Existing frameworks for the statistical analysis of spatial survey data offer a clear workflow towards the estimation of absolute and relative abundance of wildlife, in association with present and future environmental profiles (whether naturally or anthropogenically effected). At the same time, more broadly in applied ecology, there is a keen interest in integrated analysis and adaptive resource management. Momentum behind these ideas is encouraging the incorporation of different sources of spatial information onto a single, joint inference framework, so that statistical power can be greatly enhanced, even if the data themselves cannot be directly pooled because of their qualitative differences. The present project used systematic literature review, expert knowledge on survey methodology, bespoke model development and sensitivity analyses on realistic simulation data to derive methodological and quantitative guidelines for best practice in conducting such joint inference for multi-platform seabird survey data. We subdivide our recommendations into six distinct categories.