Local Western Australian commercial beekeeper livelihoods depend on ecosystem services generated from healthy, mature native flora. Commercial beekeepers rely on local ecological knowledge developed through migratory practices to enhance productivity, yet their concerns over environmental decline have been reticently received by local scientists and land managers. International experts confronting the global biodiversity crisis highlight the potential of engaging diverse knowledge systems to develop an enriched picture of ecosystem health and decline. To achieve this, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services recommends a Multiple Evidence Base approach (MEB), but identify that novel tools to support knowledge integration, validation and synthesis are required. This case study employed a pragmatic and reflexive approach to develop methods that weaved together diverse knowledge systems of beekeepers, scientists and land managers to develop an enriched picture of the situational concerns of local beekeepers.
To this end, geospatial technologies assisted field data collection across the case study region. Data was primarily collected using traditional qualitative methods such as interviews, focus groups and in-field photography. A priori generation of geospatial data supported focus group discussions at long term biodiversity monitoring sites. Post hoc use of historic satellite imagery validated the accuracy and significance of oral accounts of fire regimes. This then supported development of vignettes demonstrating causal mechanisms contributing to observed declines in environmental health. A GIS platform was instrumental in the synthesis and analysis of data collected in the field as well as for the communication of results. There were a number of technical and logistical considerations identified through the process of integrating geospatial technologies in a more traditional qualitative research design. This presentation will elucidate the pros and cons of using geospatial data and platforms to support collection, validation and synthesis of diverse knowledge systems as part of an MEB approach.