Rapid Lateglacial tree population dynamics and ecosystem changes in the eastern Baltic region

A growing body of evidence implies that the concept of 'treeless tundra' in eastern and northern Europe fails to explain the rapidity of Lateglacial and postglacial tree population dynamics of the region, yet the knowledge of the geographic locations and shifting of tree populations is fragmentary. Pollen, stomata and plant macrofossil stratigraphies from Lake Kurjanovas in the poorly studied eastern Baltic region provide improved knowledge of ranges of north‐eastern European trees during the Lateglacial and subsequent plant population responses to the abrupt climatic changes of the Lateglacial/Holocene transition. The results prove the Lateglacial presence of tree populations (Betula, Pinus and Picea) in the eastern Baltic region. Particularly relevant is the stomatal and plant macrofossil evidence showing the local presence of reproductive Picea populations during the Younger Dryas stadial at 12 900–11 700 cal. a BP, occurring along with Dryas octopetala and arctic herbs, indicating semi‐open vegetation. The spread of Pinus–Betula forest at ca. 14 400 cal. a BP, the rise of Picea at ca. 12 800 cal. a BP and the re‐establishment of Pinus–Betula forest at ca. 11 700 cal. a BP within a span of centuries further suggest strikingly rapid, climate‐driven ecosystem changes rather than gradual plant succession on a newly deglaciated land. Copyright © 2009 John Wiley & Sons, Ltd.     

Palomar Testbed Interferometer Observations of Young Stellar Objects

We present observations of a sample of Herbig AeBe stars, as well as the FU Orionis object V1057 Cygni. Our K-band (2.2μm) observations from the Palomar Testbed Interferometer (PTI) used baselines of 110 m and 85 m, resulting in fringe spacings of ∼4 mas and 5 mas, respectively. Fringes were obtained for the first time on V1057 Cygnias well as V594 Cas. Additional measurements were made of MWC147, while upper limits to visibility-squared are obtained for MWC297, HD190073, and MWC614. These measurements are sensitive to the distribution of warm, circumstellar dust in these sources. If the circumstellar infrared emission comes from warm dust in a disk, the inclination of the disk to the line of sight implies that the observed interferometric visibilities should depend upon hour angle. Surprisingly, the observations of Millan-Gabet, Schloerb and Traub (2001)(hereafter MST) did not show significant variation with hour angle. However, limited sampling of angular frequencies on the sky was possible with the IOTA interferometer, motivating us to study a subset of their objects to further constrain these systems. This revised version was published online in July 2006 with corrections to the Cover Date.     

一类龙虾树优美

本文使用交错的与非交错的标号法,证明了一类龙虾树优美。     

单体褶牡蛎(Crassostrea sp.)的研究

采用肾上腺素(EPI)和去甲肾上腺素(NE)诱导、颗粒固着基和先固着后脱基三种方法,对褶牡蛎的眼点幼虫进行处理,产生单体蛎苗。EPI和NE诱导的最适浓度为10~(-4)M,最适处理时间为3h,幼虫的不固着变态率分别达47.7％和46.6％。处理时间延长,变态率增加不明显。EPI和NE的诱导效果差别不明显,其诱导作用对稚贝的生长无明显副作用。颗粒固着基以贝壳粉为佳,最适颗粒的规格为幼虫的壳长。幼虫先固着后脱基而形成单体蛎苗,以可弯曲的灰色塑料板效果最好,固着的小贝可通过来回弯曲塑料板而从上面脱落下来,小贝长至1～2cm大小时,脱基最容易。     

Economic assessment of production of flour from Prosopis alba and P. pallida pods for human food applications.

This paper presents a detailed cost accounting of the processes involved in the production of flours from the mesocarps of the fruits of Prosopis alba and P. pallida to be used in human foods. The flours from both species are similar and contain 7–8% protein, 0·8–2% fat, 26–32% total dietary fiber and 48–59% sucrose. The data were based on actual production rates in use at the Universidad de Piura pilot plant using simple machinery. Machinery hourly charges were based on equivalent rental costs in order to avoid interest charge calculations. As opposed to other analyses which only included processing costs delivered to the plant, our analysis ascribed value to the owner of the trees in order to provide incentives to plant and care for the trees. The sensitivity analyses showed that the most economically important factor was the conversion efficiency of pods into flour. When a 40% conversion efficiency, such as that which has been obtained in both Peru and Argentina was obtained, and by-product credits for high fiber containing fraction were assumed, the wholesale selling price with a 5% marketing cost and a 40% profit direct costs was estimated to be $0·66 kg⁻¹ for Peru (using a $0·40 h⁻¹ labor rate ) and $1·76 kg⁻¹ for Argentina (using a $1·5 h⁻¹ labor rate).     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

The impact of rural land management changes on soil hydraulic properties and runoff processes: results from experimental plots in upland UK

To develop an evidence base to help predict the impacts of land management change on flood generation, four experimental sites were established on improved grassland used for sheep grazing at the Pontbren catchment in upland Wales, UK. At each site, three plots were established where surface runoff was measured, supplemented by measurements of soil infiltration rates and soil and vegetation physical properties. Following baseline monitoring, treatments were applied to two of the plots: exclusion of sheep (ungrazed) and exclusion of sheep and planting with native broadleaf tree species (tree planted), with the third plot acting as a control (grazed pasture). Due to a particularly dry summer that occurred pre‐treatment, the soil hydrological responses were initially impacted by the effects of the climate on soil structure. Nevertheless, treatments did have a clear influence on soil hydrological response. On average, post‐treatment runoff volumes were reduced by 48% and 78% in ungrazed and tree‐planted plots relative to the control, although all results varied greatly over the sites. Five years following treatment application, near‐surface soil bulk density was reduced and median soil infiltration rates were 67 times greater in plots planted with trees compared to grazed pasture. The results illustrate the potential use of upland land management for ameliorating local‐scale flood generation but emphasise the need for long‐term monitoring to more clearly separate the effects of land management from those of climatic variability. Copyright © 2013 John Wiley & Sons, Ltd.     

Recultivation of abandoned agricultural lands in Ukraine: Patterns and drivers

The recent rise in agricultural commodity prices and the expectation that high price will persist have triggered a wave of farmland expansion in regions where land resources are still available. One such region is the former Soviet Union, where the collapse of socialism caused massive agricultural abandonment and where some of these lands are now being brought back into production. Yet, the extent and spatial patterns of recultivation, and what determines these patterns, remains unclear. We examined the extent of recultivation of abandoned agricultural land in Ukraine since 2007 using a new, satellite-based recultivation map and assessed the effect of biophysical and socioeconomic determinants on recultivation patterns using boosted regression trees. We found key predictors of recultivation to be related to the suitability of land for agriculture (i.e., soil quality, temperature). Accessibility to major cities was also important, with most recultivation happening closer to settlements, but this influence varied across Ukraine. Variables related to agricultural management (fertilizer input, mechanization) and demography were negligible in explaining recultivation in our analyses. These factors suggest that recultivation patterns were primarily driven by factors related to land productivity, with recultivation focusing on the most promising areas. Given the remaining large amount of unused agricultural land in Eastern Europe and the former Soviet Union, and considering that much abandonment occurred in areas only marginally suited to agriculture, our findings provide important insights into where recultivation can be expected to happen and thus for assessing the potential socioeconomic and environmental impacts of recultivation.