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. This article is protected by copyright. All rights reserved

Biological and geophysical feedbacks with fire in the Earth system

Roughly 3% of the Earth's land surface burns annually, representing a critical exchange of energy and matter between the land and atmosphere via combustion. Fires range from slow smouldering peat fires, to low-intensity surface fires, to intense crown fires, depending on vegetation structure, fuel moisture, prevailing climate, and weather conditions. While the links between biogeochemistry, climate and fire are widely studied within Earth system science, these relationships are also mediated by fuels—namely plants and their litter—that are the product of evolutionary and ecological processes. Fire is a powerful selective force and, over their evolutionary history, plants have evolved traits that both tolerate and promote fire numerous times and across diverse clades. Here we outline a conceptual framework of how plant traits determine the flammability of ecosystems and interact with climate and weather to influence fire regimes. We explore how these evolutionary and ecological processes scale to impact biogeochemical and Earth system processes. Finally, we outline several research challenges that, when resolved, will improve our understanding of the role of plant evolution in mediating the fire feedbacks driving Earth system processes. Understanding current patterns of fire and vegetation, as well as patterns of fire over geological time, requires research that incorporates evolutionary biology, ecology, biogeography, and the biogeosciences

Allometric equations for integrating remote sensing imagery into forest monitoring programmes

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able – for the first time – to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed – specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large-scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation model