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Améliorer les connaissances sur les processus écologiques régissant les dynamiques de populations d'auxiliaires de culture : modélisation couplant paysages et populations pour l'aide à l'échantillonnage biologique dans l'espace et le temps

Abstract : A promising alternative to the chemical control of pests consists in favoring their natural enemies populations by managing the agricultural landscape structure. Identifying favorable spatio-temporal structures can be performed through the exploration of landscape scenarios using coupled models of landscapes and population dynamics. In this approach, population dynamics are simulated on virtual landscapes with controlled properties, and the observation of population patterns allows for the identification of favorable structures. Population modeling however relies on a good knowledge about the ecological processes and their variability within the landscape elements. Current state of knowledge about the ecological mechanisms underlying natural enemies’ of the carabid family population dynamics remains a major obstacle to in silico investigation of favorable landscape scenarios. Literature about the relationship between carabid population and landscape properties allows the formulation of competing hypotheses about these processes. Reducing the number of these hypotheses by analyzing the convergence between their associated population patterns and investigating the stability of their convergence along a landscape gradient appears to be a necessary tep towards a better knowledge about ecological processes. In a first step, we propose a heuristic method based on the simulation of reaction-diffusion models carrying these competing hypotheses. Comparing the population patterns allowed to set a model typology according to their response to the landscape variable, through a classification algorithm, thus reducing the initial number of competing hypotheses. The selection of the most likely hypothesis from this irreducible set must rely on the observation of population patterns on the field. This implies that population patterns are described with spatial and temporal resolutions that are fine enough to select a unique hypothesis among the ones in competition. In the second part, we propose a heuristic method that allows determining a priori sampling strategies that maximize the robustness of ecological hypotheses selection. The simulation of reaction-diffusion models carrying the ecological hypotheses allows to generate virtual population data in space and time. These data are then sampled using strategies differing in the total effort, number of sampling locations, dates and landscape replicates. Population patterns are described from these samples. The sampling strategies are assessed through a classification algorithm that classifies the models according to the associated patterns. The analysis of classification performances, i.e. the ability of the algorithm to discriminate the ecological processes, allows the selection of optimal sampling designs. We also show that the way the sampling effort is distributed between its spatial and temporal components is strongly impacting the ecological processes inference. Reducing the number of competing ecological hypotheses, along with the selection of sampling strategies for optimal model inference both meet a strong need in the process of knowledge improvement about the ecological processes for the exploration of landscape scenarios favoring ecosystem services. In the last chapter, we discuss the implications and future prospects of our work.
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https://tel.archives-ouvertes.fr/tel-01887527
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Submitted on : Thursday, October 4, 2018 - 10:50:26 AM
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Benoit Bellot. Améliorer les connaissances sur les processus écologiques régissant les dynamiques de populations d'auxiliaires de culture : modélisation couplant paysages et populations pour l'aide à l'échantillonnage biologique dans l'espace et le temps. Sciences agricoles. Université Rennes 1, 2018. Français. ⟨NNT : 2018REN1B008⟩. ⟨tel-01887527⟩

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