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Watson, Simon J., Taylor, Rick S., Nimmo, Dale G., Kelly, Luke T., Haslem, Angie, Clarke,
Michael F. and Bennett, Andrew F. 2012, Effects of time since fire on birds : how
informative are generalized fire response curves for conservation management?, Ecological
applications, vol. 22, no. 2, pp. 685-696.
Available from Deakin Research Online:
http://hdl.handle.net/10536/DRO/DU:30046985
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Copyright : 2012, Ecological Society of America
Ecological Applications, 22(2), 2012, pp. 685–696
Ó 2012 by the Ecological Society of America
Effects of time since fire on birds: How informative are generalized
fire response curves for conservation management?
SIMON J. WATSON,1,2,5 RICK S. TAYLOR,3 DALE G. NIMMO,1 LUKE T. KELLY,1,4 ANGIE HASLEM,3 MICHAEL F. CLARKE,3
1
AND ANDREW F. BENNETT
1
Landscape Ecology Research Group, School of Life and Environmental Sciences, Deakin University, 221 Burwood Highway,
Burwood, Victoria 3125 Australia
2
Institute for Land, Water and Society, School of Environmental Science, Charles Sturt University, Thurgoona,
New South Wales 2640 Australia
3
Department of Zoology, La Trobe University, Melbourne, Victoria 3086 Australia
4
School of Botany, University of Melbourne, Parkville, Victoria 3010 Australia
Abstract. Fire is both a widespread natural disturbance that affects the distribution of
species and a tool that can be used to manage habitats for species. Knowledge of temporal
changes in the occurrence of species after fire is essential for conservation management in fireprone environments. Two key issues are: whether postfire responses of species are
idiosyncratic or if multiple species show a limited number of similar responses; and whether
such responses to time since fire can predict the occurrence of species across broad spatial
scales. We examined the response of bird species to time since fire in semiarid shrubland in
southeastern Australia using data from surveys at 499 sites representing a 100-year
chronosequence. We used nonlinear regression to model the probability of occurrence of 30
species with time since fire in two vegetation types, and compared species’ responses with
generalized response shapes from the literature. The occurrence of 16 species was significantly
influenced by time since fire: they displayed six main responses consistent with generalized
response shapes. Of these 16 species, 15 occurred more frequently in mid- or later-successional
vegetation (.20 years since fire), and only one species occurred more often in early succession
(,5 years since fire). The models had reasonable predictive ability for eight species, some
predictive ability for seven species, and were little better than random for one species. Bird
species displayed a limited range of responses to time since fire; thus a small set of fire ages
should allow the provision of habitat for most species. Postfire successional changes extend for
decades and management of the age class distribution of vegetation will need to reflect this
timescale. Response curves revealed important seral stages for species and highlighted the
importance of mid- to late-successional vegetation (.20 years). Although time since fire
clearly influences the distribution of numerous bird species, predictive models of the spatial
distribution of species in fire-prone landscapes need to incorporate other factors in addition to
time since fire.
Key words: birds; chronosequence; conservation; disturbance; fire; mallee; semiarid shrubland;
southeastern Australia; succession; time since fire.
INTRODUCTION
Fire is a natural disturbance that influences the
structure of ecological communities throughout the
world (Bowman et al. 2009). Fire is also used for land
management and conservation; for example, to reduce
fuel loads to prevent unplanned fires (Gill and Allan
2008), or to provide particular postfire age classes of
vegetation to benefit species (Parr and Andersen 2006).
The response of many species to fire is poorly
understood, and inappropriate fire regimes are a
Manuscript received 10 May 2011; revised 28 September
2011; accepted 28 September 2011. Corresponding Editor: R. L.
Knight.
5 Present address: Institute for Land, Water and Society,
Charles Sturt University, P.O. Box 789, Albury, New South
Wales 2640 Australia. E-mail: [email protected]
threatening process for species and communities worldwide (e.g., Woinarski 1999, Covert-Bratland et al. 2006,
Fuhlendorf et al. 2006, Sara et al. 2006, Slik and Balen
2006). In Australia, inappropriate fire regimes are
associated with five extinct species or subspecies of
birds, (e.g., Paradise Parrot Psephotus pulcherrimus,
Chisholm 1922, Woinarski 1999), and are recognized as
a threatening process for more than 50 species (Woinarski 1999). A key challenge for fire management is to
develop an understanding of the responses of species to
fire so that fire management can be tailored toward
species conservation.
Knowledge of the pattern of occurrence or abundance
of species with time since fire (‘‘fire response curves’’) is
essential for fire management (Driscoll et al. 2010). Fire
response curves indicate the extent to which a species
depends on particular postfire ages and may also
685
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SIMON J. WATSON ET AL.
