Landscape allocation: stochastic generators and statistical inference

In agricultural landscapes, the composition and spatial configuration of cultivated and semi-natural elements strongly impact species dynamics, their interactions and habitat connectivity. To allow for landscape structural analysis and scenario generation, we here develop statistical tools for real landscapes composed of geometric elements including 2D patches but also 1D linear elements such as hedges. We design generative stochastic models that combine a multiplex network representation and Gibbs energy terms to characterize the distributional behavior of landscape descriptors for land-use categories. We implement Metropolis-Hastings for this new class of models to sample agricultural scenarios featuring parameter-controlled spatial and temporal patterns (e.g., geometry, connectivity, crop-rotation). Pseudolikelihood-based inference allows studying the relevance of model components in real landscapes through statistical and functional validation, the latter achieved by comparing commonly used landscape metrics between observed and simulated landscapes. Models fitted to subregions of the Lower Durance Valley (France) indicate strong deviation from random allocation, and they realistically capture small-scale landscape patterns. In summary, our approach of statistical modeling improves the understanding of structural and functional aspects of agro-ecosystems, and it enables simulation-based theoretical analysis of how landscape patterns shape biological and ecological processes

Gibbsian T-tessellation model for agricultural landscape characterization

A new class of planar tessellations, named T-tessellations, was introduced in ([10]). A model was proposed to be considered as a completely random T-tessellation model (CRTT) and its Gibbsian variants were discussed. A general simulation algorithm of Metropolis-Hastings-Green type was derived for model simulation, involving three local transformations of T-tessellations. The current paper focuses on statistical inference for Gibbs models of T-tessellations. Statistical methods originated from point pattern analysis are implemented on the example of three agricultural landscapes approximated by T-tessellations. The choice of model statistics is guided by their capacity to highlight the differences between the landscape patterns. Model parameters are estimated by Monte Carlo Maximum Likelihood method, yielding a baseline for landscapes comparison. In the last part of the paper a global envelope test based on the empty-space function is proposed for assessing the goodness-of-fit of the model

Structure of agricultural landscapes and epidemic risk, a demo-genetic approach.

L'intensification de l'agriculture a amélioré de façon considérable la production alimentaire ces dernières cinquante années mais elle s'est accompagnée d'un impact croissant sur l'environnement. En particulier, la modernisation de l'agriculture a impliqué une simplification de la structure des paysages agricoles rendant nos agro-ecosystèmes plus sensibles au risque épidémique. L'utilisation de la diversité génétique des cultures est une solution prometteuse pour réduire le risque d'occurrence et de propagation des maladies des cultures. Elle nécessite cependant une gestion collective des espaces agricoles. En conséquences, l'échelle d'étude ne doit plus se focaliser sur la parcelle mais sur le paysage. Dans cette thèse, nous nous interessons aux processus se déroulant à l'échelle du paysage et au rôle de la diversité des plantes cultivées pour le contrôle des épidémies. Nous avons identifié trois questions: comment les populations pathogènes se propagent-elles dans un paysage d'hôtes hétérogène ? Comment les différents génotypes composant la population pathogène entrent-ils en compétition au sein d'une population hôte diversifiée ? et, à plus long terme, comment les populations pathogènes évoluent-elles en réponse à la structure des populations hôtes ? Chacune de ces questions a été approfondie grâce à l'analyse de données obtenues en condition de production mais aussi par des approches théoriques. Nous avons montré que la composition et la structure spatiale des populations hôtes influence fortement la population pathogène. Cependant, les recommandations que peut fournir ce travail pour gérer la diversité génétique dépendent de l'objectif visé.Agriculture intensification has improved food production impressively in the past 50 years but this came with an increasing impact on the environment. In particular, modern agriculture has led to the simplification of the environmental structure over vast areas. As a consequence, agro-ecosystems are particularly susceptible to epidemics. The increase of crop genetic diversity is a promising way for reducing the risk of occurrence and development of diseases in crops but the technical and organisational conditions required to manage the genetic resources at this scale have not been established yet. This will require shifting the scale of crop protection investigations from the field to the agricultural landscape. In this PhD thesis we focus on landscape-scale processes and on the potential role of functional diversity in cultivated landscapes to better control plant diseases. We identified three questions: how does a pathogen population spread over a heterogeneous host landscape? How do pathogen genotypes compete in a diversified host population? And, in a longer term, how do pathogen populations evolve in response to host landscape structure? Each of these questions is investigated through the analysis of real data and the development of theoretical approaches. We demonstrate that the composition and the spatial structure of the host landscape greatly influence the pathogen population dynamics and evolution. The recommendations that this work could provide in order to practically manage the genetic resources will depend on the desired aim and will request further collaborative work withthe professional operators

