  Streams and rivers are biodiverse and provide valuable ecosystem services.
Maintaining these ecosystems is an important task, so organisations often
monitor the status and trends in stream condition and biodiversity using field
sampling and, more recently, autonomous in-situ sensors. However, data
collection is often costly and so effective and efficient survey designs are
crucial to maximise information while minimising costs. Geostatistics and
optimal and adaptive design theory can be used to optimise the placement of
sampling sites in freshwater studies and aquatic monitoring programs.
Geostatistical modelling and experimental design on stream networks pose
statistical challenges due to the branching structure of the network, flow
connectivity and directionality, and differences in flow volume. Thus, unique
challenges of geostatistics and experimental design on stream networks
necessitates the development of new open-source software for implementing the
theory. We present SSNdesign, an R package for solving optimal and adaptive
design problems on stream networks that integrates with existing open-source
software. We demonstrate the mathematical foundations of our approach, and
illustrate the functionality of SSNdesign using two case studies involving real
data from Queensland, Australia. In both case studies we demonstrate that the
optimal or adaptive designs outperform random and spatially balanced survey
designs. The SSNdesign package has the potential to boost the efficiency of
freshwater monitoring efforts and provide much-needed information for
freshwater conservation and management.
