Post‐Soviet farmland abandonment,forest recovery,and carbon sequestration in western Ukraine

Land use is a critical factor in the global carbon cycle, but land‐use effects on carbon fluxes are poorly understood in many regions. One such region is Eastern Europe and the former Soviet Union, where land‐use intensity decreased substantially after the collapse of socialism, and farmland abandonment and forest expansion have been widespread. Our goal was to examine how land‐use trends affected net carbon fluxes in western Ukraine (57 000 km²) and to assess the region's future carbon sequestration potential. Using satellite‐based forest disturbance and farmland abandonment rates from 1988 to 2007, historic forest resource statistics, and a carbon bookkeeping model, we reconstructed carbon fluxes from land use in the 20th century and assessed potential future carbon fluxes until 2100 for a range of forest expansion and logging scenarios. Our results suggested that the low‐point in forest cover occurred in the 1920s. Forest expansion between 1930 and 1970 turned the region from a carbon source to a sink, despite intensive logging during socialism. The collapse of the Soviet Union created a vast, but currently largely untapped carbon sequestration potential (up to～150 Tg C in our study region). Future forest expansion will likely maintain or even increase the region's current sink strength of 1.48 Tg C yr^?1. This may offer substantial opportunities for offsetting industrial carbon emissions and for rural development in regions with otherwise diminishing income opportunities. Throughout Eastern Europe and the former Soviet Union, millions of hectares of farmland were abandoned after the collapse of socialism; thus similar reforestation opportunities may exist in other parts of this region.     

Optimization of a crossing system using mate selection

A simple model based on one single identified quantitative trait locus (QTL) in a two-way crossing system was used to demonstrate the power of mate selection algorithms as a natural means of opportunistic line development for optimization of crossbreeding programs over multiple generations. Mate selection automatically invokes divergent selection in two parental lines for an over-dominant QTL and increased frequency of the favorable allele toward fixation in the sire-line for a fully-dominant QTL. It was concluded that an optimal strategy of line development could be found by mate selection algorithms for a given set of parameters such as genetic model of QTL, breeding objective and initial frequency of the favorable allele in the base populations, etc. The same framework could be used in other scenarios, such as programs involving crossing to exploit breed effects and heterosis. In contrast to classical index selection, this approach to mate selection can optimize long-term responses.     

Single-molecule anisotropy imaging

A novel method, single-molecule anisotropy imaging, has been employed to simultaneously study lateral and rotational diffusion of fluorescence-labeled lipids on supported phospholipid membranes. In a fluid membrane composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine, in which the rotational diffusion time is on the order of the excited-state lifetime of the fluorophore rhodamine, a rotational diffusion constant, D(rot) = 7 x 10(7) rad(2)/s, was determined. The lateral diffusion constant, measured by direct analysis of single-molecule trajectories, was D(lat) = 3.5 x 10(-8) cm(2)/s. As predicted from the free-volume model for diffusion, the results exhibit a significantly enhanced mobility on the nanosecond time scale. For membranes of DPPC lipids in the L(beta) gel phase, the slow rotational mobility permitted the direct observation of the rotation of individual molecules characterized by D(rot) = 1.2 rad(2)/s. The latter data were evaluated by a mean square angular displacement analysis. The technique developed here should prove itself profitable for imaging of conformational motions of individual proteins on the time scale of milliseconds to seconds.     

Analyses of short-chain fatty acids and exhaled breath volatiles in dietary intervention trials for metabolic diseases

As an alternative to pharmacological treatment to diseases, lifestyle interventions, such as dietary changes and physical activities, can help maintain healthy metabolic conditions. Recently, the emerging analyses of volatile organic compounds (VOCs) from breath and short-chain fatty acids (SCFAs) from plasma/feces have been considered as useful tools for the diagnosis and mechanistic understanding of metabolic diseases. Furthermore, diet-induced changes of SCFAs in individuals with diagnosed metabolic abnormalities have been correlated with the composition changes of the gut microbiome. More interestingly, the analysis of exhaled breath (breathomics) has gained attention as a useful technique to measure the human VOC profile altered as a result of dietary interventions. In this mini-review, we examined recent clinical trials that performed promising dietary interventions, SCFAs analysis in plasma/feces, and VOC profile analysis in exhaling breath to understand the relationship between dietary intervention and metabolic health.     

