A Pragmatic Approach for Developing Landbase Cumulative Effects Assessments with Aggregated Impacts Crossing Multiple Ecological Values

Abstract

AbstractIn strategic cumulative effects assessments, significant methodological challenges exist for classifying and aggregating impacts when using multiple indicators to determine relative risks upon ecological values from anthropogenic developments. We present a strategic spatial modeling case study CEA (2012–2112) in a 909,000 ha forested landscape of Southwestern British Columbia. We explore decisions needed to calculate and aggregate modeled indicators of cumulative anthropogenic footprints on landscape conditions by examining the choice of quantitative methods. We compare how aggregated impact conclusions may differ for seven indicators grouped in two ways to represent three ecological values (Forest Ecosystems, Riparian Ecosystems and Species at Risk): four expert-defined policy-driven valued components (VCs) or three analytically derived environmental resource factors (ERFs). By explicitly demonstrating methodological choices at each step of impact estimation and aggregation, we outline a practical systematic approach to customize strategic CEAs of this type and retain transparency for interpreting impacts among values. Aggregated impacts for VCs appeared dominated by those estimated from “condition” indicators describing the degree of expected deviations in indicator states from desired conditions; aggregated impacts of ERFs were dominated by “pressure” indicators linked to underlying causal processes assumed important for describing changes in future ecological conditions. High spatial congruence occurred between impact statements for some VCs compared to ERFs representing the same ecological value; poor congruence between others likely occurred because they represented different ecological processes. Aggregated impact classifications may usefully signal impact severity and risk but are dependent on indicator grouping, hence choices for aggregation are integral to the assessment process.