Spatial distribution patterns in freshwater macrozooplankton: variation with scale

SUMMARYThis paper presents residual‐based turbulence models for problems with moving boundaries and interfaces. The method is developed via a hierarchical application of variational multiscale ideas and the models are cast in an arbitrary Lagrangian–Eulerian (ALE) frame to accommodate the deformation of domain boundaries. An overlapping additive decomposition of velocity and pressure fields into coarse and fine scale components leads to coarse and fine scale mixed‐field problems. The problem governing fine scales is subjected to a further decomposition of the fine scale velocity into overlapping components termed as fine scales level I and level II. In turn, in the bottom‐up integration of scales, the model for level II fine scales serves to stabilize the problem governing level I fine scales, and model for level I fields yields the turbulence models. From the computational perspective, the coarse scales are represented in terms of the standard Lagrange shape functions, whereas level I and level II scales are represented via quadratic and fourth order polynomial bubbles, respectively. Because of the bubble functions approach employed in the consistently derived fine scale models, the resulting method is free of any embedded or tunable parameters. The proposed turbulence models share a common feature with the LES models in that the largest scales in the flow are numerically resolved, whereas the subgrid scales are modeled. The method is applied to flow around a plunging airfoil at Re = 40,000, and results are compared with experimental and numerical data published in the literature. Also presented are the results for the plunging airfoil at Re = 60,000 to show the robustness and range of applicability of the method. Copyright © 2013 John Wiley & Sons, Ltd.

A three dimensional adaptive multiscale method for crack growth in Silicon

BackgroundBiological invasions pose severe threats to global biodiversity and human well-being. Invading populations often experience negative growth rates during the ‘lag phase’, leading to Allee effects, a density-dependent phenomenon. Allee effects reduce species fitness or plant performance due to low-density populations. The rapid spread and range expansion of an invader, Hyptis suaveolens (L.) Poit. has been reported to have negative impacts on local biodiversity in the invaded regions of the Vindhyan highlands, India. The present study examines the effects of varied population densities of H. suaveolens on its vegetative trait performance, reproductive output, and density-dependent plant population regulations. Understanding the relationship between the population density and trait modulation ability of H. suaveolens at fine and coarse scales could help strategize for management.MethodsThe study was conducted in invaded habitats of H. suaveolens in the Vindhyan highlands, India. Population density was divided into low-, medium-, and high-density groups. Plant performance was assessed at two scales—fine scale and coarse scale. Plant performance traits, vegetative growth, and reproductive output were estimated as plant traits (PlTs) at the fine scale and patch traits (PaTs) at the coarse scale. The plasticity response index (PI) was also estimated among three population densities.ResultsResults showed that PlTs-vegetative and reproductive traits, such as plant height, biomass, and number of seeds, were significantly different across densities, with medium-density individuals showing maximum plant height and plant biomass and high-density individuals exhibiting a higher number of seeds per plant. PaTs analysis revealed that plant biomass per patch was similar for medium- and high-density populations, whereas the number of seeds per patch was similar in low- and medium-density populations. PI values revealed that PlTs showed low, medium, and high plastic responses, while PaTs exhibited low and high plastic responses.ConclusionsThe study concludes that H. suaveolens exhibits density-dependent plant population regulations. As population density increases, low-density populations grow more rapidly, resulting in denser populations. These populations can negatively impact recipient habitats and, if left unchecked, grow into high-density populations with higher seed production. The study suggests that low-density areas should be considered a high priority for developing efficient and cost-effective management strategies. The present study emphasizes the importance of incorporating Allee effects dynamics in invasion studies for predicting high-risk/priority areas for strategizing invasive species management.

