Abstract. Invasive species continue to pose major challenges for managing coupled human-environmental systems. Predictive tools are essential to maximize invasion monitoring and conservation efforts in regions reliant on abundant freshwater resources to sustain economic welfare, social equity, and ecological services. Past studies have revealed biotic and abiotic heterogeneity, along with human activity, can account for much of the spatial variability of aquatic invaders; however, improvements remain. This study was created to (1) examine the distribution of aquatic invasive species richness (AISR) across 126 lakes in the Adirondack Region of New York; (2) develop and compare global and local models between lake and landscape characteristics and AISR; and (3) use geographically weighted regression (GWR) to evaluate non-stationarity of local relationships, and assess its use for prioritizing lakes at risk to invasion. The evaluation index, AISR, was calculated by summing the following potential aquatic invaders for each lake: Asian Clam (Corbicula fluminea), Brittle Naiad (Najas minor), Curly-leaf Pondweed (Potamogeton crispus), Eurasian Watermilfoil (Myriophyllum spicatum), European Frog-bit (Hydrocharis morsus-ranae), Fanwort (Cabomba caroliniana), Spiny Waterflea (Bythotrephes longimanus), Variable-leaf Milfoil (Myriophyllum heterophyllum), Water Chestnut (Trapa natans), Yellow Floating Heart (Nymphoides peltata), and Zebra Mussel (Dreissena polymorpha). The Getis-Ord Gi à statistic displayed significant spatial hot and cold spots of AISR across Adirondack lakes. Spearman's rank (q) correlation coefficient test (r s ) revealed urban land cover composition, lake elevation, relative patch richness, and abundance of game fish were the strongest predictors of aquatic invasion. Five multiple regression global Poisson and GWR models were made, with GWR fitting AISR very well (R 2 = 76-83%). Local pseudo-t-statistics of key explanatory variables were mapped and related to AISR, confirming the importance of GWR for understanding spatial relationships of invasion. The top 20 lakes at risk to future invasion were identified and ranked by summing the five GWR predictive estimates. The results inform that inexpensive and publicly accessible lake and landscape data, typically available from digital repositories within local environmental agencies, can be used to develop predictions of aquatic invasion with remarkable agreement. Ultimately, this transferable modeling approach can improve monitoring and management strategies for slowing the spread of invading species.

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This research utilized surficial sediment core sample data that were collected in 1969/1973 and 2002 from Lake Huron as part of the Environment Canada Great Lakes Sediment Assessment Program. Concentrations for mercury and lead were analyzed due their persistence in the lake ecosystem and their detrimental environmental effects. The analysis area included the main basin of Lake Huron, Georgian Bay, and the North Channel. Comprehending overall pollution levels strictly on the basis of point data is a difficult task, however spatial analysis techniques combined with Geographic Information Systems can be used to gain a better understanding of lake-wide trends. The Geostatistical Analyst extension of the ESRI ArcGIS software was used to carry out ordinary kriging analyses on the datasets. They produced statistically valid concentration estimates with log-normal data transformation procedures occasionally being performed to obtain suitable prediction estimates. Geospatial analysis (including kriging) allows for samples that vary in number and location to be analyzed and compared with each other based on areal estimates. Overall decreases in contamination levels were observed between the historical and contemporary surveys. Mercury has seen a dramatic reduction in concentrations from 1969/1973 to 2002, while the lead results indicate that high levels of contamination (compared to background concentrations) still persist in the contemporary dataset, although they have subsided from historic values. Higher contaminant concentrations were generally found in depositional basins. The interpolated kriging surfaces are more informative than i.e. conventional dot and/or proportional circle maps in the amount of information they present. They also provide an increased understanding of both the spatial distribution and temporal trends in sediment contamination in Lake Huron.

This research analyzed sediment contamination concentrations for mercury and lead in Lakes Ontario and Erie using a GIS-based kriging approach. Environment Canada provided sediment survey data for Lake Ontario (1968 and 1998) and Lake Erie (1971 and 1997/98). Collation and mapping of point measurement data without the application of interpolation methods does not allow for spatial data trends to be fully analyzed. The kriging technique enables the creation of interpolated prediction surfaces, with the advantage that the results can be statistically validated. Although data normality is not required, the kriging results for the historical datasets suggest that it may be desirable, as statistical validity was reduced due to some individual stations having very high contaminant concentrations. Three of the four models developed for the 1997/98 data were statistically valid. For both lakes, the more recent data reveal reduced concentrations of mercury and lead, and there has been an overall reduction in contamination levels. However, sediments in some areas still exceeded Canadian sediment quality guidelines. The areas of greatest sediment contamination in Lake Ontario were within the major depositional basins, presumably as a result of historical industrial activities in watersheds along the southern and western shoreline including the Niagara River. In Lake Erie, areas of greatest sediment contamination continue to be located in the western and south central portions of the lake in proximity to the Detroit River and major urban/industrial centres.