Bird diversity increases after patchy prescribed fire - CSIRO Publishing

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International Journal of Wildland Fire 2015, 24, 690–701 http://dx.doi.org/10.1071/WF14123

Bird diversity increases after patchy prescribed fire: implications from a before–after control–impact study Holly Sitters A,C, Julian Di Stefano A, Fiona J. Christie A, Paul Sunnucks B and Alan York A A

Fire Ecology and Biodiversity Group, School of Ecosystem and Forest Sciences, University of Melbourne, 4 Water Street, Creswick, Vic. 3363, Australia. B School of Biological Sciences, Monash University, Clayton, Vic. 3800, Australia. C Corresponding author. Email: [email protected]

Abstract. Increasingly, patchy prescribed fire of low severity is used by land managers to mitigate wildfire risk, but there are relatively few experimental studies on the effects of low-severity fire on fauna. We used a before–after control–impact experiment to examine avian responses to prescribed fire at two scales in topographically variable, tall-open eucalypt forest in south-east Australia. We surveyed birds at control and impact areas twice before and twice after fire, and applied mixed models to investigate responses of avian turnover, richness and the occurrence of selected species. Approximately half of the impact area was burnt and topographic variation generated a finger-like configuration of burnt patches on ridges and unburnt patches in gullies. Our findings at the smaller scale (0.8 ha) indicated that the fire resulted in increased bird diversity because a patchwork of burnt and unburnt areas provided a mosaic of distinct successional states in which different species occurred. Additionally, we found that the effect of fire on species richness and occurrence was a function of the presence of unburnt topographic refuges. In contrast, we found no compelling evidence to suggest that birds responded to the fire at the larger scale (400 ha). We conclude that application of low-severity fire in a patchy manner enhanced avian diversity and facilitated the persistence of the birds detected in pre-fire surveys. Although the levels of patchiness required to sustain diverse taxa warrant further study, our findings highlight the importance of formally incorporating patchiness into prescribed burning for the ecologically sensitive management of contemporary landscapes. Received 16 July 2014, accepted 25 January 2015, published online 2 April 2015

Introduction Prescribed fire is used primarily as a means of reducing the risk of high-severity wildfire, but also for biodiversity conservation (Penman et al. 2011). However, its effects on fauna are poorly defined (Clarke 2008). The effect of fire on terrestrial ecosystems is reflected in fire severity (Pastro et al. 2014), which describes spatial patterns of vegetation damage and ranges across a spectrum from unburnt to crown fire (Bradstock et al. 2010). Prescribed burns are often characterised by patchy, lowseverity fire (Penman et al. 2007) and the literature identifies two conceptual frameworks through which fauna may respond to patchy fire. First, a patchwork of fire severities is expected to increase animal diversity because it constitutes a mosaic of successional states in which different species occur (Bradstock et al. 2005; Parr and Andersen 2006). Second, unburnt patches can provide faunal refuges by retaining resources (Robinson et al. 2013; Robinson et al. 2014). These frameworks are not mutually exclusive and both can depict the responses of different animal species to a single patchy fire. Relationships between avian diversity and patchy fire have been identified in ecosystems featuring marked structural and floristic contrast between early and late successional vegetation (Smucker et al. 2005; Fontaine and Kennedy 2012). In contrast, mid–late successional vegetation is often of disproportionate Journal compilation Ó IAWF 2015

importance to fauna in ecosystems featuring overlap in the structural and floristic elements of different successional states (e.g. birds, Taylor et al. 2012; arboreal marsupials, Lindenmayer et al. 2013; small mammals, Kelly et al. 2012; and reptiles, Nimmo et al. 2013). Given the paucity of early successional specialists in south-east Australia, it has been recommended that land managers prioritise the retention of mid–late successional vegetation (e.g. Taylor et al. 2012). Most studies of the effects of fire on animals have been undertaken following wildfire, which often burns at high severity and generates mosaics of coarse resolution (Hutto 1995; Herrando et al. 2003; Smucker et al. 2005; Murphy et al. 2010; Nappi et al. 2010; Zozaya et al. 2011). To our knowledge, there are few experimental studies incorporating pre-treatment data on the effects of low-severity, prescribed fire undertaken at the scale at which fire is applied by land managers (but see Russell et al. 2010; Pastro et al. 2011; Sutton et al. 2013). Much vegetation structure and associated resources remain intact following low-severity fire (Penman et al. 2007). It is therefore plausible that patchy, prescribed fire has a positive effect on faunal diversity by (i) retaining refuge habitat and allowing species that rely on long-unburnt vegetation to persist, and (ii) providing a patchwork of successional states in which different species occur. www.publish.csiro.au/journals/ijwf

