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Keywords:

  • Cumulative effects assessment;
  • Framework;
  • Regression trees;
  • Stream monitoring

Abstract

Increased land use intensity has been shown to adversely affect aquatic ecosystems. Multiple landscape stressors interact over space and time, producing cumulative effects. Cumulative Effects Assessment (CEA) is the process of evaluating the impact a development project may have on the ecological surroundings, but several challenges exist that make current approaches to cumulative effects assessment ineffective. The main objective of this study was to compare results of different methods used to link landscape stressors with stream responses in a highly developed watershed, where past work has shown that the river has experienced significant water quality and quantity changes to improve approaches to CEA. The study site was the lower reaches of the Athabasca River, Canada that have been subjected to a diverse range of intense anthropogenic developments since the late 1960s. Linkages between landscape change and river response were evaluated using correlation analyses, stepwise, multiple regression, and regression trees. Notable landscape changes include increased industrial development and forest cut-blocks, made evident from satellite imagery and supporting ancillary data sets. Simple regression analyses showed water use was closely associated with total phosphorus (TP) and Na+ concentrations, as well as specific conductance. The regression trees for total organic carbon (TOC), TP, and Na+ showed that the landscape variables that appear as the first characteristic were the same variables that showed significant relations for their respective simple regression models. Simple, stepwise, and multiple regressions in conjunction with regression trees were useful in this study for capturing the strongest associations between landscape stressors and river response variables. The results highlight the need for improved scaling methods and monitoring strategies crucial to managing cumulative effects to river systems. Integr Environ Assess Manag 2013;9:392–404. © 2012 SETAC