41
Habitat use by two tropical species of waterfowl
in central Malaysia
ABDOLLAH SALARI1,2*, MOHAMED ZAKARIA1 & MARK S. BOYCE2
1Department
of Park and Ecotourism, Universiti Putra Malaysia, Serdang, Selangor,
Malaysia.
2Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.
*Correspondence author. E-mail: abdsalari@gmail.com
Abstract
Two tropical species of waterfowl, the Lesser Whistling-duck (LWD) Dendrocygna
javanica and Cotton Pygmy-goose (CPG) Nettapus coromandelianus, are patchily
distributed across Malaysia and little is known about their habitat requirements. We
studied patterns of habitat use for LWD and CPG at the Paya Indah Wetlands
Reserve, Malaysia (c. 3,050 ha), by counting the birds from observation points and
using a zero-altered negative binomial model to describe their abundance and
distribution at the site. Habitat use by LWD and CPG was highly correlated; for
instance both species frequented shallow, nutrient-rich lakes in the study area. Finescale measures of vegetation characteristics influenced local distribution, whereas a
combination of anthropogenic activities and other habitat features best predicted
abundance. Overall, LWD selected the more stable but densely-vegetated marshy
shoreline while CPG used vegetated areas near the central deeper portions of the
lake. Our habitat-selection models give insight into the ecology of LWD and CPG in
Malaysia and can provide a tool for identifying areas for possible habitat restoration
and conservation in the region.
Key words: Cotton Pygmy-goose, count data, habitat selection, hurdle models,
Lesser Whistling-duck, Paya Indah Wetlands, zero-altered negative binomial.
Patterns of habitat use by animals are likely
to be a result of the influence of natural
selection on survival and reproduction,
which determine the fitness consequences
of exploiting different habitats (Morris et al.
2008; Gaillard et al. 2010). Typically, the
extent of habitat use suggests the quality
and abundance of resources in those
© Wildfowl & Wetlands Trust
areas, which in turn reflects the fitness
consequences of exploiting that habitat
(Fretwell 1969), although there are
exceptions (Horne 1983). In general, birds
(Loegering & Fraser 1995; Bock & Jones
2004) and mammals (McLoughlin et al.
2006; McLoughlin et al. 2007) aggregate
in higher-quality habitats. Availability of
Wildfowl (2016) 66: 41–59
42 Lesser Whistling-duck and Cotton Pygmy-goose habitat use
adequate food and shelter play a vital role
in influencing habitat use by waterfowl
(McKinney et al. 2006; Guadagnin &
Maltchik 2007; Tavares et al. 2015), with
access to food being influenced by water
depth and by the type, structure and density
of vegetation in wetland habitats (Baschuk et
al. 2012). Many waterfowl forage by surface
feeding, dabbling through the water and
upending, so access to food can also be
constrained by their morphology (e.g. leg
length or bill length), particularly for
tactile foragers (Green 1998). Vegetation
additionally provides shelter and increases
the bird’s ability to avoid predators (Forman
& Brain 2004), although the specific
characteristics of vegetation that affect
these decisions are not known. Moreover,
disturbance from human activities can cause
temporary changes in behaviour and affect
temporal and spatial distribution of
waterfowl locally (Madsen 1995).
Little is currently known about factors
affecting the distribution and abundance of
Lesser Whistling-duck (LWD) Dendrocygna
javanica and Cotton Pygmy-goose (CPG)
Nettapus coromandelianus. LWDs are mediumsize waterfowl native to most areas of south
Asia, east Asia and southeast Asia (IUCN
2013). Like all of the whistling ducks
Dendrocygna sp. they differ from other duck
species in having longer legs, a squarish
head and an erect goose-like posture when
alert (Johnsgard 1976). Although abundant
through most of its range in southern
and southeast Asia, with population size
estimates varying from 100,000 to 1,000,000
birds during the 1980s–1990s (Perennou et
al. 1994; Rose & Scott 1997; Choudhury
2005), numbers are thought to be in decline
© Wildfowl & Wetlands Trust
(Wetlands International 2015). The CPG
distribution is similar to that of the LWD,
although it extends to northeast Australia
(IUCN 2013). Physically the two species are
very different, however, with CPGs having a
rounded head, short bill and short legs
(Johnsgard 1976). Population size estimates
for the CPG are of 100,000 for the South
Asian population and up to 1,000,000 for
the East/South East Asian population,
with population trends classed as
“unknown” (Perennou et al. 1994; Wetlands
International 2015). Both species are mostly
gregarious and frequent tropical freshwater
wetlands with sufficient aquatic vegetation
(Johnsgard 1976 and Fig. 1). They are nonmigratory and have a patchy distribution in
our study area.
