Managing multiple species or communities? Considering variation in plant species abundances in response to fire interval, frequency and time since fire in a heathy Eucalyptus woodland

Forest Ecology and Management 289 (2013) 393–403

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Managing multiple species or communities? Considering variation in plant species abundances in response to fire interval, frequency and time since fire in a heathy Eucalyptus woodland Thomas J. Duff a,⇑, Tina L. Bell b, Alan York a a b

Forest and Fire Ecology Group, Department of Forest and Ecosystem Science, The University of Melbourne, Creswick 3363, Australia Faculty of Agriculture, Food and Natural Resources, University of Sydney, Redfern, 2015 New South Wales, Australia

a r t i c l e

i n f o

Article history: Received 3 August 2012 Received in revised form 17 October 2012 Accepted 19 October 2012 Available online 28 November 2012 Keywords: Vital attributes Fire ecology Generalised additive model Key fire response species Plant functional types Quantitative ecology

a b s t r a c t With the prospect of altered levels of fire in mediterranean-type environments due to changing climatic conditions, managers are faced with the need to regulate fire with an incomplete knowledge of the ecological outcomes of their actions. Burning intervals for natural vegetation are often determined by using methods which provide an indication of ‘appropriate’ intervals based on the tolerances of key fire response species. The research presented here is an investigation into variation in species abundance in response to fire frequency, interval and time since fire. An ordination of species composition constrained by the corresponding fire history matrix revealed limited response at a community level. However, when species were considered individually using quantitative models, over half of the species responded significantly to at least one of the assessed components of fire regime. Species richness exhibited little variation; however there was change in species diversity (Shannon H) in response to time since fire. Quantitative models showed a range of species responses to fire, indicating that any given regime will favour some species at the cost of others. This study demonstrated that while current techniques provide guidance on tolerable fire intervals, such an approach is not necessarily ideal for all species. The determination of ecologically meaningful fire regimes may be better served when the changing abundances of species of interest are also taken into account. Ó 2012 Elsevier B.V. All rights reserved.

1. Introduction Mediterranean-type climate regions are characterised by hot dry summers, cool wet winters and sclerophyllous vegetation. These combine to promote the prevalence of summer wildfire (Cowling et al., 1996). For many species in such areas, fire plays an integral role in stimulating or facilitating reproductive processes (Bond and Keeley, 2005) and therefore has an important role in defining vegetation composition (Cary and Morrison, 1995; Capitanio and Carcaillet, 2008). However, changes in human populations have resulted in the profound alteration of fire patterns through clearing, grazing, fire suppression, management burning and increases in accidental or intentional ignition (Gill, 1981; Keeley, 2002; Pausas, 2004; Seydack et al., 2007). Such changes have subsequent effects on vegetation, including altering the extent of communities (Gil-Romera et al., 2010), species distributions and local abundances (Vlok and Yeaton, 2000; Watson et al., 2009). Recent significant fire events (Pyne, 2008; Cameron et al., 2009; Keeley et al., 2009) in conjunction with changing weather patterns ⇑ Corresponding author. Tel.: +61 418 552 726; fax: +61 3 5321 4166. E-mail address: [email protected] (T.J. Duff). 0378-1127/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.foreco.2012.10.032

(Whetton et al., 1993; Pausas, 2004), vegetation equilibria (Melillo et al., 1993; Bradstock, 2010) and projected increases in fire occurrence (Clark, 1988; Hennessy et al., 2005; Marlon et al., 2009) have resulted in political pressure to mitigate fire risk through practices such as prescribed burning (Teeter, 2008; Victorian Government, 2008). Plant species in mediterranean-type ecosystems exhibit a variety of strategies to survive fire (Noble and Slatyer, 1980), however the efficacy of these strategies is strongly related to properties of the historic pattern of fire in an area; the fire regime (Bradstock et al., 1997; Menges, 2007). Regime properties such as fire frequency (Andersen et al., 2005), intensity (Keeley et al., 2005), season (Knox and Clarke, 2006), interval length (Zedler et al., 1983), time since fire (Specht et al., 1958) and fire size (Ooi et al., 2006) have been shown to influence vegetation parameters including biomass, cover abundance and the densities of individuals. Particular regimes can adversely affect some species while at the same time favouring others (Pausas, 1999; Knox and Morrison, 2005). The determination of fire regimes that maximise ecological values while maintaining manageable levels of fire has been a focus of landscape planning in mediterranean-type environments over the last 20 years (Noble and Slatyer, 1981; Gill and McCarthy, 1998;

