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Forest Ecology and Management 415–416 (2018) 85–97 Contents lists available at ScienceDirect Forest Ecology and Management journal homepage: doc.001pp.com/locate/foreco Fire planning for multispecies conservation: Integrating growth stage and T ?re severity Matthew Swana, N, Holly Sittersa, Jane Cawsonb, Thomas Du?b, Yohannes Wibisonoa,1, Alan Yorka a School of Ecosystem and Forest Sciences, University of Melbourne, Creswick Campus, 4 Water Street, Creswick, Vic. 3363, Australia b School of Ecosystem and Forest Sciences, University of Melbourne, Burnley Campus, 500 Yarra Boulevard, Richmond, Vic. 3121, Australia ARTICLE INFO Keywords: Fire regime Pyrodiversity Geometric mean abundance Time since ?re Eucalyptus regnans Fire severity Biodiversity Fire management Wild?re ABSTRACT Setting suitable conservation targets is an important part of ecological ?re planning. Growth-stage optimisation (GSO) determines the relative proportions of post-?re growth stages (categorical representations of time since ?re) that maximise species diversity, and is a useful method for determining such targets. Optimisation methods can accommodate various predictor variables, but to date have only been applied using post-?re growth stages as the primary landscape variable. However, other aspects of ?re regimes such as severity may in?uence species diversity but have not yet been considered in determining conservation targets in ?re planning. Here we use a space-for-time substitution to address two objectives, 1. To determine the e?ects of growth stage and ?re severity on plant and vertebrate species’ occurrence, and 2. To determine the optimal mix of growth stages and ?re severities for sustaining the diversity of these groups. We used the tall wet forests of southeast Australia as the focal system because ?re severity is expected to create distinct successional pathways and in?uence species’ responses. We found that growth stage predicted the occurrence of many species, and severity of the most recent ?re was an important factor over and above growth stage for a small subset of species. The optimal distribution of growth stages for both plants and animals included a substantial proportion of young forest, however when ?re severity was considered, areas burned at low severity were most important in driving the diversity of both groups. Growth stage is a good surrogate for developing conservation targets in tall wet forests, however growth stage alone does not capture the full range of species’ ?re responses. More complex versions of growth stage optimisation that accommodate multiple ?re-regime variables need to be explored to yield ecologically meaningful conservation goals. 1. Introduction Fire is an important natural process that in?uences ecosystem structure and function (Bowman et al., 2009). It is also used as a forest management tool globally, both for ecological objectives and to reduce wild?re risk (Penman et al., 2011; Fernandes et al., 2013). Managing ?re for biodiversity conservation is challenging because of the competing needs of multiple species, stochasticity associated with ?re regimes and uncertainty about species’ ?re responses (Bowman et al., 2016). Varying the properties of ?re regimes across space and time has been advocated as a way of accommodating the needs of multiple species, in various guises (e.g. patch mosaic burning, pyrodiversity begets biodiversity Martin and Sapsis, 1992; Parr and Andersen, 2006). However; there is uncertainty about appropriate levels of pyrodiversity and which properties of ?re regimes should be varied (Parr and Andersen, 2006; Bowman et al., 2016; Kelly et al., 2017). Growth-stage optimisation (GSO) is a recent approach to biodiversity conservation in ?re-prone landscapes that provides a strategy to manage ?re in a way that maximises biodiversity values based on the requirements of multiple species (Di Stefano et al., 2013). GSO determines the relative proportions of post-?re growth stages that maximise a species diversity index, where growth stages are categorical representations of a successional pathway as determined by time since ?re (Cheal, 2010). GSO provides tangible operational goals linked to ecosystem resilience, which can be achieved through planned burning, ?re suppression or other management interventions (DELWP, 2015). GSO has been incorporated into ?re management policy in south eastern Australia because it has a strong theoretical basis, input data can be obtained using standard ecological survey techniques, and it provides a uni?ed framework for addressing conservation goals associated with multiple species (DELWP, 2015). To date, optimisation methods in ?re management have only been N Corresponding author. E-mail address: swanm@unimelb.edu.au (M. Swan). 1 Current Address: Environment and Forestry Research and Development Institute of Manokwari, Jl. Inamberi-Susweni, Manokwari, 98313 Papua Barat, Indonesia. https://doi.org/10.1016/j.foreco.2018.01.003 Received 5 September 2017; Received in revised form 29 December 2017; Accepted 4 January 2018 Available online 24 February 2018 0378-1127/ © 2018 Elsevier B.V. All rights reserved. M. Swan et al. Forest Ecology and Management 415–416 (2018) 85–97 applied using growth stage as the primary landscape property. This method, however, can be applied in any context where there is a landscape attribute of interest that may in?uence multiple species and can potentially be in?uenced by management. For example, ?re regime elements such as severity, frequency, interval and season (Gill, 1975) have the potential to be strong drivers of community organisation but have not yet been used in GSO (Kelly et al., 2015). Developing a better understanding of the role of such factors in driving community composition in addition to growth stage will be important for determining ecologically meaningful conservation targets. Fire severity, related to ?re intensity, is a measure of the loss of above- and below-ground organic matter caused by ?re (Keeley, 2009). Indicators of ?re severity are typically associated with vegetation or soils. Vegetation-related indicators include the amount of unburnt, scorched and burnt vegetation in each vegetation stratum and the scorch height. A range of organisms respond to ?re severity; for plants, severity is directly related to mortality and has important implications for mechanisms of persistence (e.g. seeding vs resprouting) and the subsequent availability of resources such as light and nutrients (Wang and Kemball, 2005; Kuenzi et al., 2008). For animals, severity has important implications for a species’ capacity to survive ?re, and for forest structure which in turn a?ects resources used as shelter and forage in the post-?re environment (Smucker et al., 2005; Bassett et al., 2017). Recent work has shown that ?re severity in?uences species’ occurrence and abundance (Chia et al., 2016; Lindenmayer et al., 2016; Gordon et al., 2017) and spatiallyheterogeneous ?re severities can bene?t species diversity at a landscape scale (Tingley et al., 2016). Here we investigate the in?uence of growth stage and ?re severity on ?ora and fauna using the tall wet forests of south eastern Australia as a case study system. These forests are highly valued for biodiversity, timber, water and carbon storage (Burns et al., 2015). Furthermore, they exhibit large di?erences in successional response between high and low severity ?re (McCarthy and Lindenmayer, 1998; Benyon and Lane, 2013). A recent wild?re in 2009 has provided an opportunity to examine the role of ?re severity. We use a space-for-time substitution to address two objectives, 1. to determine the e?ects of growth stage and ?re severity on plant and vertebrate species’ occurrence, and 2. to determine the optimal mix of growth stages and ?re severities for sustaining the diversity of these groups. 2. Methods 2.1. Study area We conducted this study in the Central Highlands region of Victoria, Austral 内容过长，仅展示头部和尾部部分文字预览，全文请查看图片预览。 J. Ecol. 22, 404–411. White, P.S., Jentsch, A., 2001. The Search for Generality in Studies of Disturbance and Ecosystem Dynamics. In: Esser, K., Lüttge, U., Kadereit, J.W., Beyschlag, W. (Eds.), Progress in Botany: Genetics Physiology Systematics Ecology. Springer, Berlin, Heidelberg, pp. 399–450. Wickham, H., 2011. The split-apply-combine strategy for data analysis. J. Stat. Softw. 40, 1–29. Zeileis, A., Hothorn, T., 2002. Diagnostic checking in regression relationships. R News 2, 7–10. 97 [文章尾部最后500字内容到此结束,中间部分内容请查看底下的图片预览]请点击下方选择您需要的文档下载。

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