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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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ISSN (up) 0169-5347 ISBN Médium
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Numéro d'Appel MARBEC @ alain.herve @ collection 2447
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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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ISSN (up) 0304-3800 ISBN Médium
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Notes WOS:000514022500015 Approuvé pas de
Numéro d'Appel MARBEC @ isabelle.vidal-ayouba @ collection 2675
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Auteur Benedetti, F.; Vogt, M.; Righetti, D.; Guilhaumon, F.; Ayata, S.-D.
Titre Do functional groups of planktonic copepods differ in their ecological niches? Type Article scientifique
Année 2018 Publication Revue Abrégée J. Biogeogr.
Volume 45 Numéro 3 Pages 604-616
Mots-Clés climate-change; copepods; species distribution models; north-atlantic; calanus-finmarchicus; mediterranean sea; environmental niche; functional groups; lipid pump; marine ecosystem; oithona-similis; pseudo-absences; trait biogeography; zooplankton; zooplankton fecal pellets
Résumé Aim: To assess the degree of overlap between the environmental niches of marine planktonic copepods and test if the distribution of copepod functional groups differs across environmental gradients. Location: The Mediterranean Sea. Methods: Functional groups were defined based on clustering of functional traits in 106 marine copepod species using a multivariate ordination analysis. Functional traits included maximum body length, feeding mode, spawning strategy and trophic group. Simultaneously, the global distribution of the species was used to model their environmental niches with six environmental variables. For each of these predictors, four niche parameters were derived from the univariate response curve of each species to summarise their environmental preferences and ordinate the species in niche space through a PCA. Finally, the differences in the position in niche space of functional groups were tested with variance analysis. Results: We identified seven copepod functional groups with different distributions along the environmental gradients covered by our study. While carnivorous functional groups were affiliated with oligotrophic and tropical conditions, large and small current-feeding herbivores are associated with colder, more seasonally varying and productive conditions. Small cruising detritivores and other small current-feeding herbivores were not affiliated with specific conditions as their constituting species were scattered in niche space. Main conclusions: Since copepod functional groups occupy distinct ecological niches, ecosystem processes related to these groups are expected to vary across environmental gradients. Conditions favouring large current-feeding herbivores should allow for enhanced fluxes of energy and nutrients through Mediterranean Sea ecosystems, while such fluxes should be weakened where large carnivores and small passive ambush-feeding copepods dominate. Our study supports the development of trait-based zooplankton functional groups in marine ecosystem models.
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ISSN (up) 0305-0270 ISBN Médium
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Numéro d'Appel MARBEC @ alain.herve @ collection 2311
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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
Éditeur de collection Titre de collection Titre de collection Abrégé
Volume de collection Numéro de collection Edition
ISSN (up) 0305-0270 ISBN Médium
Région Expédition Conférence
Notes WOS:000483602900019 Approuvé pas de
Numéro d'Appel MARBEC @ isabelle.vidal-ayouba @ collection 2640
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Auteur Authier, M.; Saraux, C.; Péron, C.
Titre Variable selection and accurate predictions in habitat modelling: a shrinkage approach Type Article scientifique
Année 2017 Publication Revue Abrégée Ecography
Volume 40 Numéro 4 Pages 549-560
Mots-Clés account; distributional data; Ecology; indian-ocean; inference; Mediterranean Sea; regression methods; small pelagic fish; spatial autocorrelation; species distribution models
Résumé Habitat modelling is increasingly relevant in biodiversity and conservation studies. A typical application is to predict potential zones of specific conservation interest. With many environmental covariates, a large number of models can he investigated but multi-model inference may become impractical. Shrinkage regression overcomes this issue by dealing with the identification and accurate estimation of effect size for prediction. In a Bayesian framework we investigated the use of a shrinkage prior, the Horseshoe, for variable selection in spatial generalized linear models (GLM). As study cases, we considered 5 datasets on small pelagic fish abundance in the Gulf of Lion (Mediterranean Sea, France) and 9 environmental inputs. We compared the predictive performances of a simple kriging model, a full spatial GLM model with independent normal priors for regression coefficients, a full spatial GLM model with a Horseshoe prior for regression coefficients and 2 zero-inflated models (spatial and non-spatial) with a Horseshoe prior. Predictive performances were evaluated by cross validation on a hold-out subset of the data: models with a Horseshoe prior performed best, and the full model with independent normal priors worst. With an increasing number of inputs, extrapolation quickly became pervasive as we tried to predict from novel combinations of covariate values. By shrinking regression coefficients with a Horseshoe prior, only one model needed to be fitted to the data in order to obtain reasonable and accurate predictions, including extrapolations.
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Langue English Langue du Résumé Titre Original
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Volume de collection Numéro de collection Edition
ISSN (up) 0906-7590 ISBN Médium
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Numéro d'Appel MARBEC @ alain.herve @ collection 2130
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