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Pausas, 1999; Uys et al., 2004; Keeley et al., 2005), however the optimal means of determining appropriate fire regimes remains elusive. A feature of many fire-prone systems is that historic or ‘natural’ fire regimes cannot be easily determined from evidence within existing landscapes (Keeley, 1992; Miller et al., 2007). Consequently a key method used to determine ‘appropriate’ fire patterns has been through the evaluation of the reproductive ‘vital attributes’ of extant species (Noble and Slatyer, 1980). Vital attribute approaches (also described as plant functional type approaches) are predominantly focused on disturbance interval and are used to provide indicative fire return intervals under which all species can maintain reproductive capacity. The life cycle responses to disturbance of each individual species is evaluated to determine thresholds at which disturbance rates are either too frequent or infrequent to satisfy reproductive requirements (Pausas, 1999; Bradstock and Kenny, 2003). Suitable thresholds for a system are determined by using the reproductive limits of the most sensitive species (Gill and Bradstock, 1992), the ‘key fire response species’ (KFRS (Department of Sustainability and Environment, 2004b)). It is assumed that fire interval thresholds determined according to KFRS will facilitate the persistence of all species at a site. Vital attribute approaches have been studied extensively and are well supported in theory and practice (Pausas, 1999; Vlok and Yeaton, 2000; Bell, 2001; Keeley, 2002; Bradstock and Kenny, 2003; Clarke et al., 2005; Knox and Morrison, 2005; Penman et al., 2009). These methods are in use in regions of the world with relatively high fire frequency (Franklin et al., 2001; Pausas, 2001) and are a dominant paradigm in policy in southern Australia (Department of Natural Resources and Environment, 2003; NSW National Parks and Wildlife Service, 2004; Burrows, 2008). Vital attribute approaches provide a formal pathway for considering fire ecology in management. However as applied, they are solely focused on interval; other aspects of fire regime are yet to be formally considered. Vital attribute approaches provide constraints on minimum and maximum fire intervals but no guidance on the ideal times for disturbance within these constraints. In addition, current applications of the methods do not recognise changes or variation in species abundance and all disturbances are considered equivalent. These issues were recognised as a limitations when the theory was developed (Noble and Slatyer, 1980), but have not been substantially addressed since. There can be considerable variation in responses to fire even within a single species (Glasgow and Matlack, 2007; Vivian et al., 2010), and as fire thresholds are based on a limited number of KFRS, results may be misleading if not validated at each location (Trollope et al., 1989). In addition, other fire regime properties have been demonstrated to influence plant species composition. Compounding on these is variation due to interactions between species (Keith and Bradstock, 1994; McMahon et al., 1994; Vlok and Yeaton, 2000; Tozer and Bradstock, 2002). As a result, species loss can still occur when fire intervals are considered ‘appropriate’ under vital attribute methodologies. The incorporation of quantitative estimates could provide for the recognition of continuous responses of both species and communities to fire and allow planning to take into consideration species change with greater precision. Quantitative properties such as cover abundance and frequency are routinely collected in vegetation surveys (Sun et al., 1998) but such data are generally used for comparative analysis of fire effects (Fox and Fox, 1986; Watson and Wardell-Johnson, 2004) and are rarely used to quantify continuous responses to fire (Specht et al., 1958; Nieuwenhuis, 1987; Trollope et al., 1989; Capitanio and Carcaillet, 2008). This study aims to assess the potential for using vegetation abundance models to contribute to the understanding of community and individual species responses to fire frequency, interval and time since fire using a case study in an Australian heathy Eucalyptus woodland.

2. Methods 2.1. Study area The study area was located in dry heathy woodland in south western Victoria, Australia (37°350 S, 141°120 E, Fig. 1). The climate is mediterranean-type (Dodson, 2001), with hot dry summers and cool wet winters varying between 620 and 800 mm annual rainfall across the study area, with higher rainfall in the south. There is limited variation in relief, with the area being an average of 115 m above sea level (Land Conservation Council, 1972). The area consists of pockets of edaphic dry sclerophyllous woodland associated with deep nutrient-poor acidic sands naturally fragmented by open grasslands on richer clay soils (Gibbons and Downes, 1964). Broad site parameters are summarised by fire frequency in Table A1. The vegetation is dominated by a low tree overstorey of Eucalyptus baxterii (Benth.) Maiden & Blakely or Eucalyptus arenacea Marginson & P. Ladiges, with a diverse heathy understorey which includes representatives from the families Epacridaceae, Proteaceae, Myrtaceae, Fabaceae and Xanthorrhoeaceae (Department of Sustainability and Environment, 2004a). The area is subject to wildfire and periodic prescribed burning (Department of Sustainability and Environment, 2004c). Of an approximate forest area of 100,000 ha, an average 2910 ha are burnt annually by wildfires and prescribed burns (with a standard deviation of 3650 ha and a coefficient of variation of 1.25) equating to a fire cycle (see Li, 2002) of 34 years. Fire perimeters have been reliably recorded for approximately 45 years, however details are limited on season, intensity and cause (prescribed burn or wildfire). A vital attribute approach has been used by the management agency to define a minimum inter-fire interval of 10 years and a maximum of 50 years, with an ‘ideal’ fire cycle of 20 years (Department of Sustainability and Environment, 2004c). No other elements of fire regime are formally considered.