FIG. 1. Generalized postfire response curves of bird species, adapted from the literature (e.g., Whelan et al. 2001, Kavanagh et
al. 2004). These curves show eight possible relationships between the frequency of occurrence of bird species and time since fire:
incline (lowest occurrence in young vegetation, increases monotonically to be highest in old vegetation); decline (highest occurrence
in young vegetation, decreases monotonically to be lowest in old vegetation); bell (defined peak in occurrence in mid-aged
vegetation); plateau (lowest occurrence in young vegetation, peaks mid-age vegetation, maintained in older vegetation); irruptive
response 1 (occurrence peaks in young vegetation, rapidly declines, lowest in mid-aged and old vegetation); irruptive response 2
(occurrence increases and peaks in young vegetation, rapidly declines, lowest in mid-aged and old vegetation); delayed incline (low
occurrence until mid- or old-aged vegetation before increasing in occurrence); null (occurrence not influenced by postfire age).
identify time-since-fire thresholds necessary to ensure
required habitat resources (Keith et al. 2001, Driscoll et
al. 2010). ‘‘Generalized’’ fire response curves, whereby a
limited number of patterns represent the responses of
many species (see Fig. 1), are an attractive prospect for
conservation managers, as they suggest that a limited
number of postfire ages will provide habitat for a range
of species. However, knowledge of temporal responses
to fire is scarce for many species, even in fire-prone
environments. Furthermore, there have been few empirical investigations to test the extent to which knowing a
species’ response can be used to predict its occurrence.
A diverse range of faunal responses to time since fire
has been documented (Whelan et al. 2001). Some species
favor recently burned vegetation (e.g., Hutto 2008),
whereas others prefer long-unburned vegetation (e.g.,
Clarke et al. 2005). Comparison of responses among
multiple species may provide insights into the importance of fire in structuring faunal assemblages and the
generality of responses among species.
Studies of species’ responses to fire over long time
frames commensurate with the duration of successional
processes remain rare. Additionally, studies that do
investigate long time frames seldom do so across broad
geographic scales, which may result in localized effects
obscuring successional patterns (Johnson and Miyanishi
2008). Investigations of long-term trends at spatial and
temporal scales as large as those at which ecosystem
processes such as fire can operate are an important
complement to detailed short-term and manipulative
studies. The former may reveal patterns not apparent at
short timescales, allowing for more informed management decisions (Clarke et al. 2010).
In this study, we examine the influence of fire on the
occurrence of bird species in semiarid Eucalyptus
shrublands of southeastern Australia. We use data from
systematic surveys at 499 sites representing a 100-year
chronosequence in time since fire, to examine the shape
of fire response curves over an extended time frame. We
investigate whether fire response curves display generality among species in an assemblage and whether they
conform to a priori patterns identified in the literature
(e.g.Whelan et al. 2001, Kavanagh et al. 2004) (Fig. 1);
and examine the role of different successional stages in
supporting bird species. Finally, we test the capacity for
postfire response patterns to predict species’ distributions across an extensive fire-prone region.
METHODS
Study area
The study area encompassed ;104 000 km2 in the
Murray Mallee region of Australia, centered on the
intersection of the states of Victoria, New South Wales
and South Australia. The region has a semiarid climate
March 2012
AVIAN FIRE RESPONSE CURVES
with hot, dry summers and mild winters. Climatic
variation across the region forms a south to north
gradient of increasing aridity (Pausas and Bradstock
2007), with mean annual rainfall declining from ;350 to
200 mm across this gradient. Mean daily maximum and
minimum temperatures in the warmest month range
from 328 to 338C and 148 to188C, respectively; and in the
coolest from 158 to 168C and 48 to 68C, respectively (raw
data obtained from the Australian Bureau of Meteorology).
The study was conducted within the widespread ‘‘tree
mallee’’ vegetation of the region, dominated by a low
canopy (,10 m tall) of multistemmed Eucalyptus
species, and an understory of shrubs and perennial and
ephemeral grasses (Bradstock and Cohn 2002). Here, we
recognize three broad vegetation types (on the basis of
floristic surveys) for which spatially explicit maps were
generated (Haslem et al. 2010): Triodia Mallee, Chenopod Mallee (also incorporating Shrubby Mallee), and
Heathy Mallee. Triodia Mallee vegetation occurs on
sandy soils: the overstory is dominated by Eucalyptus
dumosa and E. socialis, and the understory typically
includes the hummock grass Triodia scariosa, and
commonly also contains shrubs such as Acacia rigens,
A. wilhelmiana, and Beyeria opaca. Chenopod Mallee
occurs on soils with higher clay content, typically in
swales between dunes. It has a sparse understory of
shrubs (e.g., Olearia spp., Zygophyllum spp.) and
chenopod species (e.g., Maireana pentatropis, Enchylaena tomentosa var. tomentosa, and M. pyramidata) and an
overstory of E. oleosa subsp. oleosa and E. gracilis
(Haslem et al. 2010). Sites in Heathy Mallee were
excluded from this analysis (see study design).
Mallee vegetation is highly flammable (Bradstock
and Cohn 2002). Large fires (.10 000 ha) occur
regularly in the study region (16 since 1972), with very
large fires (.100 000 ha) occurring once every 10–20
years. Smaller fires (,10 000 ha and mostly less than
100 ha) occur every year. The majority of fires are
ignited by lightning (S. C. Avitabile, K. E. Callister,
L. T. Kelly, L. Fraser, A. Haslem, D. G. Nimmo, S. J.
Watson, S. A. Kenny, R. S. Taylor, L. M. SpenceBailey, A. F. Bennett, and M. F. Clarke, unpublished
manuscript). Fires in tree mallee vegetation typically
remove both understory and canopy vegetation (Noble
and Vines 1993). Mallee eucalypts resprout from
underground lignotubers following fire, whereas understory plants regenerate via resprouting from surviving vegetative matter (i.e., stems, roots, and
lignotubers) and from seed (Parsons 1968, Cheal et
al. 1979). Postfire regeneration produces a relatively
dense vegetation layer below 2 m until 15 years postfire,
after which vegetation cover reduces in the mid stratum
(0.5–2 m) as shrubs become sparser and mallee
eucalypts develop a canopy above this height. Canopy
height reaches a mean of 5–8 m at ;60 years postfire
(Haslem et al. 2011).
