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Auteur Morato, T.; Gonzalez-Irusta, J.-M.; Dominguez-Carrio, C.; Wei, C.-L.; Davies, A.; Sweetman, A.K.; Taranto, G.H.; Beazley, L.; Garcia-Alegre, A.; Grehan, A.; Laffargue, P.; Murillo, F.J.; Sacau, M.; Vaz, S.; Kenchington, E.; Arnaud-Haond, S.; Callery, O.; Chimienti, G.; Cordes, E.; Egilsdottir, H.; Freiwald, A.; Gasbarro, R.; Gutierrez-Zarate, C.; Gianni, M.; Gilkinson, K.; Wareham Hayes, V.E.; Hebbeln, D.; Hedges, K.; Henry, L.-A.; Johnson, D.; Koen-Alonso, M.; Lirette, C.; Mastrototaro, F.; Menot, L.; Molodtsova, T.; Duran Munoz, P.; Orejas, C.; Pennino, M.G.; Puerta, P.; Ragnarsson, S. a; Ramiro-Sanchez, B.; Rice, J.; Rivera, J.; Roberts, J.M.; Ross, S.W.; Rueda, J.L.; Sampaio, I.; Snelgrove, P.; Stirling, D.; Treble, M.A.; Urra, J.; Vad, J.; van Oevelen, D.; Watling, L.; Walkusz, W.; Wienberg, C.; Woillez, M.; Levin, L.A.; Carreiro-Silva, M.
Titre Climate-induced changes in the suitable habitat of cold-water corals and commercially important deep-sea fishes in the North Atlantic Type Article scientifique
Année 2020 Publication Revue Abrégée Glob. Change Biol.
Volume Numéro Pages
Mots-Clés calcification rates; climate change; cod gadus-morhua; cold-water corals; deep-sea; envelope models; fisheries; fishes; habitat suitability modelling; lophelia-pertusa; ocean acidification; octocorals; protected areas; scleractinian corals; scleractinians; species distribution models; species distribution models; thermal tolerance; vulnerable marine ecosystems
Résumé 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 commercially important deep-sea fish species under present-day (1951-2000) environmental conditions and to project changes under severe, high emissions future (2081-2100) climate projections (RCP8.5 scenario) for the North Atlantic Ocean. Our models projected a decrease of 28%-100% in suitable habitat for cold-water corals and a shift in suitable habitat for deep-sea fishes of 2.0 degrees-9.9 degrees towards higher latitudes. The largest reductions in suitable habitat were projected for the scleractinian coral Lophelia pertusa and the octocoral Paragorgia arborea, with declines of at least 79% and 99% respectively. We projected the expansion of suitable habitat by 2100 only for the fishes Helicolenus dactylopterus and Sebastes mentella (20%-30%), mostly through northern latitudinal range expansion. Our results projected limited climate refugia locations in the North Atlantic by 2100 for scleractinian corals (30%-42% of present-day suitable habitat), even smaller refugia locations for the octocorals Acanella arbuscula and Acanthogorgia armata (6%-14%), and almost no refugia for P. arborea. Our results emphasize the need to understand how anticipated climate change will affect the distribution of deep-sea species including commercially important fishes and foundation species, and highlight the importance of identifying and preserving climate refugia for a range of area-based planning and management tools.
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ISSN 1354-1013 ISBN Médium
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Auteur Stephenson, F.; Goetz, K.; Sharp, B.R.; Mouton, T.L.; Beets, F.L.; Roberts, J.; MacDiarmid, A.B.; Constantine, R.; Lundquist, C.J.
Titre Modelling the spatial distribution of cetaceans in New Zealand waters Type Article scientifique
Année 2020 Publication Revue Abrégée Diversity and Distributions
Volume 26 Numéro 4 Pages 495-516
Mots-Clés boosted regression tree models; cetacean distribution; New Zealand; relative environmental suitability models; spatial management; species distribution models
Résumé Aim Cetaceans are inherently difficult to study due to their elusive, pelagic and often highly migratory nature. New Zealand waters are home to 50% of the world's cetacean species, but their spatial distributions are poorly known. Here, we model distributions of 30 cetacean taxa using an extensive at-sea sightings dataset (n > 14,000) and high-resolution (1 km2) environmental data layers. Location New Zealand's Exclusive Economic Zone (EEZ). Methods Two models were used to predict probability of species occurrence based on available sightings records. For taxa with <50 sightings (n = 15), Relative Environmental Suitability (RES), and for taxa with ≥50 sightings (n = 15), Boosted Regression Tree (BRT) models were used. Independently collected presence/absence data were used for further model evaluation for a subset of taxa. Results RES models for rarely sighted species showed reasonable fits to available sightings and stranding data based on literature and expert knowledge on the species' autecology. BRT models showed high predictive power for commonly sighted species (AUC: 0.79–0.99). Important variables for predicting the occurrence of cetacean taxa were temperature residuals, bathymetry, distance to the 500 m isobath, mixed layer depth and water turbidity. Cetacean distribution patterns varied from highly localised, nearshore (e.g., Hector's dolphin), to more ubiquitous (e.g., common dolphin) to primarily offshore species (e.g., blue whale). Cetacean richness based on stacked species occurrence layers illustrated patterns of fewer inshore taxa with localised richness hotspots, and higher offshore richness especially in locales of the Macquarie Ridge, Bounty Trough and Chatham Rise. Main conclusions Predicted spatial distributions fill a major knowledge gap towards informing future assessments and conservation planning for cetaceans in New Zealand's extensive EEZ. While sightings datasets were not spatially comprehensive for any taxa, these two best available approaches allow for predictive modelling of both more common, and of rarely sighted, cetacean species with limited available information.