2.2. Vegetation data A set of 161 sites was surveyed in the spring of 2007. Site locations were defined before measurement using stratified random sampling. Plot points were randomly located within cells of a 2 km grid covering the entire study area to ensure broad environmental coverage. Sampling was restricted to a single vegetation group, heathy woodland with an E. baxterii or E. arenaceae overstorey (Department of Sustainability and Environment, 2004a) to reduce confounding effects due to changes in vegetation as a result of factors unrelated to fire. A minimum distance constraint of 40 m was used to prevent site overlap. Pre-defined points were located in the field using a Global Positioning System. A census of all vascular plant species was undertaken within a 20  20 m quadrat and understorey cover abundance was measured using a point intersection technique (Bonham, 1989) by dropping a metal pin vertically at 1 m intervals and recording the primary species intersected. In each quadrat, a total of 420 intersections was sampled. When the pin did not intersect with vegetation, either ‘bareground’ (bareground or leaf litter) or ‘woody debris’ (fallen tree branches over 50 mm diameter) was recorded. Species which were present but not intersected were recorded as a single interception, resulting in an arbitrary cover of 1 count in addition to the measured pin cover counts (Chiarucci et al., 1999). Cryptic, ephemeral, annual and geophytic species were excluded from analysis to reduce errors due to detectability and seasonality. Names and authorities of species follow those used in Ross and Walsh (2003). The abundances recorded for each species within each quadrat were processed to obtain Shannon’s H diver-

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Fig. 1. Location of the study area within the state of Victoria, Australia. Heathy woodland within the scope of the study is indicated in black.

sity (Keylock, 2005) and species richness. Vital attribute information was obtained from the government management agency and condensed to three categories (accessed from Department of Sustainability and Environment corporate data library, 2010); Obligate Seeders (OS – killed by fire and reproducing only by seed), Resprouters (R – able to survive fire via vegetative recovery) or Unknown (U). The ability to reproduce in the absence of disturbance is a key of determining species sensitive to disturbance (Noble and Slatyer, 1980). Methods of persistence were defined as Tolerant (T – able to reproduce in competition with other species in the absence of fire), Intolerant (I – is dependent on disturbance for reproduction) or Unknown (U). Obligate seeders and species that are unable to reproduce without disturbance are most sensitive to short and long fire intervals respectively. 2.3. Fire regime Annual fire perimeters were obtained as GIS shapes for the entire study area (accessed from Department of Sustainability and Environment corporate data library, 2007). Fire records from before 1960 were not consistently collected, so fire history values were truncated at a maximum of 45 years since fire. Historic perimeters were predominantly digitised from paper maps, so some adjustments were made to ensure mapped boundaries were consistent with surveyed cadastre. Annual fire perimeters were processed to obtain four regime metrics for the landscape; time since fire (truncated at maximum of 45 years), fire frequency, length of the last inter-fire interval and minimum inter-fire interval. For sites at which two fires had occurred, the minimum and last intervals were identical. Intervals were only modelled for sites that had more than one fire. To reduce covariation of predictors, the value for minimum fire interval values were truncated at a maximum of 10 years. This is congruent with the KFRS determined minimum interval (Department of Sustainability and Environment, 2004c). Limitations in the long-term reliability of fire history data prevent

the maximum recommended inter-fire interval boundary of 50 years being tested. The historic fire conditions for each site were determined using the site coordinates to query the fire history spatial database. Seventy-six of the 161 sites had been burnt more than once within the study period. Of these, 36 sites had minimum interval periods of less than 11 years, indicating that at some sites fire intervals were less than recommended according to constraints obtained through vital attributes assessment. Fig. 2 illustrates the variation in fire frequency in the centre of the study area. 2.4. Statistical analysis Abundance models were developed by fitting Generalised Additive Models (GAMs) (Hastie and Tibshirani, 1987). GAMs of species cover abundances and the proportion of bareground were fitted using a negative binomial link function, as pin cover counts are typically over-dispersed (Damgaard, 2008). Species richness and diversity were modelled using Gaussian link functions. Species that were present but rarely intersected due to low cover abundances (i.e. a range of less than five intersections) and species which had fewer than 10 occurrences across all sites were excluded from modelling due to lack of power. Due to data limitations, interactions were not considered. Abundance models are sensitive to zero inflation (Martin et al., 2005; Miller, 2007), so abundance was modelled using only sites where the species was found to be present. The selection of the smoothness term, the degrees of freedom (df), of each model was undertaken using an iterative process based on generalised cross-validation for models where a dispersion parameter had to be estimated (negative binomial) and an unbiased risk estimator/Akaike’s information criterion process for known distributions (Gaussian) (Wood, 2008). The models included a penalty term which allowed predictors to be penalised to zero and excluded where there was no evidence for a relationship. To prevent overfitting, model shapes were limited to a maximum of three df. The statistical package R (R Development Core

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Fig. 2. Fire frequency map of part of the study area indicating the number of times each point has been burnt in the 45 years prior to assessment. Unsampled farmland has been masked and is indicated by stippling. Sample plots are marked as points.

Team, 2009) was used for modelling in conjunction with the mgcv library (Wood, 2008). As individual response curves were fitted with a maximum of three df, the number of general forms that curves could take was limited. The GAM curves were classified visually according to broad trends in response, similar to that used to assess veld condition in South Africa (Trollope et al., 1989). For time since fire and last inter-fire interval, four categories were used; increasing, decreasing, unimodal and bimodal. For minimum inter-fire interval and fire frequency, two categories were used due to the smaller range of values; increasing and decreasing. As ordination is typically used to evaluate community change in fire ecology, for comparative purposes, we assessed the influence of measured aspects of fire regime on community pattern using Constrained Correspondence Analysis (CCA) (Ter Braak, 1987) of the species abundance matrix constrained by the fire matrix.

