Impacts of invasive Australian acacias: implications for management and restoration : Australian acacias: linking impacts and restoration

There is increasing interest in developing better predictive tools and a broader conceptual framework to guide the restoration of degraded land. Traditionally, restoration efforts have focused on re-establishing historical disturbance regimes or abiotic conditions, relying on successional processes to guide the recovery of biotic communities. However, strong feedbacks between biotic factors and the physical environment can alter the efficacy of these successional-based management efforts. Recent experimental work indicates that some degraded systems are resilient to traditional restoration efforts owing to constraints such as changes in landscape connectivity and organization, loss of native species pools, shifts in species dominance, trophic interactions and/or invasion by exotics, and concomitant effects on biogeochemical processes. Models of alternative ecosystem states that incorporate system thresholds and feedbacks are now being applied to the dynamics of recovery in degraded systems and are suggesting ways in which restoration can identify, prioritize and address these constraints.

Different components of global environmental change are often studied and managed independently, but mounting evidence points towards complex non-additive interaction effects between drivers of native species decline. Using the example of interactions between land-use change and biotic exchange, we develop an interpretive framework that will enable global change researchers to identify and discriminate between major interaction pathways. We formalise a distinction between numerically mediated versus functionally moderated causal pathways. Despite superficial similarity of their effects, numerical and functional pathways stem from fundamentally different mechanisms of action and have fundamentally different consequences for conservation management. Our framework is a first step toward building a better quantitative understanding of how interactions between drivers might mitigate or exacerbate the net effects of global environmental change on biotic communities in the future.