1. Introduction
The objectives of environmental monitoring and State of the Environment reporting include assessing the state, condition, or health of environments and the detection of change with time, in the form of monotonic trends, or other temporal patterns. State or trends are frequently reported for individual sites. The power of State of Environment Reporting (SER) can be increased if spatial patterns in state or trends can be detected because this increases information about the extent of the environmental management issues being monitored. Detecting environmental patterns, however, is difficult because of the scale of the task. Environmental classifications offer an efficient method for detecting environmental patterns. Data collected from a limited set of sites is grouped to determine whether a consistent state or trend can be associated with a particular environment type or 'class'. The classification is then used to interpolate the detected state or trend to environmentally similar locations. This approach allows information about the extent of areas in a particular state, or subject to particular trends, to be inferred from the available data. An additional benefit of environmental classifications in pattern analysis is that the environmental context provided by classifications may reveal the causes of the detected patterns, thus helping direct future management effort.
SER needs to be made within a spatial and/or temporal context, and with comparison to guidelines for desirable or acceptable conditions. A spatial context allows comparisons to be made between sites (e.g. whether a site is degraded compared with 'pristine' sites?). A temporal context allows comparisons to be made over time. A context can be a combination of the spatial and temporal (e.g. whether conditions at a site have changed through time relative to reference sites). Guidelines provide a point of reference where state at a site can be compared with a criterion, which can also be combined with a spatial framework (e.g. what is the mean condition of these environment types relative to a criterion?). Thus a powerful state of environment analysis is one that determines spatial patterns in both state, relative to guidelines, and trends over time.
The River Environment Classification (REC) (Snelder and Biggs, 2002) was developed to provide a spatial framework for state of environment monitoring and reporting. It provides a mapped classification of rivers according to a hierarchy of functionally important physical characteristics or 'controlling factors'. The first three controlling factors include natural features of the landscape that cannot be influenced by human activities being carried out in the catchment. These are climate, topography (referred to by the REC as 'source of flow'), and geology. Land cover, which is closely correlated with land use, and is therefore influenced by human activities, is included as a fourth classification factor. The REC is an a priori classification, which codifies existing general knowledge of the causes of differences in physical and biological characteristics of rivers. REC is, therefore, is essentially a series of hypotheses concerning how physical patterns in rivers are reflected in patterns of water quality and biological communities. Subdividing rivers into REC classes and then examining monitoring data collected from sites in each class is a useful method for partitioning water quality and biological data. It is expected that broad differences in water quality and biological variables will occur among classes, and there should be a high degree of similarity within a class.
In this study, the relationship between REC classes and data collected at long-term water chemistry and biological monitoring sites has been examined. Our objectives were to determine the mean state in each class and to detect temporal trends. The analysis aimed to detect environmental patterns at various spatial scales. The REC has been specifically developed to examine patterns at a range of spatial scales, enabling analysis to 'zoom' from broad 'national' scale to finer 'regional' scales. The REC provides a spatial context where small patterns or 'patches' that may be insignificant at a national scale can be identified by decreasing the scale of analysis to a regional or local scale. In the analysis we used the National Rivers Water Quality Network (NRWQN) to provide data to examine patterns in state and trends for New Zealand's rivers at the national scale. To zoom the analysis to a regional scale, we used data provided by the Environment Southland Water Quality Network (ESWQN).
The specific objectives of this study were:
- To characterise patterns in the current state of rivers in New Zealand at varying spatial scales within the spatial framework provided by the REC.
- To detect spatial patterns in trends in water quality and biological variables at varying spatial scales within the spatial framework provided by the REC.
