Among vertebrates, nearly all oviparous animals are considered to have either obligate aquatic or terrestrial oviposition, with eggs that are specialized for developing in those environments. The terrestrial environment has considerably more oxygen but is dry and thus presents both opportunities and challenges for developing embryos, particularly those adapted for aquatic development. Here, we present evidence from field experiments examining egg-laying behavior, egg size, and egg jelly function of 13 species of Central and South American treefrogs in the genus Dendropsophus, which demonstrates that flexible oviposition (individuals laying eggs both in and out of water) and eggs capable of both aquatic and terrestrial development are the likely factors which enable the transition from aquatic to terrestrial reproduction. Nearly half of the species we studied had previously undescribed degrees of flexible oviposition. Species with obligate terrestrial reproduction have larger eggs than species with aquatic reproduction, and species with flexible reproduction have eggs of intermediate sizes. Obligate terrestrial breeding frogs also have egg masses that absorb water more quickly than those with flexible oviposition. We also examined eight populations of a single species, Dendropsophus ebraccatus , and document substantial intraspecific variation in terrestrial oviposition; populations in rainy, stable climates lay fewer eggs in water than those in drier areas. However, no differences in egg size were found, supporting the idea that the behavioral component of oviposition evolves before other adaptations associated with obligate terrestrial reproduction. Collectively, these data demonstrate the key role that behavior can have in facilitating major evolutionary transitions.
Abstract A unilateral lesion of the rat nigrostriatal pathway with 6‐hydroxydopamine (6‐OHDA) results in a decrease in the basal extracellular level of striatal glutamate, a nearly complete loss of tyrosine hydroxylase (TH) immunolabeling, an increase in the density of glutamate immunogold labeling within nerve terminals making an asymmetrical synaptic contact, and an increase in the number of apomorphine‐induced contralateral rotations. [Meshul et al. ( 1999 ) Neuroscience 88:1–16; Meshul and Allen ( 2000 ) Synapse 36:129–142]. In Parkinson's disease, a lesion of either the subthalamic nucleus (STN) or the motor thalamic nucleus relieves the patient of some of the motor difficulties associated with this disorder. In this rodent model, either the STN or motor thalamic nucleus was electrolytically destroyed 2 months following a unilateral 6‐OHDA lesions. Following a lesion of either the STN or motor thalamic nucleus in 6‐OHDA‐treated rats, there was a significant decrease (40–60%) in the number of apomorphine‐induced contralateral rotations compared to the 6‐OHDA group. There was a significant decrease (<30%) in the basal extracellular level of striatal glutamate in all of the experimental groups compared to the sham group. Following an STN and/or 6‐OHDA lesion, the decrease in striatal extracellular levels was inversely associated with an increase in the density of nerve terminal glutamate immunolabeling. There was no change in nerve terminal glutamate immunogold labeling in either the motor thalamic or motor thalamic plus 6‐OHDA lesion groups compared to the sham group. The decrease in the number of apomorphine‐induced rotations was not due to an increase in TH immunolabeling (i.e., sprouting) within the denervated striatum. This suggests that alterations in striatal glutamate appear not to be directly involved in the STN or motor thalamic lesion‐induced reduction in contralateral rotations. Synapse 51:287–298, 2004. Published 2003 Wiley‐Liss, Inc.
Abstract Whether at the undergraduate, graduate, or post-graduate level, Applied Statistics with R: A Practical Guide for the Life Sciences teaches readers to properly analyze data in an efficient, accessible, plainspoken, frank, and occasionally humorous manner. Readers will come away with the knowledge of which analyses they should use and when they should use them, an important skill in an age when the statistical analyses used in the life-sciences are becoming increasingly advanced. This book uses the statistical language R, which is the choice of ecologists worldwide and is rapidly becoming the ‘go-to’ stats program throughout the life-sciences. Written around a single real-world dataset, Applied Statistics with R which encourages readers to become deeply familiar with an imperfect but realistic set of data, much like they themselves might collect. Early chapters are designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach also demonstrates the importance of viewing data through different lenses, facilitating an easy and natural progression from linear and generalized linear models through to mixed effects versions of those same analyses. Readers will also learn advanced plotting and data-wrangling techniques, and gain an introduction to writing their own functions. Applied Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners throughout the life-sciences, whether in the fields of ecology, evolution, environmental studies, or computational biology.
Bufo americanus eggs on our laboratory experiment showing heavy mold infection in a replicate without Rana sylvatica tadpoles and healthy, mold-free eggs in two replicates where Rana sylvatica tadpoles ate the mold.
Abstract Chapter 7 introduces one of the most useful statistical frameworks for the modern life scientist: the generalized linear model (GLM). GLMs extend the linear model to an array of non-normally distributed data such as Poisson, negative binomial, binomial, and Gamma distributed data. These models dramatically improve the breadth of data that can be properly analysed without resorting to non-parametric statistics. Using the same RxP dataset, readers learn how to assess the error distribution of their data and evaluate competing models to achieve the best, most robust analysis possible. Diagnostic plots and assessing model fit is continually taught as is how to interpret the model output and calculate summary statistics. Plotting non-normal error distributions with ggplot2 is taught, as is using the predict() function.
Abstract Mixed effects models are powerful techniques for controlling for non-independence of data or repeated measures, and can be harnessed for both normal and non-normal data structures. Chapter 8 teaches readers how to code, assess, interpret, and troubleshoot both linear and generalized linear mixed models using the same RxP dataset which has been used throughout the book, although now it is viewed through a new lens. Readers are taught how to code likelihood ratio tests to calculate statistical significance and how to use multiple packages, such as lme4 and glmmTMB.
Abstract Chapter 2 is perhaps the most important in the whole book, in that it is a discussion of essential principles of experimental design and data analysis that are universal to every field in the life sciences. The merits of different experimental designs are discussed as well as best practices for data analysis.
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