Using Generalized Additive Models to Assess, Explore and Unify Environmental Monitoring Datasets

An on-going challenge for decision makers is the interpretation of temporal trends from monitoring data given that environmental processes often generate complex data that are multivariate and potentially nonlinear. Generalized additive models (GAMs) is a wellsuited modelling framework for uncovering such trends and unifying datasets. This approach allows flexible specification of regression splines to represent the functional relationships between a response variable (the parameter of interest) and a suite of temporal and spatial covariates that can be continuous or discrete using a link function and smooth functions of the covariates. We highlight the utility of using GAMs through three case studies. The first highlights the use of a GAM to unify the findings of an established longterm water quality-monitoring program with those of a focused short-term monitoring program. In the second, a GAM is used to evaluate the spatial patterns in a biomonitoring dataset whilst simultaneously accounting for variability in oyster size, which can have a confounding effect on such data. The final case study focuses on a 12 month continuous monitoring program of oceanographic data as part of an evaluation of the environmental conditions for a desalination plant intake pipe. The context for these studies is predominantly water quality in the coastal zone, however the benefits and widespread application to other research areas is clearly evident.