Invasive Species Compendium

Detailed coverage of invasive species threatening livelihoods and the environment worldwide

Abstract

Propagating uncertainty from catchment experiments to estimates of streamflow reduction by invasive alien plants in southwestern South Africa.

Abstract

Long-term catchment experiments from South Africa have demonstrated that afforestation of grasslands and shrublands significantly reduces surface-water runoff. These results have guided the country's forestry policy and the implementation of a national Invasive Alien Plant (IAP) control programme for the past few decades. Unfortunately, woody IAP densities continue to increase, compounding existing threats to water security from population growth and climatic change. Decision makers need defensible estimates of the impacts of afforestation or invasions on runoff to weigh up alternative land use options, or guide investment of limited resources into ecosystem restoration through IAP clearing versus engineering-based water-augmentation schemes. Existing attempts to extrapolate the impacts observed in catchment afforestation experiments to broad-scale IAP impacts give no indication of uncertainty. Globally, the uncertainty inherent in the results from paired-catchment experiments is seldom propagated into subsequent analyses making use of these data. We present a fully reproducible Bayesian model that propagates uncertainty from input data to final estimates of changes in streamflow when extrapolating from catchment experiments to broader landscapes. We apply our model to South Africa's catchment experiment data, estimating streamflow losses to plantations and analogous plant invasions in the catchments of southwestern South Africa, including uncertainty. We estimate that regional streamflow is reduced by 304 million m3 or 4.14% annually as a result of IAPs, with an upper estimate of 408 million m3 (5.54%) and a lower estimate of 267 million m3 (3.63%). Our model quantifies uncertainty associated with all parameters and their contribution to overall uncertainty, helping guide future research needs. Acknowledging and quantifying inherent uncertainty enables more defensible decisions regarding water resource management.