SSNdesign—An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks

Abstract

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, 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. Geostatistical models for stream network data and their unique features already exist. Some basic theory for experimental design in stream environments has also previously been described. However, open source software that makes these design methods available for aquatic scientists does not yet exist. To address this need, 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 implemented in existing open-source software packages. The SSNdesign package has the potential to boost the efficiency of freshwater monitoring efforts and provide much-needed information for freshwater conservation and management.

Integrating spatial stream network models and environmental DNA to estimate current and future distributions of nonnative Smallmouth Bass

“…This is not overly surprising because of the nature of eDNA transport in rivers, but also, our data set contained many site pairs in close proximity (1 km) to identify the upstream-edge boundaries in tributaries. Furthermore, quantification of spatial autocorrelation in our eDNA data set adds valuable information to the body of literature focused on optimally designing sampling on river networks (Som et al 2014;Pearse et al 2020;Carraro et al 2021). Our sites were most correlated at hydrological distances ≤10 km, implying sampling at finer intervals for species distribution modeling may be redundant.…”

Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management

“…Furthermore, the optimal placement of sensors will optimize simulations, improving performance in the light of low computational costs [233]. In streamflows, the efficiency of distributed computing has been used to enhance the monitoring system by optimizing adaptive design [234]. However, some obstacles of water bodies change over time and their remote location has determined the monitoring efficiency of the network from different sensor locations [235].…”

Assessing the ability of adaptive designs to capture trends in hard coral cover

“…Adaptive design has also been used in video surveillance applications to adjust the sampling rates of cameras based on the present circumstances (Jain & Chang, 2004; Jiao et al, 2004), and in habitat monitoring to change sampling frequencies depending on observed behavior (Mainwaring et al, 2002). Only a few studies have used adaptive designs in other fields (Morgan et al, 2014), and, in particular, adaptive design has received little attention in ecological and environmental monitoring (Falk et al, 2014; Pearse et al, 2020). Despite the fact that adaptive ecological monitoring has seen some interest, the approaches that have been proposed tend to rely on changes in survey designs as new problems occur (Lindenmayer et al, 2011) rather than using adaptive design to update the survey design based on feedback between models and the data being collected, to efficiently estimate specific quantities of interest.…”