Two CCA analyses were done, one with all (161) sites assessing correspondence with time since fire and fire frequency. The second with all variables of interest evaluating only sites that had valid interval data (a fire frequency of >1, 76 sites). The significance of the ordinations were evaluated with permutation tests. Ordination was undertaken in R using the vegan library (Oksaken et al., 2011).

3. Results 3.1. Species richness, diversity and proportion of bareground Species richness did not vary significantly in relation to any of the aspects of fire regime tested. However, there was significant variation in species diversity in relation to time since fire (12.5% explained deviance, p < 0.001) and length of the last inter-fire

397

2.2

2.4

Shannon’s H

2.6 2.4 2.0

2.0

2.2

Shannon’s H

2.6

2.8

3.0

B

2.8

A

3.0

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0

10

20

30

0

40

Time since fire

10

20

30

40

Last inter−fire interval

Fig. 3. Quadrat diversity (Shannon’s H) in response to (A) time since fire, and (B) last inter-fire interval. Solid line indicate predicted value, dashed lines indicate 2  SE.

interval (12.7% explained deviance, p = 0.02) (Fig. 3). Species diversity showed a unimodal response in relation to time since fire, with an increase in diversity after fire approaching a maximum at about 15 years (Fig. 3A). In contrast, species diversity decreased as the length of the last fire interval increased (Fig. 3B). The proportion of bareground was found to vary strongly with time since fire (37.0% explained deviance, p < 0.001), and the length of the last fire interval (14.3% explained deviance, p = 0.01) (Fig. 4). Bareground showed a bimodal pattern in relation to time since fire, decreasing as plants reoccupy a site after fire to a minimum at about 15 years, before plants decline and the amount of bareground begins to increase again, although not to the levels found immediately post-fire. Bareground was greater at sites where the previous inter-fire interval was long. The proportion of bareground was inversely associated with species diversity, with greater amounts of unoccupied space associated with lower plant diversity (Figs. 3 and 4). CCA analysis indicated that the aspects of fire regime assessed accounted for only a small proportion of variation in floristic composition (2.0% for all sites with frequency and time since fire, 5.5% for the subset of sites with all variables) when calculated as a proportion of constrained inertia (all sites: Total inertia 5.62, Constrained 0.11, Unconstrained 5.51, subset: Total inertia 5.34, Constrained 0.29, Unconstrained 5.05). Permutation tests of the CCA showed that despite low explanatory power, fire frequency

3.2. Species responses A total of 74 species met the criteria for modelling. Over half (39) of the species analysed exhibited a significant response (p < 0.05) to at least one aspect of the fire regime. The vast majority of the species modelled (70 of 74 species) exhibited resprouting capabilities or were classified as tolerant of competition and able to reproduce without fire. Dillwynia glaberrima Sm. occurred in 88 sites across the study area and was the only obligate seeder which requires fire to be found in more than 20% of the sites sampled, in contrast to 47 species which resprout or reproduce without disturbance. The regeneration response of approximately one third of species sampled was unknown. While species curves were classified into four classes based on general form, there was substantial variation in abundances and rates of change. The component of fire regime that had the strongest effect on species abundance was time since fire, with 32 species showing significant variation in abundance. The responses of individual species were categorised (Table 1) and typical examples of species which exhibited generalised curve shapes are presented in Fig. 5. Slightly more species were predicted to increase (12) than decrease

0

10

20

30

Time since fire

40

200 150 100

100

Bareground (interceptions)

150

B

50

Bareground (interceptions)

A

and time since fire had a significant influence on composition when considering all sites (F = 1.60, p < 0.01), but there was no significance evident when interval was included (F = 1.02, p = 0.36).

0

10

20

30

40

Last inter−fire interval

Fig. 4. Cover abundance of bareground in response to (A) time since fire, and (B) length of last interfire interval. Solid line indicate predicted value, dashed lines indicate 2  SE.

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Table 1 Species exhibiting significant responses to time since fire categorised by generalised additive model response form. Species

Family

Response type

Deviance explained (%)

p-value

n

Increasers Acrotriche serrulata Astroloma conostephioides Banksia ornata Brachyloma ciliatum Drosera peltata Isopogon ceratophyllus Lepidosperma carphoides Leptocarpus tenax Monotoca scoparia Styphelia adscendens Xanthorrhoea australis Xanthorrhoea caespitosa

Epacridaceae Epacridaceae Proteaceae Epacridaceae Droseraceae Proteaceae Cyperaceae Restionaceae Epacridaceae Epacridaceae Xanthorrhoeaceae Xanthorrhoeaceae

R/T R/T OS/I U/I R/T R/I R/I U/U R/T U/I R/I R/I

5 16 51 7 11 15 32 38 12 9 10 9

0.029 <0.001 0.014 0.026 0.023 0.001 0.016 0.047 0.010 0.048 0.032 0.008

137 142 13 98 70 104 43 24 108 98 68 100

Decreasers Acacia mitchellii Epacris impressa Eucalyptus baxterii Gompholobium ecostatum Gonocarpus tetragynus Hibbertia fasciculata Leucopogon ericoides Leucopogon virgatus

