STATISTICAL TREATMENT AND COMPARATIVE-ANALYSIS OF SCALE-DEPENDENT AQUATIC TRANSECT DATA IN ESTUARINE LANDSCAPES

Estuarine ecosystem dynamics have evolved around and respond to landsc ape-level influences that are dynamic in space and time. The estuarine water column is effectively the physical and biologial integrator of these landscape inputs. In this paper, we present a floating window An alysis of Covariance (ANCOVA) technique to statistically compare and c ontrast aquatic transect data that were taken at different times and u nder different tidal conditions, yet were geographically parallel and spatially articulate. The floating window ANCOVA compared two transect s by testing whether the means of the dependent variable were signific antly different while also testing whether the slopes of patterns in t he dependent variable were significantly different. By varying the siz e of the floating window where the ANCOVA was run, we were able to exa mine how scale affected the magnitude and spatial pattern of that vari able. The percentages of total models run, at a given window size, tha t generated significantly different magnitudes (means) and patterns (s lopes) in the dependent variable were referred to as the ''degree of d issimilarity''. Plots of window size versus degree of dissimilarity el ucidated temporal and spatial variability in water column parameters a t a range of scales. The advantages of this new statistical method in relation to traditional spatial statistics are discussed. We demonstra ted the efficacy of the floating window ANCOVA method by comparing chl orophyll and salinity transect data taken at the North Inlet, SC estua ry during flooding and ebbing tides in Winter, Spring, and Summer 1991 . Chlorophyll concentrations represented the biological characteristic s of the estuarine water column and salinity represented the physical processes affecting that water column. We found total dissimilarity in the magnitude of salinity data from one season to the next at all sca les, but inter-seasonal similarity in spatial patterns over both short (hourly) and long (monthly) time scales. We also found a large season al dissimilarity in the magnitude of chlorophyll levels, as expected. Spatial patterns in phytoplankton biomass (as chlorophyll concentratio ns) appeared to be largely controlled by the physical processes repres ented with the salinity data. Often, we observed greater dissimilarity in biological and physical parameters from one tide to the next [on a given day] than from one season to the next. In these cases, the grea test flood-ebb differences were associated with landscape-level influe nces - from rivers and the coastal ocean - that varied greatly with di rection of tidal flow. We are currently using spatially articulate aqu atic transect data and the floating window ANCOVA technique to validat e spatial simulation models at different scales. By using this variabl e-scale statistical technique to determine coherence between the actua l transect data and model output from simulations run at different sca les, we will test hypotheses about the scale-dependent relationships b etween data resolution and model predictability in landscape analysis.