Temporal changes in mesoscale aggregations and spatial distribution scenarios of the Peruvian anchovy (Engraulis ringens)

2019 ◽  
Vol 159 ◽  
pp. 75-83 ◽  
Author(s):  
Giancarlo Moron ◽  
Paola Galloso ◽  
Dimitri Gutierrez ◽  
Josymar Torrejon-Magallanes
Keyword(s):  
Spatial Distribution ◽  
Temporal Changes ◽  
Engraulis Ringens

scholarly journals SPATIAL AND TEMPORAL CHANGES OF AEROSOL OPTICAL DEPTH AND ITS DRIVING FACTORS BASED ON MODIS IN JIANGSU PROVINCE

2018 ◽  
Vol XLII-3 ◽  
pp. 645-649
Author(s):  
C. Jiang ◽  
Q. Xu ◽  
Y. K. Gu ◽  
X. Y. Qian ◽  
J. N. He
Keyword(s):  
Spatial Distribution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Temporal Changes ◽  
Jiangsu Province ◽  
Driving Factors ◽  
Air Mass ◽  
Spatial And Temporal Changes ◽  
Clear Point ◽  
Precipitation And Temperature

Aerosol Optical Depth (AOD) is of great value for studying air mass and its changes. In this paper, we studied the spatial-temporal changes of AOD and its driving factors based on spatial autocorrelation model, gravity model and multiple regression analysis in Jiangsu Province from 2007 to 2016. The results showed that in terms of spatial distribution, the southern AOD value is higher, and the high-value aggregation areas are significant, while the northern AOD value is lower, but the low-value aggregation areas constantly change. The AOD gravity centers showed a clear point-like aggregation. In terms of temporal changes, the overall AOD in Jiangsu Province increased year by year in fluctuation. In terms of driving factors, the total amount of vehicles, precipitation and temperature are important factors for the growth of AOD.

scholarly journals Temporal changes in the spatial distribution of carabid beetles around arable field-woodlot boundaries

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Michal Knapp ◽  
Miroslav Seidl ◽  
Jana Knappová ◽  
Martin Macek ◽  
Pavel Saska
Keyword(s):  
Spatial Distribution ◽  
Temporal Changes ◽  
Carabid Beetles ◽  
Arable Field

Temporal changes in the spatial distribution of physicochemical properties during postharvest ripening of mango fruit

2020 ◽  
Vol 14 (2) ◽  
pp. 992-1001
Author(s):  
Busarakorn Mahayothee ◽  
Parika Rungpichayapichet ◽  
Pasinee Yuwanbun ◽  
Pramote Khuwijitjaru ◽  
Marcus Nagle ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Physicochemical Properties ◽  
Temporal Changes ◽  
Mango Fruit ◽  
Postharvest Ripening

Lévy trajectories of Peruvian purse-seiners as an indicator of the spatial distribution of anchovy (Engraulis ringens)

2005 ◽  
Vol 62 (3) ◽  
pp. 477-482 ◽  
Author(s):  
Sophie Bertrand ◽  
Julian M. Burgos ◽  
François Gerlotto ◽  
Jaime Atiquipa
Keyword(s):  
Spatial Distribution ◽  
Fractal Dimension ◽  
Spatial Organization ◽  
Length Distribution ◽  
Fish Distribution ◽  
Acoustic Survey ◽  
Fishing Vessels ◽  
Top Predators ◽  
System Data ◽  
Engraulis Ringens

Abstract Analogous to other top predators foraging on patchy resources, the spatial behaviour of fishers provides information on the spatial organization of fish. Focusing on the Peruvian anchovy purse-seine fishery, vessel monitoring system data are used to describe fishing vessels' trajectories, and acoustic survey data to characterize anchovy spatial distribution. Vessel trajectories were analysed in terms of move-length distribution, whereas fish distribution was characterized by spatial extent, concentration, and fractal dimension. Fishers perform Lévy flights that can be characterized by a single statistic (μ); μ is significantly correlated with the fractal dimension of fish distribution. It is argued that the Lévy statistic is a good candidate for an ecosystem indicator that might contribute to real-time monitoring of ecosystems.

Using Tree Growth and Yield Simulators to Create Ecological Yield Tables for Silvicultural Prescriptions

1994 ◽  
Vol 9 (3) ◽  
pp. 91-94 ◽  
Author(s):  
Tara M. Barrett ◽  
Lawrence S. Davis ◽  
Frieder G. Schurr
Keyword(s):  
Spatial Distribution ◽  
Tree Growth ◽  
Forest Structure ◽  
Temporal Changes ◽  
Wildlife Habitat ◽  
Growth And Yield ◽  
Target Distribution ◽  
Dynamic Target ◽  
Silvicultural Prescriptions

Abstract Several authors have suggested using silviculture to create a dynamic target distribution of forest structures across the landscape in managing for both biodiversity and production of outputs (Harris 1984, Oliver 1992, Swanson and Franklin 1992). If forest structure (the dynamic spatial distribution and composition of tree vegetation) is known, it can be used for predicting changes in wildlife habitat, biodiversity, and output production. This paper describes a method for using tree growth and yield simulator output to classify temporal changes in forest structure associated with applying silvicultural prescriptions. West. J. Appl. For. 9(3):91-94.

scholarly journals Temporal variation of spatial autocorrelation of COVID-19 cases identified in Poland during the year from the beginning of the pandemic

2021 ◽  
Vol 94 (3) ◽  
pp. 355-380
Author(s):  
Alfred Stach
Keyword(s):  
Spatial Distribution ◽  
Spatial Autocorrelation ◽  
Temporal Variation ◽  
Temporal Changes ◽  
Space Time ◽  
Spatial Component ◽  
Complex Combination ◽  
Source Data ◽  
Temporal And Spatial

The spread of the COVID-19 pandemic has a simultaneous temporal and spatial component. This pattern results from a complex combination of factors, including social ones, that lead to significant differences in the evolution of space-time distributions, both between and within countries. The aim of this study was to assess changes in the regularity of the spatial distribution of the number of diagnosed COVID-19 cases in Poland over more than a year of the pandemic. The analysis utilized daily and weekly data for 380 counties (poviats), using the local – Poisson risk semivariogram – measure of spatial autocorrelation. Despite the heterogeneity and errors in the source data, it was possible to identify clear patterns of temporal changes in the spatial distribution of COVID-19 cases, manifested by differences in the nature and extent of their autocorrelation.

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