Bird responses to patchy fire

Given increasingly widespread application of prescribed fire in many regions (Castellnou et al. 2010; Stephens et al. 2012; Attiwill and Adams 2013), enhanced understanding of the effect of patchy prescribed fire on fauna is critical. To address this knowledge gap, we used a before–after control–impact (BACI) design to investigate the responses of birds to prescribed fire at two spatial scales. The scale of analysis can have a profound influence on the responses that are detected (Wiens et al. 1987; Cushman and McGarigal 2004) so we examined both a coarse scale (400 ha) – which was commensurate with the scale at which fire management is practised in the region (Department of Environment and Primary Industries 2013) – and a fine scale (0.8 ha) that corresponded to the smallest territory sizes of forest birds occurring in the region; for example, superb fairy-wren (Malurus cyaneus) territory sizes range between 0.8 and 2.6 ha (Nias 1984). Our study area was a topographically variable landscape of tall-open eucalypt forest in south-east Australia, where prescribed fire is extensively applied (Attiwill and Adams 2013). We anticipated that the spatial arrangement of burnt and unburnt patches would be a function of topography (Wood et al. 2011; Leonard et al. 2014). Although the interplay between topography, fire weather and direction of fire movement determines patterns of fire severity, rather than topography per se, the probability of fire is usually lowest in gullies and highest on ridges (Bradstock et al. 2010; Wood et al. 2011; Collins et al. 2012). We made two general predictions regarding avian responses to the prescribed fire. First, we predicted that fire would result in increased avian diversity because species present in older vegetation would remain in unburnt patches and early successional species would be attracted to burnt patches. Second, we hypothesised that the effect of fire on species richness and occurrence would be a function of the presence of topographic refuges. Materials and methods Study area The study was undertaken in the Otway Ranges (the Great Otway National Park) in south-east Australia (Fig. 1). The study area features complex topography (70–350 m above sea level) and a mild climate (mean annual minimum and maximum temperatures, 10.5 and 18.28C; mean annual rainfall, 1229 mm) (Bureau of Meteorology 2014, http://www.bom.gov.au/climate/ data). Vegetation is tall-open forest dominated by mountain grey gum (Eucalyptus cypellocarpa), Tasmanian blue gum (E. globulus), messmate (E. obliqua), narrow-leaved peppermint (E. radiata) and manna gum (E. viminalis) (Department of Environment and Primary Industries 2012). Drier mid-slopes and ridges of fine-grained and duplex loams support shrubby foothills forest, which is characterised by an open shrub stratum and a ground stratum of tussock grass (Department of Environment and Primary Industries 2012). Wetter gullies support shrubby wet forest on fertile loams or loamy clays and contain a scattered understorey of dense, tall shrubs and a species-rich ground stratum including ferns and mesic forbs. Prior to the study, the region was burnt by wildfires in 1939 and 1983. Both the shrubby foothills forest and shrubby wet forest were therefore at a mature successional stage

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(Cheal 2010). Prescribed burns were infrequent in the Otway Ranges until 2008. Since then, they have been applied during spring and autumn, and each fire typically covers an area of ,400 ha (Department of Environment and Primary Industries 2013). Burns are normally implemented in a patchy manner, such that 30–70% of the treated area remains unburnt (Department of Environment and Primary Industries 2013). Study design We used a BACI design to investigate the influence of prescribed fire on birds, which involved bird surveys before and after prescribed fire and concurrent monitoring of a control area (Stewart-Oaten et al. 1986). We selected a 400-ha impact area scheduled for prescribed burning and identified a comparable control area of similar size (Fig. 1). We refer to the scale of the control and impact areas as the ‘coarse scale’. The prescribed burn was not replicated because we were able to identify one location that had a high probability of being burnt at a known time. The region’s topography and unpredictable weather can generate dramatic changes in fire behaviour, necessitating an adaptive approach to prescribed burning. The location we identified was typical of large portions of the region and captured a range of natural variation in the landscape. Impact and control areas were 7 km apart, contained the same broad vegetation types and exhibited a similar degree of topographic variation. Both areas were stratified with respect to topography and 48 survey sites were selected in each using a restricted random protocol. First, a random point along a gully was selected using ArcMap 10 (ESRI 2011). From this initial point, a series of sites was placed in the gully at 200-m intervals (Fig. 1). Then, ridge sites were placed perpendicular to each gully site at the point of highest elevation and mid-slope sites were positioned midway between each ridge–gully pair, resulting in ‘blocks’ of three sites at each topographic position (gully, mid-slope and ridge). We ensured that all blocks were $200 m apart and that all sites within a block were $100 m apart. Blocks were also equally distributed between north- and south-facing slopes. The impact area was burnt between 30 March and 2 April 2012 using drip torches and aerial incendiaries. The principal management objective was to create an irregular mosaic of fuel reduction to protect a nearby town from wildfire. Overall, 52% of the area remained unburnt (208 ha) and 48% was burnt (192 ha; Fig. 1). Fire reached the canopy in 8 ha and elsewhere it affected only understorey and mid-storey vegetation (Loschiavo 2012). Given that there was relatively little variation in fire severity within burnt patches, we mapped the spatial arrangement of burnt and unburnt patches instead of a spectrum of severities. Boundaries between patches were mapped on the ground with a handheld GPS (GARMIN GPSMAP 62s, Olathe, Kansas, USA), and used to produce a vector layer in ArcMap (ESRI 2011). We defined the ‘fine scale’ by delineating circles of 50-m radius at sites within the impact area using ArcMap (Fig. 1) and calculating the proportion burnt using the Patch Analyst add-in (Rempel et al. 2012). The mean (95% confidence interval) proportions of gully, mid-slope and ridge sites burnt were 0.03 (0.03), 0.46 (0.21) and 0.84 (0.14).

