Evaluating regional water security through a freshwater ecosystem service flow model: A case study in Beijing-Tianjian-Hebei region,China

Freshwater ecosystem service is essential to human’s survival and development. Many studies have documented the spatial differences in the supply and demand of ecosystem services and proposed the concept of ecosystem services flows. However, few studies characterize freshwater ecosystem service flow quantitatively. Therefore, our paper aims to quantify the effects of freshwater ecosystem service flow on downstream areas. We developed a freshwater ecosystem service flow model and applied it in the Beijing–Tianjin–Hebei (BTH) region, China, for the year of 2000, 2005, and 2010. We assessed the regional water security with an improved freshwater security index by integrating freshwater service provision, consumption and flow; and found that most areas of the BTH region (69.2%) were affected by upstream freshwater flows. The areas achieving water security in the region also expanded to 66.9%, 66.1%, and 71.3%, which were 6.4%, 6.8% and 5.7% increments compared to no-flow situation, in 2000, 2005 and 2010, respectively. Setting quota for human water consumption is suggested to further improve water security. These results highlight the need to fully understand the connections between distant freshwater ecosystem service provision and local freshwater ecosystem service consumption. This approach may also help managers to choose more sustainable strategies for critical freshwater resource management across different regions.     

Correcting for finite spatial scales of self-similarity when calculating fractal dimensions of real-world structures

Fractal geometry is a potentially valuable tool for quantitatively characterizing complex structures. The fractal dimension (D) can be used as a simple, single index for summarizing properties of real and abstract structures in space and time. Applications in the fields of biology and ecology range from neurobiology to plant architecture, landscape structure, taxonomy and species diversity. However, methods to estimate the D have often been applied in an uncritical manner, violating assumptions about the nature of fractal structures. The most common error involves ignoring the fact that ideal, i.e. infinitely nested, fractal structures exhibit self-similarity over any range of scales. Unlike ideal fractals, real-world structures exhibit self-similarity only over a finite range of scales.Here we present a new technique for quantitatively determining the scales over which real-world structures show statistical self-similarity. The new technique uses a combination of curve-fitting and tests of curvilinearity of residuals to identify the largest range of contiguous scales that exhibit statistical self-similarity. Consequently, we estimate D only over the statistically identified region of self-similarity and introduce the finite scale- corrected dimension (FSCD). We demonstrate the use of this method in two steps. First, using mathematical fractal curves with known but variable spatial scales of self-similarity (achieved by varying the iteration level used for creating the curves), we demonstrate that our method can reliably quantify the spatial scales of self-similarity. This technique therefore allows accurate empirical quantification of theoretical Ds. Secondly, we apply the technique to digital images of the rhizome systems of goldenrod (Solidago altissima). The technique significantly reduced variations in estimated fractal dimensions arising from variations in the method of preparing digital images. Overall, the revised method has the potential to significantly improve repeatability and reliability for deriving fractal dimensions of real-world branching structures.     

Monte Carlo-based assessment of the trade-off between spatial resolution,field-of-view and scattered radiation in the variable resolution X-ray CT scanner

ObjectiveThe purpose of this work is to evaluate the impact of optimization of magnification on performance parameters of the variable resolution X-ray (VRX) CT scanner.MethodsA realistic model based on an actual VRX CT scanner was implemented in the GATE Monte Carlo simulation platform. To evaluate the influence of system magnification, spatial resolution, field-of-view (FOV) and scatter-to-primary ratio of the scanner were estimated for both fixed and optimum object magnification at each detector rotation angle. Comparison and inference between these performance parameters were performed angle by angle to determine appropriate object position at each opening half angle.ResultsOptimization of magnification resulted in a trade-off between spatial resolution and FOV of the scanner at opening half angles of 90°–12°, where the spatial resolution increased up to 50% and the scatter-to-primary ratio decreased from 4.8% to 3.8% at a detector angle of about 90° for the same FOV and X-ray energy spectrum. The disadvantage of magnification optimization at these angles is the significant reduction of the FOV (up to 50%). Moreover, magnification optimization was definitely beneficial for opening half angles below 12° improving the spatial resolution from 7.5 cy/mm to 20 cy/mm. Meanwhile, the FOV increased by more than 50% at these angles.ConclusionIt can be concluded that optimization of magnification is essential for opening half angles below 12°. For opening half angles between 90° and 12°, the VRX CT scanner magnification should be set according to the desired spatial resolution and FOV.     

To aggregate or not? Capturing the spatio-temporal complexity of the thermal regime

