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Aubin, J., Callier, M., Rey-Valette, H., Mathe, S., Wilfart, A., Legendre, M., et al. (2019). Implementing ecological intensification in fish farming: definition and principles from contrasting experiences. Rev. Aquac., 11(1), 149–167.
Résumé: Ecological intensification is a new concept in agriculture that addresses the double challenge of maintaining a level of production sufficient to support needs of human populations and respecting the environment in order to conserve the natural world and human quality of life. This article adapts this concept to fish farming using agroecological principles and the ecosystem services framework. The method was developed from the study of published literature and applications at four study sites chosen for their differences in production intensity: polyculture ponds in France, integrated pig and pond polyculture in Brazil, the culture of striped catfish in Indonesia and a recirculating salmon aquaculture system in France. The study of stakeholders' perceptions of ecosystem services combined with environmental assessment through Life Cycle Assessment and Emergy accounting allowed development of an assessment tool that was used as a basis for co-building evolution scenarios. From this experience, ecological intensification of aquaculture was defined as the use of ecological processes and functions to increase productivity, strengthen ecosystem services and decrease disservices. It is based on aquaecosystem and biodiversity management and the use of local and traditional knowledge. Expected consequences for farming systems consist of greater autonomy, efficiency and better integration into their surrounding territories. Ecological intensification requires territorial governance and helps improve it from a sustainable development perspective.
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Authier, M., Saraux, C., & Péron, C. (2017). Variable selection and accurate predictions in habitat modelling: a shrinkage approach. Ecography, 40(4), 549–560.
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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