The aim of this study was to describe
patterns of habitat use and selection for the
two species, based on the premise that
abundance and distribution will vary
spatially in relation to certain habitat
characteristics. Since waterfowl abundance
represents a recognised metric for habitat
selection, our analyses have a working
hypothesis that the models should reveal
environmental conditions that are
functionally related to both distribution and
abundance (Baschuk et al. 2012). For
instance, marshes with high abundance of
vegetation provide suitable environments
for breeding, feeding and predatory
avoidance for most dabbling ducks (Scheffer
& van Nes 2007). A modelling approach has
been found by other authors to be useful
for investigating waterbird distribution/
abundance relative to environmental factors,
habitat selection and species-specific
requirements and that these models
Wildfowl (2016) 66: 41–59
Lesser Whistling-duck and Cotton Pygmy-goose habitat use 43
Figure 1. Lesser Whistling-duck (right) and Cotton Pygmy-goose (left) in their shared habitat.
(Photograph by J.M. Garg.)
therefore also can provide useful insights for
management and conservation (Found et al.
2008; Tavares et al. 2015).
Methods
Study area
The Paya Indah Wetlands (PIW) (Malay
translation: “beautiful wetland”) reserve
encompasses 3,050 ha, of which 450 ha are
under the administration of the Department
of Wildlife and National Parks (DWNP)
Peninsular Malaysia. It is adjacent to
Malaysia’s administrative centre of Putrajaya
(at 2.85°–2.88°N, 101.60°–101.63° E), is
a part of the Kuala Langat North Forest
Reserve (a permanent peat swamp forest),
and comprises degraded tin-mining lakes,
© Wildfowl & Wetlands Trust
logged peat swamp forest and large open
lakes. Twenty-one lakes are located within
the PIW, with heterogeneous ecosystems
such as marshes, swamps and open water
lakes, and with contrasting hydrochemical
and structural attributes in both their
spatial and temporal dimensions, but for
our study we considered only 17 lakes that
were under reserve management (Fig. 2).
Two main activities, conducted erratically
by local authorities and farmers in the PIW
area: (1) sand mining, and (2) construction
and maintenance of canals to irrigate oil
palm plantations, both cause changes in
the hydrochemical, structural, and spatial
characteristics of the lakes. Because of
its location 50 km south of Kuala
Lumpur, 12 km west of Putra Jaya, and
Wildfowl (2016) 66: 41–59
44 Lesser Whistling-duck and Cotton Pygmy-goose habitat use
Figure 2. Satellite image (from World View 2) of the of Paya Indah Wetlands, Malaysia. Acquired
through http://www.digitalglobe.com in January 2010.
15 km north of Kuala Lumpur International
Airport (KLIA), the site is considered
to be a “green lung” for the region and
also a “super corridor” for migratory
species. Rajpar and Zakaria (2010) recorded
13,872 birds from 100 species during a
14-month study at the PIW in 2007–
2009, of which 22.3% of individuals and
25% of species were waterbirds, mostly
Anatidae.
Species occurrence and abundance
data
Two non-migratory waterfowl species –
LWD and CPG – were monitored in the
PIW. These are the only duck species in the
area, both are permanent residents that
breed at the site, and they have shared
© Wildfowl & Wetlands Trust
freshwater wetland habitats (Choudhury
2005; Fullagar 2005).
Point-count methods have been used to
monitor the abundance of many types of
birds (Ralph 1993; Forcey et al. 2006;
Mordecai et al. 2011). We initially chose 288
sampling locations, distributed at random
along the shoreline of 17 wetlands but with
at least 150 m distance between them to
achieve proportional allocation across the
site (Fig. 3). Because of time and logistical
constraints in undertaking our study, we then
marked 48 of the sampling points, again
selected at random from within the initial
sample. Observations were made within a
150 m fixed radius around each marked
sampling point (McKinney & Paton 2009;
Leal et al. 2011), by the observer scanning in
Wildfowl (2016) 66: 41–59
Lesser Whistling-duck and Cotton Pygmy-goose habitat use 45
Figure 3. Random available locations (n = 288), 150 m radius around fixed observation points (n = 48,
land excluded) and the names of lakes surveyed in the Paya Indah Wetlands, Malaysia.
a 180º arc to the front during 10-minute
visits to each marked point at random times
but between 07:00–12:00 h every week. All
observations were conducted over two
seasons (October–January 2010 and 2012)
by the same observer. Variability in observer
detection errors therefore could be assumed
to be constant, although we believe
detection was nearly 100% (Mordecai et al.
