275
Views
7
CrossRef citations to date
0
Altmetric
MANAGEMENT BRIEF

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

, , &
Pages 935-942 | Received 05 Dec 2016, Accepted 04 May 2017, Published online: 20 Jul 2017
 

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

ACKNOWLEDGMENTS

We thank the Minnesota Department of Natural Resources and the Long-Term Monitoring Program for providing equipment and personnel assistance. Additionally, we appreciate the Legislative-Citizen Commission on Minnesota Resources for allocating funds to make this research possible.

Log in via your institution

Log in to Taylor & Francis Online

There are no offers available at the current time.

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.