A dynamic modeling tool to anticipate the effectiveness of invasive plant control and restoration recovery trajectories in South African fynbos

Abstract

Invasive alien plants negatively impact ecosystems, necessitating intricate management actions. In a critically endangered vegetation type within the fynbos biome of South Africa, a study was performed comparing different management interventions over plots invaded by Acacia saligna. A dynamic modeling approach was designed to analyze field data and simulate the effectiveness of several restoration methods. Field data for vegetation recovery rates over the course of 2 years were fed into the model, which allowed the extrapolation of multiple recovery trajectories over a long time-span, not possible to obtain from traditional short-term field surveys. Our model simulations show that different treatments in similarly degraded states at the time of clearing can result in vastly different recovery trajectories. Active seed sowing was initially most expensive but resulted in the most successful native shrub recovery, decreasing costs of longer-term follow-up acacia clearing. Clearing without burning was cheapest but resulted in limited establishment of both native and acacia cover, providing an opportunity for secondary invasion by alien forbs. In this case, biotic thresholds may have been crossed which prevented recovery of certain vegetation components. Active sowing can partially reverse thresholds by restoring shrub cover but not structural diversity. Therefore, even applying this treatment did not resemble vegetation structure of the reference condition after an extended period of 30 years, but does show how restoration can be improved by native seed sowing compared to passive restoration alone. Our model simulations provide a useful tool to support decision-making by providing management recommendations for optimizing alien plant-clearing protocols.

Active seed sowing can overcome constraints to passive restoration of a critically endangered vegetation type

“…Therefore it appears that none of the tested treatments will necessarily facilitate restoration of a pristine ecosystem even in the long-term. This is supported by the results from having developed a model using this data to predict long-term recovery trajectories (Hall et al, 2020).…”

Scaling up restoration efforts by simulating the effects of fire to circumvent prescribed burns when preparing restoration sites in South African fynbos ecosystems

“…Selection of species and sowing rates used in the seed mix were guided mostly by seed availability. However, the species' significance in the ecosystem's structure and functioning was also considered as recommended by specialists or guided by literature (Frischie et al, 2020;Hall et al, 2017Hall et al, , 2021 (Appendix S1). So too were methods and areas of seed collection, cleaning and storage (Appendix S1).…”

“…An experimental area of 3-ha deemed as low restoration potential and long-invaded (more than 70 years) with Acacia saligna was delineated on a portion of the reserve and subdivided into three 1-ha blocks. The area was pre-surveyed for density, cover and richness of both alien and native species to indicate its restoration potential and degree of invasion before and after felling acacia stands (Hall et al, 2021). In March 2019, the area was cleared of alien biomass by subcontractors from the City of Cape Town (CoCT)'s Biodiversity Management Branch's Invasive Species Unit.…”

“…Meanwhile, the acacias sheltered recovering natives from wind erosion and desiccation by creating microsites of windbreaking and hydro lifting. Working on the same site, Hall et al (2021) indicated that high density acacia recruitment within 6 months post fire, resulted in very poor recovery of native vegetation. Consequently, follow-up clearing was required after 6 months together with follow-up sowing (Hall, 2018).…”

A Dynamic Modeling Framework to Evaluate the Efficacy of Control Actions for a Woody Invasive Plant, Hakea sericea

“…Dynamic models can capture the complexity of interactions among key ecological components by combining environmental conditions, effects of time, and stochastic factors that are difficult to understand otherwise (Jørgensen, 1999). Therefore, this type of models has the potential to support decision-making in IAS management (Santos et al, 2015;Büyüktahtakın and Haight, 2018), for example, for risk evaluation (Guisan et al, 2013), spread dynamics (Ferrari et al, 2014;Martins et al, 2016), management effectiveness (Hyder et al, 2008;Chalak et al, 2011;Portela et al, 2020) of IAS, and restoration of invaded ecosystems (Hall et al, 2020). System dynamics (SD) is a process-based and problem-oriented modeling able to study, analyze, and visualize the behavior of complex systems, in which time is a critical component (Mashaly and Fernald, 2020).…”