RÉPARTITION SPATIO-TEMPORELLE DES PRÉSENCES D’AILÉS D’APHIS GOSSYPII (HEMIPTERA: APHIDIDAE) EN CULTURE COTONNIÈRE (MALVACEAE)

1999 ◽  
Vol 131 (6) ◽  
pp. 813-824 ◽  
Author(s):  
Léonide Célini ◽  
Jean Vaillant
Keyword(s):  
Monte Carlo ◽  
Edge Effect ◽  
Central African Republic ◽  
Spatial Scales ◽  
Aphis Gossypii ◽  
Temporal Distribution ◽  
Significant Edge ◽  
Short Period ◽  
Cotton Plants ◽  
Spatio Temporal

AbstractThe spatio-temporal distribution of cotton plants, Gossypium hirsutum L., infested with Aphis gossypii (Glover) winged adults, is described in a plot located in Bangui, Central African Republic. Each cotton plant growing in the plot was examined visually for a short period of time to ascertain the presence or absence of A. gossypii and to construct weekly infestation maps. Tests of autocorrelation and dispersion were carried out at different spatial scales by means of Monte Carlo procedures on embedded counting grids. Edge effect tests were also carried out. The statistical analyses show significant overdispersion of the infested plants and positive spatial autocorrelation. Positive temporal autocorrelation and significant edge effect are detected intermittently throughout the season.

scholarly journals Dynamics and spatio-temporal distribution of geomagnetic disturbances during periods of increased solar activity and magnetic storms

2020 ◽  
Vol 196 ◽  
pp. 02009
Author(s):  
Oksana Mandrikova ◽  
Anastasia Rodomanskay
Keyword(s):  
Cross Correlation ◽  
Temporal Distribution ◽  
Temporal Analysis ◽  
Auroral Zone ◽  
Magnetic Storms ◽  
Magnetic Data ◽  
Geomagnetic Disturbances ◽  
Short Period ◽  
Adaptive Thresholds ◽  
Spatio Temporal

A detailed spatio-temporal analysis of magnetic data was performed during the periods of magnetic storms on October 02, 2013 and September 27, 2019 based on measurements of the station network. In this work, we used a method developed by us for the analysis of magnetic data, based on the use of wavelet transform and adaptive thresholds. The method allows us to identify short-period field disturbances and estimate their intensity from the data of the H-component of the geomagnetic field. The features of the occurrence and propagation of geomagnetic disturbances in the auroral zone and at meridionally located stations have been studied. Dynamic spectra of disturbances of different intensity and duration are obtained. The paper confirms the possibility of occurrence of short-period weak geomagnetic disturbances at stations from high latitudes to the equator, preceding magnetic storms and correlating with fluctuations of the southern Bz-component of the interplanetary magnetic field and increases in the auroral indices of geomagnetic activity. Cross-correlation dependences of the intensity of geomagnetic disturbances on the parameters of the interplanetary medium during magnetic storms were obtained from the data of the network of magnetic stations. A statistically significant influence of the magnitude of the scope of the Bz-component of the IMF and the speed of the solar wind on the development of magnetic storms during the initial and main phases of magnetic storms was revealed.

scholarly journals The spatio-temporal distribution of COVID-19 infection in England between January and June 2020

2021 ◽  
Vol 149 ◽  
Author(s):  
Richard Elson ◽  
Tilman M. Davies ◽  
Iain R. Lake ◽  
Roberto Vivancos ◽  
Paula B. Blomquist ◽  
...  
Keyword(s):  
Urban Areas ◽  
Temporal Dynamics ◽  
Disease Risk ◽  
Spatial Scales ◽  
Temporal Distribution ◽  
Health Resources ◽  
Epidemiological Studies ◽  
Accurate Identification ◽  
Multiple Introductions ◽  
Spatio Temporal

Abstract The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed. A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk. Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout. In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

scholarly journals Multi-Scape Interventions to Match Spatial Scales of Demand and Supply of Ecosystem Services

2021 ◽  
Vol 4 ◽  
Author(s):  
Emilio A. Laca
Keyword(s):  
Ecosystem Services ◽  
Spatial Scales ◽  
Supply And Demand ◽  
Temporal Distribution ◽  
Ecosystem Functions ◽  
Spatial Design ◽  
Spatial And Temporal Distributions ◽  
Quantitative Basis ◽  
Spatio Temporal ◽  
And Function

