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Auteur |
CORMON, X.; LOOTS, C.; VAZ, S.; VERMARD, Y.; MARCHAL, P. |
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Titre |
Spatial interactions between saithe (Pollachius virens) and hake (Merluccius merluccius) in the North Sea |
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Article scientifique |
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Année |
2014 |
Publication |
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Revue Abrégée |
Ices Journal Of Marine Science |
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Volume |
71 |
Numéro |
6 |
Pages |
1342-1355 |
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Mots-Clés |
biotic interactions; competition; Generalized linear models; Hake; North sea; overlap; predator-prey relationship; saithe; species distribution modelling |
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Résumé |
Spatial interactions between saithe (Pollachius virens) and hake (Merluccius merluccius) were investigated in the North Sea. Saithe is a well-established species in the North Sea, while occurrence of the less common hake has recently increased in the area. Spatial dynamics of these two species and their potential spatial interactions were explored using binomial generalized linear models (GLM) applied to the International Bottom Trawl Survey (IBTS) data from 1991 to 2012. Models included different types of variables: (i) abiotic variables including sediment types, temperature, and bathymetry; (ii) biotic variables including potential competitors and potential preys presence; and (iii) spatial variables. The models were reduced and used to predict and map probable habitats of saithe, hake but also, for the first time in the North Sea, the distribution of the spatial overlap between these two species. Changes in distribution patterns of these two species and of their overlap were also investigated by comparing species' presence and overlap probabilities predicted over an early (1991–1996) and a late period (2007–2012). The results show an increase in the probability over time of the overlap between saithe and hake along with an expansion towards the southwest and Scottish waters. These shifts follow trends observed in temperature data and might be indirectly induced by climate changes. Saithe, hake, and their overlap are positively influenced by potential preys and/or competitors, which confirms spatial co-occurrence of the species concerned and leads to the questions of predator–prey relationships and competition. Finally, the present study provides robust predictions concerning the spatial distribution of saithe, hake, and of their overlap in the North Sea, which may be of interest for fishery managers. |
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1054-3139 |
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MARBEC @ isabelle.vidal-ayouba @ |
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1135 |
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Poggiale, J.-C.; Dantigny, P.; De Wit, R.; Steinberg, C. |
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Titre |
Modeling in Microbial Ecology |
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Chapitre de livre |
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Année |
2015 |
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847-882 |
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Biofilm models; Biotic interactions; Chemostat; Fermenter models; Metabolic models; Microbial Ecology; Population dynamics |
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Résumé |
The bases and the principles of modeling in microbial community ecology and biogeochemistry are presented and discussed. Several examples are given. Among them, the fermentation process is largely developed, thus demonstrating how the model allows determining the microbial population growth rate, the death rate, and the maintenance rate. More generally, these models have been used to increase the development of bioenergetic formulations which are presently used in biogeochemical models (Monod, Droop, DEB models). Different types of interactions (competition, predation, and virus–bacteria) are also developed. For each topic, a complete view of the models used in the literature cannot be presented. Consequently, the focus has been done on the demonstration how to build a model instead of providing a long list of existing models. Some recent results in sediment biogeochemistry are provided to illustrate the application of such models. |
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Springer Netherlands |
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Éditeur |
Bertrand, J.-C.; Caumette, P.; Lebaron, P.; Matheron, R.; Normand, P.; Sime-Ngando, T. |
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en |
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Environmental Microbiology: Fundamentals and Applications |
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978-94-017-9117-5 978-94-017-9118-2 |
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MARBEC @ alain.herve @ |
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1395 |
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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. |
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Titre |
Outstanding Challenges in the Transferability of Ecological Models |
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Article scientifique |
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2018 |
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Trends Ecol. Evol. |
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33 |
Numéro |
10 |
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790-802 |
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abundance; biotic interactions; climate-change; decision-making; distributions; habitat selection; niche; predictive models; species distribution models; temporal transferability |
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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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English |
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0169-5347 |
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MARBEC @ alain.herve @ |
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2447 |
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