2011; Farmer et al. 2012). All ducks seen
from the 48 marked points were recorded
except for those in flight. Spatial maps
developed from World View 2 (WV2) satellite
imagery (http://www.digitalglobe.com) and
from field work were used to extract
environmental gradients for covariates used
in analysis.
© Wildfowl & Wetlands Trust
Weekly counts were summed to provide
monthly counts to reduce temporal
autocorrelation. A resource unit (extending
over a 150 m radius, excluding land, from 48
observation points) was considered to have
been used when LWD or CPG were
recorded in the unit during either the first or
second sampling season. The same protocol
was applied for abundance values, where
abundance was taken to be the total number
of birds counted in the used resource units.
Available resource units for all distribution
and abundance analyses were defined as
being the entire set of 288 possible resource
units (following Johnson et al. 2006), which
included the subset of 48 observation units
from which counts were made.
Wildfowl (2016) 66: 41–59
46 Lesser Whistling-duck and Cotton Pygmy-goose habitat use
Environmental explanatory variables
Remotely sensed data has proven to be a
powerful way to describe environmental
conditions for ecological purposes
(Pettorelli et al. 2011). WV2-derived
Normalized Difference Vegetation Index
(NDVI) gives an index of primary
productivity that can be linked to ecological
mechanisms (Pettorelli et al. 2005; Williams
& Peterson 2009).
Variation in water depth influences the
species composition and abundance of
emergent and submersed vegetation in
wetlands (Robel 1961; Anderson 1978; van
der Valk et al. 1999; van der Valk & Murkin
2002). Vegetation, in turn, influences the
amount of available food, nesting sites and
cover for marsh birds and waterfowl
(Murkin et al. 1997; Tavares et al. 2015). In
addition, water depth affects available food
resources because it limits access by some
avian species (Pöysä 1983; Lantz et al. 2011;
Lunardi et al. 2012). Water depth can also
influence invertebrate populations that are
important food for waterfowl (Cox et al.
1998). Furthermore, waterfowl are known
to select lakes based on water quality
(Hansson et al. 2010). Anthropogenic
disturbance has been shown to negatively
affect activities and behaviour of most
waterfowl (Dahlgren & Korschgen 1992;
Fox & Madsen 1997; Väänäanen 2001;
Pease et al. 2005).
To identify those environmental
characteristics that were predictive of the
distribution and abundance of LWD and
CPG in PIW, several variables were
measured, using fieldwork, satellite-image
analysis, and spatial statistics. Identification
of the dominant emergent vegetation was
© Wildfowl & Wetlands Trust
determined from a coarse-scale aquatic
vegetation map of wetlands in combination
with numerous field visits for validation.
NDVI values, extracted based on Red and
Near Infra-Red 2 bands, were inspected and
used with visually interpreted data to create
a map of land-use/land-cover for the PIW
area, and inside wetlands polygons were
grouped into two classes: “open water” and
“vegetated”. Annual mean water-quality
index (WQI) of lakes and lake-level
fluctuation data for 2010 were obtained
from PIW reserve management. The WQI
was derived using measurements of
dissolved oxygen (DO), biological oxygen
demand (BOD), chemical oxygen demand
(COD), ammonical nitrogen (AN),
suspended solids (SS) and pH. Numerous
depth samples from all 17 lakes were
obtained by field surveys during the first
season of data collection. ArcGIS 10
spatial analysis tools were used to estimate
aquatic vegetation percentage covers,
interpolation, and distance and density
measurements.
Procedures for estimating the fine-grain
explanatory variables used in this study
from World View 2 satellite imagery are
described in greater detail in Salari et al.
(2014). Additionally, regional climate data
permits analysis of relationships between
climate variables and habitat selection
(Huston 1999). For instance, nest-site
selection in waterfowl is largely influenced
by microclimate conditions such as
temperature and humidity (Gloutney &
Clark 1997). Furthermore, in those Anatids
with female-only incubation, maternal
behaviour is influenced by these
environmental factors which in turn might
Wildfowl (2016) 66: 41–59
Lesser Whistling-duck and Cotton Pygmy-goose habitat use 47
affect population growth rate and
abundance (Hepp et al. 2006). Hence,
monthly climate data were obtained from
KLIA International Airport for each of the
observation periods to explore the possible
effect of local weather conditions on habitat
use by our study species.