687
Study design and data collection
We surveyed birds at sites representing a chronosequence from ,1 year to 164 years postfire. Time since
fire was measured from the year 2007 (i.e., sites burned
in 2006 ¼ 1 year since fire). The postfire age of vegetation
at each site was ascertained by using two methods. For
sites burned after 1972, the year of burning was
determined from maps of fire history based on 15
individual years of satellite imagery, combined with
local knowledge of exact fire dates (S. C. Avitabile, K. E.
Callister, L. T. Kelly, L. Fraser, A. Haslem, D. G.
Nimmo, S. J. Watson, S. A. Kenny, R. S. Taylor, L. M.
Spence-Bailey, A. F. Bennett, and M. F. Clarke,
unpublished manuscript). For sites burned prior to
1972, fire age was estimated using regression models of
the strong positive relationship between the diameter of
eucalypt stems and years since fire, which were validated
with independent data (Clarke et al. 2010).
This investigation was part of a study examining the
influence of the properties of fire mosaics on flora and
fauna; consequently, bird survey sites were arranged in
28 clusters (landscape units), each encompassing 20 sites
(mean distance between landscapes ¼ 130 km, range ¼ 6–
260 km; see Haslem et al. [2010] for a map of study
landscapes). Within each landscape, sites generally were
separated by 500 m to maximize independence (mean
distance between sites within landscapes ¼ 1.5 km, range
¼ 0.34–3.61 km). We excluded from analysis 17 sites
predicted to be .100 years since fire (i.e., 101–164 years)
as there were too few sites to adequately represent this
time period. We also excluded all sites in Heathy Mallee
vegetation (n ¼ 44) due to poor representation of some
age classes: only one site was ,19 years since fire. The
resulting data set consisted of 499 sites representing
postfire ages between 1 and 100 years: 176 sites in
Chenopod Mallee and 323 in Triodia Mallee (see
Appendix A for details of the distribution of survey
sites in relation to time since fire).
Surveys were conducted on four survey rounds, once
each in the Austral spring and autumn of 2006/2007 and
2007/2008. All sites were surveyed four times, twice each
by two observers (S. Watson and R. Taylor). At each
site, all individuals were counted and recorded within a
60 m radius during a 5-min period. Surveys commenced
within 15 min of sunrise, during the time of greatest
vocal activity for birds. We recorded the distance from
the center of the point count to the location of the first
detection of individual birds to permit analysis of
detectability. For visual detections, distance was measured using an OPTi-LOGIC 800LH laser range finder
(Opti-Logic, Tullahoma, Tennessee, USA). For aural
detections, distance was estimated by the observer.
Observers trained together to ensure comparability of
procedures.
STATISTICAL ANALYSIS
Variation in detectability of different species, or of the
same species in different habitats, is a potential source of
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SIMON J. WATSON ET AL.
variation in ecological studies (Buckland et al. 2001). To
ascertain whether detectability was likely to be a
problem in interpreting our data, we undertook multiple
covariate distance sampling, MCDS (Buckland et al.
2004, Marques et al. 2007), using the program Distance
5.0 release 2 (Thomas et al. 2006). We modeled variation
in detectability of individual species with increasing
distance from the observer, also incorporating vegetation density as a covariate in the model. Density of
vegetation at each site was obtained by recording the
number of contacts of vegetation with a 2-m structure
pole at each of four height intervals (0 m, 0.5–1 m, 1–2
m, .2 m), at 50 3 1 m intervals along a transect. Species
recorded too infrequently to model individually were
grouped with more common species that displayed
similar detection characteristics, following Alldredge et
al. (2007). All species were successfully detected at the
farthest extremities of the point count. The lowest
probability of detection at a point was for the combined
taxa, Variegated Fairy-wren (Malurus lamberti ) and
Splendid Fairy-wren (Malurus splendens) (probability of
detection ¼ 0.44, 95% CI 0.290.66, N ¼ 72). Vegetation
density did not significantly reduce the detectability of
any species. Considering these results, and that our
primary aim was to assess site occupancy, modeling
presence–absence is an appropriate approach that
reduces potential influence of differential detectability
of species in different postfire ages.
We used generalized additive mixed models, GAMMs
(Wood 2006), to model the change in species occurrence
with time since fire. Generalized additive models
(GAMs) are a nonparametric form of regression
modeling that use smoothing functions to model
nonlinear relationships (Wood 2006, 2008). Because
many of the response shapes that we selected a priori are
nonlinear (Fig. 1), GAMs presented an appropriate
method (Zuur et al. 2009). Models were fitted using
presence–absence data to model the probability of
occurrence of species, where presence represents the
detected occurrence of a species at a site in any of the
four survey rounds. We used presence–absence rather
than abundance or density because it is less prone to
error from localized data anomalies (Buckland et al.
