<abstract xmlns="http://www.w3.org/1999/xhtml"> Records from community-based coastal management initiatives indicate that local communities who are key actors in activities that aim at safeguarding the health status of terrestrial and marine ecosystems face a lot of challenges associated with adapting and applying indicators that are scientifically abstracted and methodologically too reified, given varying social, contextual and technical conditions prevailing amongst them.This paper brings into view possible challenges of adapting and applying scientific indicators in community-based monitoring of mangrove ecosystem and suggests a new approach that may lead to development of indicators which are less reified, more congruent to users (coastal communities) and likely to attract a wider social learning in the mangrove restoration context. It also sets a bridge for scientific institutions (including universities), to understand various social, cultural and contextual needs that determine epistemological access between them and local communities, which need to be addressed prior to engaging target communities in participatory monitoring programmes.The paper attempts to analyse learning at the interface of knowledge that scientific institutions produce and the potential knowledge that exists in local context (traditional ecological knowledge) for purposes of widening and improving knowledge sharing and safeguarding the health status of mangrove species and fisheries that use them as key habitats.The paper stems from a study which employs processes of abstraction and experiential learning techniques such as Experiential Learning Intervention Workshop carried out in 2012, to unlock knowledge that local communities have, as an input for underlabouring existing scientific indicators in the eastern coast of Tanzania.It brings into view the need to consider contextual realities on ground, the level of education that the participating group has, the minimum level of participation that is required, structures that govern coastal monitoring practices at local level and the need for scientific institutions to consider the knowledge that local people have as an input for enhancing or improving coastal monitoring, especially monitoring of mangrove and fishery resources.The paper finally comes up with a framework of indicators which is regarded by coastal communities as being less reified, more contextually and culturally congruent and which can easily be used in detecting environmental trends, threats, changes and conditions of mangrove and fisheries resources, and attract wider social learning processes.</abstract>

Records from community-based coastal management initiatives indicate that local communities who are key actors in activities that aim at safeguarding the health status of terrestrial and marine ecosystems face a lot of challenges associated with adapting and applying indicators that are scientifically abstracted and methodologically too reified, given varying social, contextual and technical conditions prevailing amongst them.This paper brings into view possible challenges of adapting and applying scientific indicators in community-based monitoring of mangrove ecosystem and suggests a new approach that may lead to development of indicators which are less reified, more congruent to users (coastal communities) and likely to attract a wider social learning in the mangrove restoration context. It also sets a bridge for scientific institutions (including universities), to understand various social, cultural and contextual needs that determine epistemological access between them and local communities, which need to be addressed prior to engaging target communities in participatory monitoring programmes.The paper attempts to analyse learning at the interface of knowledge that scientific institutions produce and the potential knowledge that exists in local context (traditional ecological knowledge) for purposes of widening and improving knowledge sharing and safeguarding the health status of mangrove species and fisheries that use them as key habitats.The paper stems from a study which employs processes of abstraction and experiential learning techniques such as Experiential Learning Intervention Workshop carried out in 2012, to unlock knowledge that local communities have, as an input for underlabouring existing scientific indicators in the eastern coast of Tanzania.It brings into view the need to consider contextual realities on ground, the level of education that the participating group has, the minimum level of participation that is required, structures that govern coastal monitoring practices at local level and the need for scientific institutions to consider the knowledge that local people have as an input for enhancing or improving coastal monitoring, especially monitoring of mangrove and fishery resources.The paper finally comes up with a framework of indicators which is regarded by coastal communities as being less reified, more contextually and culturally congruent and which can easily be used in detecting environmental trends, threats, changes and conditions of mangrove and fisheries resources, and attract wider social learning processes.

Analysing Learning at the Interface of Scientific and Traditional Ecological Knowledge in a Mangrove Ecosystem Restoration Scenario in the Eastern Coast of Tanzania

Environmental Learning Research Centre (ELRC), Rhodes University, South Africa

Transylvanian Review of Systematical and Ecological Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Records from community-based coastal management initiatives indicate that local communities who are key actors in activities that aim at safeguarding the...

Analysing Learning at the Interface of Scientific and Traditional Ecological Knowledge in a Mangrove Ecosystem Restoration Scenario in the Eastern Coast of Tanzania

Publié en ligne: 30 déc. 2014

Pages: 185 - 210

DOI: https://doi.org/10.2478/trser-2013-0027

© 2015

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.