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Une question fondamentale en écologie des populations est l’identification des facteurs influençant la dynamique d’une population. L’objectif principal de cette thèse est de déterminer quelles sont les réponses démographiques et populationnelles de prédateurs marins supérieurs face aux fluctuations d’abondance de leurs proies, aux variations climatiques, à la densité-dépendance tout en tenant compte de l’hétérogénéité inter et intra-individuelle (âge, expérience, sexe, qualité ou stratégie). Pour ce faire, nous nous baserons sur l’analyse de suivis à long-terme individuels et populationnels d’oiseaux marins longévifs et prédateurs apicaux phylogénétiquement très proches dans deux biomes contrastés : le labbe de McCormick Catharacta maccormicki sur l’archipel de Pointe Géologie en Antarctique et le labbe subantarctique Catharacta lonnbergi sur l’archipel des Kerguelen en milieu subantarctique. Nous tirerons parti d’estimations d’abondances de leurs proies respectives : le manchot Adélie Pygoscelis adeliae et le manchot empereur Aptenodytes forsteri en Antarctique, et le pétrel bleu Halobaena caerulea et le prion de Belcher Pachyptila belcheri à Kerguelen. Ces jeux de données offrent une opportunité unique de pouvoir déterminer et quantifier simultanément les différentes sources de variabilité dans les changements de taille de populations naturelles occupant l’un des niveaux trophiques les plus élevés des réseaux alimentaires antarctiques et subantarctiques. Nous avons mis en évidence de la variation dans plusieurs traits vitaux des deux populations influencées par les performances des individus et de l’hétérogénéité individuelle latente. Nous discutons des mécanismes par lesquels la variabilité climatique, l’abondance de proie et la densité de population peuvent affecter différentiellement les différentes classes d’âges de chaque trait vital, et les conséquences potentielles de futurs changements environnementaux. / A fundamental endeavor in population ecology is to identify the drivers of population dynamics. The main objective of this thesis is to determine what are the demographic and population responses of superior marine predators to the fluctuations of their prey abundance, to climatic variations, to density-dependence while taking into account inter and intra individual heterogeneity (age, experience, sex, quality or strategy). To do this, we analysed long-term individual and population-based monitoring of long-lived seabirds and phylogenetically close apical predators in two contrasting biomes: the south polar skua Catharacta maccormicki at Pointe Géologie archipelago, Antarctica, and the brown skua Catharacta lonnbergi on the sub-Antarctic Kerguelen Archipelago. We will use direct abundance of their respective prey: Adélie penguin Pygoscelis adeliae and emperor penguin Aptenodytes forsteri in Antarctica, and the blue petrel Halobaena caerulea and the thin-billed prion Pachyptila belcheri prion in Kerguelen islands. These datasets provide a unique opportunity to simultaneously disentangle and quantify the different sources of variability driving variation in natural populations occupying one of the highest trophic levels of the Antarctic and sub-Antarctic food webs. We found variation in several vital traits of both populations influenced by individual performance and latent individual heterogeneity. We discuss the mechanisms by which climatic variability, prey abundance, and population density can differentially affect the different age classes of each age class, and the potential consequences of future environmental changes.

Range, areas of concentrated activity, and dispersal characteristics for juvenile Steller sea lions Eumetopias jubatus in the endangered western population (west of 144° W in the Gulf of Alaska) are poorly understood. This study quantified space use by analyzing post-release telemetric tracking data from satellite transmitters externally attached to n = 65 juvenile (12-25 months; 72.5 to 197.6 kg) Steller sea lions (SSLs) captured in Prince William Sound (60°38'N -147°8'W) or Resurrection Bay (60°2'N -149°22'W), Alaska, from 2003-2011. The analysis divided the sample population into 3 separate groups to quantify differences in distribution and movement. These groups included sex, the season when collected, and the release type (free ranging animals which were released immediately at the site of capture, and transient juveniles which were kept in captivity for up to 12 weeks as part of a larger ongoing research program). Range-use was first estimated by using the minimum convex polygon (MCP) approach, and then followed with a probabilistic kernel density estimation (KDE) to evaluate both individual and group utilization distributions (UDs). The LCV method was chosen as the smoothing algorithm for the KDE analysis as it provided biologically meaningful results pertaining to areas of concentrated activity (generally, haulout locations). The average distance traveled by study juveniles was 2,131 ± 424 km. The animals mass at release (F[subscript 1, 63] = 1.17, p = 0.28) and age (F[subscript 1, 63] = 0.033, p = 0.86) were not significant predictors of travel distance. Initial MCP results indicated the total area encompassed by all study SSLs was 92,017 km², excluding land mass. This area was heavily influenced by the only individual that crossed over the 144°W Meridian, the dividing line between the two distinct population segments. Without this individual, the remainder of the population (n = 64) fell into an area of 