306 research outputs found
Data set: Average daily minimum temperature in January and February in Corsica
No full textRaster providing the average of the daily minimum temperature in Celsius degrees over January and February in Corsica from 1995 to 2003 with a 0.016667x0.0166671 resolution in latitude and longitude.
Construction: This raster was constructed from the freely available database (PVGIS © European Communities, 2001-2008) providing, in particular, monthly averages of the daily minimum temperature reconstructed over a grid with 11km spatial resolution (Huld et al., 2006). These monthly averages correspond to the period 1995-2003 and were used by Abboud et al. (2019, 2020) to model Xylella fastidious dynamics in South Corsica.
Load the raster in the R statistical software (v4.1.2):
library(raster)
ADMT=raster("average-daily-minimum-temperature_Corsica_Abboud-et-al_Forecasting.grd")
print(ADMT)
plot(ADMT)
Summary information:
class : RasterLayer
dimensions : 108, 78, 8424 (nrow, ncol, ncell)
resolution : 0.016667, 0.016667 (x, y)
extent : 8.400708, 9.700734, 41.30018, 43.10021 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : average-daily-minimum-temperature_Corsica_Abboud-et-al_Forecasting.grd
names : layer
values : -0.6748945, 6.75789 (min, max)
References:
- Abboud, C., Bonnefon, O., Parent, E., and Soubeyrand, S. (2019). Dating and localizing an invasion from post-introduction data and a coupled reaction–diffusion–absorption model. Journal of Mathematical Biology 79, 765–789.
- Abboud, C., Parent, E., Bonnefon, O., and Soubeyrand, S. (2022). Forecasting pathogen dynamics with Bayesian model-averaging: Application to Xylella fastidiosa. Preprint.
- Huld, T. A., Suri, M., Dunlop, E. D., and Micale, F. (2006). Estimating average daytime and daily temperature profiles within Europe. Environmental Modelling & Software 21, 1650–1661
Air mass trajectory and connectivity data generated with tropolink (Richard et al., 2023)
No full text<p>Archive containing trajectory and connectivity data generated with tropolink for the preparation of the manuscript Richard et al. (2023, <a href="https://doi.org/10.1029/2023GH000885">https://doi.org/10.1029/2023GH000885</a>), as well as the corresponding specifications (node coordinates, dates and other tropolink options). The archive contains specifications, trajectories and connectivities for the three applications presented in the manuscript:</p><p>- the study of airborne connectivity between areas of production of sugar beet, with starting altitude equal to 250m, 500m and 750m above ground level;</p><p>- the study of airborne connectivity between potyvirus populations;</p><p>- the study of invasion risk of Spodoptera frugiperda in Europe, North Africa and western Asia;</p><p> </p><p>Web application tropolink: https://tropolink.fr/</p><p>Associated gitlab: https://forgemia.inra.fr/tropo-group</p><p>Accompanying wiki: https://forgemia.inra.fr/tropo-group/tropolink/-/wikis</p><p>R code for analyzing tropolink output: https://forgemia.inra.fr/tropo-group/tropolink/-/wikis/Examples</p><p>Richard H., Martinetti D., Lercier D., Fouillat Y., Hadi B., Elkahky M., Ding J., Michel L., Morris C.E., Berthier K., Maupas F., <br>Soubeyrand S. (2023). Computing geographical networks generated by air-mass movement. GeoHealth 7:e2023GH000885. <a href="https://doi.org/10.1029/2023GH000885">https://doi.org/10.1029/2023GH000885</a>.</p>
briskaR NTL Simulation Web App for Biological Risk Assessment
No full textbriskaR NTL Simulation Web App is an Web Application associated to the External Scientific Report:
Virgile Baudrot, Andreas Lang, Constanti Stefanescu, Samuel Soubeyrand and Antoine Messéan, 2020. Development of the extended “briskaR-NTL” model to assess potential adverse effects of Bt-maize pollen on non-target Lepidoptera at landscape level. EFSA supporting publication 2021:EN-6443. 98 pp. doi:10.2903/sp.efsa.2021.EN-6443
Description of briskaR: A spatio-temporal exposure-hazard model for assessing biological risk and impact. The model is based on stochastic geometry for describing the landscape and the exposed individuals, a dispersal kernel for the dissemination of contaminants, a set of tools to handle spatio-temporal dataframe and ecotoxicological equations.
