Primary Faculty Mentor Name

Clayton Williams

Status

Undergraduate

Student College

Rubenstein School of Environmental and Natural Resources

Program/Major

Environmental Sciences

Primary Research Category

Food & Environment Studies

Secondary Research Category

Biological Sciences

Presentation Title

Accounting for Spatial Autocorrelation in Great Lake Coastal Wetland Ecological Responses

Time

1:00 PM

Location

Silver Maple Ballroom - Biological Sciences

Abstract

The Great Lakes Coastal Wetland Monitoring Program (CWMP) collects wetland biota, habitat, and water quality in order to provide information on the health of the Great Lakes. My research used CWMP data on fish, birds, amphibians, wetland vegetation, aquatic macroinvertebrates, and water quality from 185 wetlands across the Great Lakes collected during the peak growing season of 2016, 2017, and 2018. My research goal was to determine if wetland vegetation cover helped shape the water quality of the wetland. However, in order to investigate these connections, I needed to first overcome the spatial influence in the data. The project data was manipulated using Microsoft Excel and R in order remove data gaps and inconsistencies, then ArcGIS was used to determine the best way to account for spatial autocorrelation in the data. Spatial autocorrelation can be used along with spatial data to make accurate generalizations about relatedness over a greater area, the idea being that close points are more similar than further ones. This is generally a good geospatial tool to use when gaps are present in the data. For the CWMP data, however, it presents a challenge because of the diverse nature of the Great Lake ecosystem and strong spatial gradient of ecosystem quality from Lake Superior (North) to Lake Ontario (Southeast). As such, to investigate connections between wetland, water quality, and vegetation across wetlands, the within lake spatial patterns must be accounted for. In order to accurately gauge the health of the wetlands across the study area, my research attempts to account for spatial autocorrelation in these data. The final products of this research will be maps of the study wetlands and the Great Lakes that display how location influences ecological relationships across the Great Lakes coastal wetlands.

This document is currently not available here.

Share

COinS
 

Accounting for Spatial Autocorrelation in Great Lake Coastal Wetland Ecological Responses

The Great Lakes Coastal Wetland Monitoring Program (CWMP) collects wetland biota, habitat, and water quality in order to provide information on the health of the Great Lakes. My research used CWMP data on fish, birds, amphibians, wetland vegetation, aquatic macroinvertebrates, and water quality from 185 wetlands across the Great Lakes collected during the peak growing season of 2016, 2017, and 2018. My research goal was to determine if wetland vegetation cover helped shape the water quality of the wetland. However, in order to investigate these connections, I needed to first overcome the spatial influence in the data. The project data was manipulated using Microsoft Excel and R in order remove data gaps and inconsistencies, then ArcGIS was used to determine the best way to account for spatial autocorrelation in the data. Spatial autocorrelation can be used along with spatial data to make accurate generalizations about relatedness over a greater area, the idea being that close points are more similar than further ones. This is generally a good geospatial tool to use when gaps are present in the data. For the CWMP data, however, it presents a challenge because of the diverse nature of the Great Lake ecosystem and strong spatial gradient of ecosystem quality from Lake Superior (North) to Lake Ontario (Southeast). As such, to investigate connections between wetland, water quality, and vegetation across wetlands, the within lake spatial patterns must be accounted for. In order to accurately gauge the health of the wetlands across the study area, my research attempts to account for spatial autocorrelation in these data. The final products of this research will be maps of the study wetlands and the Great Lakes that display how location influences ecological relationships across the Great Lakes coastal wetlands.