Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.

/pdf/phyloseq-an-r-package-for-reproducible-interactive-analysis-1ez1xx3dlr.pdf

phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.

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.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/j.2041-210x.2012.00261.x

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

Humans are altering the composition of biological communities through a variety of activities that increase rates of species invasions and species extinctions, at all scales, from local to global. These changes in components of the Earth's biodiversity cause concern for ethical and aesthetic reasons, but they also have a strong potential to alter ecosystem properties and the goods and services they provide to humanity. Ecological experiments, observations, and theoretical developments show that ecosystem properties depend greatly on biodiversity in terms of the functional characteristics of organisms present in the ecosystem and the distribution and abundance of those organisms over space and time. Species effects act in concert with the effects of climate, resource availability, and disturbance regimes in influencing ecosystem properties. Human activities can modify all of the above factors; here we focus on modification of these biotic controls. The scientific community has come to a broad consensus on many aspects of the re- lationship between biodiversity and ecosystem functioning, including many points relevant to management of ecosystems. Further progress will require integration of knowledge about biotic and abiotic controls on ecosystem properties, how ecological communities are struc- tured, and the forces driving species extinctions and invasions. To strengthen links to policy and management, we also need to integrate our ecological knowledge with understanding of the social and economic constraints of potential management practices. Understanding this complexity, while taking strong steps to minimize current losses of species, is necessary for responsible management of Earth's ecosystems and the diverse biota they contain.

/pdf/effects-of-biodiversity-on-ecosystem-functioning-a-consensus-13j8vabcus.pdf

Effects of biodiversity on ecosystem functioning: a consensus of current knowledge

http://talklab.psy.gla.ac.uk/KeepItMaximalR2.pdf

Random effects structure for confirmatory hypothesis testing: Keep it maximal

vegan: Community Ecology Package

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.

/pdf/generalized-linear-mixed-models-a-practical-guide-for-3gh61pkir0.pdf

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

Tropical forests include a diversity of habitats, which has led to specialization in plants. Near Iquitos, in the Peruvian Amazon, nutrient-rich clay forests surround nutrient-poor white-sand forests, each harboring a unique composition of habitat specialist trees. We tested the hypothesis that the combination of impoverished soils and herbivory creates strong natural selection for plant defenses in white-sand forest, while rapid growth is favored in clay forests. Recently, we reported evidence from a reciprocal-transplant experiment that manipulated the presence of herbivores and involved 20 species from six genera, including phylogenetically independent pairs of closely related white-sand and clay specialists. When protected from herbivores, clay specialists exhibited faster growth rates than white-sand specialists in both habitats. But, when unprotected, white-sand specialists outperformed clay specialists in white- sand habitat, and clay specialists outperformed white-sand specialists in clay habitat. Here we test further the hypothesis that the growth-defense trade-off contributes to habitat specialization by comparing patterns of growth, herbivory, and defensive traits in these same six genera of white-sand and clay specialists. While the probability of herbivore attack did not differ between the two habitats, an artificial defoliation experiment showed that the impact of herbivory on plant mortality was significantly greater in white-sand forests. We quantified the amount of terpenes, phenolics, leaf toughness, and available foliar protein for the plants in the experiment. Different genera invested in different defensive strategies, and we found strong evidence for phylogenetic constraint in defense type. Overall, however, we found significantly higher total defense investment for white-sand specialists, relative to their clay specialist congeners. Furthermore, herbivore resistance consistently exhibited a significant trade-off against growth rate in each of the six phylogenetically independent species-pairs. These results confirm theoretical predictions that a trade-off exists between growth rate and defense investment, causing white-sand and clay specialists to evolve divergent strategies. We propose that the growth-defense trade-off is universal and provides an important mechanism by which herbivores govern plant distribution patterns across resource gradients.

The Growth–Defense Trade‐Off And Habitat Specialization By Plants In Amazonian Forests

A number of authors have suggested that, within areas a few square meters to many square kilometers in size, species diversity appears to peak at moderate levels of productivity, and this pattern is currently unexplained. Among the best examples of this pattern have been descriptions of vegetation in which species richness declines as soil fertility increases. We tested two hypotheses that have been proposed to explain this pattern. The interspecific competitive exclusion hypothesis proposes that dominant species suppress the growth of competitively subordinate species and exclude subordinate species as fertility rises. In contrast, the assemblage-level thinning hypothesis proposes that individuals of all species tend to become larger as fertility rises, and individuals of all species tend to exclude subordinate individuals of each species. Because total density declines, samples of finite numbers of individuals will result in fewer species by chance alone. To test these hypotheses, we established an experimental productivity gradient in a first- year old field using four levels of slow-release NPK fertilizer (0, 8, 16, and 32 g N/M2). At the end of the growing season, we sampled aboveground biomass and numbers of stems for each species in 72 20 X 20 cm subplots (18 reps X 4 levels), with an average sample size of 260 individual stems per plot. We observed an 80% decline in stem density with increasing fertility, and a 50% decline in species richness along this fertility gradient. A simulation of random thinning along a fertility gradient showed a nearly identical decline in species richness, supporting the assemblage-level thinning hypothesis. We also found that responses of individual species to the soil fertility gradient showed virtually no support for interspecific competitive exclusion. The overwhelming influence of density found in this study suggests that plant species richness along many productivity gradients may be strongly influenced by total stem density, and that differences in competitive ability among species, although generally important, are not necessary to create dramatic changes in species richness along fertility gradients.

Plant density determines species richness along an experimental fertility gradient

Resource heterogeneity has often been proposed to explain the maintenance of plant species diversity and patterns of species diversity along productivity gradients. Resource heterogeneity should maintain biodiversity by preventing competitive exclusion because different species are superior competitors in different parts of a heterogeneous environment. In natural systems, however, resource heterogeneity covaries with average resource supply rate, making the effect of heterogeneity difficult to isolate. Using a novel experimental approach, we tested the independent effects of resource heterogeneity and average supply rate on plant species diversity. We show that the average supply rate of the most limiting resource controlled species diversity, whereas heterogeneity of this resource had virtually no effect. These findings also suggest that biodiversity declines with increasing productivity because at high enough levels of productivity one resource may always be driven to sufficiently short supply to exclude many species.

M. Henry H. Stevens

Papers

vegan: Community Ecology Package

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

The Growth–Defense Trade‐Off And Habitat Specialization By Plants In Amazonian Forests

Plant density determines species richness along an experimental fertility gradient

Resource quantity, not resource heterogeneity, maintains plant diversity