Structure of agricultural landscapes and epidemic risk, a demo-genetic approach.

L'intensification de l'agriculture a amélioré de façon considérable la production alimentaire ces dernières cinquante années mais elle s'est accompagnée d'un impact croissant sur l'environnement. En particulier, la modernisation de l'agriculture a impliqué une simplification de la structure des paysages agricoles rendant nos agro-ecosystèmes plus sensibles au risque épidémique. L'utilisation de la diversité génétique des cultures est une solution prometteuse pour réduire le risque d'occurrence et de propagation des maladies des cultures. Elle nécessite cependant une gestion collective des espaces agricoles. En conséquences, l'échelle d'étude ne doit plus se focaliser sur la parcelle mais sur le paysage. Dans cette thèse, nous nous interessons aux processus se déroulant à l'échelle du paysage et au rôle de la diversité des plantes cultivées pour le contrôle des épidémies. Nous avons identifié trois questions: comment les populations pathogènes se propagent-elles dans un paysage d'hôtes hétérogène ? Comment les différents génotypes composant la population pathogène entrent-ils en compétition au sein d'une population hôte diversifiée ? et, à plus long terme, comment les populations pathogènes évoluent-elles en réponse à la structure des populations hôtes ? Chacune de ces questions a été approfondie grâce à l'analyse de données obtenues en condition de production mais aussi par des approches théoriques. Nous avons montré que la composition et la structure spatiale des populations hôtes influence fortement la population pathogène. Cependant, les recommandations que peut fournir ce travail pour gérer la diversité génétique dépendent de l'objectif visé.Agriculture intensification has improved food production impressively in the past 50 years but this came with an increasing impact on the environment. In particular, modern agriculture has led to the simplification of the environmental structure over vast areas. As a consequence, agro-ecosystems are particularly susceptible to epidemics. The increase of crop genetic diversity is a promising way for reducing the risk of occurrence and development of diseases in crops but the technical and organisational conditions required to manage the genetic resources at this scale have not been established yet. This will require shifting the scale of crop protection investigations from the field to the agricultural landscape. In this PhD thesis we focus on landscape-scale processes and on the potential role of functional diversity in cultivated landscapes to better control plant diseases. We identified three questions: how does a pathogen population spread over a heterogeneous host landscape? How do pathogen genotypes compete in a diversified host population? And, in a longer term, how do pathogen populations evolve in response to host landscape structure? Each of these questions is investigated through the analysis of real data and the development of theoretical approaches. We demonstrate that the composition and the spatial structure of the host landscape greatly influence the pathogen population dynamics and evolution. The recommendations that this work could provide in order to practically manage the genetic resources will depend on the desired aim and will request further collaborative work withthe professional operators

Structure du paysage agricole et risque épidémique, une approche démo-génétique.

