|
|
|
|
|
| Morato, Telmo; González‐irusta, José‐manuel; Dominguez‐carrió, Carlos; Wei, Chih‐lin; Davies, Andrew; Sweetman, Andrew K.; Taranto, Gerald H.; Beazley, Lindsay; García‐alegre, Ana; Grehan, Anthony; Laffargue, Pascal; Murillo, Francisco Javier; Sacau, Mar; Vaz, Sandrine; Kenchington, Ellen; Arnaud-haond, Sophie; Callery, Oisín; Chimienti, Giovanni; Cordes, Erik; Egilsdottir, Hronn; Freiwald, André; Gasbarro, Ryan; Gutiérrez‐zárate, Cristina; Gianni, Matthew; Gilkinson, Kent; Wareham Hayes, Vonda E.; Hebbeln, Dierk; Hedges, Kevin; Henry, Lea‐anne; Johnson, David; Koen‐alonso, Mariano; Lirette, Cam; Mastrototaro, Francesco; Menot, Lenaick; Molodtsova, Tina; Durán Muñoz, Pablo; Orejas, Covadonga; Pennino, Maria Grazia; Puerta, Patricia; Ragnarsson, Stefán Á.; Ramiro‐sánchez, Berta; Rice, Jake; Rivera, Jesús; Roberts, J. Murray; Ross, Steve W.; Rueda, José L.; Sampaio, Íris; Snelgrove, Paul; Stirling, David; Treble, Margaret A.; Urra, Javier; Vad, Johanne; Oevelen, Dick; Watling, Les; Walkusz, Wojciech; Wienberg, Claudia; Woillez, Mathieu; Levin, Lisa A.; Carreiro‐silva, Marina. |
| The deep sea plays a critical role in global climate regulation through uptake and storage of heat and carbon dioxide. However, this regulating service causes warming, acidification and deoxygenation of deep waters, leading to decreased food availability at the seafloor. These changes and their projections are likely to affect productivity, biodiversity and distributions of deep‐sea fauna, thereby compromising key ecosystem services. Understanding how climate change can lead to shifts in deep‐sea species distributions is critically important in developing management measures. We used environmental niche modelling along with the best available species occurrence data and environmental parameters to model habitat suitability for key cold‐water coral and... |
| Tipo: Text |
Palavras-chave: Climate change; Cold-water corals; Deep-sea; Fisheries; Fishes; Habitat suitability modelling; Octocorals; Scleractinians; Species distribution models; Vulnerable marine ecosystems. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00610/72211/71007.pdf |
| |
|
|
| 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... |
| Tipo: Text |
|
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00466/57728/59909.pdf |
| |
|
|
|
