Combining optimization and simulation modelling to measure the cumulative impacts of prescribed fire and wildfire on vegetation species diversity. Issue 3 (29th December 2018)

• Record Type: Journal Article
• Authors: Chick, Matthew P.; York, Alan; Sitters, Holly; Di Stefano, Julian; Nitschke, Craig R.
• Editors: Driscoll, Don
• Is Part Of: Journal of applied ecology. Volume 56:Issue 3(2019)
• Page Start: 722
• Page End: 732
• Publication Date: 2018-12-29
• Subjects: biodiversity conservation -- fire -- management objective -- optimization -- prescribed fire -- simulation modelling -- species diversity -- wildfire; Agriculture -- Periodicals; Biology, Economic -- Periodicals; Agricultural ecology -- Periodicals; Applied ecology -- Periodicals; 577
• DOI: 10.1111/1365-2664.13314
• Languages: English
• ISSNs: 0021-8901

Abstract

Abstract: Growth‐stage optimization (GSO) offers a new approach to biodiversity conservation in fire‐prone regions by estimating the optimal distribution of vegetation growth stages that maximize a species diversity index. This optimal growth‐stage structure provides managers an operational goal explicitly linked to a positive conservation outcome but does not define the fire regime needed to achieve it. We paired GSO with LANDIS II, a landscape succession and disturbance simulation model, to (a) estimate the optimal growth‐stage structure that maximized vegetation diversity in a south‐east Australian heathy woodland, (b) define the fire regime needed to achieve it, and (c) determine the cumulative effects of different fire‐regime scenarios on vegetation diversity over a 60‐year period. Scenarios included 0%, 2%, 5%, and 10% of the landscape burnt per year by prescribed fire only, or in combination with three alternative wildfire regimes. Furthermore, we investigated the differences in the optimal growth‐stage structure relating to above‐ground, soil seedbank, and total (above and soil seedbank) diversity datasets. The growth‐stage structure that maximized total vegetation diversity comprised approximately even proportions of all stages. In contrast, separately analysed above‐ground and soil seedbank data resulted in a greater proportion of younger and older growth‐stages, respectively. Scenarios including 5% prescribed burning per year (with and without wildfire) resulted in diversity values within 1.5% of the theoretical maximum value. Scenarios including 2% and 10% prescribed fire resulted in diversity values 8%–12% and 1.5%–5% lower than the maximum, respectively. Scenarios without prescribed fire caused diversity to fall 30%–70%. Trends across the 60 years showed that wildfire depressed diversity and subsequent prescribed fire drove recovery within 15 years. The largest threat to vegetation diversity was the absence of fire. Synthesis and applications . Combining growth‐stage optimization and simulation modelling is a powerful way of defining a conservation‐based fire management goal and identifying the prescribed fire regime needed to achieve it. We demonstrated that vegetation diversity in heathy woodland was increased by prescribed fire, with and without the cumulative effect of wildfire, and declined sharply when fire was excluded. Our method provides a flexible platform for developing long‐term fire management strategies that seek to balance human safety and biodiversity conservation. Including both plants and animals in GSO will help land managers meet the needs of multiple taxa. Abstract : Combining growth‐stage optimization and simulation modelling is a powerful way of defining a conservation‐based fire management goal and identifying the prescribed fire regime needed to achieve it. We demonstrated that vegetation diversity in heathy woodland was increased by prescribed fire, with and without the cumulative effect of wildfire, and declined sharply when fire was excluded. Our method provides a flexible platform for developing long‐term fire management strategies that seek to balance human safety and biodiversity conservation. Including both plants and animals in GSO will help land managers meet the needs of multiple taxa.