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Impact area

0 7501500 3000 4500 6000 Metres

0

Lorne

125 250

500

750

1000 Metres

Control area

Bass Strait G M R

Block of three sites

Survey site 50 m radius Contour (10 m) Road Gully Burnt area

0

125 250

500

750

1000 Metres

Fig. 1. Locations of the study sites in south-east Australia. Sites are positioned in ‘blocks’ of three; one in a gully (G) and the others on a mid-slope (M) and ridge (R). Proportion burnt was calculated within a 50-m radius of sites at the impact area.

Bird data Birds were surveyed at each site using 10-min point counts (Bibby et al. 1994). We sought to capture the immediate responses of birds to changes in resource distribution precipitated by the fire, as well as changes during the spring breeding season (November). We therefore conducted surveys during four seasons: twice before fire (November 2011 and February 2012) and twice after fire (6 weeks after fire, in May 2012 and 6 months after fire, in November 2012). Each bird detection was assigned to one of five distance classes from the site (0–10 m, 10–25 m, 25–50 m, 50–100 m and .100 m) and was recorded as seen, heard or flying over. Surveys were undertaken on days of good weather (no rain or strong wind) and repeat surveys at each site were conducted by different observers to limit the potential influence of observer bias. Multiple-covariate distance sampling (Buckland et al. 2004; Marques et al. 2007) in Distance version 6.0 (Thomas et al. 2010) was used to evaluate variation in detectability between species

and topographic position, which was a potential source of bias (Buckland et al. 2001). Birds recorded as flying over were omitted and species recorded infrequently were grouped with species assumed to have similar detectability to generate a common detection function for 21 groups (see Supplementary Material Table S1, available online only; Alldredge et al. 2007). Variation in detectability was modelled with increasing distance from the observer and topographic position was included as a categorical predictor. The probability of detection was reasonably consistent among species and species groups (mean probability of detection ¼ 0.49, 95% confidence intervals 0.41–0.56, n ¼ 19) and topographic position did not have a significant influence on detection probabilities. Consequently, we analysed the data without adjusting for differences in detectability and modelled presence–absence of individual species within 50 m of sites. We also used presence–absence data to generate two measures of diversity: species richness and temporal species turnover (b diversity). Richness was quantified per site for each of


the four survey seasons. Species turnover was calculated using the multiple-site generalisation of the Simpson dissimilarity index, which quantifies the replacement of some species by others between periods (Baselga et al. 2013). Turnover between the before- and after-fire sampling periods was calculated for each site using presence–absence data from spring pre-fire surveys and spring post-fire surveys. The index ranges from zero (complete similarity in species at a site between time periods) to one (complete dissimilarity in species) and was calculated in the R statistical environment (R Core Team 2014) using the package betapart (Baselga et al. 2013). Statistical analyses We used generalised linear mixed models (GLMMs) to investigate avian responses to the prescribed fire (Zuur et al. 2009) at the coarse and fine scales. We recorded 57 species in total (Table S1), 15 of which were observed frequently enough for modelling given the largest number of possible parameters estimated in models (Wintle et al. 2005). Models of species richness and turnover were implemented with Gaussian errors because assumptions of normality and homogeneity of variance were satisfied, and models of individual species occurrence were applied with a logit link function and binomial errors. We selected random effects by running a full model containing all fixed effects and their interactions, and using Akaike’s Information Criterion corrected for small sample size (AICc) to compare levels of support for models containing different random effects (Zuur et al. 2009). The random effects we considered were survey season, block and site. Inclusion of survey season and block in the full model resulted in the lowest AICc so we specified them as crossed random effects throughout analysis at both the coarse and fine scales (block was the only random effect in models of species turnover because data from different survey seasons were pooled) (Zuur et al. 2009). Specification of survey season as a random effect accounted for variance associated with natural seasonal fluctuations in bird communities. Modelling was undertaken in R (R Core Team 2014); GLMMs were run using the package lme4 (Bates et al. 2013). At the coarse scale, fixed effects were the two-level categorical variables time (before, after) and treatment (control, impact), and the three-level categorical variable topographic position (gully, mid-slope, ridge). Candidate sets for species richness and occurrence consisted of four models; the candidate set for species turnover consisted of only three models due to the omission of the time variable, which was used in calculating turnover between the before and after sampling periods (Table S2). At the fine scale, before-fire bird data were used as a basis for comparison with after-fire data (Stewart-Oaten and Bence 2001). Fixed effects were time and a direct measure of the proportion burnt within 50 m of survey sites (termed ‘proportion burnt’). Candidate sets consisted of two models: proportion burnt in additive and interactive combination with time (we ran only one model of species turnover, containing proportion burnt, because the time variable was omitted; Table S2). Information-theoretic model selection was used to investigate the influences of predictor variables on birds (Burnham and Anderson 2002). Within each candidate model set, levels of support for models were compared using AICc, and Akaike