2001) and because the natural rarity of some species
results in many zeros across the data set, rendering
Poisson and Gaussian models inappropriate.
We developed GAMs in a mixed-modeling framework
(GAMMs) to account for the spatial clustering of study
sites in landscapes. Mixed models reduce the potentially
confounding problems of spatial autocorrelation and are
recommended where systematic structuring is present in
the data (Zuur et al. 2009). Thus, landscape unit was
included as a random factor in each species’ model. To
ascertain whether species’ response shapes varied
between vegetation types (Triodia Mallee vs. Chenopod
Mallee), we fitted models allowing a separate smoothed
term for time since fire to be fitted for each level of this
categorical variable (Wood 2006). We also included
vegetation type as a separate factor variable; an
additional variable, ‘‘northing,’’ was included to allow
for a site’s location along the gradient of aridity in the
region.
The amount of smoothing used to model the response
to time since fire was automatically selected in the
model-fitting process (Wood 2008). Wood (2006) warns
that P values for smoothed terms are approximated;
consequently, we approach P values near 0.05 with
caution, following Zuur et al. (2009). The modeled
response of the probability of occurrence of each species
to time since fire was plotted, and species were then
allocated to a response shape based on its similarity to a
priori response shapes adapted from Whelan et al.
(2001) (see Fig. 1).
We evaluated the GAMMs using deviance explained
(D2) as a measure of model fit, and seven-fold crossvalidation to test predictive accuracy (Pearce and Ferrier
2000). This procedure involved splitting the data into
seven groups (‘‘folds’’), fitting the GAMMs to data from
six folds and predicting the occurrence of species to sites
in the seventh fold, which is independent of the data
used to generate the model. These predictions for sites in
the seventh fold were compared with observed responses
to test the predictive accuracy of the model. This process
was continued iteratively until predictions had been
made for all sites. Evaluation of the predictive performance of models was based on the mean discrimination
(and standard error) across all folds. Model discrimination was ascertained using the relative operating
characteristic (ROC) curve. The area under the curve
(AUC) gives a measure of the usefulness of the model in
predicting species occurrence. Scores from 0.5 to 0.7 are
considered to have some discrimination, 0.7 to 0.9
indicate reasonable discrimination, and 0.9 to 1.0 show
very good discrimination (Pearce and Ferrier 2000).
All GAMMs were built in the R statistical environment (R Development Core Team 2009), using the
‘‘mgcv’’ package (Wood 2004, Wood 2006). Crossvalidation methods and source scripts were adapted
from Elith et al. (2008).
RESULTS
We detected 70 species of birds during point counts.
We modeled species that occurred at .20 sites and that
occurred across the entire region (north and south of the
Murray River, a biogeographic barrier), resulting in
models for 30 of the 70 species. One species (Greyfronted Honeyeater, Lichenostomus plumulus) was recorded only north of the Murray River, and two species
(Mallee Emu-wren and Purple-gaped Honeyeater, Lichenostomus cratitius) only south of the river; these
species were not included in the analysis.
Response of species to time since fire
Of the 30 species modeled, 16 showed significant
variation in probability of occurrence with time since fire
(Table 1). All 16 responded to time since fire in Triodia
March 2012
AVIAN FIRE RESPONSE CURVES
Mallee vegetation, but only four species displayed a
significant response in Chenopod Mallee (Table 1).
We assigned each significant time-since-fire response
to one of the a priori response shapes (Tables 1 and 2,
Fig. 2). There was uncertainty for some species in
distinguishing between an inability to detect a significant
response and a null response to time since fire. This was
particularly evident in the less fire-prone Chenopod
Mallee, for which there was a lower sample size,
particularly in younger vegetation. Accordingly, we
assigned a category of ‘‘nondetection’’ to species that
displayed nonsignificant trends, for which a null
response may not be appropriate (e.g., where P values
were close to alpha, 0.05 , P , 0.1).
In Triodia Mallee, 16 species displayed significant
responses to time since fire. Of these, eight displayed an
incline, five a bell shape, two a plateau, and one an
irruptive response shape (Table 2). In Chenopod Mallee,
four species displayed significant time-since-fire responses: of these, two species displayed an incline, one species
a decline, and one species a plateau (Table 2). Overall,
the null response was the most frequently encountered in
both vegetation types (Table 2).
There was little concordance between responses of
species in the two vegetation types (Table 1). Of the
species that displayed a significant response to time since
fire, only two (Yellow-plumed Honeyeater, Spinycheeked Honeyeater) had the same response shape in
both vegetation types. Two species (Chestnut-rumped
Thornbill, White-browed Babbler) displayed different
response shapes in each vegetation type, and eight
species showed a significant response in one vegetation
type and a null response in the other (Fig. 3). A further
four species responded in one vegetation type and
displayed a nonsignificant trend (nondetection) in the
other.
Evaluation of model fit and predictive discrimination
The deviance explained by models ranged from 0.04
to 0.34 (Table 3), suggesting a high level of variability in
their explanatory power. Note that the probability of
occurrence of several species was also significantly
related to the non-fire variables in the model, northing
and vegetation type (Table 3; details in Appendix B).
