Use of Wind Fetch and Shoreline Relief to Predict Nearshore Substrate Composition in a North Temperate Lake

Abstract

Spawning habitat assessments often focus on substrate composition, but few studies have predicted shoal substrates by using environmental factors. We developed a model for predicting shoal substrates in Belle Lake, Minnesota, using wind fetch and shoreline relief characteristics. Percent composition of four substrate classes (silt, sand, gravel, and rock), water depth estimated at 1 m from shore (shoal slope), effective wind fetch measured using a GIS model, and riparian bank height derived from LIDAR imaging were determined at 50 transects. Classification and regression tree (CART) analysis grouped substrates into categories, and general additive modeling described the effects of three predictor variables on the percent composition of substrate classes. The CART analysis correctly grouped 39 of 50 transects into four categories, and misclassifications primarily resulted from the movement of sand. Effective fetch most influenced silt (low fetch) and rock (high fetch) substrate classes, shoal slope was predictive of rock, and riparian height was useful in distinguishing sand from gravel. These results demonstrate the utility of a single empirical model for determining shoal substrate composition. Fisheries managers can use this technique to determine potential fish spawning locations and identify potential areas for habitat restoration or protection projects. Received December 5, 2016; accepted May 5, 2017 Published online July 19, 2017

Using GIS to predict habitat in lakes: An example using nearshore substrate categories

“…In lentic systems, GIS has been used to predict aquatic macrophytes (e.g., Narumalani et al ) and spawning grounds (e.g., Muskellunge Esox masquinongy ; Nohner and Diana ). To our knowledge, Schall et al () is the only study where GIS was used to predict physical habitat (substrate distributions) within a lake. Physical habitat predictions would be most beneficial if they could be done across multiple lakes, but this would require establishing framework for quantification and prediction.…”