Fabaceae Epacridaceae Myrtaceae Fabaceae Haloragaceae Dilleniaceae Epacridaceae Epacridaceae

R/I R/I R/I U/I R/T U/I R/U R/I

77 36 4 25 46 24 18 8

0.005 <0.001 0.020 0.003 0.030 <0.001 0.020 0.019

13 65 161 47 126 112 51 135

Unimodal Calytrix tetragona Correa reflexa Daucus glochidiatus Lomandra sp. Persoonia juniperina Tetratheca ciliata

Myrtaceae Rutaceae Apiaceae Xanthorrhoeaceae Proteaceae Tremandraceae

R/T R/T OS/T R/U R/I R/T

20 27 46 14 31 22

0.022 <0.001 0.030 0.041 0.017 <0.001

57 90 20 64 33 119

Bimodal Acacia mearnsii Astroloma humifusum Dillwynia glaberrima Hydrocotyle laxiflora Hibbertia sericea Pultenaea sp.

Fabaceae Epacridaceae Fabaceae Apiaceae Dilleniaceae Fabaceae

OS/I R/T OS/I R/T R/I U/U

68 16 11 30 25 25

0.002 0.016 0.046 0.020 0.015 0.003

17 71 88 34 42 48

Response types represented are: R = Resprouter, OS = obligate seeder, I = Intolerant; unable to reproduce without fire, T = Tolerant; able to reproduce without fire, U = unknown.

(8) with increasing time since fire. Fewer species exhibited bimodal (6) or unimodal responses (6). Species which decrease after a fire were generally shrub species, with the exception of E. baxterii. There was no strong evidence of consistent patterns of response type by plant family. A number of species were found to respond significantly to changes in fire frequency by increasing (6) or decreasing (2) in abundance as fire frequency increased (Table 2). Species which responded to fire frequency did not appear to be associated by family or response type. A variety of responses were evident in relation to the length of the last inter-fire interval, with one species Hydrocotyle laxiflora DC., Prodr. 4:61 (1830). increasing, three species decreasing, three species bimodal, and one species, Banksia marginata Cav., showing a unimodal shape (Table 3). Eucalyptus willisii Ladiges, Humphries & Brooker was absent in all sites that had not burnt for 40 years, although it cannot be determined whether this was due to sensitivity to fire interval or chance due to the few observations (one-tailed Fischer exact p = 0.13). There was no evidence of species loss in the study area due to long periods without fire. Only one species, Hibbertia fasciculata R.Br. ex DC., showed a significant response to minimum inter-fire interval, with an increasing in abundance as the fire interval became shorter. There was no evidence of species loss in the study area due to short fire intervals. A large proportion of species (35 of 74) showed no response to any of the aspects of fire regime that were measured in this study (Table 4). While some of these species were recorded in low num-

bers, increasing the potential for type II errors, many of the species were common and occupied substantial proportions of quadrats (e.g. Pteridium esculentum (G. Forst.) Cockayne, Hypolaena fastigiata R.Br., Leptospermum sp., Dianella revoluta R.Br. and a number of species from the Fabaceae family). In addition to the 74 species for which quantitative models were produced, there were 23 species that were found at 10 or more sites that had insufficient ranges of cover abundance (<5) for modelling (Table A2).

4. Discussion 4.1. Efficacy of ordination in describing fire responses Fire was found to have an important role in determining the composition of vegetation in the study area; however results contrasted strongly between community and species levels. Constrained ordination indicated little change in broad community pattern due to fire. In comparison, when species were considered individually, over half varied significantly in abundance in response to at least one of the aspects of fire assessed. The weak community (CCA) relationships recognised in this study is in contrast to the relatively strong findings of other studies in Australian fire prone environments (Morrison et al., 1995; Myerscough and Clarke, 2007). This is likely to be due to unique characteristics of the study area; in particular, the relative proportion of obligate seeders. Other studies of heathy vegetation have frequently found large proportions of obligate seeders (Nieuwenhuis, 1987; Brad-

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15 10

Cover (interceptions)

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80 60 40 0

Cover (interceptions)

B

20

A

20

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0

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40

0

10

Time since fire

30

40

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14

D

15 10 5

Cover (interceptions)

10 8 6 4

0

0

2

Cover (interceptions)

12

C

20

Time since fire

0

10

20

30

40

0

10

Time since fire

20

30

40

Time since fire

Fig. 5. Typical response shapes of species identified to respond to time since fire. Response shapes are (A) Xanthorrhoea caespitosa with an increasing trend, (B) Epacris impressa with a decreasing trend, (C) Correa reflexa with a unimodal trend, and (D) Acacia mearnsii with a bimodal trend. Solid line indicate predicted value, dashed lines indicate 2  SE.