The models for the 16 species that showed a
significant response to time since fire had AUC values
that ranged between 0.54 and 0.86 (Table 3). There was
reasonable predictive discrimination for eight species
(AUC . 0.7), some predictive discrimination for seven
species (0.6 , AUC , 0.7), and predictive discrimination little better than chance for one species AUC ¼ 0.54
(Table 3).
DISCUSSION
Influence of time since fire on the occurrence
of bird species
Our findings are consistent with the view that fire is an
important disturbance process that influences avifaunal
689
communities (Brawn et al. 2001). The frequency of
occurrence of species can continue to change up to 100
years since fire in mallee ecosystems, as has also been
found in contemporaneous work on small mammals and
reptiles in this system (Kelly et al. 2011, Nimmo et al., in
press). This finding emphasizes the importance of
interpreting time-since-fire response curves on a temporal scale that corresponds with that at which the
disturbance process can operate. In mallee vegetation,
the fire return interval is generally .35 years (S. C.
Avitabile, K. E. Callister, L. T. Kelly, L. Fraser, A.
Haslem, D. G. Nimmo, S. J. Watson, S. A. Kenny, R. S.
Taylor, L. M. Spence-Bailey, A. F. Bennett, and M. F.
Clarke, unpublished manuscript), and some sites are
likely to be .150 years since fire (Clarke et al. 2010).
In this study, many sites of similar time since fire were
products of different fire events spread across a vast
region. Thus, the significant responses show that species
respond in a similar way to vegetation of similar postfire
age generated by different fire events. This result is
notable, given the effects that the complexity of
individual fire events (Whelan et al. 2001, Bain et al.
2008, Lindenmayer et al. 2009) and other factors, such
as rainfall (Monamy and Fox 2000, Letnic 2003), can
have on species succession. However, moderate model fit
and predictive ability for several species indicate that
factors additional to time since fire also are influential.
In addition to general responses of species to fire,
represented by the overall frequency of occurrence (or
abundance) of a species in postfire age classes, species
may respond unexpectedly to particular fire events. For
example, the Eastern Bristlebird (Dasyornis brachypterus), considered to prefer older vegetation in temperate
heathland of eastern Australia and to take many years
for population recovery after fire (Baker 2000), was
found to quickly colonize recently burned sites after a
fire that burned in a ‘‘patchy’’ manner (Bain et al. 2008,
Lindenmayer et al. 2009). Fire response curves derived
from large sample sizes and over broad spatial scales can
inform management about general responses of species
to fire and their preferred seral stages, but may not
necessarily be able to predict responses to particular fire
events.
Shapes of species’ responses to time since fire
Modeling results for 30 species of birds revealed six
generalized response shapes that resembled those
identified a priori in the literature. Similar responses
have also been described for birds in mountain
rangeland systems in Europe (Pons and Clavero 2010),
suggesting that these patterns, and potentially similar
processes, may occur in diverse ecosystems. Different
postfire responses can result from interactions of species
traits with aspects of the environment influenced by fire
(Whelan et al. 2001, Moretti and Legg 2009).
The shape of a species’ response to fire is influenced by
multiple factors. These include the influence of fires on
mortality of the species (Whelan et al. 2001); the
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TABLE 1. Relationship between species occurrence and time since fire for 30 bird species in the Murray Mallee region of
southeastern Australia, derived from generalized additive mixed models.
Triodia Mallee
Chenopod Mallee
Years since file
Species
Australian
Ringneck,
Barnardius
zonarius
Mulga Parrot,
Psephotus varius
Variegated Fairywren, Malurus
lamberti
Striated Grasswren,
Amytornis
striatus
Shy Heathwren,
Calamanthus
cautus
Weebill, Smicrornis
brevirostris
Chestnut-rumped
Thornbill,
Acanthiza
uropygialis
Inland Thornbill,
Acanthiza apicalis
Spotted Pardalote,
Pardalotus
punctatus
Striated Pardalote,
Pardalotus
striatus
White-eared
Honeyeater,
Lichenostomus
leucotis
Yellow-plumed
Honeyeater,
Lichenostomus
ornatus
White-fronted
Honeyeater,
Purnella albifrons
Spiny-cheeked
Honeyeater,
Acanthagenys
rufogularis
Red Wattlebird,
Anthochaera
carunculata
Brown-headed
Honeyeater,
Melithreptus
brevirostris
Striped Honeyeater,
Plectorhyncha
lanceolata
White-browed
Babbler,
Pomatostomus
superciliosus
Chestnut Quailthrush,
Cinclosoma
castanotus
Gilbert’s Whistler,
Pachycephala
inornata
N
Shape
F
P
Maximum
occurence
Minimum
occurrence Years since fire
Shape
F
P
54 null
0.11
0.745
nd
2.96
0.086
36 null
1.25
0.264
null
1.95
0.163
23 null
1.13
0.312
null
1.60
0.207
37 bell
6.84 ,0.001
0–10, 70–100 null
2.09
0.149
69 null
0.16
null
0.10
0.757
null
0.02
0.9
decline
6.47
0.011
null
0.39
0.535
1.71
0.183
20–50
0.692
324 plateau
5.52 ,0.001
114 irruptive 1
4.20
0.017
61 null
2.13
0.145
225 bell
4.19
0.013
153 incline
9.62 ,0.001
90–100
0–10
nd
2.42
0.12
219 bell
3.9
30–50
90–100
null
0.17
0.679
50–100
0–10
286 plateau
21
0.018
,0.001
113 null
0.9
0.343
147 incline
5.42
0.021
28 null
1.03
46 nd
20–100
0–10
0–10
20–100
30–50
0–10, 90–100 nd
plateau 11.59 ,0.001
null
0.18
0.674
incline
5.23
0.022
0.352
null
1.66
0.199
2.88
0.056
null
0
0.971
20 incline
8.04
0.005
90–100
nd
2.67
0.065
39 bell
3.3
0.018
60–80
5.90
0.016
148 null
0.42
0.598
nd
3.03
0.083
null
0.16
0.694
23 incline
11.7
,0.001
90–100
90–100
0–10
0–10
0–40, 90–100 incline
0–10
Maximum Minimum
occurence occurence
0–10
90–100
50–100
0–10
90–100
0–10
90–100
0–10
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AVIAN FIRE RESPONSE CURVES
691
TABLE 1. Continued.
