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Assessing cetacean surveys throughout the Mediterranean Sea: a gap analysis in environmental space ArchiMer
Mannocci, Laura; Roberts, Jason J.; Halpin, Patrick N.; Authier, Matthieu; Boisseau, Oliver; Bradai, Mohamed Nejmeddine; Canadas, Ana; Chicote, Carla; David, Lea; Di-meglio, Nathalie; Fortuna, Caterina M; Frantzis, Alexandros; Gazo, Manel; Genov, Tilen; Hammond, Philip S.; Holcer, Drasko; Kaschner, Kristin; Kerem, Dani; Lauriano, Giancarlo; Lewis, Tim; Di Sciara, Giuseppe Notarbartolo; Panigada, Simone; Antonio Raga, Juan; Scheinin, Aviad; Ridoux, Vincent; Vella, Adriana; Vella, Joseph.
Heterogeneous data collection in the marine environment has led to large gaps in our knowledge of marine species distributions. To fill these gaps, models calibrated on existing data may be used to predict species distributions in unsampled areas, given that available data are sufficiently representative. Our objective was to evaluate the feasibility of mapping cetacean densities across the entire Mediterranean Sea using models calibrated on available survey data and various environmental covariates. We aggregated 302,481 km of line transect survey effort conducted in the Mediterranean Sea within the past 20 years by many organisations. Survey coverage was highly heterogeneous geographically and seasonally: large data gaps were present in the eastern and...
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Ano: 2018 URL: https://archimer.ifremer.fr/doc/00626/73789/75004.pdf
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Geographical differences in habitat relationships of cetaceans across an ocean basin ArchiMer
Mannocci, Laura; Roberts, Jason J.; Pedersen, Eric J.; Halpin, Patrick N..
The distributions of highly mobile marine species such as cetaceans are increasingly modeled at basin scale by combining data from multiple regions. However, these basin‐wide models often overlook geographical variations in species habitat relationships between regions. We tested for geographical variations in habitat relationships for a suite of cetacean taxa between the two sides of the North Atlantic basin. Using cetacean visual survey data and remote sensing data from the western and eastern basin in summer, we related the probability of presence of twelve cetacean taxa from three guilds to seafloor depth, sea surface temperature and primary productivity. In a generalized additive model framework, we fitted 1) basin‐wide (BW) models, assuming a single...
Tipo: Text Palavras-chave: Environmental predictors; Geographical variation; Habitat relationships; Highly mobile marine species; North Atlantic Ocean; Species distribution modeling.
Ano: 2020 URL: https://archimer.ifremer.fr/doc/00630/74163/73721.pdf
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OBIS-SEAMAP The World Data Center for Marine Mammal, Sea Bird, and Sea Turtle Distributions ArchiMer
Halpin, Patrick N.; Read, Andrew J.; Fujioka, Ei; Best, Ben D.; Donnelly, Ben; Hazen, Lucie J.; Kot, Connie; Urian, Kim; Labrecque, Erin; Dimatteo, Andrew; Cleary, Jesse; Good, Caroline; Crowder, Larry B.; Hyrenbach, K. David.
The science needed to understand highly migratory marine mammal, sea bird, and sea turtle species is not adequately addressed by individual data collections developed for a single region or single time period. These data must be brought together into a common, global map based on a coherent, interoperable, and openly accessible information system. This need was clearly articulated by the National Oceanographic Partnership Program (NOPP) and the Alfred P. Sloan Foundation when they co-sponsored a new effort to directly address this issue in 2002. The result is OBIS-SEAMAP: the world data-center for marine mammal, sea bird, and sea turtle information. OBIS-SEAMAP brings together georeferenced distribution, abundance, and telemetry data with tools to query...
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Ano: 2009 URL: https://archimer.ifremer.fr/doc/00660/77167/78557.pdf
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Outstanding Challenges in the Transferability of Ecological Models ArchiMer
Yates, Katherine L.; Bouchet, Phil J.; Caley, M. Julian; Mengersen, Kerrie; Randin, Christophe F.; Parnell, Stephen; Fielding, Alan H.; Bamford, Andrew J.; Ban, Stephen; Marcia Barbosa, A.; Dormann, Carsten F.; Elith, Jane; Embling, Clare B.; Ervin, Gary N.; Fisher, Rebecca; Gould, Susan; Graf, Roland F.; Gregr, Edward J.; Halpin, Patrick N.; Heikkinen, Risto K.; Heinanen, Stefan; Jones, Alice R; Krishnakumar, Periyadan K.; Lauria, Valentina; Lozano-montes, Hector; Mannocci, Laura; Mellin, Camille; Mesgaran, Mohsen B.; Moreno-amat, Elena; Mormede, Sophie; Novaczek, Emilie; Oppel, Steffen; Crespo, Guillermo Ortuno; Peterson, A. Townsend; Rapacciuolo, Giovanni; Roberts, Jason J.; Ross, Rebecca E.; Scales, Kylie L.; Schoeman, David; Snelgrove, Paul; Sundblad, Goran; Thuiller, Wilfried; Torres, Leigh G.; Verbruggen, Heroen; Wang, Lifei; Wenger, Seth; Whittingham, Mark J.; Zharikov, Yuri; Zurell, Damaris; Sequeira, Ana M. M..
Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability...
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Ano: 2018 URL: https://archimer.ifremer.fr/doc/00466/57728/59909.pdf
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