Table 2 Species exhibiting significant responses to fire frequency categorised by general generalised additive model response form. Species

Family

Response type

Deviance explained (%)

p-value

n

Increasers Correa reflexa Dillwynia sericea Epacris impressa Gompholobium ecostatum Gonocarpus tetragynus Leucopogon ericoides

Rutaceae Fabaceae Epacridaceae Fabaceae Haloragaceae Epacridaceae

R/T U/I R/I U/I R/T R/U

8 29 10 34 9 13

0.034 <0.001 0.038 <0.001 0.001 0.046

90 73 65 47 126 51

Decreasers Lepidosperma carphoides Xanthorrhoea australis

Cyperaceae Xanthorrhoeaceae

R/I R/I

17 8

0.013 0.043

43 68

Response types represented are: R = Resprouter, OS = obligate seeder, I = Intolerant; unable to reproduce without fire, T = Tolerant; able to reproduce without fire, U = unknown.

stock et al., 1997), whereas our study area was dominated by resprouters, both in number of species and occurrences. Obligate seeders typically respond with synchronised pulses of germination after fire, resulting broad signals easily detected by ordination. In contrast, resprouting species continue growing from existing rootstock, and our quantitative results indicated that there is high variation in rates of response. Ordination methods are weak in conditions where there is substantial variation between components or where variation occurs in a non-linear or non-monotonic manner (Ruokolainen and Salo, 2006). Such methods are common in fire ecology (Morrison et al., 1995; Bradstock et al., 1997; Wat-

son and Wardell-Johnson, 2004; Myerscough and Clarke, 2007); however our results indicate that they may be poorly representative of some patterns of vegetation response. 4.2. Quantitative changes in plant properties This study found species richness to be relatively robust, with no measurable effect caused by time since fire, inter-fire interval or fire frequency. This is despite a number of sites having intervals shorter than would be recommended based on vital attribute thresholds (Department of Sustainability and Environment,

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Table 3 Species exhibiting significant responses to inter-fire interval categorised by general generalised additive model response form. Species Length of last inter-fire interval Increasers Hydrocotyle laxiflora Decreasers Acacia myrtifolia Eucalyptus willisii Leptocarpus tenax Bimodal Acacia verticillata Amperea xiphoclada Drosera whittakeri Unimodal Banksia marginata Minimum inter-fire interval Decreasers Hibbertia fasciculata

Family

Response type

Deviance explained (%)

p-Value

n (freq > 1)

Apiaceae

R/T

98

<0.001

29

34

Fabaceae Myrtaceae Restionaceae

OS/I U/U U/U

41 61 65

0.015 0.023 0.034

17 10 14

30 13 24

Fabaceae Euphorbiaceae Droseraceae

OS/I R/I R/T

87 19 38

0.001 0.023 0.007

11 41 23

23 66 65

Proteaceae

R/T

14

0.024

Dilleniaceae

U/I

28

0.022

n (all)

67

141

n (Int < 10)

n (all)

23

112

Response types represented are: R = Resprouter, OS = obligate seeder, I = Intolerant; unable to reproduce without fire, T = Tolerant; able to reproduce without fire, U = unknown.

Table 4 Species exhibiting no detectable quantitative response to fire. Species

Family

Response type

n

Species

Family

Response type

n

Acacia melanoxylon Acacia oxycedrus Acacia suaveolens Allocasuarina paludosa Billardiera scandens Boronia nana Boronia pilosa Bossiaea cinerea Brachyloma daphnoides Calytrix alpestris Caustis pentandra Centrolepis sp. Daviesia brevifolia Dianella revoluta Eucalyptus ovata Eucalyptus viminalis subsp. cyg. Eucalyptus viminalis subsp. vim. Ficinia nodosa Gahnia radula

Fabaceae Fabaceae Fabaceae Casuarinaceae Pittosporaceae Rutaceae Rutaceae Fabaceae Epacridaceae Myrtaceae Cyperaceae Centrolepidaceae Fabaceae Liliaceae Myrtaceae Myrtaceae Myrtaceae Cyperaceae Cyperaceae

R/T OS/I R/I U/T U/U U/U R/T R/T R/T U/U U/T U/I R/T U/U U/I U/I R/T R/T R/T

17 33 13 30 10 57 56 40 20 49 34 28 27 133 11 13 14 18 24

Hakea rostrata Hibbertia riparia Hibbertia stricta Hibbertia virgata Hypolaena fastigiata Kennedia prostrata Lepidobolus drapetocoleus Lepidosperma viscidum Leptospermum continentale Leptospermum myrsinoides Oxalis sp. Patersonia sp. Pinus radiata Platylobium triangulare Poa sp. Pteridium esculentum Viola sp. Wahlenbergia sp.

Proteaceae Dilleniaceae Dilleniaceae Dilleniaceae Restionaceae Fabaceae Restionaceae Cyperaceae Myrtaceae Myrtaceae Oxalidaceae Iridaceae Pinaceae Fabaceae Poaceae Dennstaedtiaceae Violaceae Campanulaceae

R/I U/U R/T R/T OS/I R/I R/I R/I R/I R/T R/I R/T R/I R/T R/T R/T R/T R/I

24 53 87 27 130 24 17 29 98 145 18 13 19 33 122 105 51 47

Response types represented are: R = Resprouter, OS = obligate seeder, I = Intolerant; unable to reproduce without fire, T = Tolerant; able to reproduce without fire, U = unknown.

