Suchergebnisse

Résumé La critique écoféministe, en ses différentes dimensions (épistémologique, morale et sociale), vise à révéler que les conséquences de la longue prédominance masculine dans les différents champs où elle a pu s'exercer ont éminemment une signification écologique : en sciences, où les savoirs et les méthodes ont été conçus selon un modèle qui se révèle impuissant à saisir la nature des problèmes environnementaux ; en morale, où paternalisme et anthropocentrisme échangent leurs caractères ; dans les contextes sociaux, où la subordination économique et politique des femmes fait de ces dernières à la fois les premières victimes de la dégradation de l'environnement et bien souvent des actrices-clefs de la mise en œuvre des mesures de protection.

Virginie Maris, philosophe de l'environnement, revient dans cet entretien sur sa rencontre, pendant son doctorat, avec un texte majeur de l'écoféminisme et explique la façon dont cette lecture a transformé son travail depuis lors. Partant de la philosophie analytique pour venir à la technicité de la notion de biodiversité et aux enjeux politiques des sciences de la conservation, son parcours de recherche s'inscrit dans les débats vifs autour de la notion d'anthropocène. Cette conversation retrace notamment l'importance de la théorisation par Val Plumwood de la logique du dualisme et du « modèle du maître » dans la lutte contre la destruction du monde vivant. Virginie Maris souligne la portée philosophique de la distinction entre dualisme et différenciation et la façon dont cela a joué un rôle dans l'élaboration de son ouvrage défendant « la part sauvage du monde » comme altérité radicale du milieu naturel.

The present paper analyses the links between Payments for Environmental Services (PES) and biodiversity conservation in developing countries. We first discuss some of the inherent complexities and uncertainties when linking biodiversity to ecosystem services and the related inconsistencies to deal with ecosystem services monitoring, quantification and biodiversity economic valuation. We then apply such theoretical framework in 11 biodiversity PES field projects to evaluate the impacts on biodiversity conservation and rural development. We find that PES designed for conserving ecosystem biodiversity with no concrete species targeting, require less monitoring and payments are done on a per surface basis. Biodiversity PES projects targeting concrete species require more monitoring and control, and payments are done on a per family basis. These projects have not addressed the polemic of the lack of consistent links between ecosystem functions and biodiversity, and have adopted a practical approach where biodiversity depends on land uses or targets specific species threaten by extinction. We finally suggest some design features to better adjust PES to local needs while coping with forthcoming socio-economic challenges.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

Réponses et adaptations aux changements globaux : quels enjeux pour la recherche sur la biodiversité ? Prospective de recherche.