MODISTools - downloading and processing MODIS remotely sensed data in R

Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per-location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta-analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time-series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R2 values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub (https://github.com/seantuck12/MODISTools)

Geometric quantization of Hamiltonian actions of Lie algebroids and Lie groupoids

We construct Hermitian representations of Lie algebroids and associated unitary representations of Lie groupoids by a geometric quantization procedure. For this purpose we introduce a new notion of Hamiltonian Lie algebroid actions. The first step of our procedure consists of the construction of a prequantization line bundle. Next, we discuss a version of K\"{a}hler quantization suitable for this setting. We proceed by defining a Marsden-Weinstein quotient for our setting and prove a ``quantization commutes with reduction'' theorem. We explain how our geometric quantization procedure relates to a possible orbit method for Lie groupoids. Our theory encompasses the geometric quantization of symplectic manifolds, Hamiltonian Lie algebra actions, actions of families of Lie groups, foliations, as well as some general constructions from differential geometry.Comment: 40 pages, corrected version 11-01-200

Atmospheric boundary layer wind profile at a flat coastal site – wind speed lidar measurements and mesoscale modeling results

Wind profiles up to 600 m height are investigated. Measurements of mean wind speed profiles were obtained from a novel wind lidar and compared to model simulations from a mesoscale model (WRF-ARW v3.1). It is found that WRF is able to predict the mean wind profile rather well and typically within 1–2 m s^−1 to the individual measured values. WRF underpredicts the normalized wind profile, especially for stable conditions. The effect of baroclinicity on the upper part of the wind profile is discussed

Erratum to "A watershed model of individual differences in fluid intelligence" [Neuropsychologia 91 (2016) 186-198].

The publisher regrets that due to an error the full text of Appendix A was missing in the original publication. The missing text is included below. The publisher would like to apologise for any inconvenience caused

Gut microbiota, metabolism and psychopathology:A critical review and novel perspectives

Psychiatric disorders are often associated with metabolic comorbidities. However, the mechanisms through which metabolic and psychiatric disorders are connected remain unclear. Pre-clinical studies in rodents indicate that the bidirectional signaling between the intestine and the brain, the so-called microbiome-gut-brain axis, plays an important role in the regulation of both metabolism and behavior. The gut microbiome produces a vast number of metabolites that may be transported into the host and play a part in homeostatic control of metabolism as well as brain function. In addition to short chain fatty acids, many of these metabolites have been identified in recent years. To what extent both microbiota and their products control human metabolism and behavior is a subject of intense investigation. In this review, we will discuss the most recent findings concerning alterations in the gut microbiota as a possible pathophysiological factor for the co-occurrence of metabolic comorbidities in psychiatric disorders