Bird monitoring in a tropical savanna conservation reserve suggests Noisy Miners Manorina melanocephala and adaptive fire management should be a ...
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Australian Field Ornithology 2021, 38, 131–136
http://dx.doi.org/10.20938/afo38131136
Bird monitoring in a tropical savanna conservation reserve
suggests Noisy Miners Manorina melanocephala and adaptive
fire management should be a future management focus
A.S. Kutt1,2,3,4*, L. Hales1, P. Hales1, P. Young1, C. Edwards5, B. Warren5,
K. Shurcliff5 and G. Harrington5
Bush Heritage Australia, P.O. Box 329, Flinders Lane, Melbourne VIC 8009, Australia
1
2
Tasmanian Land Conservancy, P.O. Box 2112, Lower Sandy Bay TAS 7005, Australia
3
School of BioSciences, The University of Melbourne, Parkville VIC 3010, Australia
4
School of Natural Sciences, University of Tasmania, Hobart TAS 7005, Australia
5
BirdLife Northern Queensland, P.O. Box 571, Malanda QLD 4885, Australia
*Corresponding author. Email: akutt@tasland.org.au
Abstract. Long-term monitoring of bird communities over time can provide important data for management, and the adaptation
of that management over time. We examined data from bird surveys across 37 sites sampled in five different years from 2009
to 2017 in a 56,000-ha tropical savanna conservation reserve, in northern Queensland. Because of the limitations of the survey
method and the lack of environmental data for sites, we examined broad patterns in the abundance of small- and large-bodied
birds, abundance of Noisy Miners Manorina melanocephala, land type, survey year, and time since last fire. There was some
variation in bird species richness and abundance across the land types, years sampled and Noisy Miner abundance; however,
the clearest pattern was decreasing numbers of small-bodied birds and increasing Noisy Miner abundance, and an association
between time since fire (i.e. 5 years), Noisy Miner abundance and diversity in other birds. The apparent and
potentially compounding interaction of Noisy Miners and fire could be an emerging problem. Future fire management needs
to be embedded in a program of targeted question-driven monitoring and adaptive management, to provide more assured
approaches to prescribed burning that enhances bird conservation.
Introduction to environmental variation is important to identify emerging
threats (Rossiter et al. 2003), especially in sites where
Long-term monitoring in protected areas is an important environmental shifts are more gradual or thought to be
tool to track natural perturbations or changes in wildlife more benign, such as the tropical savannas in northern
over time in response to management (Lindenmayer et al. Australia.
2015). Monitoring is critical for assessing the response of Regular monitoring that informs and guides adaptive
biota to management actions considered to be beneficial management is a critical component of biodiversity
to biodiversity conservation, and the adaptation of those
conservation (Deb et al. 2019). In this study we examined
actions, to increase the resilience and persistence of
5 years of bird monitoring data (spanning a period from
native wildlife (Wintle 2018). For example, the removal
2009 to 2017) in a conservation reserve in north-eastern
of grazing of domestic stock or changing fire patterns
Australia, where Noisy Miners are present and sometimes
can have positive, negative, or neutral consequences
abundant. We examined three questions using this data:
for wildlife, and not necessarily in linear or predictable
(1) Are there any notable changes in bird abundance and
fashion (Kutt et al. 2012a; Legge et al. 2019). Coupled
species richness over time, since the conversion from a
with weather patterns that are now becoming increasingly
pastoral station to a conservation reserve?; (2) Is there
more variable or extreme (Whetton et al. 2012), this can
create unexpected changes in species’ responses after, any variation in changes in bird abundance and species
for example, the removal of cattle (Kemp & Kutt 2020). richness across land type, survey year or fire history?; and
Perverse outcomes like the over-abundance of native or (3) Is there any evidence of Noisy Miners affecting small-
introduced species can be the consequence (Colman et bodied birds on the reserve?