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weights were used to indicate the relative likelihood that a particular model was the most parsimonious (Burnham and Anderson 2002). Model selection was undertaken using the package MuMIn (Barton 2013). GLMMs were evaluated using R2 as a measure of model fit and seven-fold cross-validation as a test of predictive potential (Pearce and Ferrier 2000). Measures of model fit consist of two values: marginal R2 is the variance explained by fixed factors and conditional R2 is the variance explained by both fixed and random factors (Nakagawa and Schielzeth 2013). Seven-fold cross-validation involved randomly dividing data into seven groups and using a model fitted to data from six of the groups to predict data from the seventh. Mean correlation coefficients and 95% confidence limits between predicted and observed data were generated from 100 iterations for Gaussian models. The relative operating characteristic (ROC) curve was used in examining the predictive ability of binomial models, which is measured by the area under the curve (AUC). Mean AUC values and 95% confidence limits were calculated from 100 iterations; a value of 0.5 indicates no predictive discrimination and a value of 1.0 implies perfect discrimination (Pearce and Ferrier 2000). Cross-validation was conducted using the R packages dismo (Gaussian and binomial models; Hijmans et al. 2013) and pROC (binomial models; Robin et al. 2011). Finally, we tested for spatial autocorrelation in the residuals of top-ranked models to ensure that the mixed model structure accounted for any spatial autocorrelation in the response variables (Keitt et al. 2002). We used the R package ncf to generate spline correlograms from 1000 permutations (Bjornstad 2013). The ‘spline.correlog’ function uses a modified nonparametric spatial covariance function to derive a generalised estimate of spatial dependency as a function of distance, and a bootstrap algorithm estimates point-wise 95% confidence intervals (Bjørnstad and Falck 2001). At the coarse scale, we produced separate spline correlograms for the control and impact areas. The spline correlograms showed that correlation values for model residuals were close to zero across the range of distances (0–2500 m) (see Fig. S1 for examples), providing validity to the assumption that model residuals were spatially independent. Results Coarse scale Top-ranked models of both species turnover and richness contained topographic position alone, indicating that there was no detectable effect of the fire on bird diversity (Table 1). The species richness model’s Akaike weight was high and its fit and predictive capacity were moderate; richness was greater in gullies than on mid-slopes and ridges (Fig. 2a). There was no detectable effect of the fire on the overall occurrence of the 15 species (Table 1). However, the occurrence of several species changed at particular topographic positions after fire at the impact area. Models that tested consistent changes in occurrence at different topographic positions after fire were top ranked for two species (Table 1): occurrence of superb fairy-wren increased at all topographic positions after fire (Fig. 2d ), whereas occurrence of eastern yellow robin (Eopsaltria australis) decreased at all topographic positions (Fig. 2e). An interaction between time, treatment and

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Table 1. Responses of avian species turnover, richness and the probability of occurrence of 15 species to prescribed fire and topographic position derived from generalised linear mixed models Predictor variables are time, treatment (Tr) and topographic position (topog), which consists of the levels gully (G), mid-slope (M) and ridge (R); estimates associated with M and R represent contrasts with G. Models were ranked using the small sample size adjustment of Akaike’s information criterion (AICc), and top-ranked models are displayed with Akaike weights. Parameter estimates with 95% confidence intervals (CI) are included, as well as their statistical significance (P). Two measures of fit are presented for each model; marginal R2 (R2(m)) and conditional R2 (R2(c)). For Gaussian species turnover and richness models, mean cross-validation correlations (R) are included as measures of predictive accuracy; for binomial models of the probability of occurrence of individual species, areas under the curve (AUC) are displayed Response variable

Model

Species turnover

Topog M R Topog M R Topog M R Topog M R Time Treatment þ Topog M R Time Tr Topog M R Topog M R Topog M R Time Treatment þ Topog M R Time Tr Topog M R Time Treatment þ Topog M R Time Tr Topog M R Topog M R Topog M R Time Treatment Topog Time Tr Time Tr M Time Tr R Time Treatment þ Topog M R Time Tr Topog M R

Species richness

Crimson rosella, Platycercus elegans White-throated treecreeper, Cormobates leucophaea Superb fairy-wren Malurus cyaneus

White-browed scrubwren, Sericornis frontalis Striated thornbill, Acanthiza lineata Brown thornbill, Acanthiza pusilla Eastern spinebill, Acanthorhynchus tenuirostris White-naped honeyeater, Melithreptus lunatus Red wattlebird, Anthochaera carunculata

Crescent honeyeater, Phylidonyris pyrrhopterus Golden whistler, Pachycephala pectoralis Grey shrike-thrush, Colluricincla harmonica Grey fantail, Rhipidura albiscapa

