Spatial Regression Modeling for Compositional Data With Many Zeros

Compositional data analysis considers vectors of nonnegative-valued variables subject to a unit-sum constraint. Our interest lies in spatial compositional data, in particular, land use/land cover (LULC) data in the northeastern United States. Here, the observations are vectors providing the proportions of LULC types observed in each 3 km×3 km grid cell, yielding order 10⁴ cells. On the same grid cells, we have an additional compositional dataset supplying forest fragmentation proportions. Potentially useful and available covariates include elevation range, road length, population, median household income, and housing levels. We propose a spatial regression model that is also able to capture flexible dependence among the components of the observation vectors at each location as well as spatial dependence across the locations of the simplex-restricted measurements. A key issue is the high incidence of observed zero proportions for the LULC dataset, requiring incorporation of local point masses at 0. We build a hierarchical model prescribing a power scaling first stage and using latent variables at the second stage with spatial structure for these variables supplied through a multivariate CAR specification. Analyses for the LULC and forest fragmentation data illustrate the interpretation of the regression coefficients and the benefit of incorporating spatial smoothing.     

Sequencing of Culex quinquefasciatus establishes a platform for mosquito comparative genomics

Culex quinquefasciatus (the southern house mosquito) is an important mosquito vector of viruses such as West Nile virus and St. Louis encephalitis virus, as well as of nematodes that cause lymphatic filariasis. C. quinquefasciatus is one species within the Culex pipiens species complex and can be found throughout tropical and temperate climates of the world. The ability of C. quinquefasciatus to take blood meals from birds, livestock, and humans contributes to its ability to vector pathogens between species. Here, we describe the genomic sequence of C. quinquefasciatus: Its repertoire of 18,883 protein-coding genes is 22% larger than that of Aedes aegypti and 52% larger than that of Anopheles gambiae with multiple gene-family expansions, including olfactory and gustatory receptors, salivary gland genes, and genes associated with xenobiotic detoxification.     

Forest restitution and protected area effectiveness in post-socialist Romania

The effectiveness of protected areas can diminish during times of pronounced socio-economic and institutional change. Our goals were to assess the effectiveness of Romanian protected areas at stemming unsanctioned logging, and to assess post-socialist logging in their surrounding landscapes, during a time of massive socio-economic and institutional change. Our results suggest that forest cover remained fairly stable shortly before and after 1990, but forest disturbance rates increased sharply in two waves after 1995 and 2005. We found substantial disturbances inside protected areas, even within core reserve areas. Moreover, disturbances in the matrix surrounding protected areas were even lower than inside protected area boundaries. We suggest that these rates are largely the result of high logging rates, triggered by rapid ownership and institutional changes. These trends compromise the goals of Romania’s protected area network, lead to an increasing loss of forest habitat, and more isolated and more fragmented protected areas. The effectiveness of Romania’s protected area network in terms of its ability to safeguard biodiversity is therefore most likely decreasing.     

Socioeconomics drive woody invasive plant richness in New England, USA through forest fragmentation

Woody invasive plants are an increasing component of the New England flora. Their success and geographic spread are mediated in part by landscape characteristics. We tested whether woody invasive plant richness was higher in landscapes with many forest edges relative to other forest types and explained land use/land cover and forest fragmentation patterns using socioeconomic and physical variables. Our models demonstrated that woody invasive plant richness was higher in landscapes with more edge forest relative to patch, perforated, and especially core forest types. Using spatially-explicit, hierarchical Bayesian, compositional data models we showed that infrastructure and physical factors, including road length and elevation range, and time-lagged socioeconomic factors, primarily population, help to explain development and forest fragmentation patterns. Our social–ecological approach identified landscape patterns driven by human development and linked them to increased woody plant invasions. Identifying these landscape patterns will aid ongoing efforts to use current distribution patterns to better predict where invasive species may occur in unsampled regions under current and future conditions.