In tropical rainforests, seedling performance at fine ecological scales has traditionally been explained by seed dispersal limitation and negative density dependence. Soil properties have strong patch structures at fine scales, and we investigated whether such heterogeneity contributes to seedling coexistence. From January 2011 to January 2014, in a one-hectare area of tropical rainforest in Southwest China, we sampled seedlings from ninety-nine 2-m2 quadrats, and measured light condition (canopy openness), and the height (>10 cm) of seedlings at bi-monthly intervals. In addition, we measured soil pH, total nitrogen, ammonium, nitrate, and available phosphorus and potassium three times during the survey period. These data were used to examine the effects of soil properties and light on seedling spatial distribution, survival, and growth. Seedling spatial distribution was significantly associated with light and spatial heterogeneity in all the examined soil properties except available K. These fine scale soil properties and light heterogeneity also promoted seedling growth, whereas they had no discernible effects on seedling survival. This study highlights the effects of fine scale soil property heterogeneity on seedling spatial distribution and growth, and indicates their importance in the maintenance of seedling biodiversity.

Fine-scale spatial and temporal variation in earthworm surface casting activity in agroforestry fields, western Honduras

Spatial patterns of species diversity have important influences on the functioning of ecosystems, and the effect of livestock grazing on spatial heterogeneity can differ depending on the scale of the analysis. This study examined the effects of grazing on the spatial patterns of species distributions and whether the effects of grazing on the spatial distributions of a species vary with the scale of the analysis. Data were collected at three locations in the subalpine grasslands of Ordesa-Monte Perdido National Park and Aisa Valley, Central Pyrenees, Spain, which differed in mean stocking rates. Aspect explained about one-third of the environmental variation in species distributions. In flat areas, spatial variation in species composition varied with grazing intensity at two scales. At a coarse scale (among vegetation patches), grazing promoted patchiness, and among-transect variation in species diversity and grazing intensity were positively correlated. At a fine scale (within vegetation patches), the disruption of the self-organizing processes of the species spatial distributions resulted in a reduction in the long-range spatial autocorrelations of some of the characteristic species and in the homogenization of species spatial distributions. The presence of encroaching Echinospartum horridum had a significant influence on the effect of grazing on south-facing grassland slopes.

The study of the spatial and temporal evolution of annual mean PM<sub>2.5</sub> concentration and population exposure risk in Hunan Province can further analyze the influence of the landscape pattern index on the changes in annual mean PM<sub>2.5</sub> concentration and population exposure risk. The spatial and temporal evolution of the coupling relationship between people, air, and land at a fine scale and its spatial heterogeneity were investigated by the population exposure risk model, the moving window method, the spatial auto correlation model, and correlation analysis. The results show that: (1) from 2000 to 2020, the annual average PM<sub>2.5</sub> concentration in Hunan Province showed a slow increase and then a significant decrease, and its spatial distribution was high in the northeast and low in the southwest. (2) From 2000 to 2016, the average proportion of the population exposed to high annual average PM<sub>2.5</sub> concentrations (>45 μg/m<sup>3</sup>) in Hunan Province reached 74.24%, and from 2016 to 2020, the proportion of the population exposed to low annual average PM2.5 concentrations (<35 μg/m<sup>3</sup>) increased year by year. In 2020, there were regions with annual average concentrations of 15-25 μg/m<sup>3</sup>, and the regional concentrations were all less than 45 μg/m3. The spatial distribution is dominated by low-risk areas, with an overall scattered distribution, and high-risk areas are concentrated in the Chang - Zhu - Tan urban agglomeration. (3) Cultivated land and forested land, as the main land types, have relatively obvious characteristics of changes in dynamics and attitudes; water bodies and impervious surfaces are second; cultivated land has a high degree of patch aggregation, high edge density and low fragmentation, which increases the risk of annual average PM<sub>2.5</sub> concentration and population exposure; forested land has a high degree of patch aggregation, low edge density and low fragmentation, which is conducive to reducing the risk of annual average PM<sub>2.5</sub> concentration and population exposure; and water bodies and impervious surfaces have a high degree of aggregation, low edge density, and low fragmentation, which is conducive to reducing the risk of annual average PM<sub>2.5</sub> concentration and population exposure. The higher the degree of aggregation, the higher the edge density, the more complex the degree of fragmentation of water bodies and impervious surfaces, and the higher the risk of annual average PM<sub>2.5</sub> concentration and population exposure. The results of this study can provide a theoretical basis for mitigating air pollution, improving the human environment, optimizing the landscape pattern, and promoting ecologically sustainable development.