Eastern yellow robin, Eopsaltria australis

Silvereye, Zosterops lateralis

Akaike weight

Estimate 95% CI

P

0.57 0.03 0.09 0.12 0.09

0.457 0.011

3.09 0.74 1.65 0.74

,0.001 ,0.001

0.00 0.54 0.23 0.55

1.000 0.401

0.03 0.50 0.37 0.51

0.898 0.156

1.94 1.15 3.23 1.12 1.43 1.14

0.001 ,0.001 0.014

2.32 0.62 2.43 0.62

,0.001 ,0.001

0.46 0.52 0.62 0.52

0.085 0.019

0.37 0.65 1.13 0.61

0.256 ,0.001

0.25 0.53 0.96 0.58 0.49 0.92

0.349 0.001 0.294

0.87 0.65 1.37 0.74

0.008 ,0.001

0.24 0.51 0.61 0.54 0.70 0.88

0.393 0.027 0.130

0.78 0.56 1.83 0.60

0.007 ,0.001

1.16 0.59 2.02 0.70

,0.001 ,0.001

0.41 0.59 0.61 0.61

0.175 0.047

0.30 1.47 0.40 2.16 1.36 2.47

0.689 0.713 0.277

0.90 0.56 1.05 0.58 1.05 0.95

0.002 ,0.001 0.030

1.18 0.54 1.81 0.57

,0.001 ,0.001

0.94

0.47

0.54

0.41

0.91

0.85

0.62

0.55

0.94

0.41

0.62

0.81

0.87

0.88

0.97

0.53

R2(m)

R2(c)

R or AUC 95% CI

0.05

0.31

0.25 0.03

0.15

0.18

0.39 0.01

0.00

0.15

0.51 0.01

0.01

0.03

0.52 0.01

0.33

0.36

0.78 0.01

0.27

0.30

0.71 0.01

0.02

0.02

0.56 0.01

0.06

0.06

0.62 0.01

0.08

0.08

0.61 0.01

0.09

0.11

0.65 0.01

0.08

0.17

0.62 0.01

0.11

0.33

0.66 0.01

0.15

0.27

0.70 0.01

0.02

0.12

0.57 0.01

0.19

0.29

0.66 0.01

0.10

0.10

0.63 0.01

0.12

0.28

0.67 0.01



White–browed scrubwren

Species richness

8 6 4 2 0

G

M

1.0

(c) 1.0

0.8

0.8

Silvereye

(b)

(a) 10

0.6 0.4 0.2

0.6 0.4 0.2

0

R

695

G

M

0

R

G

M

R

Topographic position (e)

Impact

0.8 0.6 0.4 0.2

Control

1.0

(f )

Impact

0.6 0.4

G

M

R

G

M

R

Impact

0.6 0.4 0.2

0.2

0

0

0

Control

1.0 0.8

0.8

Grey fantail

Control

1.0

Eastern yellow robin

Superb fairy–wren

(d )

G

M

R

G

M

R

G

M

R

G

M

R

Topographic position Fig. 2. Responses of species richness (a) and the probability of occurrence of species (b–f ) to topographic position (levels are gully (G), mid-slope (M) and ridge (R)) (a–c); and to topographic position and treatment (control or impact) before (pale grey bars) and after (dark grey bars) prescribed fire (d–f ). Predictions are from generalised linear mixed models and errors are 95% confidence intervals.

topographic position was the top-ranked model of grey fantail (Rhipidura albiscapa) occurrence (Table 1), which decreased on ridges after fire (Fig. 2f ); no clear changes were evident at other topographic positions. Twelve of the 15 modelled species responded to topographic position (Table 1). The occurrence of most species was greater in gullies than on mid-slopes and ridges (Fig. 2); superb fairywren was an exception that occurred more frequently on ridges (Fig. 2d ). We observed large differences in the fit and predictive capacity of statistically significant models: topographic position was most strongly related to the occurrence of superb fairywren, white-browed scrubwren (Sericornis frontalis) and silvereye (Zosterops lateralis) (Table 1). Fine scale Fire resulted in an increase in species turnover (Table 2; Fig. 3a). In contrast, we were unable to detect an effect of the fire on species richness (Table 2). Modelling of individual species occurrence revealed four different responses to fire (Table 2). The occurrence of eastern yellow robin decreased at all topographic positions irrespective of the proportion of burnt habitat (Fig. 3f ). Likewise, the occurrence of white-browed scrubwren, grey fantail and silvereye was lower after fire, but the decrease was stronger where a larger proportion of habitat was burnt (Fig. 3c, e, g). The occurrence of brown thornbill also decreased where a higher proportion of habitat was burnt, but its occurrence increased in unburnt habitat (Fig. 3d ). Only superb fairywren had ubiquitously higher occurrence after fire (Fig. 3b).

