Scale of analysis drives the observed ratio of spatial to non-spatial variance in microbial water quality: insights from two decades of citizen science data

Abstract

While fecal indicator bacteria (FIB) testing is used to monitor surface waters for potential health hazards, recent studies report substantial variation in FIB levels and that observed variation appeared dependent on scale of analysis (SOA). Citizen science data and random effects models were used to quantify variance in FIB levels attributable to spatial versus temporal factors. Separately, Bayesian models were used to quantify the ratio of spatial to nonspatial variance in FIB levels, and identify associations between environmental factors and FIB levels. Separate analyses were performed for each stream, lake, and watershed as well as at the state-level (three SOAs). As SOA increased (from waterway to watershed to statewide models), variance attributable to spatial sources generally increased and variance attributable to temporal sources generally decreased. While relationships between FIB levels and environmental factors, such as flow conditions, were constant across SOA, the effect of land cover was both highly dependent on SOA and consistently smaller than the effect of outfalls. This study demonstrates the importance of scale when designing monitoring programs or using FIB monitoring to inform management. Moreover, these data represent a comprehensive survey of water quality in Upstate NY, and the study highlights a valuable publicly available database.