Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models

1.Nakagawa & Schielzeth extended the widely used goodness-of-fit statistic R2 to apply to generalized linear mixed models (GLMMs). However, their R2GLMM method is restricted to models with the simplest random effects structure, known as random intercepts models. It is not applicable to another common random effects structure, random slopes models.<p></p> 2.I show that R2GLMM can be extended to random slopes models using a simple formula that is straightforward to implement in statistical software. This extension substantially widens the potential application of R2GLMM.<p></p&gt

The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

The coefficient of determinationR2quantifies the proportion of varianceexplained by a statistical model and is an important summary statisticof biological interest. However, estimatingR2for generalized linear mixedmodels (GLMMs) remains challenging. We have previously introduced a ver-sion ofR2that we calledR2GLMMfor Poisson and binomial GLMMs, but notfor other distributional families. Similarly, we earlier discussed how to estimateintra-class correlation coefficients (ICCs) using Poisson and binomial GLMMs.Inthis paper, we generalize our methodsto allothernon-Gaussian distributions,in particular to negative binomial and gamma distributions that are commonlyused formodellingbiological data. Whileexpanding ourapproach,we highlighttwo useful concepts for biologists, Jensen’s inequality and the delta method,both of which help us in understanding the properties of GLMMs. Jensen’sinequality has important implications for biologically meaningful interpretationof GLMMs, whereas the delta method allows a general derivation of varianceassociated with non-Gaussian distributions. We also discuss some special con-siderations for binomial GLMMs with binary or proportion data. We illustratethe implementation of our extension by worked examples from the field of ecol-ogy and evolution in theRenvironment. However, our method can be usedacross disciplines and regardless of statistical environments

The use of algorithms to predict surface seawater dimethyl sulphide concentrations in the SE Pacific, a region of steep gradients in primary productivity, biomass and mixed layer depth

Dimethyl sulphide (DMS) is an important precursor of cloud condensation nuclei (CCN), particularly in the remote marine atmosphere. The SE Pacific is consistently covered with a persistent stratocumulus layer that increases the albedo over this large area. It is not certain whether the source of CCN to these clouds is natural and oceanic or anthropogenic and terrestrial. This unknown currently limits our ability to reliably model either the cloud behaviour or the oceanic heat budget of the region. In order to better constrain the marine source of CCN, it is necessary to have an improved understanding of the sea-air flux of DMS. Of the factors that govern the magnitude of this flux, the greatest unknown is the surface seawater DMS concentration. In the study area, there is a paucity of such data, although previous measurements suggest that the concentration can be substantially variable. In order to overcome such data scarcity, a number of climatologies and algorithms have been devised in the last decade to predict seawater DMS. Here we test some of these in the SE Pacific by comparing predictions with measurements of surface seawater made during the Vamos Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) in October and November of 2008. We conclude that none of the existing algorithms reproduce local variability in seawater DMS in this region very well. From these findings, we recommend the best algorithm choice for the SE Pacific and suggest lines of investigation for future work

High dose atorvastatin associated with increased risk of significant hepatotoxicity in comparison to simvastatin in UK GPRD cohort

Background and Aims: Occasional risk of serious liver dysfunction and autoimmune hepatitis during atorvastatin therapy has been reported. We compared the risk of hepatotoxicity in atorvastatin relative to simvastatin treatment. Methods: The UK GPRD identified patients with a first prescription for simvastatin [164,407] or atorvastatin [76,411] between 1997 and 2006, but with no prior record of liver disease, alcohol-related diagnosis, or liver dysfunction. Incident liver dysfunction in the following six months was identified by biochemical value and compared between statin groups by Cox regression model adjusting for age, sex, year treatment started, dose, alcohol consumption, smoking, body mass index and comorbid conditions. Results: Moderate to severe hepatotoxicity [bilirubin >60μmol/L, AST or ALT >200U/L or alkaline phosphatase >1200U/L] developed in 71 patients on atorvastatin versus 101 on simvastatin. Adjusted hazard ratio [AHR] for all atorvastatin relative to simvastatin was 1.9 [95% confidence interval 1.4–2.6]. High dose was classified as 40–80mg daily and low dose 10–20mg daily. Hepatotoxicity occurred in 0.44% of 4075 patients on high dose atorvastatin [HDA], 0.07% of 72,336 on low dose atorvastatin [LDA], 0.09% of 44,675 on high dose simvastatin [HDS] and 0.05% of 119,732 on low dose simvastatin [LDS]. AHRs compared to LDS were 7.3 [4.2–12.7] for HDA, 1.4 [0.9–2.0] for LDA and 1.5 [1.0–2.2] for HDS. Conclusions: The risk of hepatotoxicity was increased in the first six months of atorvastatin compared to simvastatin treatment, with the greatest difference between high dose atorvastatin and low dose simvastatin. The numbers of events in the analyses were small

The use of geophysical equipment in hydrogeologic investigations, and the measurement of stream discharge

Guidebook for field trips in southwestern Maine: New England Intercollegiate Geological Conference, 78th annual meeting, Bates College, Lewiston, Maine, October 17, 18, and 19, 1986: Trip A-

Estimating the size of dog populations in Tanzania to inform rabies control

Estimates of dog population sizes are a prerequisite for delivering effective canine rabies control. However, dog population sizes are generally unknown in most rabies-endemic areas. Several approaches have been used to estimate dog populations but without rigorous evaluation. We compare post-vaccination transects, household surveys, and school-based surveys to determine which most precisely estimates dog population sizes. These methods were implemented across 28 districts in southeast Tanzania, in conjunction with mass dog vaccinations, covering a range of settings, livelihoods, and religious backgrounds. Transects were the most precise method, revealing highly variable patterns of dog ownership, with human/dog ratios ranging from 12.4:1 to 181.3:1 across districts. Both household and school-based surveys generated imprecise and, sometimes, inaccurate estimates, due to small sample sizes in relation to the heterogeneity in patterns of dog ownership. Transect data were subsequently used to develop a predictive model for estimating dog populations in districts lacking transect data. We predicted a dog population of 2,316,000 (95% CI 1,573,000–3,122,000) in Tanzania and an average human/dog ratio of 20.7:1. Our modelling approach has the potential to be applied to predicting dog population sizes in other areas where mass dog vaccinations are planned, given census and livelihood data. Furthermore, we recommend post-vaccination transects as a rapid and effective method to refine dog population estimates across large geographic areas and to guide dog vaccination programmes in settings with mostly free roaming dog populations

Evolution of drug-tolerant nematode populations in response to density reduction

Resistance to xenobiotics remains a pressing issue in parasite treatment and global agriculture. Multiple factors may affect the evolution of resistance, including interactions between life-history traits and the strength of selection imposed by different drug doses. We experimentally created replicate selection lines of free-living Caenorhabditis remanei exposed to Ivermectin at high and low doses to assess whether survivorship of lines selected in drug-treated environments increased, and if this varied with dose. Additionally, we maintained lines where mortality was imposed randomly to control for differences in density between drug treatments and to distinguish between the evolutionary consequences of drug-treatment versus ecological processes due to changes in density-dependent feedback. After 10 generations, we exposed all of the selected lines to high-dose, low-dose and drug-free environments to evaluate evolutionary changes in survivorship as well as any costs to adaptation. Both adult and juvenile survival were measured to explore relationships between life-history stage, selection regime and survival. Intriguingly, both drug-selected and random-mortality lines showed an increase in survivorship when challenged with Ivermectin; the magnitude of this increase varied with the intensity of selection and life-history stage. Our results suggest that interactions between density-dependent processes and life history may mediate evolved changes in susceptibility to control measures