Discussion Understanding the effect of patchy low-severity fire on biota is crucial given the extensive application of prescribed fire in many regions (Stephens et al. 2012; Attiwill and Adams 2013). Our BACI experiment produced insights into avian responses to patchy fire by showing that species turnover increased after fire at a fine scale, despite a predominance of mid–late successional species in the study region (Taylor et al. 2012; Lindenmayer et al. 2014) Further, it revealed that when fire is suitably low intensity and patchy, faunal refuges can enable species to persist. Our results indicate that the burn applied in the experiment did not have a detrimental effect on birds, and can help inform use of fire as a conservation management tool, particularly in topographically variable landscapes. Does patchy fire increase species diversity? At the fine scale, results supported our prediction that bird diversity would increase after fire due to species preferences for particular successional states (Bradstock et al. 2005; Parr and Andersen 2006). This finding is consistent with ecological paradigms regarding relationships between species diversity and landscape heterogeneity (e.g. Huston 1994). It also supports studies that demonstrate different successional states are required to sustain bird diversity (Smith 2000). For example, much research conducted in northern temperate and boreal forest shows that early successional post-fire forest supports a distinct bird assemblage with fire-associated species and patchy fire has a positive influence on avian diversity (Smucker et al. 2005;

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Table 2. Responses of avian species turnover, richness and the probability of occurrence of 15 species to time and the proportion burnt within a 50-m radius of sites (prop. burnt) derived from generalised linear mixed models Time consists of the levels before fire and after; estimates associated with after represent contrasts with before fire. Models were ranked using the small sample size adjustment of Akaike’s information criterion (AICc), and top-ranked models are displayed with Akaike weights. Parameter estimates with 95% confidence intervals (CI) are included, as well as their statistical significance (P). Two measures of fit are presented for each model; marginal R2 (R2(m)) and conditional R2 (R2(c)). For Gaussian species turnover and richness models, mean cross-validation correlations (R) are included as measures of predictive accuracy; for binomial models of the probability of occurrence of individual species, areas under the curve (AUC) are displayed Response variable

Model

Species turnover Species richness

Prop. burnt Prop. burnt þ Time Prop. burnt After Prop. burnt þ Time Prop. burnt After Prop. burnt þ Time Prop. burnt After Prop. burnt Time Prop. burnt After Prop. burnt Time Prop. burnt Time Prop. burnt After Prop. burnt Time Prop. burnt þ Time Prop. burnt After Prop. Burnt Time Prop. burnt After Prop. burnt Time Prop. burnt þ Time Prop. burnt After Prop. burnt þ Time Prop. burnt After Prop. burnt þ Time Prop. burnt After Prop. burnt þ Time Prop. burnt After Prop. burnt þ Time Prop. burnt After Prop. burnt þ Time Prop. burnt After Prop. burnt Time Prop. burnt After Prop. burnt Time Prop. burnt þ Time Prop. burnt After Prop. burnt Time Prop. burnt After Prop. burnt Time

Crimson rosella

White–throated treecreeper Superb fairy–wren


Striated thornbill

Brown thornbill

Eastern spinebill

White–naped honeyeater

Red wattlebird

Crescent honeyeater

Golden whistler

Grey shrike–thrush

Grey fantail


Silvereye

Akaike weight NA 0.75

Estimate 95% CI

P

0.16 0.12

0.012

1.47 1.06 0.29 1.40

0.007 0.523

0.07 0.73 0.26 1.40

0.842 0.712

0.06 0.69 0.39 0.84

0.874 0.348

6.23 4.06 3.90 4.01 3.91 4.19

0.002 0.055 0.066

2.02 1.07 0.20 1.04 1.58 1.74

,0.001 0.705 0.073

0.04 0.70 0.30 0.58

0.916 0.460

0.06 1.23 1.47 1.40 2.75 1.91

0.920 0.038 0.004

1.57 0.82 0.07 1.13

,0.001 0.898

0.93 1.03 0.08 0.80

0.074 0.839

0.51 0.75 0.50 0.64

0.174 0.123

1.90 0.88 0.86 2.41

,0.001 0.484

2.23 1.03 0.51 1.85

,0.001 0.585

0.56 0.83 0.24 0.68

0.196 0.491

1.07 1.10 0.07 1.77 1.70 1.69

0.054 0.934 0.048

0.99 0.78 0.85 0.64

0.012 0.008

0.84 1.09 0.14 1.66 2.0 1.87

0.128 0.863 0.035

0.68

0.74

0.87

0.65

0.72

0.97

0.74

0.74

0.73

0.71

0.74

0.69

0.72

0.70

0.79

R2(m)

R2(c)

R or AUC 95% CI

0.12 0.04

0.30 0.07

0.32 0.06 0.21 0.02

0.00

0.11

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Proportion burnt Fig. 3. The response of species turnover between the before- and after-fire sampling periods (a), and responses of the probability of occurrence of species to the proportion burnt within a 50-m radius of sites before (dotted line) and after (dashed line) prescribed fire (b–g). Bird responses to proportion burnt before fire are a function of topographic position. Predictions are from generalised linear mixed models and errors are 95% confidence intervals.