Freshwater stream systems are under immense pressure from various anthropogenic impacts, including climate change. Stream systems are increasingly being altered by changes to the magnitude, timing, frequency, and duration of their thermal regimes, which will have profound impacts on the life-history dynamics of resident biota within their home range. Although temperature regimes have a significant influence on the biology of instream fauna, large spatio-temporal temperature datasets are often reduced to a single metric at discrete locations and used to describe the thermal regime of a system; potentially leading to a significant loss of information crucial to stream management. Models are often used to extrapolate these metrics to unsampled locations, but it is unclear whether predicting actual daily temperatures or an aggregated metric of the temperature regime best describes the complexity of the thermal regime. We fit spatial statistical stream-network models (SSNMs), random forest and non-spatial linear models to stream temperature data from the Upper Condamine River in QLD, Australia and used them to semi-continuously predict metrics describing the magnitude, duration, and frequency of the thermal regime through space and time. We compared both daily and aggregated temperature metrics and found that SSNMs always had more predictive ability than the random forest models, but both models outperformed the non-spatial linear model. For metrics describing thermal magnitude and duration, aggregated predictions were most accurate, while metrics describing the frequency of heating events were better represented by metrics based on daily predictions generated using a SSNM. A more comprehensive representation of the spatio-temporal thermal regime allows researchers to explore new spatio-temporally explicit questions about the thermal regime. It also provides the information needed to generate a suite of ecologically meaningful metrics capturing multiple aspects of the thermal regime, which will increase our scientific understanding of how organisms respond to thermal cues and provide much-needed information for more effective management actions.     

Scale‐dependent variation in visual estimates of grassland plant cover

Abstract. Plant cover was visually estimated by five observers, independent of each other, in a species‐rich grassland in the Bílé Karpaty Mts., southeastern Czech Republic, in seven plots ranging from 0.001 to 4 m². Variation of total plant cover among the observers was high at small scales: 0.001–0.016 m²; coefficient of variation, CV = 35 to 45%, but much lower at larger scales: 0.06–4 m²; CV = 7 to 15%. Differences between visual estimates of plant cover of individual species made by different observers were affected by plot size, total cover and morphology of particular plants. CV of the cover of individual species ranged from 0 to 225% and decreased with increasing plot size. For abundant plants the CV attained ca. 50%, independent of plot size. In spite of a very high number of sterile plants with similar leaf morphology and colour, the observed variation in cover estimates in the studied grassland was comparable with results reported from other vegetation types. Differences between estimates by individual observers were often larger than usual year to year changes in undisturbed grasslands. Therefore, I suggest that to avoid difficulties in the interpretation of results based on plant cover data obtained from visual estimates, several observers should always work together, adjusting their extreme estimates.     

Distinct classes of lagging chromosome underpin age-related oocyte aneuploidy in mouse

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The small-scale pattern of Cynodon dactylon in Mediterranean pastures

The abundance of Cynodon dactylon was recorded in 50 plots presenting different geomorphological conditions and along a transect of 16 500 2×2 cm contiguous quadrats within a small basin 330 m in length, in the granite pediment of the Sierra de Guadarrama (Central Spain). Soil analyses were undertaken on samples from the 50 plots and the soil information matrix obtained was analysed using Principal Components Analysis and Discriminant Analysis. Results showed that in the Mediterranean pastures Cynodon dactylon was restricted to deep, well developed soils with relatively high values of cations, conductivity and organic matter, and could be used as an indicator of such soil conditions in these grasslands. The spatial pattern of the species was analysed using New Local Variances which revealed the existence of a small-scale pattern, ranging from 8 to 18 cm, present at all levels of the analysis. The size of small-scale patterns was positively correlated with species abundance and was interpreted as a relation between plant vigour and favourable soil conditions.I would like to thank Professor González Bernáldez for his interest and encouragement.     

Validating Landsat-based landscape metrics with fine-grained land cover data

ContextModerate-grained data may not always represent landscape structure in adequate detail which could cause misleading results. Certain metrics have been shown to be predictable with changes in scale; however, no studies have verified such predictions using independent fine-grained data.ObjectivesOur objective was to use independently derived land cover datasets to assess relationships between metrics based on fine- and moderate-grained data for a range of analysis extents. We focus on metrics that previous literature has shown to have predictable relationships across scales.MethodsThe study area was located in eastern Connecticut. We compared a 1 m land cover dataset to a 30 m resampled dataset, derived from the 1 m data, as well as two Landsat-based datasets. We examined 11 metrics which included cover areas and patch metrics. Metrics were analyzed using analysis extents ranging from 100 to 1400 m in radius.ResultsThe resampled data had very strong linear relationships to the 1 m data, from which it was derived, for all metrics regardless of the analysis extent size. Landsat-based data had strong correlations for most cover area metrics but had little or no correlation for patch metrics. Increasing analysis areas improved correlations.ConclusionsRelationships between coarse- and fine-grained data tend to be much weaker when comparing independent land cover datasets. Thus, trends across scales that are found by resampling land cover are likely to be unsuitable for predicting the effects of finer-scale elements in the landscape. Nevertheless, coarser data shows promise in predicting fine-grained for cover area metrics provided the analysis area used is sufficiently large.     

A measure of spatial stratified heterogeneity

Spatial stratified heterogeneity, referring to the within-strata variance less than the between strata-variance, is ubiquitous in ecological phenomena, such as ecological zones and many ecological variables. Spatial stratified heterogeneity reflects the essence of nature, implies potential distinct mechanisms by strata, suggests possible determinants of the observed process, allows the representativeness of observations of the earth, and enforces the applicability of statistical inferences. In this paper, we propose a q-statistic method to measure the degree of spatial stratified heterogeneity and to test its significance. The q value is within [0,1] (0 if a spatial stratification of heterogeneity is not significant, and 1 if there is a perfect spatial stratification of heterogeneity). The exact probability density function is derived. The q-statistic is illustrated by two examples, wherein we assess the spatial stratified heterogeneities of a hand map and the distribution of the annual NDVI in China.