Data analysis
We developed models for two levels:
distribution and abundance, using count
data for LWD and CPG collected over the
8-month study period. To do this, we
applied a negative binomial model using
the hurdle function (Potts & Elith 2006)
available in the PCSL-contributed package
(Zeileis et al. 2007) for R software. This
zero-altered negative binomial (ZANB)
model assumes that a binary process
determines whether a count model should
be > 0 (binomial GLM) and then a count
process independently generates the
positive values. If the count value exceeds
unity, the presence threshold is fulfilled and
the conditional distribution of the positive
values is governed by a zero-truncated
negative binomial model. The explanatory
covariates for the two components are not
constrained to be the same in ZANB
models, although here we have used same
covariates for both components. We tested
for over-dispersion of LWD and CPG
data by calculating a variance-to-mean
ratio, where ratios of > 1 are considered
to indicate over-dispersion (Zorn 1996).
We also plotted the overall distribution
of abundance data to ensure that they
approximated a negative binomial
distribution. Over-dispersion is additionally
considered likely when the residual deviance
© Wildfowl & Wetlands Trust
of a model is significantly higher than the
residual degrees of freedom (Crawley
2012).
Strong correlations between explanatory
variables can cause problems in model
fitting and interpretation (Graham 2003;
Heikkinen et al. 2006). We began with a preselection of covariates that were most
ecologically relevant to the species. After
that, we used a variance inflation factor
(VIF) to test for collinearity; collinear
variables with VIF values larger than 3 can
cause estimation problems (Zuur 2007). We
chose a subset of 23 variables among
environmental factors believed to be causal
for habitat use by the two species at the
scale of our study using covariates that
determine the quality and quantity of
habitats for most waterfowl (Hansson
et al. 2010; Kreakie et al. 2012). After
those data-screening procedures and
based on our ecological knowledge of
the species, 13 non-collinear candidate
explanatory variables were chosen to model
habitat use by LWD and CPG in the PIW
(Table 1).
We ranked eight biologically plausible a
priori models for LWD and four for CPG to
evaluate those relationships (Tables 2 & 3)
using information-theoretic methods. Given
the lack of earlier studies of how these
species select their habitats, we chose
covariates included in these models on the
basis of our field observations and literature
on similar taxa, and as such appreciate that
these analyses are exploratory. We began our
comparison by calculating the null model,
which was a model incorporating a constant
but with no explanatory variable serving as a
reference point or null model to evaluate the
Wildfowl (2016) 66: 41–59
Variables
abbreviation
Description
Source
Units
Value Ranges
Dep_cv
Field(GIS)
–
0.52–1.22
Field(GIS)
WV2&Field(GIS)
Field(GIS)
Field(GIS)
M
M
M
km/km–2
0.20–7.85
14.35–116
89.22–779.80
0.00–16.71
WW2&Field(GIS)
VW2(GIS)
WW2&Field(GIS)
WW2&Field(GIS)
km/km–2
–
%
%
0.00–0.008
0.14–0.76
0–100
0–99
WQI
LLF
Temp
Mean coefficient of variation of water depth in a 150 m radius
around each available point
Mean water depth in a 150 m radius around each available point
Distance from available points to nearest road
Distance from units to nearest human development
Density of human linear development in a 150 m radius
around each unit
Density of roads in a 150 m radius each unit
Mean NDVI of each unit
Percent cover of Spike Rush in each unit
Percent cover of mixture of Spike Rush and Water Lily in
each unit
Mean water quality index (.5m) of each unit
Mean of water level fluctuation of each unit
Monthly mean daily temperature in the PIW
–
M
C
50.26–89.90
6.00–7.75
26–28
Humidity
Monthly mean daily relative humidity in the PIW
PIWM(GIS)
PIWM(GIS)
Sepang(KLIA)
Weather station
Sepang(KLIA)
Weather station
%
80–87
Dep
Dis_rd
Dis_hum
Hum_den
Rd_den
NDVI
Spik1
Mix1_15
Wildfowl (2016) 66: 41–59
Land-use/ land-cover features were extracted from World View 2 Imagery (WV2) with 10 m resolution. Procedures for estimating finegrain explanatory variables by using World View 2 satellite imagery are described in greater detail in Salari et al. (2014). Units = 150 m radius
around observation and random points (terrestrial areas excluded). PIWM = Paya Indah Wetland Management data. KLIA = Kuala Lumur
International Airport weather station, Sepang, Malaysia. WQI = Malaysian Water Quality Index, accessible at http://www.doe.gov.my.