Triodia Mallee
Chenopod Mallee
Years since file
Years since fire
F
P
Maximum
occurence
Minimum
occurrence Shape
F
P
4.88
,0.001
20–40
0–10, 70–100
null
0.01
0.924
,0.001
90–100
0–10
null
0.05
0.819
6.21
0.013
90–100
0–10
null
1.79
0.17
null
1.5
0.226
null
0.18
0.674
132
null
1.11
0.292
null
1.07
0.301
21
null
1.41
0.236
null
2.44
0.119
45
incline
6.48
0.011
null
0.19
0.667
117
null
0.28
0.599
null
1.06
0.304
21
null
0.07
0.796
null
1.87
0.159
53
incline
nd
2.34
0.072
Species
N
Shape
Golden Whistler,
Pachycephala
pectoralis
Rufous Whistler,
Pachycephala
rufiventris
Grey Shrike-thrush,
Colluricincla
harmonica
Crested Bellbird,
Oreoica gutturalis
Grey Butcherbird,
Cracticus
torquatus
Grey Currawong,
Strepera
versicolor
Willie Wagtail,
Rhipidura
leucophrys
Jacky Winter,
Microeca
fascinans
Red-capped Robin,
Petroica
goodenovii
Southern Scrubrobin, Drymodes
brunneopygia
21
bell
38
incline
165
incline
101
14.4
15.6
,0.001
90–100
90–100
0–10
0–10
Maximum
occurence
Minimum
occurence
Notes: The number of sites recorded (N ), the assigned response shape with time since fire in Triodia Mallee and Chenopod
Mallee, and the statistical significance of the relationship (F, P) in each vegetation type are presented for each species. Significant
responses (P , 0.05) are shown in boldface type. For significant responses, the periods of time since fire at which the species display
their highest and lowest occurrence are shown. The abbreviation ‘‘nd’’ means that a nonsignificant trend was present, such that a
null response was not appropriate.
Two ranges of values are displayed for a species’ minimum occurrence if that species displays a bell-shaped response and its
minimum occurrence is equally low at two points along the time-since-fire gradient.
dependence of the species on resources or vegetation
characteristics that are themselves affected by time since
fire, such as food (Murphy and Lehnhausen 1998,
Barlow and Peres 2004) and the structural complexity of
vegetation (Skowno and Bond 2003); and also the
ecological traits of the species, such as its fecundity
(Friend 1993), dispersal ability (Brotons et al. 2005), and
competitive ability (Fox 1982). In the case of generalized
response curves, the incline, plateau, and bell responses
indicate that fire events initially have a negative effect on
the occurrence of a species, but subsequently the species
recolonizes over time. In contrast, irruptive and decline
responses indicate that fire events initially facilitate the
occurrence of a species. Null responses suggest that
species are little affected, or are not affected in a
consistent way, by most fire events.
In the mallee ecosystem, the shapes of several species’
responses to fire tend to correspond with postfire
changes in structural attributes of the vegetation on
which they rely. Indeed, it is essential to recognize that
many bird species do not respond to fire per se, but to
the changes in their habitat associated with fire. For
instance, the Striated Pardalote, which uses tree hollows
for nesting (Higgins and Peter 2002), displays an incline
response. The proportion of hollow-bearing stems at
sites also increases with time since fire (Haslem et al.
2011). The White-browed Babbler forages under bark
on the lower branches of trees and among litter and
debris on the ground, and also displays an incline
response. Correspondingly, the amount of decorticating
bark and litter also is greater in older vegetation
(Haslem et al. 2011). The Striated Grasswren, which is
closely associated with Triodia hummock grasses that it
uses for refuge and nesting (Higgins et al. 2001), displays
a bell-shaped response. Hummock grass peaks in cover
at 20–40 years postfire (Haslem et al. 2011), and the
peak occurrence of the Striated Grasswren corresponds
with this pattern.