Importance and strength of environmental controllers of soil organic carbon changes with scale

Hierarchical analysis of spreading dynamics in complex systems

Smoothing images creates corners

For medical application, we are interested in estimation of optical flow on the face and particularly on area around the eyes. Among the methods of optical flow estimation, gradient estimation and block matching are the main ones. However the gradient based approach can only be applied for small displacement. Generally the process of block matching gives only good result if the searching strategy is judiciously selected. Our approach is based on a Markov random field model combined with an algorithm of block matching in a multiresolution scheme. The multiresolution approach leads to detect a large range of displacement amplitude. The large displacements are detected on the coarse scales and the small ones will be detected successively on finer scales in a coarse scales and the small ones will be detected successively on finer scales. The tracking of motion is achieved by a block matching algorithm. This method gives the otpical flow whatever the amplitude of the motion is, if included in the range defined by the multiresolution approach. The result show clearly the complementary of Markov random fields estimation and block matching across the scales.

Aim We used a hierarchical fractal-based sampling design to test how sampling grain influences (1) beta diversity of and (2) inferences from the modelled contribution of niche- versus dispersal-based assembly processes in structuring tree and bird assemblages. Location Coastal forest fragments, South Africa. Methods We surveyed 103 tree plots and 267 bird points within eight forest fragments and partitioned beta diversity (βsor) into its turnover (βsim) and nestedness (βnes) components. We evaluated how sampling at fine, intermediate and coarse scales influenced beta diversity components, and compared how tree and bird beta diversity responded to sampling grain variation. We then explored the relative contributions of niche- and dispersal-based assembly processes in explaining spatial turnover as a function of sampling grain and/or study taxon, by using multiple regression modelling on distance matrices and variance partitioning. Results The βsor of trees and birds was mainly explained by βsim at all sampling scales. For both taxonomic groups, βsor and βsim decreased as sampling scale increased. Beta diversity differed among trees and birds at fine, but not at coarse, sampling scales. Dispersal-based assembly processes were the best predictors of community assembly at fine scales, whereas niche-based assembly processes were the best predictors at coarse scales. However, most of the variation in tree community composition was explained at fine scales (by dispersal-based assembly processes), while most of the variation in bird community composition was explained at coarse scales (by niche-based assembly processes). Main conclusions Our study shows that inferences from beta diversity are scale dependent. By matching the grain of the data with the grain at which predictor variables and associated processes are likely to operate, multi-scale sampling approaches can help improve planning for biodiversity conservation and should be part of initiatives aimed at ecological conservation plans.

AimOne of the fundamental tools in biogeography is the classification of the Earth surface into spatially coherent units based on assemblage distinctiveness. However, spatial coherence of biogeographical regions may be scale‐dependent, that is, it may change with changing the size of spatial units used. We ask (1) how the clusters resulting from the classification of animal assemblages at different spatial scales differ in their spatial coherence, (2) whether there are geographical trends in the patterns of spatial coherence, and (3) what factors drive these patterns at different scales and in different areas of Europe.LocationEurope.MethodsWe used data from distribution atlases at two spatial scales (50 × 50 km and c. 10 × 10 km) and, for each scale, we selected four different areas across Europe, each of them covered by 250 grid cells. We classified each area based on the distributions of mammals (coarser scale only) and birds (both scales). Subsequently, we calculated the spatial coherence of resulting clusters and correlated it with environmental factors and geographical distance.ResultsCoarse‐scale classifications provided more spatially coherent clusters than the classifications at the finer scale and this pattern was closely related to different strength of distance decay of similarity in the species composition at different scales. Spatial coherence revealed latitudinal trends, so that coarse‐scale clusters were more spatially coherent in northern Europe. Geographical distance was the best predictor of spatial patterns at the coarser scale, although this effect was strong only in central and northern Europe. At the finer scale, topography and land cover composition were the most important.Main conclusionsSpatial coherence of biogeographical regionalizations depends on scale and varies geographically. It is closely related to different beta diversity patterns at different scales and in different areas. Heterogeneous areas with high beta diversity and endemism reveal more complex patterns than areas characterized by lower beta diversity but a stronger relationship between beta diversity and distance, and consequently coherent clusters. If the development of the species distribution databases provides fine‐resolution data covering large areas in the future, the issue of the scale of biogeographical regionalizations will probably become even more crucial.

