Abstract: Recent Monitoring Studies Have Evidenced The Added Value Of Water Stable Isotopes Composition Measurements In Air Dynamics Studies (Atmospheric Circulation), Providing An Integrated Tracer Of The Atmospheric Water Cycle. Water Vapour Isotopic Composition Carries A Fingerprint Of The Oceanic Evaporative Flux, Which Depends On Local Meteorological Conditions, And Reflects Fractionation Processes Occurring Along Atmospheric Transport Pathway And At Precipitation Sites. Our Study Is Focused On Those Processes, As Well As Snow-air Exchanges, Motivated By The Need To Better Understand The Drivers Of Water Vapour Isotope Variations, In Both Vapour Or Surface Snow, In East Antarctica. We Present Here New Data Of Water Vapor Isotopic Composition From Two Sites In East Antarctica (Dumont D’urville In Coastal Antarctica And Dome C On The East Antarctic Plateau). These Two Sites Have Different Characteristics: Dumont D’urville Is Under The Influence Of Katabatic Wind While The Flat Relief At Dome C Prevents The Existence Of Any Katabatic Wind, Dome C Is A Clear Sky, Dry And Cold Place. However, Both Sites Are However Related Since Dumont D’urville Is On The Main Pathway Of The Oceanic Air Masses Reaching Dome C. After Several Successful Summer Seasons On Both Sites, We Measure Continuously Water Stable Isotopic Composition Of Vapour In Low Atmosphere Using Laser Spectrometers, In Parallel, At Both Sites Since November 2018. We Focus Here On The First Results Obtained Since The End Of 2018 With A Comparison With Previous Summer Campaigns Obtained In Different Contexts (Presence Vs Absence Of Sea Ice On The Coastal Station).

Abstract: Predicting the epidemiological dynamics of a pathogen requires a quantitative understanding of its circulation in its host populations. However, data collection is often limited by logistical and cost constrains, notably in wild populations, sometimes impacting the capacity to infer epidemiological dynamics, especially when indirect infection data, such as serological data, are used. It is thus important to optimize field sampling designs by accounting for both the constrains and the quality of the inference. In this context, we first compare the efficiency of two approaches classically used in disease ecology to estimate the force of infection (i.e., the rate at which susceptible individuals become infected) from serological data: cross-sectional sampling from unmarked individuals and longitudinal capture-recapture setups, which generally involve more limited numbers of marked individuals due to cost and logistical constrains. Then, inspired from tools developed in the field of population ecology, we explore the benefits of integrating both cross-sectional (seroprevalences) and longitudinal (individuals histories) datasets. To do so, we use a simulation approach combined with empirical data collected in three colonial vertebrate populations: gulls exposed to the parasite Toxoplasma gondii, albatrosses exposed to the bacteria Pasteurella multocida and bats exposed a rabies virus. Our results highlight that key elements to determine optimal sampling designs are: host species life history, the degree of antibody persistence and the degree of a priori knowledge and uncertainty on demographic and epidemiologic parameters. In the case of long-lived species exposed to infectious agents resulting in persistent antibody responses, integrated designs are especially valuable. Overall, we argue that serology studies could greatly benefit from the opportunity of integrating cross-sectional and longitudinal designs and propose a statistical method to do so.