al. 2014). One important qualification to the framing of these
In eastern Australia, the over-abundance of Noisy Miners questions should be noted. When this monitoring was
Manorina melanocephala has acutely affected woodland initiated, it was without a well-articulated purpose, except
bird communities and is listed as a key threatening process to surveil an expected improvement in bird condition over
under national environmental legislation (Threatened time. Though adaptive management is the foundation
Species Scientific Committee 2013). The negative impact for monitoring for many not-for-profit conservation
of Noisy Miners on other birds is well established (Maron organisations (Carr et al. 2017), the application of the
et al. 2013) and the impetus for their colonisation and principles of effective monitoring is often overlooked,
ascendancy is land clearing (Maron et al. 2013). However, namely positing key hypotheses and a conceptual model,
this species, and its congener the Yellow-throated Miner M. careful design and data collection including control sites,
flavigula, also increase and exert control on the avifauna in review and modification of the data and methods over time,
locations where clearing and disturbance are less evident and clearly linking the work to measurable management
(Kutt et al. 2016). Long-term monitoring of changes linked actions (Lindenmayer & Likens 2010). However, the data132 Australian Field Ornithology A.S. Kutt et al.
now collected are valuable to review and use to refocus the Instead, we used the raw data (i.e. the abundance of
monitoring on more targeted and experimental questions each bird species per minute in the 20-minute counts) and
(e.g. effect of fire management), which is a feature of simply selected as our measure of abundance the largest
adaptive monitoring (Lindenmayer & Likens 2009). 1-minute count number. For example, if one species
was recorded in five of the 20 minutes, with an observed
abundance of 1, 2, 1, 1, 3 birds/minute in the years of our
Methods surveys analysed in our data, we simply used 3 birds/
minute as the abundance for that species in that 20-minute
This study was conducted on Bush Heritage Australia’s count. We use this as a measure of relative abundance,
Yourka Reserve (17.92°S, 145.35°E; 43,500 ha), located as several studies have demonstrated that measures of
50 km south-south-west of Ravenshoe, Queensland, and relative abundance provide patterns of population trends
on the western boundary of the Wet Tropics bioregion. This proportional to those derived from estimates of absolute
region is tropical in climate (annual rainfall ~800–1000 mm) abundance (Slade & Blair 2000; Hopkins & Kennedy 2004).
and the vegetation consists largely of tropical woodlands
There were no site-based vegetation data collected
and forests dominated by Eucalyptus, Corymbia and
at the survey sites, though the geo-reference of each
Melaleuca species. The Reserve ranges in elevation
location was assigned a broad vegetation type—Eucalypt
from 700 to 1000 m above sea level (asl). The land use
forest or Eucalypt woodland (Neldner et al. 2019)—as well
is wildlife conservation; the Reserve converted from a
as land type (sandstone, granite, alluvial or basalt) from
cattle property in 2009. The property was destocked after
available mapping (Neldner et al. 2019). Fire history—time
purchase but there is regular incursion of cattle annually
since last prescribed burn or wildfires (TSLF) in months,
from neighbouring properties. In addition, a program of
and frequency of fire (FF) for each site for each year of
regular prescribed burning to help prevent large wildfire
survey—was derived from fire scar mapping. Metrics of fire
was undertaken, though wildfire consistently occurred
intensity could not be derived. Fire scars were identified for
across the property in the period of this study. Thus, it is
a 10-year period before each survey year via examination
difficult to add recovery from grazing as a focus of the data
of a combination of satellite imagery sensors (Landsat 5, 7
analysis because of the lack of quantitative site-based or
and 8, and Sentinel 2) including the period before Yourka
property-based measures of stocking, and ancillary data
Reserve was purchased. Raw imagery was analysed in
such as vegetation structural or floristic changes. Similarly,
ArcMap 10.7.1 (ESRI 2019) to produce vectors (polygons)
the prescribed burning was focused on wildfire mitigation,
of pixel reflectance difference between pre- and post-fire
rather than explicitly using the monitoring sites to test
images. These polygons were then geo-processed using
different regimes (i.e. season or intensity) and biodiversity
tools within the Spatial Analyst extension of ArcMap to
effects. In this case we examined only coarse-grained
derive the TSLF and FF frequency metrics. As the history
environmental metrics of time since last fire, vegetation,
for each site was constrained to a 10-year period before
and land type (see below).
