Wildlife Infometrics Inc., #3-220 Mackenzie Blvd., Mackenzie, BC, V0J 2C0, Canada.
Ecora Engineering and Resource Group, #200-2045 Enterprise Way, Kelowna, BC, V1Y 9T5, Canada.
Environ Manage. 2022 May;69(5):1020-1034. doi: 10.1007/s00267-022-01632-9. Epub 2022 Mar 28.
In 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.
在战略性累积影响评估中，当使用多个指标来确定人为发展对生态价值的相对风险时，存在着对分类和综合影响的重大方法挑战。我们提出了一个战略性空间建模案例研究 CEA（2012-2112），该案例研究在不列颠哥伦比亚省西南部一个 909000 公顷的森林景观中进行。我们通过检查定量方法的选择，探索了计算和综合累积人为足迹模型指标的决策，这些指标反映了景观条件。我们比较了以两种方式分组的七个指标的综合影响结论可能会有何不同，这些指标代表了三种生态价值（森林生态系统、河岸生态系统和濒危物种）：四个专家定义的政策驱动的有价值的组件（VC）或三个分析得出的环境资源因素（ERF）。通过在影响估计和综合的每个步骤中明确展示方法选择，我们概述了一种实用的系统方法，用于定制这种类型的战略性 CEAs，并保持对不同价值之间的影响进行解释的透明度。VC 的综合影响似乎主要由描述指标状态从期望条件偏离程度的“条件”指标估计得出；ERF 的综合影响主要由与假设对描述未来生态条件变化很重要的潜在因果过程相关的“压力”指标主导。一些 VC 的影响陈述与代表相同生态价值的 ERF 之间具有高度的空间一致性；而其他的一致性较差，可能是因为它们代表了不同的生态过程。综合影响分类可以有效地表明影响的严重程度和风险，但取决于指标分组，因此聚合选择是评估过程的组成部分。
