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Abiotic Factors That Affect The Distribution of Aquatic Macrophytes In Shallow Lakes Located In Sibley County, Minnesota, USA: A Spatial Modeling Approach

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Abstract Macrophytes are an integral component of lake communities, therefore understanding the factors that affect macrophyte community structure is important for conservation and management of lakes. In Sibley County, Minnesota, USA, five lakes were surveyed using the point-intercept method. At each point the presence of macrophytes were recorded, water depth was measured, and a sediment sample was collected. Sediment samples were quantified by determining soil particle size and percent organic matter. The richness of macrophytes in all lakes were modeled via generalized linear regression with six explanatory variables: water depth, distance from shore, percent sand, percent silt, percent clay, and percent sediment organic matter. If model residuals were spatially autocorrelated, then a geographically weighted regression was used. Water depth and distance from shore were negatively related to mean species richness, and silt was either negatively or positively related to species richness depending on the lake and macrophytes present. All species richness models had pseudo-R2 values between 0.25 and 0.40. Curlyleaf pondweed (Potamogeton crispus) was related to with water depth, percent silt, and percent sediment organic matter during early season surveys.
Title: Abiotic Factors That Affect The Distribution of Aquatic Macrophytes In Shallow Lakes Located In Sibley County, Minnesota, USA: A Spatial Modeling Approach
Description:
Abstract Macrophytes are an integral component of lake communities, therefore understanding the factors that affect macrophyte community structure is important for conservation and management of lakes.
In Sibley County, Minnesota, USA, five lakes were surveyed using the point-intercept method.
At each point the presence of macrophytes were recorded, water depth was measured, and a sediment sample was collected.
Sediment samples were quantified by determining soil particle size and percent organic matter.
The richness of macrophytes in all lakes were modeled via generalized linear regression with six explanatory variables: water depth, distance from shore, percent sand, percent silt, percent clay, and percent sediment organic matter.
If model residuals were spatially autocorrelated, then a geographically weighted regression was used.
Water depth and distance from shore were negatively related to mean species richness, and silt was either negatively or positively related to species richness depending on the lake and macrophytes present.
All species richness models had pseudo-R2 values between 0.
25 and 0.
40.
Curlyleaf pondweed (Potamogeton crispus) was related to with water depth, percent silt, and percent sediment organic matter during early season surveys.

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