A notice has just been published advertising a two year post-doc contract for project MEEMO. The call accepts national, foreign and stateless candidates who hold a doctorate degree in areas related to Marine Ecology, Oceanography or Mathematics, and a scientific and professional curriculum suitable for the activity to be developed:
Assume the coordination of WP 1 (Explore: whale watching data as ocean sentinels), in liaison with José Azevedo, Marc Fernandez, Manuel Hidalgo and Albrecth Gnauck. Organize the MONICET data according to tidy data principles (Wickham, 2014). Identify the inherent biases of the dataset and develop routines to filter them out. Based on a literature review of which variables can potentially influence cetacean distributions (at different temporal and spatial scales), select climatological and environmental data from open repositories (such as Copernicus or ERDAS). Select the main variables based on an exploratory analysis of how their change over time correlates with the abundance patterns of the different species. Asses the spatial scale of influence of different variables applying scalograms (sensu Alvarez-Berastegui et al. 2014), noting that this scale may differ for different drivers and the impacted ecological processes, and ultimately on the temporal analyses. Apply wavelet coherence (Cazelles et al., 2008, Gnauck et al., 2010) and other time series tools to track periods of synchrony between cetacean abundance and environmental variables. This will be done following a multi-step approach, to better explain the dynamic occurrence of cetaceans according to orographic or oceanographic seascape features. The first three steps cover univariate and multivariate procedures to detect spatial structures and windows of time within the data. The remaining steps deal with dynamic statistical methods to investigate fluctuating frequencies within time series. Data check, computation of probability density functions, and statistical measures including rank correlation between cetacean's occurrences and environmental variables. Cluster analysis (agglomerative methods) to get information on the spatial structure of data. Determination of time scales and windows of change to get information on the temporal structure of the data set based on results of cluster analysis. Determination of spatio-temporal scales of occurrences of cetaceans to get information on seasonality and spatial variability by classical spectral analysis (computation of wavelet power spectra). Computation of wavelet coherence to get information on synchronicity between cetacean occurrences and environmental variables. Derivation and computation of quantitative temporal trends of cetacean occurrences. Support the management of the present MONICET platform and of its ongoing restructuring, in liaison with José Azevedo and Marc Fernandez. Be the lead person validating the data introduced by the whale watching companies during the 2019 and 2020 seasons, and producing the MONICET annual report. Contribute to the drawing and construction of the new MONICET platform and app, namely providing input to the configuration of the database and the products developed from it. Details of the call: http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=122104) Project description: https://fgf.uac.pt/en/content/meemo-keep-expand-and-explore-monicet-platform-cetacean-watching-opportunity-science-0 [cid:image003.jpg@01D5BF35.83F9DD60] José Manuel N. Azevedo Professor auxiliar (+351) 296 650 313 www.uac.pt<http://www.uac.pt> facebook.com/uac.fct<https://facebook.com/uac.fct>
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