48 Lesser Whistling-duck and Cotton Pygmy-goose habitat use
© Wildfowl & Wetlands Trust
Table 1. Candidate explanatory variables used for modelling distribution and abundance of Lesser Whistling-ducks and Cotton
Pygmy-geese in the Paya Indah Wetlands, Malaysia.
Lesser Whistling-duck and Cotton Pygmy-goose habitat use 49
Table 2. Ranking of a priori candidate zero-altered negative binomial (hurdle, ZANB) models
for Lesser Whistling-duck in the Paya Indah wetlands, Malaysia.
Model
No.
Model structure
K
AICc
Δi
8
Spik1+Mix1_15+NDVI+Dis_hum+WQI+
LLF+Temp+Humidity
Spik1+Mix1_15+Dep_cv+LLF+WQI+
Temp+Humidity
Spik1+Mix1_15+NDVI+Dis_hum+WQI+
Temp+Humidity
Spik1+Mix1_15+NDVI+Dis_hum+
Temp+Humidity
Spik1+Mix1_15+NDVI+Rd_den+
Temp+Humdity
Spik1+Mix1_15+Rd_den+Hum_den+
Temp+Humidity
Spik1+Mix1_15+NDVI+Temp+
Humdity
Spik1+Mix1_15+Rd_den+Temp+
Humidity
19
750.68
0.00 1.00
–356.3
17
767.55
16.30 0.00
–366.8
17
786.44
35.19 0.00
–376.0
15
807.71
55.96 0.00
–388.0
15
897.01 145.26 0.00
–433.5
15
919.82 168.07 0.00
–449.9
13
924.36 172.17 0.00
–449.2
13
933.42 181.23 0.00
–453.7
2
7
5
6
4
1
3
wi
LL
Abbreviations for variables listed in the model structure are provided in Table 1. K = total
parameters count for binary and zero truncated negative binomial parts of ZANB including
intercepts plus θ. Δi = differences among AICc scores of model i and best fitting model.
wi = AICc weights. LL = log likelihood scores.
performance of other candidate models. In
the final stage, Akaike’s information
criterion corrected for small sample sizes
(AICc), was used to select the most
parsimonious of the candidate ZANB
models (Burnham & Anderson 2002;
Johnson & Omland 2004). To adjust for
variation in resource unit size, we applied
log area of resource units as an offset
term.
© Wildfowl & Wetlands Trust
Results
Species-distribution models
A total of 1,262 LWD and 1,526 CPG were
detected during the first field season, in
2010/11, decreasing to 931 LWD and 1,417
CPG recorded in 2011/12. The two species
were detected at relatively few of the
observation points during the study (LWD
= 7/48, CPG = 4/48; Fig. 3).
Wildfowl (2016) 66: 41–59
50 Lesser Whistling-duck and Cotton Pygmy-goose habitat use
Table 3. Ranking of a priori candidate zero-altered negative binomial (hurdle, ZANB) models
for Cotton Pygmy-geese in the Paya Indah wetlands, Malaysia.
Model
No.
3
1
4
2
K
AICc
Δi
wi
LL
Spik1+Mix1_15+NDVI+Temp+Humdity 13
Spik1+Mix1_15+Temp+Humdity
11
Spik1+Mix1_15+Rd_den+Temp+Humdity 13
NDVI+Temp+Humdity
9
558.72
567.14
568.34
657.35
0.00
8.41
9.61
98.62
1.00
0.00
0.00
0.00
–265.9
–272.1
–270.5
–319.4
Model structure
Abbreviations for variables listed in the model structure are provided in Table 1. K = total
parameters count for binary and zero truncated negative binomial parts of ZANB including
intercepts plus θ. Δi = differences among AICc scores of model i and best fitting model.
wi = AICc weights. LL = log likelihood scores.
In general, the variables with the most
support for species presence in the ZANB
modelling were the same for both species at
the PIW site. The number of explanatory
variables in ZANB models for CPG
presence/absence in each of the 48
observed resource units is smaller than for
the LWD models because larger models
caused convergence errors on attempting to
fit the ZANB model (Harrell 2001). LWD
and CPG were absent from many resource
units and so the probability of their being
present was small. As the GLM produces a
response deviance on the same scale for all
nested models, we can compare results of
models with different link transformations.