2004c). On the other hand, species diversity, a compound measure of richness and evenness (Keylock, 2005), was sensitive to both time since fire and the length of the last inter-fire interval. Diversity peaked 15 years after fire and was lower in areas where the previous fire interval was greater than 30 years. A fire regime aimed at maximising plant species diversity in this vegetation type would therefore need to maintain the average time since fire at 15 years while limiting the proportion of the landscape that is older. However, this would result in decreasing abundance of species which favour long fire intervals, including a number of large shrubs (such as Banksia ornata F. Muell. ex Meisn., Monotoca scoparia (Sm.) R.Br. and Xanthorrhoea spp.), and result in increasing abundances of small shrubs which favour short intervals and frequent fire (such as Gonocarpus tetragynus Labill., Epacris impressa Labill. and Leucopogon spp. (Walsh and Entwisle, 1996)). This may have implications for habitat value. The current fire cycle for the study area is 34 years, indicating that an introduction of more fire into the landscape would be beneficial for diversity. Time since fire had the most dominant influence on the abundances of individual species, with a variety of responses evident. Species that decreased with increasing time since fire are likely to be those that establish rapidly after fire, but are poorly compet-

itive and decline through attrition as other species become dominant. These include a number of low shrubs and seedlings of E. baxterii. This is supported by the fact that the majority of these species that declined (6 of 8) are unable to reproduce without fire (I). Those with a unimodal pattern of response are likely to establish with fire, be moderately competitive but are relatively short lived and the populations begin to decline in the absence of fire. The bimodal pattern of response would be expected to be species that rapidly occupy sites after fire and also increase through reproduction as substrate becomes available as other species decline. However, two intolerant obligate seeders were in this category; Acacia mearnsii (De Wild.) and Dillwynia glaberrima. As cover abundance methods cannot discriminate between many small individuals and a fewer larger plants, it is likely that the high abundances soon after fire are due to the mass germination of seed. With increasing time since fire, attrition would reduce the number of individuals, resulting in a decline in cover. As the surviving individuals become more dominant, cover increases again. Organic growth of dominant individuals may also be responsible for the species that show continued increases after fire, as there is a high proportion of large shrub species, such as Banksia ornata and Monotoca scoparia.

T.J. Duff et al. / Forest Ecology and Management 289 (2013) 393–403

Our results indicated that a number of species responded to variation in fire frequency. Such responses are not considered under vital attribute approaches; each disturbance is considered equivalent and no enduring effects are recognised. In addition, there was some evidence of an additive effect where species abundances continue to increase or decline in response to repeated fire at similar intervals. This was observed for Xanthorrhoea australis R.Br., which had low abundance in both areas when time since fire was short and where fire frequency was high; possibly due to a heightened susceptibility of younger individuals to fire (Curtis, 1998). Other examples include Gompholobium ecostatum Kuchel and Epacris impressa, two species which exhibit high abundances soon after fire and also with more frequent fires, and Hibbertia fasciculata, which is in high abundance soon after fire and with short fire intervals. This effect may be a due to the competitive potential of a species at site after a fire being somewhat proportional to the occupancy of a site before fire (Egler, 1954). This effect is also apparent with diversity, with a decrease in diversity both with long fire intervals and when the length of the last interval was long. As studies of fire often focus only on the most recent interval, further investigation of the effects of repeated fire at particular intervals is warranted, particularly where components of regime can be more independently assessed. 4.3. Implementing quantitative assessment for improved management When suitable information is available, the barriers to assessing individual species responses are few. However, a potential issue is the convention of collecting vegetation cover abundance data using non-metric categorical scales such the Braun-Blanquet (Poore, 1955). Such scales violate a number of assumptions of generalised modelling (Podani, 2005), limiting potential applications of collected data. Metric methods are often more labour intensive but greatly increase the applications of the information collected. In addition, where quantitative methods are applied, the selected method can influence which patterns are detected. Biomass based indices, such as cover abundance, may indicate site occupancy and competitive dominance, but cannot discriminate between changes due to the sizes or frequencies of individuals. In this study, 23 species, predominantly small herbs, had insufficient measured ranges to allow quantitative assessment. Individuals of these species may have been present in high numbers but due to their small size they were not readily detected using point sampling at 1 m intervals. As cover abundance is not necessarily informative about a species contribution to ecosystem function, likelihood of persistence or habitat value, outcomes must be considered in the context of the limitations of the methodology used. To compensate, sampling could be optimised based on the predominant scale of variation (Critchley and Poulton, 1998) or be adapted to encompass multiple scales (Stohlgren et al., 1995). Vital attribute approaches to management consider vegetation properties at a coarse scale, with a small number of species used as surrogates for the needs of the majority of species. These species may not be representative, as was evident in the study area, where no intolerant obligate seeders were found on a majority of sites. There are a number of weakness inherent with such approaches (Driscoll et al., 2010), and there is no guidance on optimal fire regimes. An alternative approach is to consider finer scale variation, assessing individual species change in occupancy and abundance due to a range of fire regime properties. While such an approach would require a much greater investment in monitoring, it also would provide a greater understanding of ecosystem function. Variation is inherent in biological systems, and any form of vegetation monitoring is likely to detect change as time progresses. A key question will be ‘what level of change is biologically significant?’ While over half the species modelled in this study exhibited statis-