Interspecific competition may also influence postfire
response shapes, in particular contributing to declines in
species’ occurrence (Fox 1982). For example, of the five
species that displayed a bell-shaped response, three
(Spotted Pardalote, Golden Whistler, White-eared
Honeyeater) have closely related species (Striated
692
SIMON J. WATSON ET AL.
TABLE 2. Summary of species response types, given as the
number of bird species that displayed each response in
Chenopod Mallee and Triodia Mallee.
Shape
Triodia Mallee
Chenopod Mallee
Incline
Decline
Bell
Plateau
Irruptive 1
Irruptive 2
Delayed
Null
Not detected
8
0
5
2
1
0
0
13
1
2
1
0
1
0
0
0
20
6
Ecological Applications
Vol. 22, No. 2
Pardalote, Gilbert’s Whistler, Rufous Whistler, Yellowplumed Honeyeater) of similar body size that display
incline or plateau responses and are most prevalent in
older vegetation. These species may be competitors that
drive declines in occurrence (i.e., the downward phase of
the bell shape) in older age classes.
It is important that the shapes of species responses are
interpreted within the bounds of the temporal scale of
the study. Incline responses, for instance, must eventually resemble plateau or bell-shaped responses over
longer time frames: that is, the frequency of occurrence
of a species (e.g., Grey Shrike-thrush; Fig. 2a) must
eventually stabilize or decline at sites .100 years since
fire. Because sites .100 years since fire are rare, it may
FIG. 2. Examples of modeled postfire response curves for bird species (mean 6 SE), demonstrating the similarity of observed
and hypothetical response shapes: (a) incline response of Grey Shrike-thrush in Triodia Mallee; (b) decline response of Chestnutrumped Thornbill in Chenopod Mallee; (c) bell response of White-eared Honeyeater in Triodia Mallee; (d) plateau response of
Yellow-plumed Honeyeater in Triodia Mallee; (e) irruptive 1 response of Chestnut-rumped Thornbill in Triodia Mallee; (f ) null
response of Weebill in Chenopod Mallee.
March 2012
AVIAN FIRE RESPONSE CURVES
693
their highest frequency of occurrence in mid- or latersuccessional vegetation (.20 years since fire). Some
threatened species of mallee birds that were too rare to
model in this investigation have also been found to have
a preference for vegetation .20 years since fire (e.g.,
Malleefowl, Leipoa ocellata (Benshemesh 1990), Mallee
Emu-wren, Stipiturus mallee (Brown et al. 2009), and
Black-eared Miner, Manorina melanotis (Clarke et al.
2005)). This suggests that for many species in this
ecosystem, fire displaces populations from sites and they
subsequently recolonize over time. In the event of large
wildfires, these species will require refuges or source
populations nearby where they are able to survive until
they are able to recolonize the burned area when it
becomes suitable.
Although the influence of any given wildfire event on
mortality or emigration from sites is not clear, large
wildfires in mallee ecosystems dramatically alter vegetation structure and reduce the frequency of occurrence of
a substantial proportion of the avian assemblage for
decades. Vegetation of .20 years since fire supported a
higher occupancy of 14 of the 16 species modeled,
suggesting that mid- and later-seral stages are disproportionately important for many species. However, the
frequency of occurrence of six of these species declines
between 50 and 100 years since fire, and it is likely that
there are further reductions at .100 years since fire,
highlighting the dynamic nature of the habitats generated by fire in this ecosystem.
The rarity of species that prefer early-successional
habitats contrasts with some other systems in which
TABLE 3. Measures of model performance for models of the
relationship between frequency of occurrence of species and
time since fire.
FIG. 3. Response shapes (mean 6 SE) for species that had
different responses to time since fire in different vegetation
types (Triodia Mallee, black line; Chenopod Mallee, gray line):
(a) Chestnut-rumped Thornbill (irruptive and decline), (b)
White-browed Babbler (bell and incline), (c) White-eared
Honeyeater (bell and null).
be that stabilization or decline will seldom occur before
another fire event. Contrastingly, when fire response
curves are measured over a long timescale, as is the case
here, very short-term postfire population changes (i.e.,
,1 year postfire) may not be detectable. For instance, a
species that displays an incline response may be present
in early-succession stages, but may rapidly decline in the
months following fire.
Importance of particular seral stages
In different ecosystems, vegetation of different postfire age may prove to be of differing importance for
maintenance of biodiversity. Here, of the 16 species that
displayed a significant response to time since fire, only
one (Chestnut-rumped Thornbill) was more likely to
occur in early postfire ages. All other species displayed
AUC
2
Species
D
Mean
SE
Striated Grasswren ,à
Weebill
Chestnut-rumped Thornbill Spotted Pardaloteà
Striated Pardalote ,à
White-eared Honeyeater
Yellow-plumed Honeyeater
Spiny-cheeked Honeyeater
Striped Honeyeaterà
White-browed Babbler
Gilbert’s Whistler ,à
Golden Whistlerà
Rufous Whistler
Grey Shrike-thrush Willie Wagtail Southern Scrub-robinà
0.22
0.05
0.06
0.19
0.14
0.07
0.24
0.05
0.17
0.09
0.13
0.34
0.04
0.06
0.07
0.16
0.81
0.54
0.64
0.72
0.73
0.67
0.76
0.61
0.77
0.66
0.72
0.86
0.60
0.65
0.69
0.75
0.046
0.051
0.060
0.044
0.026
0.045
0.043
0.043
0.033
0.049
0.079
0.060
0.080
0.037
0.050
0.052
Notes: Values presented are the deviance explained (D2) and
mean area under the curve (AUC) from seven-fold crossvalidation (SE in parentheses), for species that displayed a
significant response to time since fire. Models also account for
variation related to geographic location and vegetation type.