Agriculture intensification has improved food production impressively in the past 50 years but this came with an increasing impact on the environment. In particular, modern agriculture has led to the simplification of the environmental structure over vast areas. As a consequence, agro-ecosystems are particularly susceptible to epidemics. The increase of crop genetic diversity is a promising way for reducing the risk of occurrence and development of diseases in crops but the technical and organisational conditions required to manage the genetic resources at this scale have not been established yet. This will require shifting the scale of crop protection investigations from the field to the agricultural landscape. In this PhD thesis we focus on landscape-scale processes and on the potential role of functional diversity in cultivated landscapes to better control plant diseases. We identified three questions: how does a pathogen population spread over a heterogeneous host landscape? How do pathogen genotypes compete in a diversified host population? And, in a longer term, how do pathogen populations evolve in response to host landscape structure? Each of these questions is investigated through the analysis of real data and the development of theoretical approaches. We demonstrate that the composition and the spatial structure of the host landscape greatly influence the pathogen population dynamics and evolution. The recommendations that this work could provide in order to practically manage the genetic resources will depend on the desired aim and will request further collaborative work withthe professional operators.L'intensification de l'agriculture a amélioré de façon considérable la production alimentaire ces dernières cinquante années mais elle s'est accompagnée d'un impact croissant sur l'environnement. En particulier, la modernisation de l'agriculture a impliqué une simplification de la structure des paysages agricoles rendant nos agro-ecosystèmes plus sensibles au risque épidémique. L'utilisation de la diversité génétique des cultures est une solution prometteuse pour réduire le risque d'occurrence et de propagation des maladies des cultures. Elle nécessite cependant une gestion collective des espaces agricoles. En conséquences, l'échelle d'étude ne doit plus se focaliser sur la parcelle mais sur le paysage. Dans cette thèse, nous nous interessons aux processus se déroulant à l'échelle du paysage et au rôle de la diversité des plantes cultivées pour le contrôle des épidémies. Nous avons identifié trois questions: comment les populations pathogènes se propagent-elles dans un paysage d'hôtes hétérogène ? Comment les différents génotypes composant la population pathogène entrent-ils en compétition au sein d'une population hôte diversifiée ? et, à plus long terme, comment les populations pathogènes évoluent-elles en réponse à la structure des populations hôtes ? Chacune de ces questions a été approfondie grâce à l'analyse de données obtenues en condition de production mais aussi par des approches théoriques. Nous avons montré que la composition et la structure spatiale des populations hôtes influence fortement la population pathogène. Cependant, les recommandations que peut fournir ce travail pour gérer la diversité génétique dépendent de l'objectif visé

Stratégies paysagères pour déployer efficacement et durablement la résistance : modèles et prédictions

International audienc

Structure of agricultural landscapes and epidemic risk, a demo-genetic approach.

L'intensification de l'agriculture a amélioré de façon considérable la production alimentaire ces dernières cinquante années mais elle s'est accompagnée d'un impact croissant sur l'environnement. En particulier, la modernisation de l'agriculture a impliqué une simplification de la structure des paysages agricoles rendant nos agro-ecosystèmes plus sensibles au risque épidémique. L'utilisation de la diversité génétique des cultures est une solution prometteuse pour réduire le risque d'occurrence et de propagation des maladies des cultures. Elle nécessite cependant une gestion collective des espaces agricoles. En conséquences, l'échelle d'étude ne doit plus se focaliser sur la parcelle mais sur le paysage. Dans cette thèse, nous nous interessons aux processus se déroulant à l'échelle du paysage et au rôle de la diversité des plantes cultivées pour le contrôle des épidémies. Nous avons identifié trois questions: comment les populations pathogènes se propagent-elles dans un paysage d'hôtes hétérogène ? Comment les différents génotypes composant la population pathogène entrent-ils en compétition au sein d'une population hôte diversifiée ? et, à plus long terme, comment les populations pathogènes évoluent-elles en réponse à la structure des populations hôtes ? Chacune de ces questions a été approfondie grâce à l'analyse de données obtenues en condition de production mais aussi par des approches théoriques. Nous avons montré que la composition et la structure spatiale des populations hôtes influence fortement la population pathogène. Cependant, les recommandations que peut fournir ce travail pour gérer la diversité génétique dépendent de l'objectif visé.Agriculture intensification has improved food production impressively in the past 50 years but this came with an increasing impact on the environment. In particular, modern agriculture has led to the simplification of the environmental structure over vast areas. As a consequence, agro-ecosystems are particularly susceptible to epidemics. The increase of crop genetic diversity is a promising way for reducing the risk of occurrence and development of diseases in crops but the technical and organisational conditions required to manage the genetic resources at this scale have not been established yet. This will require shifting the scale of crop protection investigations from the field to the agricultural landscape. In this PhD thesis we focus on landscape-scale processes and on the potential role of functional diversity in cultivated landscapes to better control plant diseases. We identified three questions: how does a pathogen population spread over a heterogeneous host landscape? How do pathogen genotypes compete in a diversified host population? And, in a longer term, how do pathogen populations evolve in response to host landscape structure? Each of these questions is investigated through the analysis of real data and the development of theoretical approaches. We demonstrate that the composition and the spatial structure of the host landscape greatly influence the pathogen population dynamics and evolution. The recommendations that this work could provide in order to practically manage the genetic resources will depend on the desired aim and will request further collaborative work withthe professional operators.PARIS-AgroParisTech Centre Paris (751052302) / SudocSudocFranceF

