Prioritizing ecological restoration among sites in multi-stressor landscapes

Most ecosystems are impacted by multiple local and long-distance stressors, many of which interact in complex ways. We present a framework for prioritizing ecological restoration efforts among sites in multi-stressor landscapes. Using a simple model, we show that both the economic and sociopolitical costs of restoration will typically be lower at sites with a relatively small number of severe problems than at sites with numerous lesser problems. Based on these results, we propose using cumulative stress and evenness of stressor impact as complementary indices that together reflect key challenges of restoring a site to improved condition. To illustrate this approach, we analyze stressor evenness across the world's rivers and the Laurentian Great Lakes. This exploration reveals that evenness and cumulative stress are decoupled, enabling selection of sites where remediating a modest number of high-intensity stressors could substantially reduce cumulative stress. Just as species richness and species evenness are fundamental axes of biological diversity, we argue that cumulative stress and stressor evenness constitute fundamental axes for identifying restoration opportunities in multi-stressor landscapes. Our results highlight opportunities to boost restoration efficiency through strategic use of multi-stressor datasets to identify sites that maximize ecological response per stressor remediated. This prioritization framework can also be expanded to account for the feasibility of remediation and the expected societal benefits of restoration projects.