58,898 km². The MCP area was highly variable, with a geometric average of 1,623.6 km². Only the groups differentiated by season displayed any significant difference in area size, with the Spring/Summer (SS) groups MCP area (Mdn = 869.7 km²) being significantly less than that of the Fall/Winter (FW) group (Mdn = 3,202.2 km²), U = 330, p = 0.012, r = -0.31. This result was not related to the length of time the tag transmitted (H(2) = 49.65, p = 0.527), nor to the number of location fixes (H(2) = 62.77, p = 0.449). The KDE UD was less variable, with 50% of the population within a range of 324-1,387 km2 (mean=690.6 km²). There were no significant differences in area use associated with sex or release type (seasonally adjusted U = 124, p = 0.205, r = -0.16 and U = 87, p = 0.285, r = -0.13, respectively). However, there were significant differences in seasonal area use: U = 328, p = 0.011, r = -0.31. There was no relationship between the UD area and the amount of time the tag remained deployed (H(2) = 45.30, p = 0.698). The kernel home range (defined as 95% of space use) represented about 52.1% of the MCP range use, with areas designated as "core" (areas where the sea lions spent fully 50% of their time) making up only about 6.27% of the entire MCP range and about 11.8% of the entire kernel home range. Area use was relatively limited – at the population level, there were a total of 6 core areas which comprised 479 km². Core areas spanned a distance of less than 200 km from the most western point at the Chiswell Islands (59°35'N -149°36'W) to the most eastern point at Glacier Island (60°54'N -147°6'W). The observed differences in area use between seasons suggest a disparity in how juvenile SSLs utilize space and distribute themselves over the course of the year. Due to their age, this variation is less likely due to reproductive considerations and may reflect localized depletion of prey near preferred haul-out sites and/or changes in predation risk. Currently, management of the endangered western and threatened eastern population segments of the Steller sea lion are largely based on population trends derived from aerial survey counts and terrestrial-based count data. The likelihood of individuals to be detected during aerial surveys, and resulting correction factors to calculate overall population size from counts of hauled-out animals remain unknown. A kernel density estimation (KDE) analysis was performed to delineate boundaries around surveyed haulout locations within Prince William Sound-Kenai Fjords (PWS-KF). To closely approximate the time in which population abundance counts are conducted, only sea lions tracked during the spring/summer (SS) months (May 10-August 10) were chosen (n = 35). A multiple state model was constructed treating the satellite location data, if it fell within a specified spatiotemporal context, as a re-encounter within a mark-recapture framework. Information to determine a dry state was obtained from the tags time-at-depth (TAD) histograms. To generate an overall terrestrial detection probability 1) The animal must have been within a KDE derived core-area that coincided with a surveyed haulout site 2) it must have been dry and 3) it must have provided at least one position during the summer months, from roughly 11:00 AM-5:00 PM AKDT. A total of 10 transition states were selected from the data. Nine states corresponded to specific surveyed land locations, with the 10th, an "at-sea" location (> 3 km from land) included as a proxy for foraging behavior. A MLogit constraint was used to aid interpretation of the multi-modal likelihood surface, and a systematic model selection process employed as outlined by Lebreton & Pradel (2002). At the individual level, the juveniles released in the spring/summer months (n = 35) had 85.3% of the surveyed haulouts within PWS-KF encompass KDE-derived core areas (defined as 50% of space use). There was no difference in the number of surveyed haulouts encompassed by core areas between sexes (F[subscript 1, 33] << 0.001, p = 0.98). For animals held captive for up to 12 weeks, 33.3% returned to the original capture site. The majority of encounter probabilities (p) fell between 0.42 and 0.78 for the selected haulouts within PWS, with the exceptions being Grotto Island and Aialik Cape, which were lower (between 0.00-0.17). The at-sea (foraging) encounter probability was 0.66 (± 1 S.E. range 0.55-0.77). Most dry state probabilities fell between 0.08-0.38, with Glacier Island higher at 0.52, ± 1 S.E. range 0.49-0.55. The combined detection probability for hauled-out animals (the product of at haul-out and dry state probabilities), fell mostly between 0.08-0.28, with a distinct group (which included Grotto Island, Aialik Cape, and Procession Rocks) having values that averaged 0.01, with a cumulative range of ≈ 0.00-0.02 (± 1 S.E.). Due to gaps present within the mark-recapture data, it was not possible to run a goodness-of-fit test to validate model fit. Therefore, actual errors probably slightly exceed the reported standard errors and provide an approximation of uncertainties. Overall, the combined detection probabilities represent an effort to combine satellite location and wet-dry state telemetry and a kernel density analysis to quantify the terrestrial detection probability of a marine mammal within a multistate modeling framework, with the ultimate goal of developing a correction factor to account for haulout behavior at each of the surveyed locations included in the study. / Graduation date: 2013

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