorimetric cell metabolic assays, both revealing a correlation between tube length and cytotoxicity with longer tubes having higher cytotoxicity. In addition to the size-dependent cytotoxicity, it was found that BNNT-1 exhibits concentration and cell-line dependent cytotoxic effects.

Assessment of boron nitride nanotube materials using X-ray photoelectron spectroscopy

Abstract: With increasing prevalence of boron nitride nanotubes (BNNTs), the need for routine and reliable assessment methods is becoming more critical both for research studies and for quality control in na...

Fitting Linear Mixed-Effects Models Using lme4

Abstract: Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

lmerTest Package: Tests in Linear Mixed Effects Models

Abstract: One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects. We have implemented the Satterthwaite's method for approximating degrees of freedom for the t and F tests. We have also implemented the construction of Type I - III ANOVA tables. Furthermore, one may also obtain the summary as well as the anova table using the Kenward-Roger approximation for denominator degrees of freedom (based on the KRmodcomp function from the pbkrtest package). Some other convenient mixed model analysis tools such as a step method, that performs backward elimination of nonsignificant effects - both random and fixed, calculation of population means and multiple comparison tests together with plot facilities are provided by the package as well.

Toward a metabolic theory of ecology

Abstract: Metabolism provides a basis for using first principles of physics, chemistry, and biology to link the biology of individual organisms to the ecology of populations, communities, and ecosystems. Metabolic rate, the rate at which organisms take up, transform, and expend energy and materials, is the most fundamental biological rate. We have developed a quantitative theory for how metabolic rate varies with body size and temperature. Metabolic theory predicts how metabolic rate, by setting the rates of resource uptake from the environment and resource allocation to survival, growth, and reproduction, controls ecological processes at all levels of organization from individuals to the biosphere. Examples include: (1) life history attributes, including devel- opment rate, mortality rate, age at maturity, life span, and population growth rate; (2) population interactions, including carrying capacity, rates of competition and predation, and patterns of species diversity; and (3) ecosystem processes, including rates of biomass production and respiration and patterns of trophic dynamics. Data compiled from the ecological literature strongly support the theoretical predictions. Even- tually, metabolic theory may provide a conceptual foundation for much of ecology, just as genetic theory provides a foundation for much of evolutionary biology.

Modern Applied Statistics With S

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