Effects of Habitat on Competition Between Kit Foxes and Coyotes

Abstract: San Joaquin kit foxes (Vulpes macrotis mutica) are an endangered species with a narrow geographic range whose natural populations are limited by predation by coyotes (Canis latrans). In the warm, arid grassland and shrubland habitats where kit foxes occur, coyotes are more cover dependent than kit foxes, creating the possibility of habitat segregation. Effects of habitat variation on coyote and kit fox competition are unknown. We assessed exploitation and interference competition between coyotes and kit foxes in grassland and shrubland habitats to determine if such competition varies among habitats. With respect to exploitation competition, we evaluated habitat and spatial partitioning, diet, prey abundance, and survival for kit foxes and coyotes at the Lokern Natural Area in central California, USA, from January 2003 through June 2004. Kit foxes partitioned habitat, space, and diet with coyotes. Coyotes primarily used shrubland habitats whereas kit foxes selectively used burned grasslands. Kit foxes and coyotes had high dietary overlap with regards to items used, but proportional use of items differed between the 2 species. Kit foxes selected for Heermann's kangaroo rats (Dipodomys heermanni), which were closely tied to shrub habitats. With respect to interference competition, predation was the primary source of mortality for kit foxes, and survival of individual kit foxes was inversely related to proportion of shrub habitat within their home ranges. Our results suggest that a heterogeneous landscape may benefit kit foxes by providing habitat patches where predation risk may be lower.     

Phylogenetic and coalescent analysis of three loci suggest that the Water Rail is divisible into two species, <Emphasis Type="Italic">Rallus aquaticus</Emphasis> and <Emphasis Type="Italic">R. indicus</Emphasis>

Background  

Water Rails (Rallus aquaticus) inhabit fragmented freshwater wetlands across their Palearctic distribution. Disjunct populations are now thought to be morphologically similar over their vast geographic range, though four subspecies had been recognized previously. The fossil record suggests that Water Rails (R. aquaticus) were already spread across the Palearctic by the Pleistocene ~2 million years ago, and the oldest fossil remains thought to be closely related to the common ancestor of water rails date from the Pliocene.     

Different models of genetic variation and their effect on genomic evaluation

Background

The theory of genomic selection is based on the prediction of the effects of quantitative trait loci (QTL) in linkage disequilibrium (LD) with markers. However, there is increasing evidence that genomic selection also relies on "relationships" between individuals to accurately predict genetic values. Therefore, a better understanding of what genomic selection actually predicts is relevant so that appropriate methods of analysis are used in genomic evaluations.

Methods

Simulation was used to compare the performance of estimates of breeding values based on pedigree relationships (Best Linear Unbiased Prediction, BLUP), genomic relationships (gBLUP), and based on a Bayesian variable selection model (Bayes B) to estimate breeding values under a range of different underlying models of genetic variation. The effects of different marker densities and varying animal relationships were also examined.

Results

This study shows that genomic selection methods can predict a proportion of the additive genetic value when genetic variation is controlled by common quantitative trait loci (QTL model), rare loci (rare variant model), all loci (infinitesimal model) and a random association (a polygenic model). The Bayes B method was able to estimate breeding values more accurately than gBLUP under the QTL and rare variant models, for the alternative marker densities and reference populations. The Bayes B and gBLUP methods had similar accuracies under the infinitesimal model.

Conclusions

Our results suggest that Bayes B is superior to gBLUP to estimate breeding values from genomic data. The underlying model of genetic variation greatly affects the predictive ability of genomic selection methods, and the superiority of Bayes B over gBLUP is highly dependent on the presence of large QTL effects. The use of SNP sequence data will outperform the less dense marker panels. However, the size and distribution of QTL effects and the size of reference populations still greatly influence the effectiveness of using sequence data for genomic prediction.