Auswahl der wissenschaftlichen Literatur zum Thema „Écologie pélagique – Modèles mathématiques“
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Zeitschriftenartikel zum Thema "Écologie pélagique – Modèles mathématiques"
BIDDULPH, George Elliot, Yannick Enock BOCKO, Pierre BOLA, Bart CREZEE, Greta DARGIE, Ovide EMBA, Selena GEORGIOU et al. „Connaissances actuelles et orientations futures des recherches sur le complexe de tourbières de la Cuvette centrale du Congo“. BOIS & FORETS DES TROPIQUES 355 (01.03.2023): 73–86. http://dx.doi.org/10.19182/bft2023.355.a37152.
Der volle Inhalt der QuelleDissertationen zum Thema "Écologie pélagique – Modèles mathématiques"
Amemou, Yago Ya Hilaire. „Modélisation biophysique de la dispersion et de la croissance des larves de sardinelles dans le Golfe de Guinée“. Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS185.
Der volle Inhalt der QuelleThe causes and dynamics of the ivoiro-ghanean upwelling have recently been investigated. The northern Gulf of Guinea zone supports a fishery of small pelagic fish where the most abundant the Sardinella. aurita (S. aurita). The success of the recruitment of the early stages of the larvae into juveniles is essentially conditioned by the local circulation which allows either to bring the eggs and larvae closer to the often coastal nurseries where the juveniles can develop, or either to disperse them offshore where they are subject to predation and lack of food. The mechanisms by which environmental conditions act to regulate the recruitment of S. aurita remain poorly understood in the northern Gulf of Guinea region, especially the potential interaction between dispersal, growth and mortality of larvae. The main objective of the thesis is to study the impact of food availability on the growth and the dispersion of sardinella larvae during their dispersal phase using a numerical modeling approach that allows to integrate fundamental hydrodynamic, biogeochemical and biological processes (CROCO-PISCES, ICHTHYOP). The results showed that coastal areas are the most favorable for retention because of the presence of eddies which act as barriers preventing eggs and larvae advection offshore. The most important spawning takes place during the small (February) and large (August) upwelling season. The maximum retention depths are the surface layers (0-25 m) and subsurface (25-50 m). These maximum laying depths associated with the maximum distribution of the simulated prey fields and the decrease in the intensity of the Guinea Current at depth
Mercier, Pascal. „Etude des relations espèces-environnement et analyse de la co-structure d'un couple de tableaux“. Lyon 1, 1991. http://www.theses.fr/1991LYO10140.
Der volle Inhalt der QuelleLaurence, Edward. „La résilience des réseaux complexes“. Doctoral thesis, Université Laval, 2020. http://hdl.handle.net/20.500.11794/66989.
Der volle Inhalt der QuelleReal complex systems are often driven by external perturbations toward irreversible transitions of their dynamical state. With the rise of the human footprint on ecosystems, these perturbations will likely become more persistent so that characterizing resilience of complex systems has become a major issue of the 21st century. This thesis focuses on complex systems that exhibit networked interactions where the components present dynamical states. Studying the resilience of these networks demands depicting their dynamical portraits which may feature thousands of dimensions. In this thesis, three contrasting methods are introduced for studying the dynamical properties as a function of the network structure. Apart from the methods themselves, the originality of the thesis lies in the wide vision of resilience analysis, opening with model-based approaches and concluding with data-driven tools. We begin by developing an exact solution to binary cascades on networks (forest fire type) and follow with an optimized algorithm. Because its practical range is restricted to small networks, this method highlights the limitations of using model-based and highly dimensional tools. Wethen introduce a dimension reduction method to predict dynamical bifurcations of networked systems. This contribution builds up on theoretical foundations and expands possible applications of existing frameworks. Finally, we examine the task of extracting the structural causesof perturbations using machine learning. The validity of the developed tool is supported by an extended numerical analysis of spreading, population, and neural dynamics. The results indicate that subtle dynamical anomalies may suffice to infer the causes of perturbations. It also shows the leading role that machine learning may have to play in the future of resilience of real complex systems.
Chiarello, Ernest. „Pyramide stochastique et écologie du paysage : modélisation des structures spatiales par images de synthèse“. Lyon 1, 1994. http://www.theses.fr/1994LYO10078.