401

tically significant variation, the importance of such variation would require a broader evaluation. Methods assessing individual species responses to fire are often used in research (Specht et al., 1958; Nieuwenhuis, 1987; Bradstock et al., 1997; Penman et al., 2009), but are rarely incorporated into systematic conservation planning. Management approaches commonly hedge uncertainty by incorporating variation into patterns of fire both through time (Gill and McCarthy, 1998; Menges, 2007) and space (Brockett et al., 2001), however this may not necessarily maximise biological values unless fire responses are explicitly considered (Parr and Andersen, 2006). As a particular regime will favour certain species at the expense of others, the ‘optimal’ fire regime needs to be considered in terms of explicit management goals rather than an ideal ecological outcome (Failing and Gregory, 2003). A common focus of legislation is the ‘maintenance of biological diversity’ (Department of Natural Resources and Environment, 1999; Bond and Archibald, 2003; NSW National Parks and Wildlife Service, 2004); however when considering compositional change in vegetation, a diverse array of outcomes exists so selecting an ‘appropriate’ fire regime is a complex process. Management for ecosystem function is likely to prioritise dominant species (Sasaki and Lauenroth, 2011), however management for biodiversity may put greater weight on rare species. Alternatively, specific species may be of interest if priorities are for fuel or fauna management. With increasing pressure to justify decision making, the precision offered by quantitative methods can help objectively define trade-offs.

5. Summary With the potential for more severe wildfires increasing in mediterranean-type environments, pressure is mounting on land managers to increase the incidence of prescribed burning to reduce fuels. This study has illustrated that while current approaches offer valuable guidance on tolerable fire intervals; they provide limited information on the effects of burning within these bounds and may not guarantee the persistence of all species. In addition, ordination methods, commonly used to assess community responses to fire, may not be indicative of fire response for some community configurations. Individual models of species abundance can supplement current approaches to ecological fire management by identifying patterns of change in species that cannot be discerned through qualitative methods. Consequently, ecologically sound fire regimes may be more attainable when variation in abundance is also taken into account. Furthermore, as detailed records of fire history develop, quantitative approaches will become of greater utility in evaluating the importance of other aspects of fire regime such as fire frequency, intensity, season and size.

Acknowledgments This work was undertaken by TJ Duff as part of a PhD candidacy at the University of Melbourne, Victoria, supported by a Melbourne University Research Scholarship. We gratefully acknowledge the financial support and cooperation of the Department of Sustainability and Environment. We would also like to recognise the voluntary contribution of the developers of the R statistical project, in particular the developers of the packages mgcv and Vegan. Finally, we would like to thank our anonymous reviewers who contributed to this work.

Appendix A See Tables A1 and A2.

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Table A1 Site parameters of the study area, categorised by fire frequency. Basal area (m2/ha1)

Fire frequency (count)

Slope (°)

Tree density (count)

l

r

l

r

l

0 1 2 3 4 5

0.8 1.0 1.0 0.5 0.4 0.9

1.18 1.47 1.07 0.73 0.27 n/a

12.3 10.3 10.2 11.0 7.8 3.8

12.3 10.3 10.2 11.0 7.8 n/a

16.6 14.3 15.0 18.4 12.0 8.0

Altitude (m)

Average annual rainfall (mm)

r

l

r

l

r

7.9 7.1 8.1 10.2 6.7 n/a

117 114 116 120 96 151

44.4 46.4 48.4 43.0 39.2 n/a

681 698 710 730 718 794

31.11 32.38 36.22 43.12 34.46 n/a

n

33 52 49 19 7 1

Table A2 Species for which quantitative models were unable to be produced due to low ranges of cover abundance (counts of <5) that were recorded at >9 sites. Species

Family

Response type

n

Species

Family

Response type

n

Amyema sp. Billardiera scandens Bossiaea prostrata Cassytha glabella Cassytha pubescens Comesperma calymega Comesperma volubile Drosera macrantha Drosera peltata Galium sp. Gonocarpus humilis Goodenia sp.

Loranthaceae Pittosporaceae Fabaceae Lauraceae Lauraceae Polygalaceae Polygalaceae Droseraceae Droseraceae Rubiaceae Haloragaceae Goodeniaceae

U/T U/T OS/U OS/T OS/T U/T R/T U/T R/T U/T U/U R/I

21 10 26 52 103 28 12 21 70 13 11 90

Hovea heterophylla Hydrocotyle callicarpa Leucopogon glacialis Lomandra nana Lomandra sororia Opercularia sp. Pimelea humilis Poranthera microphylla Tricoryne elatior Viola sp. Xanthosia dissecta

Fabaceae Apiaceae Epacridaceae Xanthorrhoeaceae Xanthorrhoeaceae Rubiaceae Thymaceae Euphorbiaceae Liliaceae Violaceae Apiaceae

R/I U/T U/I R/I U/U U/I R/U U/T R/I R/T U/U

56 14 135 71 16 11 62 21 14 51 19

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