Fine-scale spatial heterogeneity and incoming seed diversity additively determine plant establishment

2012 ◽  
Vol 100 (4) ◽  
pp. 939-949 ◽  
Author(s):  
Paul J. Richardson ◽  
Andrew S. MacDougall ◽  
Douglas W. Larson
Keyword(s):  
Spatial Heterogeneity ◽  
Fine Scale ◽  
Plant Establishment

scholarly journals Effects of socio-economic and environmental factors on the spatial heterogeneity of dengue fever investigated at a fine scale

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Yubing Qu ◽  
Xun Shi ◽  
Yong Wang ◽  
Rendong Li ◽  
Liang Lu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Environmental Factors ◽  
Spatial Heterogeneity ◽  
Dengue Fever ◽  
Scan Statistics ◽  
Fine Scale ◽  
Road Density ◽  
Risk Area ◽  
High Risk Area ◽  
Guangzhou City

The spatial pattern of dengue fever cases is the result of complex interactions between the virus, the host and the vector, which may be affected by environmental conditions. The largest outbreak of dengue fever in Guangzhou city, China occurred in 2014 with case numbers 2.7 times the number of cumulative cases since 1978 and a significantly non-random spatial distribution. Selecting Guangzhou City as the study area, we used scan statistics to analyze the spatial heterogeneity of dengue fever and a generalized additive model to evaluate and examine the effects of socio-economic and environmental factors on spatial heterogeneity at a fine scale. The study found that the spatial distribution of dengue fever is highly heterogeneous and various factors differ in relative importance. The junction of the central districts of Guangzhou is a high-risk area with the urban village and urban-rural fringe zone formed by urbanization as important regional factors. The low gross domestic product per capita, the high population density, the high road density were perceived as risk factors. The Asian subtropical coastal area together with the socioeconomic and environmental factors were found to be the key drivers at the fine scale explaining the high spatial heterogeneity of dengue fever in Guangzhou City.

scholarly journals Investigation of the spatial heterogeneity of soil microbial biomass carbon and nitrogen under long-term fertilizations in fluvo-aquic soil

2018 ◽  
Author(s):  
YE Hong-ling
Keyword(s):  
Microbial Biomass ◽  
Spatial Heterogeneity ◽  
Soil Microbial Biomass ◽  
Microbial Biomass Carbon ◽  
Surface Model ◽  
Fine Scale ◽  
Soil Layers ◽  
Carbon And Nitrogen ◽  
Soil Microbial

AbstractSoils are heterogeneous and microbial spatial distribution can clearly indicate the spatial characteristics of the soil carbon and nitrogen cycle. However, it is not clear how long-term fertilization affects the spatial distribution of microbial biomass in fluvo-aquic soil. We collected fluvo-aquic soil samples (topsoil 0-7.5 cm and sub-topsoil 7.5-20 cm) using a spatially-explicit design within three 40.5 m2 plots in each of four fertilization treatments. Fertilization treatments were: cropping without fertilizer inputs (CK); chemical nitrogen, phosphorus, and potassium fertilizer (NPK); chemical fertilizer with straw return (NPKS); and chemical fertilizer with animal manure (NPKM). Variables included soil microbial biomass carbon (MBC) and nitrogen (MBN), and MBC/MBN. For both soil layers, we hypothesized that: microbial biomass was lowest in CK but with the largest spatial heterogeneity; and microbial biomass was highest in NPKM and NPKS but with the lowest spatial heterogeneity. Results showed that: (1) Fertilization significantly increased MBC and MBN more in topsoil than sub-topsoil but had no MBC/MBN changes. (2) The coefficient of variation (CV) and Cochran’s C showed that variation was largest in CK in topsoil and NPK in sub-topsoil and that variation of topsoil was generally lower than in sub-topsoil. The sample size of the three variables was largest in CK in topsoil but had little variation among the other treatments. (3) The trend-surface model showed that within-plot heterogeneity varied substantially with fertilization (NPKM = NPK > NPKS > CK), but Moran’s I and the interpolation map showed that spatial variability with fertilization followed the order NPK > NPKS > CK = NPKM at a fine scale in topsoil. In sub-topsoil, the trend-surface model showed that within-plot heterogeneity followed the order NPKM = CK > NPK > NPKS and that the fine-scale pattern was NPKM>NPK=NPKS>CK. MBC had the highest spatial heterogeneity among the three variables in both soil layers. Our results indicate that the application of organic fertilizer (straw or manure) reduced the variation of MBC and MBN but increased the spatial variability of MBC and MBN. The spatial variation of the three variables was MBC > MBN > MBC/MBN regardless of whether variation was considered at the plot-scale or the fine-scale in both layers.

scholarly journals The fine-scale landscape of immunity and parasitism in a wild ungulate population

2018 ◽  
Author(s):  
Gregory F. Albery ◽  
Daniel J. Becker ◽  
Fiona Kenyon ◽  
Daniel H. Nussey ◽  
Josephine M. Pemberton
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Cervus Elaphus ◽  
Disease Ecology ◽  
Fasciola Hepatica ◽  
Common Source ◽  
Fine Scale ◽  
Animal Populations ◽  
Wide Range