Der volle Inhalt der QuelleFaivre, Bruno. „Coexistence et écologie de deux espèces jumelles d'hippolais“. Dijon, 1992. http://www.theses.fr/1992DIJOS014.
Der volle Inhalt der QuelleLeger, Jean-Benoist. „Modelling the topology of ecological bipartite networks with statistical models for heterogeneous random graphs“. Paris 7, 2014. http://www.theses.fr/2014PA077185.
Der volle Inhalt der QuelleAn ecological network is a representation of the whole set of interactions between species in a given context. Ecological scientists analyse the topological structure of such networks, in order to understand the underlying processes. The identification of sub-groups of highly-interacting species (usually called communities, or compartments) is an important stream of research. The most popular method for the search of communities in ecological networks is the modularity optimization method. However this popularity is more due to the first paper published on this topic than to a rational choice based on solid grounds. There are many other clustering methods that could be used to delimit communities in ecological networks. The analysis of complex networks is indeed a rapidly growing topic with many applications in several scientific fields. To our knowledge, no comparison of different clustering methods is available in the case of ecological networks. Here we reviewed the whole set of methods available for clustering networks and we compared them using an ecological benchmark. In order to assess the relative contribution of several processes to the network structure, we integrated exogenous information in the clustering model. We analysed two bipartite antagonistic networks with this method, a tree-fungus and tree-insect network. The results are still preliminary but the method seems to us very promising for future ecological studies. Finally we searched communities in a different kind of network, a mating network between individuals belonging to two hybridizing tree species. We used our results to discuss a concept which is central in ecology, the species concept
Blanc, Laurence. „Données spatio-temporelles en écologie et analyses multitableaux : examen d'une relation“. Lyon 1, 2000. http://www.theses.fr/2000LYO10044.
Der volle Inhalt der QuelleEl, Maksoud Walid. „Estimation de fonctions de sélection des ressources : échantillonnage et analyse de données“. Thesis, Université Laval, 2011. http://www.theses.ulaval.ca/2011/28356/28356.pdf.
Der volle Inhalt der QuelleBeffy, Jean-Luc. „Approche des structures ternaires en écologie par l'analyse en composantes principales à trois modes“. Lyon 1, 1993. http://www.theses.fr/1993LYO10057.
Der volle Inhalt der QuelleChevenet, François. „Un environnement coopératif de résolution de problèmes pour l'analyse statistique en écologie“. Lyon 1, 1994. http://www.theses.fr/1994LYO10074.
Der volle Inhalt der QuelleBücher zum Thema "Écologie pélagique – Modèles mathématiques"
Pavé, Alain. Modélisation en biologie et en écologie. Lyon: Aléas, 1994.
Den vollen Inhalt der Quelle findenUntangling ecological complexity: The macroscopic perspective. Chicago, Ill: University of Chicago Press, 1999.
Den vollen Inhalt der Quelle findenPilkey, Orrin H. Useless arithmetic: Why environmental scientists can't predict the future. New York: Columbia University Press, 2009.
Den vollen Inhalt der Quelle findenA primer of ecology. Sunderland, Mass: Sinauer Associates, 1995.
Den vollen Inhalt der Quelle findenA primer of ecology. 2. Aufl. Sunderland, Mass: Sinauer Associates, 1998.
Den vollen Inhalt der Quelle findenA primer of ecology. 4. Aufl. Sunderland, Mass: Sinauer Associates, 2008.
Den vollen Inhalt der Quelle findenJ, Hurst Christon, Hrsg. Modeling the environmental fate of microorganisms. Washington, DC: American Society for Microbiology, 1991.
Den vollen Inhalt der Quelle findenFrédéric, Briand, und Newman Charles M, Hrsg. Community food webs: Data and theory. Berlin: Springer-Verlag, 1990.
Den vollen Inhalt der Quelle findenT, Neumann, Hrsg. Introduction to the modelling of marine ecosystems. Boston: Elsevier, 2004.
Den vollen Inhalt der Quelle findenRosemary, Hails, Hrsg. An introduction to ecological modelling: Putting practice into theory. Oxford [England]: Blackwell Science, 1997.
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