the survey year, the maximum TSLF for any site was
The bird surveys were conducted in October–November 120 months, and the maximum FF was 10 (i.e. burnt
in 2009–2018. A total of 44 sites was established every year). The two fire measures are highly correlated
representing four main land types: sandstone, granite, (R = 0.59) and therefore only TSLF was used in the
alluvial and basalt. Not all sites were sampled every year subsequent analysis.
and not every year in the series was sampled. We chose
the data that represented repeated sites over the longest
time span, i.e. the same 37 sites sampled in each of the Analysis
years 2009, 2010, 2011, 2013 and 2017. Based on data
from Woodleigh Station, 10 km north of Yourka (17.68°S, For each site for each year, we derived a measure of
145.28°E; 630 m asl; mean rainfall 875 mm), the total species richness (the total number of species at each site for
rainfall for the survey years was above average in 2009 each 20-minute count) and abundance. We subsequently
(1162 mm), 2010 (1217 mm), 2011 (1039 mm), 2017 evaluated the bird data in terms of body size, given that
(1046 mm) and slightly below average in 2013 (816 mm) Noisy Miners are a component of the bird fauna and the
(Bureau of Meteorology 2020). competitive dominance and negative effect of increasing
Noisy Miner abundance on small-bodied birds is well-
The bird data used in this paper were collected via a
established (Kutt et al. 2012b). Small-bodied birds (SBB)
method known as ‘bird minutes’, which is used across
are defined here as all birds less massive than the Noisy
many Bush Heritage Australia reserves but has yet to be
Miner (i.e.Long-term bird monitoring in a tropical savanna reserve 133
We examined the variation in richness and abundance of between Noisy Miner abundance and year for SBB and
SBB, LBB and Noisy Miners in two ways: (1) the response LBB abundance (Table 2).
to land type, TSLF, survey year and the interactions
There was variation in bird abundance and species
between TSLF and year, and between land type and year;
richness across the four land types sampled; SBB richness
and (2) the response of bird body size to abundance of
and abundance were lowest in the sandstone, and highest
Noisy Miners, and the interaction of Noisy Miners with land
in the granite sites; LBB richness was largely equal in the
type, TSLF and year. We used generalised linear mixed
basalt, sandstone, and alluvial sites, and LBB abundance
(multi-level) models (Payne et al. 2010), which combine
was significantly higher in the alluvial sites and lowest in
both fixed and random terms and estimate the variance
the granite. Noisy Miner abundance was very low in the
within a group against the variance; in this case, we used
granite sites, but largely equal in the other land types
site as the random effect, and the other variables as the
(Figure 1a). For the years sampled, the richness and
fixed effects. We fitted negative binomial regression
abundance were highest in 2010 in all groups, including
models, which have the same mean structure as Poisson
Noisy Miners, and for the other years, largely equal,
regression, but with a variance estimate that is a quadratic
though LBB abundance was high in 2013 (Figure 1b). For
function of the mean (Ver Hoef & Boveng 2007). Variance
TSLF, SBB richness and abundance were highest in sites
components were estimated using maximum likelihood
unburnt for between 3 and 5 years, where Noisy Miner
for the fixed effects and dispersion components, and
abundance was lowest, with little variation in LBB richness
approximate empirical Bayes estimates of the random
and a peak of LBB abundance in sites burnt within the
effects, and significance of the fixed effect was assessed
past year and sites unburnt for >5 years (Figure 1c). There
via the Wald statistic (Payne et al. 2010).
was a distinct increase in SBB abundance and richness
in the >3–5-year fire period, matching a decline in Noisy
Miner abundance, which then reverted to lower and higher
Results
numbers, respectively, when the sites were longer unburnt
(>5 years: Figure 1c). We did not have fine-scale data to
The generalised linear mixed modelling indicated that
examine the relationship between rainfall and bird patterns
land type and year were strongly related to SBB and LBB
except at a broad scale; the rainfall for the survey years
richness and abundance, with significant interactions
was average or above average, and there is no obvious
for TSLF and year, and for land type and year for LBB
correlation between annual changes in species richness
abundance, and for land type and year for Noisy Miner
and abundance and rainfall.