AbstractSpatial heterogeneity in parasite susceptibility and exposure is a common source of confounding variation in disease ecology studies. However, it is not known whether spatial autocorrelation acts on immunity in particular at small scales, within wild animal populations, and whether this predicts spatial patterns in infection. Here we used a well-mixed wild population of individually recognised red deer (Cervus elaphus) inhabiting a heterogeneous landscape to investigate fine-scale spatial patterns of immunity and parasitism. We noninvasively collected 842 faecal samples from 141 females with known ranging behaviour over two years. We quantified total and helminth-specific mucosal antibodies and counted propagules of three gastrointestinal helminth taxa. These data were analysed with linear mixed models using the Integrated Nested Laplace Approximation (INLA), using a Stochastic Partial Differentiation Equation approach (SPDE) to control for and quantify spatial autocorrelation. We also investigated whether spatial patterns of immunity and parasitism changed seasonally. We discovered substantial spatial heterogeneity in general and helminth-specific antibody levels and parasitism with two helminth taxa, all of which exhibited contrasting seasonal variation in their spatial patterns. Notably, strongyle nematode intensity did not align with density hotspots, while Fasciola hepatica intensity appeared to be strongly influenced by the presence of wet grazing. In addition, antibody hotspots did not correlate with distributions of any parasites. Our results suggest spatial heterogeneity may be an important factor affecting immunity and parasitism in a wide range of study systems. We discuss these findings with regards to the design of sampling regimes and public health interventions, and suggest that disease ecology studies investigate spatial heterogeneity more regularly to enhance their results, even when examining small geographic areas.

scholarly journals Fine-scale spatial heterogeneity of invertebrates within cryoconite holes

2019 ◽  
Vol 53 (2) ◽  
pp. 179-190 ◽  
Author(s):  
Krzysztof Zawierucha ◽  
Jakub Buda ◽  
Diego Fontaneto ◽  
Roberto Ambrosini ◽  
Andrea Franzetti ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Fine Scale

Spatial distribution patterns in freshwater macrozooplankton: variation with scale

1991 ◽  
Vol 69 (6) ◽  
pp. 1557-1570 ◽  
Author(s):  
Bernadette Pinel-Alloul ◽  
Didier Pont
Keyword(s):  
Spatial Heterogeneity ◽  
Large Scale ◽  
Vertical Axis ◽  
Distribution Patterns ◽  
Fine Scale ◽  
Surface Distribution ◽  
Interspecific Variations ◽  
Invertebrate Predators ◽  
Mesocyclops Edax ◽  
Lake Size

The spatial heterogeneity of four macrozooplankton species (Skistodiaptomus oregonensis, Mesocyclops edax, Diaphanosoma brachyurum, and Daphnia sp.) was investigated over different scales, (fine and coarse scales: 2–40 m; lake-size scale: 10–380 m) in a small Canadian Shield lake. Values for the log s2: log [Formula: see text] relationships were established for the different scales and compared. Spatial analysis methods (space-constrained clustering analysis, spatial autocorrelation, and variogram modelling) were used for describing the surface distribution patterns observed on the whole-lake transect. The study demonstrated that spatial heterogeneity occurs on both the fine and coarse scales. Maximal spatial heterogeneity was observed on the vertical axis (depth) rather than on the horizontal axis. Macrozooplankton patchiness scales in Lake Cromwell correspond to whole-lake scale patterns for M. edax and D. brachyurum, and to fine-scale patterns for S. oregonensis and Daphnia spp. Our results confirm the general trend of interspecific variations in patch sizes of freshwater macrozooplankton. Distribution of the invertebrate predators Chaoborus spp. is inversely related to the large-scale gradient of D. brachyurum. Multiple regression analysis showed that several physical (water temperature, oxygen, wind direction) and biological (chlorophyll a) factors, in addition to mean population abundance, were correlated with macrozooplankton heterogeneity on the fine scale.

Upscaling of outcrop information for improved reservoir modelling – exemplified by a case study on chalk

2021 ◽  
pp. petgeo2020-126
Author(s):  
Dongfang Qu ◽  
Peter Frykman ◽  
Lars Stemmerik ◽  
Klaus Mosegaard ◽  
Lars Nielsen
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Petrophysical Properties ◽  
Fine Scale ◽  
Reservoir Modelling ◽  
Reservoir Models ◽  
Scale Models ◽  
Correlation Information ◽  
Scale Difference ◽  
Subsurface Reservoir

Outcrops are valuable for analogous subsurface reservoirs in supplying knowledge of fine-scale spatial heterogeneity pattern and stratification types, which are difficult to obtain from subsurface reservoir cores, well logs or seismic data. For petrophysical properties in a domain where the variations are relatively continuous and not dominated by abrupt contrasts, the spatial heterogeneity pattern can be characterized by a semivariogram model. The outcrop information therefore has the potential to constrain the semivariogram for subsurface reservoir modelling, even though it represents different locations and depths, and the petrophysical properties may differ in magnitude or variance. However, the use of outcrop derived spatial correlation information for petrophysical property modelling in practice has been challenged by the scale difference between the small support volume of the property measurements from outcrops and the typically much larger grid cells used in reservoir models. With an example of modelling the porosity of an outcrop chalk unit in eastern Denmark, this paper illustrates how the fine-scale spatial correlation information obtained from sampling of outcrops can be transferred to coarser scale models of analogue rocks. The workflow can be applied to subsurface reservoirs and ultimately improves the representation of geological patterns in reservoir models.

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