Hutto 2008; Nappi and Drapeau 2009; Fontaine and Kennedy 2012). In Australian ecosystems, species that favour early successional vegetation are comparatively scarce (Taylor et al. 2012; Lindenmayer et al. 2014), but there is evidence of positive associations between the diversity of successional states and bird species diversity (Sitters et al. 2014). Concordantly, our fine-scale results suggest that a distinct bird assemblage occurred in recently burnt areas. However, the maximum values of the Simpson dissimilarity index were reasonably low, reflecting a degree of overlap in species composition between the before- and after-fire sampling periods. Conceivably this overlap is a consequence of structural and floristic similarity between burnt and unburnt patches due to the low-severity fire. The supposition that low-severity fire creates a dichotomy of early and late successional vegetation is probably inadequate in most southern Australian ecosystems where high-severity fire is required to generate true early successional vegetation. Instead, low-severity fire yields vegetation comprising some early successional elements, such as a simplified understorey, and the regeneration of vegetation is often rapid and the shrub layer remains largely intact (Costermans 2006). It is probable that early successional bird species appear in a burnt area in greater abundance if early successional elements are plentiful due to higher fire severity or spatial fire coverage. For example, the abundance of species that feed on open ground, including buff-rumped thornbill (Acanthiza reguloides), scarlet robin (Petroica boodang) and flame robin (Petroica pheonicea), increased markedly following large, high-severity wildfire in south-east Australian eucalypt forest (Loyn 1997). We recorded these species after fire and inspection of raw data indicated that their presence contributed to the greater levels of species turnover in burnt areas. We did not detect them frequently enough to model individually.

Despite evidence that species such as buff-rumped thornbill, scarlet robin and flame robin are attracted to early successional vegetation, our results are aligned to those of other south-east Australian studies, which indicate that true early successional specialists are scarce (Taylor et al. 2012; Lindenmayer et al. 2014). It is feasible that the apparent paucity of early successional specialists in southern Australia is a consequence of the variable levels of early successional elements present in post-fire environments. This variability might be compounded by erratic climatic conditions, which generate fluctuation in rates of postfire vegetation regeneration and resource development (Kelly et al. 2012; Taylor et al. 2013). Although the species assemblages of early and mid–late successional vegetation are less distinct than in other regions of the globe (Smucker et al. 2005; Fontaine and Kennedy 2012), our findings support the expectation that bird diversity increases after patchy planned fire. Does patchy fire provide faunal refuges? At the fine scale, we observed changes in the occurrence of species after fire in accordance with our expectation that the patchy burn would provide faunal refuges. We expected changes in the occurrence of individual species to be a function of changes in resource availability resulting from differences in fire severity. Measurements of vegetation structure at our impact area (Loschiavo 2012) indicated that resource availability remained unaltered after fire in unburnt patches, where species occurrences remained similar. Correspondingly, the occurrence of five species decreased after fire in burnt patches, where resource availability was reduced. The species that declined in occurrence in burnt patches (white-browed scrubwren, brown thornbill, grey fantail, eastern yellow robin and silvereye) are commonly associated with understorey and mid-storey vegetation (Higgins and Peter 2001, 2002), the

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density of which is reduced even by low-severity fire (Penman et al. 2007). As predicted, the occurrence of these species remained similar to pre-fire levels in unburnt patches; only brown thornbill exhibited a slight increase in occurrence. These observations suggest that vegetation was close to carrying capacity before fire (sensu Hobbs and Hanley 1990). Contrary to expectations associated with faunal refuges, the occurrence of superb fairy-wren increased in burnt patches after fire. Superb fairy-wren is an edge species that frequently forages on the ground (Berry 2001) but requires understorey vegetation for shelter and nesting (Rowley and Russell 1997). It is possible that it was able to exploit food resources in freshly burnt areas because sufficient vegetation structure remained, or because the finger-like configuration of burnt and unburnt patches provided shelter in reasonably close proximity to burnt, open areas (the maximum distance from a burnt survey site to unburnt vegetation was 100 m). Null responses to patchy fire In contrast to our predictions, we detected no effect of fire on species richness. It is plausible that sufficient vegetation structure remained on ridges, and that the size and configuration of burnt and unburnt patches were conducive to species persistence. Any loss of species in burnt areas is likely to have been compensated for by the arrival of species attracted to freshly burnt vegetation. Presumably a threshold in fire coverage and severity exists beyond which resource requirements are not satisfied; unburnt patches within the boundary of a high-severity wildfire in similar tall-open eucalypt forest were shown to support higher species richness and a distinct assemblage composition to severely burnt areas (Robinson et al. 2014). Further work is required to define such thresholds, which are likely to be a function of climatic and environmental factors, as well as the traits of individual species. Occurrence of two-thirds of the species we modelled was not significantly related to fire effects at the fine scale. We were thus partially successful in predicting these species’ responses; occurrence remained at pre-fire levels in unburnt areas, where measurements of vegetation structure (Loschiavo 2012) indicated that resource availability was unaltered. However, it also remained similar to pre-fire levels in burnt areas where resource availability was reduced. We attribute the lack of response to the fact that these species are not reliant on understorey vegetation; it is unsurprising that a reduction in resource availability in lower vegetation layers did not have a marked influence on large seed eaters (e.g. crimson rosella – Platycercus elegans), canopy insectivores (e.g. striated thornbill – Acanthiza lineata) or honeyeaters (e.g. crescent honeyeater – Phylidonyris pyrrhopterus), all of which often occupy mid-storey or canopy vegetation. Evidently, the vertical vegetation strata occupied by individual species must be taken into consideration if the faunal refuges concept is to predict bird responses to patchy fire in forest ecosystems. In most ecosystems, the capacity of the refuges or successional states conceptual frameworks to depict faunal responses to patchy fire will be a function of multiple factors relating to the attributes of the fire, extrinsic factors and the traits of individual species. Replicate experimental studies within a range of ecosystem types will shed light on the generality of faunal responses

