Gestions des populations de grands cormoran (Phalacrocorax carbo) séjournant en France

*INRA, Station d'Hydrobiologie Lacustre Thonon-les-Bains (FRA) Diffusion du document : INRA, Station d'Hydrobiologie Lacustre Thonon-les-Bains (FRA)National audienc

Asymptotic properties of infinite Leslie matrices

The stable population theory is classically applicable to populations in which there is a maximum age after which individuals die. Demetrius [1972. On an infinite population matrix. Math. Biosci. 13, 133137] extended this theory to infinite Leslie matrices, in which the longevity of individuals is potentially infinite. However, Demetrius had to assume that the survival probability per time step tends to 0 with age. We generalise here the conditions of application of the stable population theory to infinite Leslie matrix models and apply these results to two examples, including or not senescence.La théorie des populations stables s'applique habituellement à des populations possédant un âge maximal au-delà duquel tous les individus meurent. Demetrius [1972. On an infinite population matrix. Math. Biosci. 13, 133137] a généralisé cette théorie à des matrices de Leslie infinies, dans lesquelles la longévité des individus est potentiellement infinie. Néanmoins, pour obtenir ces résultats, Demetrius dut supposer que la probabilité de survie par pas de temps tendait vers 0 quand l'âge tendait vers l'infini. Nous généralisons ici les conditions dans lesquelles la théorie des populations stables est applicable et appliquons ces résultats à deux exemples, incluant ou non de la sénescence

Potential of branching processes as a modeling tool for conservation biology

[Departement_IRSTEA]GT [TR1_IRSTEA]32 - GECOTER / ECOSYLVExtrait de documentThe aim of this chapter is to introduce a class of extinction models, called Discrete Time Branching Processes (BP), and to present mathematical results about them that are useful in the context of population extinction. In particular, we emphasize a paradoxical form of stability when ultimate extinction is certain, called quasi-stationarity, which provides a clear conceptual background to the interplay of persistence and extinction. Quasi-stationarity is often implicit in many PVAs, especially in relation to a geometric probability distribution of time to extinction (Goodman 1987, Woolfenden and Fitzpatrick 1991, Gabriel and Bürger 1992). Although quasi-stationarity has already been explicitly used in some stochastic finite state population models (e.g., Verboom et al. 1991, Day and Possingham 1995), BPs are among the simplest individual-based, infinite state models in which quasi-stationarity can be studied formally. We hope, in turn, to convince the reader that BPs are suitable for playing a theoretical and practical role in the study of population extinction similar to that of matrix models (e.g., Caswell 1989) in the study of population growth. Our chapter is organized as follows: after having first recalled the general features of BPs (section 2), we consider in section 3 the simplest case of density-independent growth, and introduce the key notion of quasi-stationarity. In section 4, we investigate BP that account for an age structure and apply such a BP to a population of White Storks. We then introduce density dependence and random environment to a BP, first separately, then simultaneously, together with an age structure (5). Finally, we discuss the relevance of BPs as extinction models

Potential of branching processes as a modeling tool for conservation biology

[Departement_IRSTEA]GT [TR1_IRSTEA]32 - GECOTER / ECOSYLVExtrait de documentThe aim of this chapter is to introduce a class of extinction models, called Discrete Time Branching Processes (BP), and to present mathematical results about them that are useful in the context of population extinction. In particular, we emphasize a paradoxical form of stability when ultimate extinction is certain, called quasi-stationarity, which provides a clear conceptual background to the interplay of persistence and extinction. Quasi-stationarity is often implicit in many PVAs, especially in relation to a geometric probability distribution of time to extinction (Goodman 1987, Woolfenden and Fitzpatrick 1991, Gabriel and Bürger 1992). Although quasi-stationarity has already been explicitly used in some stochastic finite state population models (e.g., Verboom et al. 1991, Day and Possingham 1995), BPs are among the simplest individual-based, infinite state models in which quasi-stationarity can be studied formally. We hope, in turn, to convince the reader that BPs are suitable for playing a theoretical and practical role in the study of population extinction similar to that of matrix models (e.g., Caswell 1989) in the study of population growth. Our chapter is organized as follows: after having first recalled the general features of BPs (section 2), we consider in section 3 the simplest case of density-independent growth, and introduce the key notion of quasi-stationarity. In section 4, we investigate BP that account for an age structure and apply such a BP to a population of White Storks. We then introduce density dependence and random environment to a BP, first separately, then simultaneously, together with an age structure (5). Finally, we discuss the relevance of BPs as extinction models

Principles and interest of GOF tests for multistates capture-recapture models

Principios e interés de los test Bondad de Ajuste (GOF) para los modelos de captura¿recaptura multiestado.¿ Los procedimientos óptimos de bondad de ajuste, aplicados a los modelos multiestado, son nuevos. Trazando un paralelismo con los correspondientes procesos de uniestado, presentamos sus particularidades y mostramos como el test general puede descomponerse en componentes susceptibles de ser interpretados. Todos los desarrollos teóricos están ilustrados con una aplicación del ya clásico estudio de los desplazamientos de la barnacla canadiense entre sus lugares de invernada. Mediante esta aplicación, presentamos un ejemplo de cómo los componentes susceptibles de ser interpretados nos proporcionan una idea de los datos que nos pueden llevar a la elección de un modelo general apropiado, pero también a veces a la invalidación de los modelos de multiestados en su conjunto. Se menciona entonces el método para calcular un factor de corrección de la sobredispersión. Aprovechamos esta ocasión para intentar también desmitificar algunas nociones estadísticas, como las Estadísticas Suficientes Mínimas, introduciéndolas intuitivamente. La conclusión es que estas pruebas deberían considerarse una parte importante del propio análisis, contribuyendo a la comprensión de los datos, de un modo que el modelaje paramétrico no siempre consigue