scholarly journals An integrated, spatio-temporal modelling framework for analysing biological invasions

2018 ◽  
Vol 24 (5) ◽  
pp. 652-665 ◽  
Author(s):  
Thomas Mang ◽  
Franz Essl ◽  
Dietmar Moser ◽  
Ingrid Kleinbauer ◽  
Stefan Dullinger
Keyword(s):  
Biological Invasions ◽  
Modelling Framework ◽  
Temporal Modelling ◽  
Spatio Temporal

scholarly journals Linking external components to a spatio-temporal modelling framework: Coupling MODFLOW and PCRaster

2009 ◽  
Vol 24 (9) ◽  
pp. 1088-1099 ◽  
Author(s):  
O. Schmitz ◽  
D. Karssenberg ◽  
W.P.A. van Deursen ◽  
C.G. Wesseling
Keyword(s):  
Modelling Framework ◽  
Temporal Modelling ◽  
Spatio Temporal

scholarly journals Imaging with spatio-temporal modelling to characterize the dynamics of plant-pathogen lesions

2022 ◽  
Author(s):  
Melen Leclerc ◽  
Stéphane Jumel ◽  
Frédéric M. Hamelin ◽  
Rémi Treilhaud ◽  
Nicolas Parisey ◽  
...  
Keyword(s):  
Plant Pathogen ◽  
Temporal Dynamics ◽  
Spatial Models ◽  
Model Parameters ◽  
Local Growth ◽  
Modelling Framework ◽  
Temporal Modelling ◽  
Spatio Temporal ◽  
Plant Pathogen Interactions ◽  
Host Tissues

Within-host spread of pathogens is an important process for the study of plant-pathogen interactions. However, the development of plant-pathogen lesions remains practically difficult to characterize and quantify beyond the common traits such as lesion area. We tackle the spatio-temporal dynamics of interactions by combining image-based phenotyping with mathematical modelling. We consider the spread of Peyronellaea pinodes on pea stipules that were monitored daily with visible imaging. We assume that pathogen propagation on host-tissues can be described by the Fisher-KPP model where lesion spread depends on both a logistic local growth and an homogeneous diffusion. Model parameters are estimated using a variational data assimilation approach on sets of registered images. This modelling framework is used to compare the spread of an aggressive isolate on two pea cultivars with contrasted levels of partial resistance. We show that the expected slower spread on the most resistant cultivar is actually due to a decrease of diffusion and, to a lesser extent, local growth. These results demonstrate that spatial models with imaging allows one to disentangle the processes involved in host-pathogen interactions. Hence, promoting model-based phenotyping of interactions would allow a better identification of quantitative traits thereafter used in genetics and ecological studies.

scholarly journals Spatio-temporal modelling of PM10 daily concentrations in Italy using the SPDE approach

2021 ◽  
Vol 248 ◽  
pp. 118192
Author(s):  
Guido Fioravanti ◽  
Sara Martino ◽  
Michela Cameletti ◽  
Giorgio Cattani
Keyword(s):  
Temporal Modelling ◽  
Spatio Temporal

scholarly journals Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil

2011 ◽  
Vol 37 (3) ◽  
pp. 371-381 ◽  
Author(s):  
Rachel Lowe ◽  
Trevor C. Bailey ◽  
David B. Stephenson ◽  
Richard J. Graham ◽  
Caio A.S. Coelho ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Disease Risk ◽  
Warning System ◽  
Temporal Modelling ◽  
Spatio Temporal

scholarly journals Quantification of potential recruitment of large woody debris in mountain catchments considering the effects of vegetation on hydraulic and geotechnical bank erosion and shallow landslides

2018 ◽  
Vol 40 ◽  
pp. 02046 ◽  
Author(s):  
Eric Gasser ◽  
Andrew Simon ◽  
Paolo Perona ◽  
Luuk Dorren ◽  
Johannes Hübl ◽  
...  
Keyword(s):  
Woody Debris ◽  
Probabilistic Approach ◽  
Temporal Distribution ◽  
Bank Erosion ◽  
Shallow Landslides ◽  
Large Woody Debris ◽  
Correction Coefficient ◽  
Model Framework ◽  
Modelling Framework ◽  
Spatio Temporal

Large woody debris (LWD) exacerbates flood damages near civil structures and in urbanized areas and the awareness of LWD as a risk is becoming more and more relevant. The recruitment of “fresh” large woody debris has been documented to play a significant role of the total amount of wood transported during flood events in mountain catchments. Predominately, LWD recruitment due to hydraulic and geotechnical bank erosion and shallow landslides contribute to high volumes of wood during floods. Quantifying the effects of vegetation on channel and slope processes is extremely complex. This manuscript therefore presents the concepts that are being implemented in a new modelling framework that aims to improve the quantification of vegetation effects on LWD recruitment processes. One of the focuses of the model framework is the implementation of the effect of spatio-temporal distribution of root reinforcement in recruitment processes such as bank erosion and shallow landslides in mountain catchments. Further, spatio-temporal precipitation patterns will be considered using a probabilistic approach to account for the spatio-temporal precipitation variability to estimate a LWD recruitment correction coefficient. Preliminary results are herein presented and discussed in form of a case study in the Swiss Prealps.

On Spatio-Temporal Modelling of Stream Network Initiation

2018 ◽  
Vol 5 (S1) ◽  
pp. 239-257 ◽  
Author(s):  
I. Papageorgaki ◽  
I. Nalbantis
Keyword(s):  
Stream Network ◽  
Temporal Modelling ◽  
Spatio Temporal

scholarly journals Bayesian spatio-temporal modelling of tobacco-related cancer mortality in Switzerland

2013 ◽  
Vol 7 (2) ◽  
pp. 219 ◽  
Author(s):  
Verena Jürgens ◽  
Silvia Ess ◽  
Harish C. Phuleria ◽  
Martin Früh ◽  
Matthias Schwenkglenks ◽  
...  
Keyword(s):  
Cancer Mortality ◽  
Temporal Modelling ◽  
Spatio Temporal

Application of geostatistical methods in spatio-temporal modelling of temperature changes of UCG experimental trial

Measurement ◽  
2021 ◽  
Vol 171 ◽  
pp. 108826
Author(s):  
Marek Laciak ◽  
Ladislav Vízi ◽  
Ján Kačur ◽  
Milan Durdán ◽  
Patrik Flegner
Keyword(s):  
Temperature Changes ◽  
Geostatistical Methods ◽  
Experimental Trial ◽  
Temporal Modelling ◽  
Spatio Temporal
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