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.     

Microhabitat use and diet of 0+ cyprinid fishes in a lentic, regulated reach of the River Great Ouse, England

In the River Great Ouse, 0+ roach, bream, chub, bleak and gudgeon were each divided into three' ecospecies' on the basis of their morphology: young larvae, old larvae and juveniles. Ecologically similar species were positively associated and dissimilar species were negatively associated. All ecospecies, except juvenile bleak preferred water < 1 m deep, within 6 m of the bank with some plant cover. Habitat use was similar for all species. It is suggested that this habitat offers increased growth rate, reduced predation risk and increased food abundance to all species, and that species overlap is due to complementary habitat use rather than aggregation. During the early larval period all species fed predominantly upon rotifers and diatoms. During the late larval period there was a switch to Cladocera, primarily Chydoridae and Ceriodaphnia sp., and chironomid larvae. Species-specific dietary selection was evident during the juvenile period, with roach feeding primarily upon aufwuchs, bream upon Cladocera, chub upon Diptera, and gudgeon upon Copepoda.     

Influence of learning on range expansion and adaptation to novel habitats

Learning has been postulated to ‘drive’ evolution, but its influence on adaptive evolution in heterogeneous environments has not been formally examined. We used a spatially explicit individual‐based model to study the effect of learning on the expansion and adaptation of a species to a novel habitat. Fitness was mediated by a behavioural trait (resource preference), which in turn was determined by both the genotype and learning. Our findings indicate that learning substantially increases the range of parameters under which the species expands and adapts to the novel habitat, particularly if the two habitats are separated by a sharp ecotone (rather than a gradient). However, for a broad range of parameters, learning reduces the degree of genetically‐based local adaptation following the expansion and facilitates maintenance of genetic variation within local populations. Thus, in heterogeneous environments learning may facilitate evolutionary range expansions and maintenance of the potential of local populations to respond to subsequent environmental changes.     

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.