The web application allows to use the R package briskaR (see https://CRAN.R-project.org/package=briskaR ) from a web browser.FR; R; [email protected]
Group Dispersal Modelling Revisited
No full textIn this paper we revisit the notion of grouped dispersal that have been introduced by Soubeyrand and co-authors \cite{soubeyrand2011patchy} to model the simultaneous (and hence dependent) dispersal of several propagules from a single source in a homogeneous environment. We built a time continuous measure valued process that takes into account the main feature of a grouped dispersal and derive its infinitesimal generator. To cope with the mutligeneration aspect associated to the demography we introduce two types of propagules in the description of the population which is one of the main innovations here. We also provide a rigorous description of the process and its generator. We derive as well, some large population asymptotics of the process unveilling the degenerate ultra parabolic system of PDE satisfied by the density of population. Finally, we also show that such a PDE system has a non-trivial solution which is unique in a certain functional space
Group Dispersal Modelling Revisited
No full textIn this paper we revisit the notion of grouped dispersal that have been introduced by Soubeyrand and co-authors \cite{soubeyrand2011patchy} to model the simultaneous (and hence dependent) dispersal of several propagules from a single source in a homogeneous environment. We built a time continuous measure valued process that takes into account the main feature of a grouped dispersal and derive its infinitesimal generator. To cope with the mutligeneration aspect associated to the demography we introduce two types of propagules in the description of the population which is one of the main innovations here. We also provide a rigorous description of the process and its generator. We derive as well, some large population asymptotics of the process unveilling the degenerate ultra parabolic system of PDE satisfied by the density of population. Finally, we also show that such a PDE system has a non-trivial solution which is unique in a certain functional space
Regression-based ranking of pathogen strains with respect to their contribution to natural epidemics
No full textGenetic variation in pathogen populations may be an important factor driving heterogeneity in disease dynamics within their host populations. However, to date, we understand poorly how genetic diversity in diseases impact on epidemiological dynamics because data and tools required to answer this questions are lacking. Here, we combine pathogen genetic data with epidemiological monitoring of disease progression, and introduce a statistical exploratory method to investigate differences among pathogen strains in their performance in the field. The method exploits epidemiological data providing a measure of disease progress in time and space, and genetic data indicating the relative spatial patterns of the sampled pathogen strains. Applying this method allows to assign ranks to the pathogen strains with respect to their contributions to natural epidemics and to assess the significance of the ranking. This method was first tested on simulated data, including data obtained from an original, stochastic, multi-strain epidemic model. It was then applied to epidemiological and genetic data collected during one natural epidemic of powdery mildew occurring in its wild host population. Based on the simulation study, we conclude that the method can achieve its aim of ranking pathogen strains if the sampling effort is sufficient. For powdery mildew data, the method indicated that one of the sampled strains tends to have a higher fitness than the four other sampled strains, highlighting the importance of strain diversity for disease dynamics. Our approach allowing the comparison of pathogen strains in natural epidemic is complementary to the classical practice of using experimental infections in controlled conditions to estimate fitness of different pathogen strains. Our statistical tool, implemented in the R package StrainRanking, is mainly based on regression and does not rely on mechanistic assumptions on the pathogen dynamics. Thus, the method can be applied to a wide range of pathogens
Approximate Bayesian computation with functional statistics
No full textFunctional statistics are commonly used to characterize spatial patterns in general and spatial genetic structures in population genetics in particular. Such functional statistics also enable the estimation of parameters of spatially explicit (and genetic) models. Recently, Approximate Bayesian Computation (ABC) has been proposed to estimate model parameters from functional statistics. However, applying ABC with functional statistics may be cumbersome because of the high dimension of the set of statistics and the dependences among them. To tackle this difficulty, we propose an ABC procedure which relies on an optimized weighted distance between observed and simulated functional statistics. We applied this procedure to a simple step model, a spatial point process characterized by its pair correlation function and a pollen dispersal model characterized by genetic differentiation as a function of distance. These applications showed how the optimized weighted distance improved estimation accuracy. In the discussion, we consider the application of the proposed ABC procedure to functional statistics characterizing non-spatial processes
Spécifier un processus caché non modélisé en déterminant le lien asymptotique entre résidus et processus caché. Application à l'analyse de la variabilité dans les expériences de propagation des rouilles du blé
No full textDiffusion du document : INRA, Centre d'Avignon, ERIST, Domaine St Paul, Site Agroparc, 84914 Avignon cedex 9 Diplôme : Dr. d'Universit
Forecasting Pathogen Dynamics with Bayesian Model-Averaging: Application to Xylella fastidiosa
No full textBeyond Xylella, Integrated Management Strategies for Mitigating Xylella fastidiosa Impact in Europe (BeXyl) (Grant Agreement 101060593). Partner/Coordinador principal: Blanca B. Landa del Castillo, Investigadora Científica del Instituto de Agricultura Sostenible (IAS-CSIC).Forecasting invasive-pathogen dynamics is paramount to anticipate eradication and containment strategies. Such predictions can be obtained using a model grounded on partial differential equations (PDE; often exploited to model invasions) and fitted to surveillance data. This framework allows the construction of phenomenological but concise models relying on mechanistic hypotheses and real observations. However, it may lead to models with overly rigid behavior and possible data-model mismatches. Hence, to avoid drawing a forecast grounded on a single PDE-based model that would be prone to errors, we propose to apply Bayesian model averaging (BMA), which allows us to account for both parameter and model uncertainties. Thus, we propose a set of different competing PDE-based models for representing the pathogen dynamics, we use an adaptive multiple importance sampling algorithm (AMIS) to estimate parameters of each competing model from surveillance data in a mechanistic-statistical framework, we evaluate the posterior probabilities of models by comparing different approaches proposed in the literature, and we apply BMA to draw posterior distributions of parameters and a posterior forecast of the pathogen dynamics. This approach is applied to predict the extent of Xylella fastidiosa in South Corsica, France, a phytopathogenic bacterium detected in situ in Europe less than 10 years ago (Italy 2013, France 2015). Separating data into training and validation sets, we show that the BMA forecast outperforms competing forecast approaches.This research was funded by a PhD grant INRAE-Région PACA (Emplois Jeunes Doctorants 2016-2019), the HORIZON XF-ACTORS Project (grant SFS-09-2016), the HORIZON BeXyl Project (grant 101060593) and the ANR BEYOND Project (grant 20-PCPA-0002).Peer reviewe
Equine Influenza dataset
No full textData used by Melina Ribaud, Edith Gabriel, Joseph Hughes, Samuel Soubeyrand (Identifying potential significant factors impacting zero-inflated proportions data. 2021. ) for studying factors impacting Equine Influenza outbreak in 2003 in race horses. Data are provided in one tab-delimited text files (EquineDiffFactors.txt) and the format is described in the file data-description.pdf
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