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Langue en Langue du Résumé Titre Original
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Volume de collection Numéro de collection Edition
ISSN 1472-4642 ISBN Médium
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Notes WOS:000510589200001 Approuvé pas de
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Auteur Schickele, A.; Leroy, B.; Beaugrand, G.; Goberville, E.; Hattab, T.; Francour, P.; Raybaud, V.
Titre Modelling European small pelagic fish distribution: Methodological insights Type Article scientifique
Année 2020 Publication Revue Abrégée Ecological Modelling
Volume 416 Numéro Pages 108902
Mots-Clés Convex hull; Pseudo-absence; Sampling bias; Small pelagic fish; Species distribution models; Uncertainty
Résumé The distribution of marine organisms is strongly influenced by climatic gradients worldwide. The ecological niche (sensu Hutchinson) of a species, i.e. the combination of environmental tolerances and resources required by an organism, interacts with the environment to determine its geographical range. This duality between niche and distribution allows climate change biologists to model potential species’ distributions from past to future conditions. While species distribution models (SDMs) have been intensively used over the last years, no consensual framework to parametrise, calibrate and evaluate models has emerged. Here, to model the contemporary (1990–2017) spatial distribution of seven highly harvested European small pelagic fish species, we implemented a comprehensive and replicable numerical procedure based on 8 SDMs (7 from the Biomod2 framework plus the NPPEN model). This procedure considers critical issues in species distribution modelling such as sampling bias, pseudo-absence selection, model evaluation and uncertainty quantification respectively through (i) an environmental filtration of observation data, (ii) a convex hull based pseudo-absence selection, (iii) a multi-criteria evaluation of model outputs and (iv) an ensemble modelling approach. By mitigating environmental sampling bias in observation data and by identifying the most ecologically relevant predictors, our framework helps to improve the modelling of fish species’ environmental suitability. Not only average temperature, but also temperature variability appears as major factors driving small pelagic fish distribution, and areas of highest environmental suitability were found along the north-western Mediterranean coasts, the Bay of Biscay and the North Sea. We demonstrate in this study that the use of appropriate data pre-processing techniques, an often-overlooked step in modelling, increase model predictive performance, strengthening our confidence in the reliability of predictions.
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Volume de collection Numéro de collection Edition
ISSN 0304-3800 ISBN Médium
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Notes WOS:000514022500015 Approuvé pas de
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Auteur Meynard, C.N.; Kaplan, D.M.; Leroy, B.
Titre Detecting outliers in species distribution data: Some caveats and clarifications on a virtual species study Type Article scientifique
Année 2019 Publication Revue Abrégée J. Biogeogr.
Volume 46 Numéro 9 Pages 2141-2144
Mots-Clés enm; observation errors; outliers; prevalence; probabilistic approach; sample bias; simulations; species distribution models; thresholds; virtual ecology; virtual species
Résumé Liu et al. (2018) used a virtual species approach to test the effects of outliers on species distribution models. In their simulations, they applied a threshold value over the simulated suitabilities to generate the species distributions, suggesting that using a probabilistic simulation approach would have been more complex and yield the same results. Here, we argue that using a probabilistic approach is not necessarily more complex and may significantly change results. Although the threshold approach may be justified under limited circumstances, the probabilistic approach has multiple advantages. First, it is in line with ecological theory, which largely assumes non-threshold responses. Second, it is more general, as it includes the threshold as a limiting case. Third, it allows a better separation of the relevant intervening factors that influence model performance. Therefore, we argue that the probabilistic simulation approach should be used as a general standard in virtual species studies.
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Langue English Langue du Résumé Titre Original
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Volume de collection Numéro de collection Edition
ISSN 0305-0270 ISBN Médium
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Notes WOS:000483602900019 Approuvé pas de
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Auteur Yates, K.L.; Bouchet, P.J.; Caley, M.J.; Mengersen, K.; Randin, C.F.; Parnell, S.; Fielding, A.H.; Bamford, A.J.; Ban, S.; Marcia Barbosa, A.; Dormann, C.F.; Elith, J.; Embling, C.B.; Ervin, G.N.; Fisher, R.; Gould, S.; Graf, R.F.; Gregr, E.J.; Halpin, P.N.; Heikkinen, R.K.; Heinanen, S.; Jones, A.R.; Krishnakumar, P.K.; Lauria, V.; Lozano-Montes, H.; Mannocci, L.; Mellin, C.; Mesgaran, M.B.; Moreno-Amat, E.; Mormede, S.; Novaczek, E.; Oppel, S.; Crespo, G.O.; Peterson, A.T.; Rapacciuolo, G.; Roberts, J.J.; Ross, R.E.; Scales, K.L.; Schoeman, D.; Snelgrove, P.; Sundblad, G.; Thuiller, W.; Torres, L.G.; Verbruggen, H.; Wang, L.; Wenger, S.; Whittingham, M.J.; Zharikov, Y.; Zurell, D.; Sequeira, A.M.M.
Titre Outstanding Challenges in the Transferability of Ecological Models Type Article scientifique
Année 2018 Publication Revue Abrégée Trends Ecol. Evol.
Volume 33 Numéro 10 Pages 790-802
Mots-Clés abundance; biotic interactions; climate-change; decision-making; distributions; habitat selection; niche; predictive models; species distribution models; temporal transferability
Résumé 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 metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.
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Langue English Langue du Résumé Titre Original
Éditeur de collection Titre de collection Titre de collection Abrégé
Volume de collection Numéro de collection Edition
ISSN 0169-5347 ISBN Médium
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