This allows for flexibility in exploring which
link function is most appropriate. We
compared results of both logit link and
complementary log-log link models and
chose the former one for the binomial part
of ZANB, not only because it is better
© Wildfowl & Wetlands Trust
understood but also because the AICc
comparison of these models did not show
an improved fit on using a clog-log link
function.
Caution must be observed in
interpretation of results in binary part of
ZANB because a zero outcome is the
prediction. The signs of the coefficients
have precisely the opposite interpretation
from that of a normal binomial model. In
other words, a positive coefficient indicates
that the variable increases the probability of
zeros and a negative coefficient indicates
that the variable decreases the probability of
zeros.
Based on the lowest AICc values, LWD
distribution is inversely related to the
percent cover of a mixture of Spikerush
Eleocharis dulcis and Water Lily Nympheae lotus,
water level fluctuation and, interestingly, the
water quality in each unit and the distance to
the nearest human development (Table 4).
Wildfowl (2016) 66: 41–59
Lesser Whistling-duck and Cotton Pygmy-goose habitat use 51
Table 4. Estimated coefficients and standard errors for zero-altered negative binomial
(hurdle, ZANB) models for the top AICc-selected model for the Lesser Whistling-duck
habitat use in the Paya Indah wetlands, Malaysia.
Variable (codes)a
Group A (zeros)b
Group ~A (counts)
Coefficient
s.e.
Coefficient
s.e.
Intercept
Spik1
Mix1_15
NDVI
Dis_hum
–3.37E+01
–1.71E+00
6.36E+00
–1.86E+01
9.23E–03
1.68E+01
1.05E+00
8.17E–01
3.46E+00
1.98E–03
3.20
0.73
0.41
6.01
2×10–3
8.42
0.33
0.42
1.10
8×10–4
WQI
6.31E–02
2.38E–02
4×10–3
1×10–2
LLF
Temp
Humidity
3.29E+00
8.84E–04
2.48E–02
7.27E–01
4.04E–01
9.00E–02
–2.82
0.05
0.07
0.76
0.12
0.03
a Code definitions for variables are provided in Table 1. b Group A estimates relate to the process
of being zero/non-zero in binary part of the ZANB model. Since a zero outcome is the
prediction in this part, the signs of the coefficients have precisely the opposite interpretation
from that of a logit model. In other words, a positive coefficient indicates that the variable
increases the probability of zeros and a negative coefficient indicates that the variable decreases
the probability of zeros. Group ~A estimates relate to the abundance (intensity of use) part of
ZANB model of Lesser Whistling-duck in Paya Indah wetlands, Malaysia.
The mean NDVI (productivity) of each unit
and proportion of Spikerush in each unit
has a positive effect on LWD distribution
(Table 4). The lowest AICc model for CPG
showed that distribution was inversely
related to the percent cover of a mixture of
Spikerush and Water Lily in each unit. As for
LWD, CPG distribution had a positive
relationship to the mean NDVI of each unit
(Table 5). Overall, it seems that LWD
selected more stable densely vegetated
© Wildfowl & Wetlands Trust
marshy edge areas while CPG frequented
vegetated areas near the central, deeper
parts of the lake.
Species abundance models
LWD and CPG were present in few
resource units, not even reaching that
expected from a standard Poisson
distribution which is sometimes used as the
basis for count data modelling (residual
deviance = 13,809, d.f. = 2,298 for the
Wildfowl (2016) 66: 41–59
52 Lesser Whistling-duck and Cotton Pygmy-goose habitat use
Table 5. Estimated coefficients and standard errors for zero-altered negative binomial
(hurdle, ZANB) models for the top AICc selected model for Cotton Pygmy-goose habitat use
in the Paya Indah Wetlands, Malaysia. a See footnote to Table 4.
Group A (zeros)a
Variable (codes)
Intercept
Spik1
Mix1_15
NDVI
Temp
Humidity
Group ~A (counts)
Coef.
s.e.
Coef.
s.e.
–3.03E+00
1.62E+00
4.91E+00
–6.18E+00
–3.74E–11
–1.17E–10
1.80E+01
6.21E–01
4.96E–01
2.00E+00
4.62E–01
1.02E–01
–14.41
3.67
3.52
–3.26
0.08
0.06
5.16
0.43
0.36
2.61
0.13
0.02
Lesser Whistling-duck
Cotton Pygmy-goose
35
30
Frequency
Frequency
15
10
5
25
20
15
10
5
0
0
0
50
100
150
200
Numbers counted
0
50
100
150
200
250
300
350
Numbers counted
Figure 4. Lesser Whistling–duck and Cotton Pygmy–goose abundance within a 150 m fixed radius
around observation points (Fig. 2) in the Paya Indah Wetlands, Malaysia, during October–January 2010,
2012.