abundance (Table 1). Noisy Miner abundance indicated
a strong relationship with richness and abundance of Chronologically across all sites combined, Noisy Miners
both bird body size classes, and there was an interacting increased and then decreased in site occupancy and
effect between Noisy Miner abundance and TSLF for mean abundance (2009: 68% sites occupied and mean
LBB abundance and species richness, and interaction abundance 2.4 birds/minute; 2010: 78% and 4.0; 2011:
Table 1. The results of the generalised linear mixed modelling for small-bodied (SBB) and large-bodied birds (LBB), testing
for the effect of the time since last fire (TSLF), land type, year of survey, and the interactions between TSLF and year
(TSLF * Year) and land type and year (Land type * Year). The Wald statistic is an equivalent to the F statistic, and P is the
significance level; ns = not significant.
Factors TSLF Land type Year TSLF * Year Land type * Year
d.f. = 1 d.f. = 3 d.f. = 4 d.f. = 4 d.f. = 12
Wald P Wald P Wald P Wald P Wald P
SBB abundance ns 35.1134 Australian Field Ornithology A.S. Kutt et al.
(a) Land type
Abundance/richness
Abundance/richness
SBB LBB SBB LBB Noisy Miner
richness richness abundance abundance SBB LBB SBB LBB
abundance
richness richness abundance abundance
Sandstone Granite Alluvial Basalt
5 Noisy Miners
(b) Year Figure 2. The mean (and standard error) of small-
(SBB) and large-bodied bird (LBB) species richness and
abundance for three categories of Noisy Miner abundance
Abundance/richness
(see methods for more detail).
land type, a proxy for productivity, and TSLF, but again
there was little general or consistent pattern. However,
SBB LBB SBB LBB Noisy Miner the association between Noisy Miner abundance, and
richness richness abundance abundance abundance richness and abundance of SBB and LBB, were quite
2009 2010 2011 2013 2017 conspicuous across all sites. There also seemed to be a
complicated interaction with fire, where Noisy Miners were
more abundant in recently burnt (5 years) sites, and that this abundance might
Abundance/richness
have contributed to the suppression of SBB.
As suggested in the methods, there are some limitations
that should be highlighted. Firstly, as the bird minute
method is not well established and the data for deriving
the indices of this method not consistently collected, we
selected a component of the data—the maximum number
SBB LBB SBB LBB Noisy Miner of birds seen in any one of the 20 1-minute counts. This
richness richness abundance abundance abundance
could under- or over-estimate abundances of some
5 years
species, depending on their behaviour and vocalisations.
Secondly, the data were collected over multiple years,
Figure 1. The mean (and standard error) of small- (SBB)
and occasionally with different observers, and observer
and large-bodied bird (LBB) richness and abundance, and
Noisy Miner abundance across (a) land type, (b) year of bias can affect the bird species and abundance counts.
survey and (c) time since last fire (see methods for more Lastly, we had only landscape-scale environmental factors
detail). Number of sites = 37. to model the bird richness and abundance, rather than
any site-based vegetation or structural information which
has not been collected at the survey sites. Therefore, we
can make only general conclusions from the data, and
76% and 2.6; 2013: 68% and 2.6; 2017: 60% and 2.2).
we might be missing some more nuanced patterns such
In terms of Noisy Miner abundance, SBB richness and
as the interplay of site-specific responses, but instead
abundance were reduced in sites where Noisy Miner
focused on body size and abundance of Noisy Miners, a
abundance was high (>5 Miners recorded) and LBB
well-established threat and regulator of avifauna. Despite
richness and abundance were highest in sites where
these caveats, and our data being more correlative rather
Noisy Miner abundance was high (Figure 2). The pattern
than mechanistic, we believe that our data provide a strong
of richness and abundance for SBB and LBB was clearly
basis for more targeted surveys that should be integrated
inversely related to Noisy Miner abundance (Table 2).
with future management.