The Dark Side of Shade: How Microclimates Drive the Epidemiological Mechanisms of Coffee Berry Disease

International audienceCoffee berry disease (CBD) can cause significant coffee yield losses along with major income losses for African smallholders. Although these farmers cannot afford to purchase pesticides to control the disease, agroecological solutions have rarely been investigated, and how epidemiological mechanisms are linked to the environment of the coffee tree and the plot remains unclear. Agroforestry systems are a promising agroecological option, but the effect of shade on CBD regulation is the subject of debate, and the use of plant species diversity remains uncertain. Here, we address how shade affects epidemiological mechanisms by modifying the microclimate. For this purpose, we developed a mechanistic susceptible-exposed-infectious-removed model and used a Bayesian framework to infer the epidemiological parameters against microclimatic covariates. We show that shade has opposing effects on different epidemiological mechanisms. Specifically, shade can limit disease dynamics by reducing disease transmission while simultaneously promoting disease dynamics by reducing the latent period of the pathogen. However, in full sun, efficient disease transmission compensates for long latent periods. As a result, the balances between microclimatic variables can counterbalance the epidemiological rates, which can dramatically alter the fate of epidemics in shade versus full sun conditions. We propose research avenues to help design cost- and environmentally effective management strategies for CBD that are notably based on the functional traits of shade trees that could hamper CBD dispersal

Markov random field models for vector-based representations of landscapes

International audienceIn agricultural landscapes the spatial distribution of cultivated and semi-natural elements strongly impacts habitat connectivity and species dynamics. To allow for landscape structural analysis and scenario generation, we here develop statistical tools for real landscapes composed of geometric elements, including 2D patches but also 1D linear elements (e.g., hedges). Utilizing the framework of discrete Markov random fields, we design generative stochastic models that combine a multiplex network representation, based on spatial adjacency, with Gibbs energy terms to capture the distribution of landscape descriptors for land-use categories. We implement simulation of agricultural scenarios with parameter-controlled spatial and temporal patterns (e.g., geometry, connectivity, crop rotation), and we demonstrate through simulation that pseudo-likelihood estimation of parameters works well. To study statistical relevance of model components in real landscapes, we discuss model selection and validation, including cross-validated prediction scores. Model validation with a view toward ecologically relevant landscape summaries is achieved by comparing observed and simulated summaries (network metrics but also metrics and appropriately defined variograms using a raster discretization). Models fitted to subregions of the Lower Durance Valley (France) indicate strong deviation from random allocation and realistically capture landscape patterns. In summary, our approach improves the understanding of agroecosystems and enables simulation-based theoretical analysis of how landscape patterns shape biological and ecological processes