Poisson model). Also, visual examination of
the count data (Fig. 4) combined with a
variance-to-mean ratio test showed overdispersion of zero-count data (variance-tomean ratio = 91), indicating that abundance
data would be best fitted using a negative
binomial model. In contrast with the
distribution model (zero hurdle), results of
abundance (count data) analysis from the
© Wildfowl & Wetlands Trust
ZANB regression models can be interpreted
in a regular manner. These indicated that
LWD abundance was positively related to
the percent cover of Spikerush, mean
NDVI of each unit, and the distance
between the sampling unit and human
activities. In addition, LWD abundance was
related to monthly mean relative humidity
and inversely related to water level
Wildfowl (2016) 66: 41–59
Lesser Whistling-duck and Cotton Pygmy-goose habitat use 53
fluctuations. The abundance model results
for CPG were similar to those for LWD in
showing that the percentage of Spikerush
cover and also the mixed Spikerush and
Water Lily cover were positively associated
with bird abundance in a unit (Tables 4 & 5).
Discussion
Anatidae are ecologically dependent on
wetland habitat for at least part of their
annual cycle. Conservation of these
waterfowl largely depends on the
recognition of key factors affecting their site
selection. Wetlands International has
published an Atlas of Key Sites for Anatidae in
the East Asian Flyway (Miyabayashi &
Mundkur 1999), which reviewed the
distribution of Anatidae species in the
region, defined population boundaries and
identified important areas for these species.
Key sites for Lesser Whistling-duck in
Thailand and Myanmar were listed in this
document, with none reported for Malaysia,
albeit information on breeding areas for
birds in the East Asian Flyway is incomplete.
Although our study provides a preliminary
assessment of the ecology for two little
known waterfowl species, it therefore
should be noted that there may be
conditions at other sites not present at our
study site that make other sites more
attractive to these birds.
In this paper we estimated model
parameters to identify factors contributing
to the distribution and intensity of use by
the Lesser Whistling-duck and Cotton
Pygmy-goose at Paya Indah Wetlands. While
data accessibility and collection were
limited, we believe that our study will not
only help to inform conservation measures
© Wildfowl & Wetlands Trust
by revealing which environmental variables
best explain species distribution and
abundance but also encourage further
research to flesh out those species’ ecology
and life history. LWD prefer freshwater
wetlands where there is sufficient aquatic
vegetation in which to hide, and they forage
mostly on aquatic plants, nibbling on their
seeds and shoots. They also feed on insects
and aquatic invertebrates. They are
gregarious and consume aquatic vegetation
by dabbling in shallow water areas
(Johnsgard 1976). CPG also preferred
habitats that are freshwater wetlands where
there is sufficient aquatic vegetation to
forage. They are gregarious and foraging is
undertaken by dabbling and picking at the
water surface or by stripping seeds and
flowers from aquatic plants (Johnsgard
1976). Most of the results from our
species/habitat analysis correspond with
our observations of the species’ biology
and habitat preferences in the field. Water
level fluctuations, water quality and
anthropogenic disturbance play a vital role
in determining the quality and quantity of
habitat for waterfowl in general (Perry &
Deller 1996; Madsen 1998; Hansson et al.
2010; Tavares et al. 2015). We evaluated the
effects of those variables on LWD and CPG
distribution and abundance in the PIW. Our
results showed that water level fluctuation
has a different effect on distribution than on
abundance. Although highly variable, the
water-level at resource units provided more
resources for both species by different
mechanisms (Hansson et al. 2010; Kreakie et
al. 2012), with fluctuations causing shallow
water habitat to shift away from the
shoreline when water levels recede and back
Wildfowl (2016) 66: 41–59
54 Lesser Whistling-duck and Cotton Pygmy-goose habitat use
towards the shorelines as water levels rise.
Although this shift in habitat location may
increase waterfowl energetic costs, it also
likely increases available food and habitat by
maintaining emergent parts of the wetlands
(Murkin et al. 1997).
Both waterfowl species were more
abundant in stable habitats with low water
level variation. Moderate water depth in
each unit supported presence of both
species in our study. Baschuk et al. (2012)
observed that shallow waters make it easier
for dabbling ducks to access submerged
aquatic vegetation for feeding. Furthermore,
emergent vegetation, especially Spikerush,
was particularly important for both species
in our study area, which may influence the
amount of available food and cover. In
addition, such environments might act as
more suitable habitats to breeding, feeding
and predatory avoidance (Murkin et al. 1997;
Frid & Dill 2002).