Biodiversity, regulating ecosystem services (RES), and vegetation productivity are key indicators to instruct natural conservation planning. Decision makers often hope that ecosystems can be protected by focusing on certain key indicators, which requires an understanding of the relationships between the indicators. Using individual case studies, many have argued that these indicators commonly have significant relationships. However, these relationships at different spatial scales are unclear. Therefore, in this study, biodiversity and ecosystem services are modelled by the ecological niche model, the universal soil loss equation, and the equation of water balance in two study areas at different scales. The influence of vegetation productivity on the spatial pattern of other ecological indicators in the two areas is examined by a spatial lag model. The contributions of the driving factors on biodiversity distribution at both scales are identified by a boosted regression tree (BRT) model. The results showed that at the fine scale, the spatial correlations were strongest for species richness, especially mammalian species richness, and water retention. However, biodiversity had no significant relationship with vegetation productivity. In contrast, at a coarser scale, the correlation was stronger between plant diversity and regulating ecosystem services. In addition, plant diversity was significantly correlated with vegetation productivity. These differences between scales were controlled by various explanatory variables. At the fine scale, biophysical and climatic factors had the strongest effects on biodiversity distribution, while Net Primary Productivity (NPP) and ecoregion also had relatively high influences on biodiversity at the coarse scale. This demonstrates the critical importance of spatial scale in selecting conservation indicators. We suggest that rare mammalian species richness or flagship mammal species are suitable as conservation surrogates in fine-scale conservation planning. However, at a coarser scale, selecting vegetation patches with more rare plant species and high productivity for each ecoregion is a workable alternative method for conservation planning.

In addition to seismic and well constraints, production data must be integrated into geostatistical reservoir models for reliable reservoir performance predictions. An iterative inversion algorithm is required for such integration and is usually computationally intensive since forward flow simulation must be performed at each iteration. This paper presents an efficient approach for generating fine-scale three dimensional (3D) reservoir models that are conditioned to multiphase production data by combining a recently developed streamline-based inversion technique with a geostatistical downscaling algorithm. Production data can not reveal fine scale details of reservoir heterogeneity. By solving the streamline pressure solution at a coarse scale consistent with the production data we are able to invert numerous geostatistical realizations. Additionally, the streamline method allows fine resolution along the 1D streamlines independent of the coarse grid pressure solution so we do not need to explicitly address multiphase scale-up. Multiple geostatistical fine scale models are up-scaled to a coarse scale used in the inversion process. After inversion, the models are each geostatistically downscaled to multiple fine scale realizations. These fine scale models are now preconditioned to the production data and can be up-scaled to any scale for final flow simulation. A 3D extension of the prior 2D sequential-self calibration method (SSC) is developed for the inversion step. This method updates the coarse models to match production data while preserving as much of geostatistical constraint as possible. A new geostatistical algorithm is developed for the downscaling step. We use Sequential Gaussian Simulation with either block kriging or Bayesian updating to "downscale" the history-matched coarse scale models to fine-scale models honoring fine-scale spatial statistics. Combining these two developments we are able to efficiently generate multiple fine scale geostatistical models constrained to well and production data.