See Appendix B for statistical details.
Significant response to vegetation type at P , 0.05.
à Significant response to geographic position (northing) at P
, 0.05.
694
Ecological Applications
Vol. 22, No. 2
SIMON J. WATSON ET AL.
species capitalize on abundant resources that occur
postfire (e.g., Murphy and Lehnhausen 1998, Woinarski
1999), or where open-country species (e.g., farmland
birds) colonize early-successional habitats (Jacquet and
Prodon 2009). In this study, recently burned vegetation
(i.e., ,5 years since fire) was generated from fires that
occurred during a period of below-average rainfall (raw
data obtained from the Australian Bureau of Meteorology); consequently, early-successional habitats may not
have experienced postfire resource flushes that can occur
in years of higher rainfall. Further, this study was
conducted in large reserves of continuous ‘‘natural’’
vegetation where bird species associated with open
country (i.e., farmland birds) generally were not
abundant in areas close to recently burned vegetation.
Early-successional habitats that are generated adjacent
to farmland, or in years following good rain, may
display increases of bird species in early-successional
environments that were not detected in this investigation.
Influence of vegetation type on postfire response patterns
Little is known about the interaction of fire with other
ecological processes (Driscoll et al. 2010). In this study,
response patterns of some species differed between
vegetation types, and four times as many species showed
a significant response in Triodia Mallee than in
Chenopod Mallee.
Fire behavior varies between vegetation types in this
region. Mallee vegetation with Triodia (i.e., Triodia
Mallee) carries fire better than other vegetation under
typical climatic conditions, due to the flammable nature
of Triodia hummocks (Noble and Vines 1993). Postfire
temporal changes in understory and midstory vegetation
differ between Triodia Mallee and Chenopod Mallee
(Haslem et al. 2011). Triodia Mallee tends to have more
distinct changes in vegetation structure with time since
fire, evidenced by a greater number of significant postfire
responses of structural attributes (Haslem et al. 2011).
This greater consistency in vegetation structural succession in Triodia Mallee is likely to be a factor that
contributes to the higher number of significant responses
of species to time since fire in this vegetation type.
Particular postfire seral stages often are identified as
important for the persistence of particular species
(Hutto 1995, Clarke 2005). This investigation reveals
that seral-stage preferences of a species may differ
between vegetation types. Thus, conservation strategies
that aim to benefit species through the provision of
particular seral stages will be most effective where they
incorporate postfire successional differences among
vegetation types.
CONCLUSION
The ability of time-since-fire models to predict species’
responses across landscapes and regions is a key issue in
determining the usefulness of postfire response curves
for management purposes. Here, although approximate-
ly half of the 30 species modeled showed significant
responses to time since fire, model fit varied greatly and
was relatively modest. The predictive capacity of these
significant relationships was considered reasonable for
about half of the species. Thus, caution is required in
using time-since-fire models as a primary method for
understanding the geographic distributions of species in
fire-prone landscapes. Further, in this study only 30 of
70 species detected could be successfully modeled;
additional response types may be represented among
the other species where they represent aspects of two or
more response curves.
Generalized fire response curves represent a significant advance in understanding how species respond to
fire and in identifying the seral stages in which multiple
species are more common. A strong empirical basis for
such response curves is essential when time since fire is
used as a metric in conservation management, such as to
determine whether vegetation should be burned or
protected to increase species diversity (Richards et al.
1999) or to create mosaics of age classes (Parr and
Andersen 2006). However, the next challenge is to go
further and better understand additional factors that
interact with time since fire in determining species
occurrence, such as postfire rainfall patterns, effects of
herbivore grazing on vegetation, and interactions with
other species (predators, competitors).
ACKNOWLEDGMENTS
This research was conducted as part of the Mallee Fire and
Biodiversity project. Funding and support for this project was
provided by Land and Water Australia, Department for
Environment and Heritage (South Australia), Parks Victoria,
Department of Sustainability and Environment (Victoria),
Mallee Catchment Management Authority (Victoria), New
South Wales National Parks and Wildlife Service, Department
of Environment and Climate Change (NSW), Lower Murray–
Darling Catchment Management Authority (NSW), Natural
Heritage Trust, Birds Australia, Australian Wildlife Conservancy, and the Murray Mallee Partnership. We are also greatly
appreciative to the Doyle and Barnes families for allowing us
access to Petro and Lethero Stations, respectively, and to Jane
Elith, who provided advice on statistical analyses.
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SUPPLEMENTAL MATERIAL
Appendix A
The distribution of survey sites in relation to time since fire in Triodia Mallee and Chenopod Mallee vegetation (Ecological
Archives A022-038-A1).
Appendix B
Relationships of the frequency of occurrence of species with the parametric variables vegetation type and northing, and tests of
significance for these variables obtained from the full species models (GAMMs) (Ecological Archives A022-038-A2).