The original focus on supply of ecosystem services is shifting toward matching supply and demand. This new focus underlines the need to consider not only the amount of ecosystem services but also their spatial and temporal distributions relative to demand. Ecosystem functions and services have characteristic or salient scales that are defined by the scales at which the producing organisms or communities exist and function. Provision of ecosystem services (ES) and functions can be managed optimally by controlling the spatio-temporal distribution of landscape and community components. A simple model represents distributions of ES as kernels centered at the location of interventions such as grassland restoration or establishment of nesting habitat for pollinators. Distribution kernels allow non-habitat patches to receive ecosystem services from species they cannot support. Simulations for three contrasting ES producing organisms (bumblebees, Northern Harriers, and oak trees) show the effects of interacting distribution of interventions and demand for ES. More ES demand is met when the intervention is spread out in the landscape and demand is evenly distributed, particularly when the kernel radius is much larger than the minimum intervention required for the ES producing unit to be established. Because different functions have different reaches and saturation points, the level of ES demand met at any point in space can be modulated by controlling the spatial distribution of landscape components created by interventions. Different ES can be promoted by the same type and quantity of intervention by controlling the continuum of scales in the distribution of interventions. This work provides a conceptual and quantitative basis to consider the spatial design of interventions to match ES supply and demand.

scholarly journals The spatio-temporal distribution of COVID-19 infection in England between January and June 2020

2021 ◽  
Author(s):  
Richard Elson ◽  
Tilman M. Davies ◽  
Iain R. Lake ◽  
Roberto Vivancos ◽  
Paula B. Blomquist ◽  
...  
Keyword(s):  
Urban Areas ◽  
Temporal Dynamics ◽  
Disease Risk ◽  
Spatial Scales ◽  
Temporal Distribution ◽  
Health Resources ◽  
Epidemiological Studies ◽  
Accurate Identification ◽  
Multiple Introductions ◽  
Spatio Temporal

AbstractThe spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed.A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk.Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first six months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on the 23rd March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout.In terms of controlling transmission, the most important practical application is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

scholarly journals A null-model analysis of the spatio-temporal distribution of earthworm species assemblages in Colombian grasslands

2009 ◽  
Vol 25 (4) ◽  
pp. 415-427 ◽  
Author(s):  
Thibaud Decaëns ◽  
Juan José Jiménez ◽  
Jean-Pierre Rossi
Keyword(s):  
Spatial Scales ◽  
Null Model ◽  
Temporal Distribution ◽  
Niche Overlap ◽  
Past Research ◽  
Distribution Patterns ◽  
Species Assemblages ◽  
Spatially Explicit ◽  
Similar Species ◽  
Spatio Temporal

Abstract:Earthworm assemblages are usually spatio-temporally structured in mosaics of patches with different species composition. We re-analysed results of past research carried out in Eastern Colombia to explore how interspecific competition accounts for this pattern. In three sown pastures and three native savannas, density data matrices were obtained from spatially explicit samplings at several successive dates, and spatio-temporal patterns of species assemblages were described through partial triadic analyses and geostatistics. This first analysis detected assemblage patchiness in the six plots at spatial scales ranging from 6 to 33 m. Species richness ranged from 5 to 6 species per plot. Null models were further used to analyse niche overlap and morphometric distribution patterns at two different scales, i.e. at the ‘plot level’ and the ‘patch level’. Seasonal and vertical niche overlaps were higher than expected by chance at both scales, indicating high environmental constraints on assemblage membership. Within-patch overlaps were lower than plot-scale overlaps. Biometric niche overlap was random at the plot level and was weakly lower than that expected by chance in patches. Body weight was significantly overdispersed and constant whatever the scale, while body length and diameter showed a similar trend within patches. These results suggest that earthworms form distinct assemblages within patches, mainly driven by deterministic responses to competition: ecologically similar species avoid competition through spatial segregation, whereas a minimal level of ecological segregation is required to allow co-existence in a given patch.

Influence of Planting Density on Root Spatio-Temporal Distribution of Different Types of Maize under High-Yielding Cultivation Conditions

2013 ◽  
Vol 38 (7) ◽  
pp. 1286-1294 ◽  
Author(s):  
Zong-Xin LI ◽  
Yuan-Quan CHEN ◽  
Qing-Cheng WANG ◽  
Kai-Chang LIU ◽  
Wang-Sheng GAO ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Planting Density ◽  
Cultivation Conditions ◽  
Different Types ◽  
Spatio Temporal
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