NDVI selection coefficient values near
zero or positive values indicated that nonvegetated water bodies such as open water
were not preferred. Our results show that
high mean NDVI of each unit was
positively related to abundance of LWD.
Growing nutritious vegetation has low redlight reflectance and high near-infrared
reflectance and thus yields high NDVI
values. High values of NDVI are also
related to higher photosynthetic activity
and, consequently, primary productivity
(Nicholson et al. 1998; Vicente-Serrano &
Heredia-Laclaustra 2004). Higher primary
productivity can, in turn, increase food
abundance in higher trophic levels, such as
arthropods, which constitutes a nutritious
food source for most waterfowl (Gordo
© Wildfowl & Wetlands Trust
2007). Furthermore, high NDVI values are
related to improved ecological conditions
for waterfowl niche exploitation (Dalby et al.
2014); therefore, this could be related to
increased habitat preference and abundance
of LWD in our units.
Several studies have sought to identify
how human activities affect waterbirds
(Klein et al. 1995; Frid & Dill 2002; Fox
et al. 2014). Most studies show that
anthropogenic activities decrease habitat
preferences by waterfowl (Dahlgren &
Korschgen 1992; Fox & Madsen 1997;
Väänänen 2001; Pease et al. 2005). But they
may still select disturbed habitats if
alternative habitats are too distant or of low
quality (Frid & Dill 2002; Gill 2007).
Anthropogenic activities play different roles
in shaping distribution and abundance
patterns on our study area. Surprisingly,
distance to human activities around each
observation point had inverse effects on
distribution of LWD, but positive effects
on LWD abundance. Most human
developments are around the shoreline in
the PIW, which our data revealed is the
LWD preferred habitat, yet the ducks were
more abundant in marshy edges when far
from anthropogenic activities.
Habitat selection often requires tradeoffs between habitat availability and
exposure to potentially detrimental factors
(Dussault et al. 2006; Bastille Rousseau et al.
2010). Our results highlight the importance
of major factors, and their potential
interactions, in determining LWD and CPG
distribution and abundance. We had
insufficient sampling to include seasonal
factors in our analysis, which will
undoubtedly exert a significant effect on
Wildfowl (2016) 66: 41–59
Lesser Whistling-duck and Cotton Pygmy-goose habitat use 55
LWD and CPG presence and abundance
through time. Future studies should
incorporate time-series remote-sensing
imagery to understand changes through the
habitat and seasonal dietary analysis
(Elmberg et al. 2003) to better document
seasonal variation in LWD and CPG diets,
as well as spatial and temporal distributions
in relation to landscape change and
food/shelter availability.
We believe that our best models reflect
the ecological processes affecting habitat
use by waterfowl in PIW. The majority of
the explanation provided by our binary
model was a description of how LWD and
CPG selected habitats determining patterns
of distribution. Given that waterfowl
were absent from the majority of sectors,
explanation of ecological processes that
determined variation in abundance was of
interest, examined using truncated negative
binomial in zero-altered models. We found
evidence that both LWD and CPG
preferred shallow, nutrient-rich “marshy”
lakes which many studies have shown to be
important for waterfowl (Murphy et al. 1984;
Bayley & Prather 2003). In addition, our
distribution model indicated medium to low
sensitivity to human disturbance by LWD.
However, the abundance model indicated
that counts were low in areas close to
anthropogenic activities, reflecting the
restricted availability of suitable habitats
in the PIW. In general, distribution
models might enlighten management,
but abundance models provide more
information, better our understanding of
processes, and ultimately can contribute to
better management and conservation. In
particular, management should focus on
© Wildfowl & Wetlands Trust
establishing screened buffer zones around
important waterfowl roosting and feeding
areas as well as manipulating vegetation
composition and configuration through
effective water level management strategies,
to ensure future abundance of both LWD
and CPG in the PIW.
Acknowledgements
The authors are grateful to the Malaysian
governmental agencies NAHRIM and
DWNP for providing data and allowing us
to conduct this research. We thank Paya
Indah Wetlands Reserve guards and staff for
their help during field surveys. We also
thank Department of Biological Sciences,
University of Alberta and Faculty of
Forestry, Universiti Putra Malaysia staff and
students for their support.
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