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.     

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.     

2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models

One of the challenges of remote sensing and computer vision lies in the three-dimensional (3-D) reconstruction of individual trees by using automated methods through very high-resolution (VHR) data sets. However, a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions. In this paper, we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models (DSMs). The symmetry of the citrus trees in a DSM is handled by an orientation-based radial symmetry transform which is computed in a unique way. Next, we propose an efficient strategy to accurately build influence regions of each tree, and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy. We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds. Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes. Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.     

Genetic variability of the Lessepsian migrant mussel Brachidontes pharaonis (Bivalvia: Mytilidae) in Tunisia

The present study used two mitochondrial markers (16S rRNA and COI) to assess the genetic diversity of a newly founded Lessepsian migrant mussel, Brachidontes pharaonis, in Tunisian waters. The species appears to be restricted to only one population in Rades Harbour, in the northern part of the country. Phylogenetic analyses revealed the monophyly of B. pharaonis in Tunisia. Both molecular markers revealed high genetic variability of the B. pharaonis population. Haplotype networks and demographic analyses confirmed the recent expansion events within this population. Multiple human-mediated introduction events involving several founder populations and intensive population growth rates are probably the main causes of the high polymorphism observed within this invasive mollusc.     

Determinants of sediment properties and organic matter in beach and dune environments based on boosted regression trees

Grain size properties and the variation of organic matter in coastal beach and dune environments are assumed to be controlled by the intensity of aeolian processes, time and the sediment source. However, assumptions are based on relatively limited empirical studies. In this study, we examined which environmental variables are the main predictors of multiple topsoil properties. To achieve this, we analysed an extensive dataset systematically collected across all beach zones and a large geographical area at the Finnish Baltic Sea coast characterized by post‐glacial land uplift. We included a comprehensive set of predictors in the analysis and applied boosted regression trees, a modern modelling technique particularly suited for analysis without prior assumptions of the data model. The results suggest that mean grain size and sorting are mainly determined by northing and fetch. Northing, disturbance and fetch predicted the variation of soil organic matter while litter cover was strongly related to disturbance. Based on the analyses, we were able to identify the main drivers of multiple topsoil properties on land uplift beaches. Parent material is suggested to determine sediment textural properties, which largely masks the effects of transient processes. Mean grain size and sorting are highly interdependent: grains become finer and sorting improves with increasing shore exposure. The intensity of momentary geomorphic processes controls the accumulation of litter whereas the slower accumulation of organic matter in the soil is influenced also by the static exposure setting. Skewness and kurtosis of the grain size distribution are mainly influenced by unmeasured processes, potentially relating to the geomorphological origin of the sediment. Copyright © 2014 John Wiley & Sons, Ltd.     

祁连山北坡水热条件对林草分布的影响

采用气候学方法,计算了祁连山北坡不同海拔处的年降水量和乔木林年蒸散量,据此确定了满足乔木林需水的高度带。分析所得的7月均温10℃和6℃,可分别作为乔木林和灌木林生长的温度下限指标。据祁连山北坡水热条件的分布特点,确定了海拔2500—3200米处适宜发展乔木林,其生长以海拔2700—2900米处为最佳,海拔3200—3700米处适宜发展灌木林,其余山区只能生长草类。     

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).     

A continental analysis of correlations between tree patterns in African savannas and human and environmental variables

This study analyses possible relationships between natural processes taking place in savannas and the tree patterns found in savannas. This can lead to new hypotheses about which processes are driving savanna physiognomy. To do so tree patterns were quantified for African savannas from historical aerial photographs applying frequently used landscape metrics. Also, additional data for these areas were collected to quantify the processes taking place at these locations. Correlations between tree pattern indices and explaining factors were analysed. We found a negative trend between tree cover and density of sheep and goats, but no relationship between tree cover and density of cattle, suggesting that small livestock have an effect on tree cover, but that larger livestock (or obligate grazers) do not. Also, a positive correlation between human population density and tree cover was found. Possible explanations for the found relations are discussed. Subsequent ways to analyse the latter correlation are discussed, and the potential of the presented historical database of aerial photographs is highlighted.     

Phylogenomic analysis of transcriptomic sequences of mitochondria and chloroplasts for marine red algae (Rhodophyta) in China

The chloroplast and mitochondrion of red algae （Phylum Rhodophyta） may have originated from different endosymbiosis. In this study, we carried out phylogenomic analysis to distinguish their evolutionary lin-eages by using red algal RNA-seq datasets of the 1 000 Plants （1KP） Project and publicly available complete genomes of mitochondria and chloroplasts of Rhodophyta. We have found that red algae were divided into three clades of orders, Florideophyceae, Bangiophyceae and Cyanidiophyceae. Taxonomy resolution for Class Florideophyceae showed that Order Gigartinales was close to Order Halymeniales, while Order Graci-lariales was in a clade of Order Ceramials. We confirmed Prionitis divaricata （Family Halymeniaceae） was closely related to the clade of Order Gracilariales, rather than to genus Grateloupia of Order Halymeniales as reported before. Furthermore, we found both mitochondrial and chloroplastic genes in Rhodophyta under negative selection （Ka/Ks〈1）, suggesting that red algae, as one primitive group of eukaryotic algae, might share joint evolutionary history with these two organelles for a long time, although we identified some dif-ferences in their phylogenetic trees. Our analysis provided the basic phylogenetic relationships of red algae, and demonstrated their potential ability to study endosymbiotic events.