The variation in richness and abundance of both LBB
Discussion and SBB changed little over time but did vary across land
type (sandstone and granite sites in particular) and TSLF
In this study we examined the changes in richness and (recently or longer unburnt). In other studies, in tropical
abundance over multiple years of bird survey, which was savannas, changes in avifauna occurred over 5 years,
intended to track the change and potential recovery of the indicating that shifts in species and their abundance can
avifauna on a conservation reserve after conversion from occur, but mostly because of contrasting local factors such
a pastoral property. These data indicated that there was as natural variation in vegetation structure, and small-
little clear trajectory of change in abundance or richness scale thinning and clearing (Kutt et al. 2012c). Similarly,
of bird species over time, though there was a notable for a range of terrestrial fauna (including birds), changes
degree of variability in some years (2010 for all groups, after the removal of grazing manifested across different
and LBB abundance in 2013). There was some effect of land types, but not specifically from the removal of cattleLong-term bird monitoring in a tropical savanna reserve 135
(Kutt et al. 2012c). It often takes ≥10 years to demonstrate interpretation of the patterns recorded) are required; (2) fire
distinct changes in vegetation after the removal of management needs to take a more adaptive management
grazing (Kemp & Kutt 2020), and even in monitoring over approach, and develop a system where fire, vegetation
decades of Kakadu National Park, where populations of and bird data are collected annually and in concert, so that
small mammals precipitously collapsed, bird communities any positive or perverse outcomes of fire management
remained resilient and more attuned to regional climate or are readily described and available; and (3) a more
other environmental variation (Woinarski et al. 2012). In our mechanistic and question-driven approach to monitoring
study, the data suggested some stability in the avifauna, or needs to be undertaken, in order to unravel the putative
at least a time frame too short to capture clear trajectories mechanism for changes in birds over time, including any
of change. Elsewhere in northern Australia, shifts in fire increase in Noisy Miner abundance and their despotism.
regime across different land types can facilitate a change Our study inferred that the threat of Noisy Miners could
in bird communities, linked to the effect on resources be enhanced by frequent fires (i.e. 5-year fire category,
The lack of inclusion of site and landscape factors in our and Noisy Miners increased in abundance, the data were
analysis might have missed more subtle determinants of highly variable, and need more careful investigation. Fire
avian community dynamics in our study area over time. management in northern Australian landscapes needs to
incorporate and test the critical recommendations for bird
Land clearing in south-eastern Australia triggered the conservation in tropical savannas, for example, testing
rise of a despotic native bird, the Noisy Miner, resulting the value of retaining larger proportions of long-unburnt
in a cascading impact on SBB (Maron et al. 2013). In (>3 years) vegetation (Woinarski & Legge 2013). Future
intact environments, with smaller-scale and more limited monitoring and management at this important tropical
disturbance, there is evidence that pastoral land use has savanna conservation reserve need to embrace a more
triggered a similar effect (Kutt et al. 2012d). One notable thorough adaptive management approach that poses more
result from our data was the incipient relationship between critical questions, and therefore purposeful monitoring.
Noisy Miner abundance and the increase or decrease in
abundance and species richness of SBB and LBB. The
abundance of Noisy Miners was perhaps more influential Acknowledgements
on the avifauna compared with fire history and land type,
This study was supported by Bush Heritage Australia and
though there were clearly some complex interacting effects, was conducted under Queensland Scientific Purposes Permit
especially with respect to TSLF. The effect of Noisy Miners WISP18503317 and Animal Ethics approval CA 2019/07/1304.
on the depletion of SBB is well established (Crates et al. We thank additional BirdLife Northern Queensland volunteers
2020), and there is evidence that, even without large-scale who undertook the bird surveys and data collection. We thank
land clearing, smaller-scale changes in vegetation structure Dr Pippa Kern (Australian Wildlife Conservancy) for review and
can promote the increase in abundance, and there can assistance and Professor John Woinarski and an anonymous
still be an effect causing a decline in smaller birds (Mac referee whose comments improved the final manuscript. We
acknowledge the Jirrbal and Warungu people as the Traditional
Nally et al. 2014). Our data suggest that this might be a
Owners of Yourka Reserve and recognise and respect the
burgeoning issue in our study area, and especially the link enduring relationship they have with their lands and waters, and
between Noisy Miner abundance, other birds and a shorter we pay our respects to Elders past, present and emerging.
or longer time since fire. Fire is a critical ecological process
in tropical savannas though the timing, intensity and extent
is highly influential on the positive or negative effects on References
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