H. Sitters et al.

to patchy fire. However, the species-specific nature of bird responses to fire observed in this experiment is a feature common to studies undertaken in diverse environments (Kotliar et al. 2007; Zozaya et al. 2011; Taylor et al. 2013). Spatial scale Most studies of relationships between landscape patchiness and fauna are undertaken at a single cartographic scale (Kupfer 2012) but it is widely acknowledged that species respond to heterogeneity across a spectrum of scales (Wiens et al. 1987; Cushman and McGarigal 2004). Our findings showed that avian responses to patchy fire varied between the coarse and fine scales. At the coarse scale, our prediction of increased bird diversity associated with a mosaic of successional states was not supported. Further, changes in the occurrence of some individual species at different topographic positions after fire were not statistically significant, suggesting that the resource base was not diminished at this scale, and providing only weak support for the faunal refuges concept. At the fine scale, results were broadly congruent with both conceptual frameworks, indicating that birds were more strongly influenced by changes in resource availability at fine spatial scales. For example, the fire responses of white-browed scrubwren and silvereye were detected convincingly at the fine scale but were entirely absent at the coarse scale. Our results supported those of several studies that show scale dependency in avian responses to disturbance (Murakami et al. 2008; Parrish and Hepinstall-Cymerman 2012; Pelosi et al. 2014). Although our investigation of scale was far from exhaustive, our findings indicate that animal responses to lowseverity patchy fire might be overlooked if analyses are confined to a single spatial scale. Management implications and future directions In many regions, the challenge of developing fire management practices that benefit biodiversity conservation is compounded by a lack of information regarding the influence of fire on fauna (Clarke 2008; Pons and Clavero 2010; Fontaine and Kennedy 2012). The implications of patchy, low-severity fire for fauna are intuitive but there are scant empirical data associating fauna with spatial variation in fire severity. The results of this study indicated that application of fire in a patchy manner, such that total fire coverage was around 50%, enhanced avian diversity and facilitated the persistence of the birds detected in pre-fire surveys. We suggest that the presence of unburnt patches is likely to support faunal persistence and recommend that patchiness is formally incorporated into prescribed burning; but caution that the degree of patchiness needed to sustain diverse taxa under different environmental conditions warrants further investigation. The scale dependency of bird responses to the patchy fire highlights the importance of examining fire–fauna responses at multiple spatial scales. To our knowledge, land management agencies rarely monitor animal responses to fire at fine spatial scales, and it is possible that key fire–fauna relationships are currently missed. The monitoring of animal responses to patchy fire and the quantification of ideal levels of patchiness will necessitate mapping of fire coverage within fire perimeters. Mapping of gradients in fire severity is desirable (Kotliar et al. 2007) although coarse maps depicting burnt and unburnt


areas might be adequate in cases where burnt patches are of uniform severity. Although it is probably impractical for land managers to manipulate fire severity at fine resolutions, our findings support the notion that topographically variable landscapes generate patchy fire, thus facilitating the management of fire for fauna (Collins et al. 2012; Leonard et al. 2014). Preservation of complex vegetation structure in unburnt patches is likely to be of particular importance in ecosystems where early successional species are scarce (Taylor et al. 2012; Lindenmayer et al. 2014). More generally, we suggest that the incorporation of patchiness into prescribed burning is conducive to ecologically sensitive management of contemporary landscapes. Acknowledgements This work is part of a collaborative study of the effects of fire on biodiversity in the Otway Ranges, funded by the Department of Environment, Land, Water and Planning and Parks Victoria. Additional funds were provided by the Holsworth Wildlife Research Endowment, the Stuart Leslie Bird Research Award and Birdlife Australia Victoria. We are grateful to Matthew Swan, Carolina Galindez Silva and Amanda Ashton for their assistance with selecting the study area, and we thank Peter Collins and Dale Tonkinson for undertaking bird surveys. We are grateful to anonymous reviewers for helpful comments on earlier drafts of the manuscript. This research was carried out under a scientific research permit issued by the Department of Environment, Land, Water and Planning (permit number 10005514) and approved by the University of Melbourne School of Land and Environment Ethics Committee (Register No. 1011632.5).

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