“In a tree by the brook, there’s a songbird who sings”: Woodlands in an agricultural matrix maintain functionality of a wintering bird community
ªIn a tree by the brook, there's a songbird who singsº: Woodlands in an agricultural matrix maintain functionality of a wintering bird community
Biang La Nam Syiem 0 1
Varun R. Goswami 1
Divya Vasudev 1
0 Post-graduate Programme in Wildlife Biology and Conservation, Wildlife Conservation Society India Program and National Centre for Biological Sciences , Bangalore , India , 2 Wildlife Conservation Society, India Program , Bangalore , India , 3 Centre for Wildlife Studies , Bangalore , India , 4 Conservation Initiatives, Guwahati , India
1 Editor: Govindhaswamy Umapathy, Centre for Cellular and Molecular Biology , INDIA
The agricultural matrix has increasingly been recognized for its potential to supplement Protected Areas (PAs) in biodiversity conservation. This potential is highly contextual, depending on composition and spatial configuration of matrix elements and their mechanistic relationship with biological communities. We investigate the effects of local vegetation structure, and proximity to a PA on the site-use of different guilds in a wintering bird community within the PA, and in wooded land-use types in the surrounding matrix. We used occupancy models to estimate covariate±guild relationships and predict site-use. We also compared species richness (estimated through capture±recapture models) and species naïve site-use between the PA and the matrix to evaluate taxonomic changes. We found that tree cover did not limit the siteuse of most guilds of the community, probably due to high canopy cover across all chosen sites. Exceptions to this were guilds comprising generalist species. Shrub cover and bamboo cover had important effects on some woodland-associated guilds, suggesting a change in limiting factors for site-use under adequate tree cover. Site-use across the matrix was high for all analyzed guilds. This was found to be due to three non-exclusive reasons: (i) presence of one or more ubiquitous species (found all across the landscape) within some guilds, (ii) redundancy of species within guilds that buffered against a decrease in site-use, and (iii) turnover in guild composition/abundances to more generalist species from PA to matrix. Estimated species richness was higher in the matrix (107± 11; mean ± SE) than in the PA (90± 7), which may have been in part due to the addition of generalist species in the matrix. Understanding factors that limit biological communities is crucial to better managing the ever-increasing matrix for biodiversity conservation. Our study provides insights into the effects of different components of vegetation structure on the bird community in wooded land-use types in the matrix. We highlight the value of woodlands surrounding PAs in maintaining multiple guilds, and hence, the functionality of a wintering bird community. However, we caution that the matrix may fall short in retaining some specialized species of the community.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: This project was financially supported by
the Department of Science and Technology,
Government of India and Wildlife Conservation
Society India Program. The funders had no role in
study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
Agricultural expansion continues to be a major driver of large-scale conversion of tropical
forests into human-use areas [
]. The resultant habitat loss is generally detrimental to
biodiversity, leading to the loss of species, as well as community structure and function [
]. Efforts to
prevent forest conversion and protect biodiversity in the tropics±±including threatened
wildlife species in particular±±have largely best been met by the establishment of Protected Areas
]. However, under current rates of human population growth, associated land-use
change and inefficient policy, the scope for expansion of PAs is greatly limited [
continuing conversion of forests into agricultural land. There is also increased recognition that
PAs are not insular, and biodiversity within PAs interacts with agricultural land-uses outside
]. As such, conservationists have brought to attention the need to evaluate and target the
ever-increasing agricultural land-use types for their potential to supplement biodiversity
conservation in the tropics.
Agricultural land-uses surrounding PAs, collectively termed the matrix (non-optimal
habitat areas in conservation landscapes), are characterized by a mosaic of managed land-use
types interspersed with natural and semi-natural habitats that vary in their ability to support
]. This conservation potential of the matrix is highly contextual, depending
on the land-use type, taxon, spatial location and other factors. For example, biodiversity is
better supported in agro-forests than intensive agriculture, while birds as a taxon have been found
to be highly sensitive to forest conversion into agriculture based on multiple measures of
biodiversity . With approximately half of the closed-canopy forests in the tropics converted
predominantly into agriculture [
], there is an ever-present need to understand the interaction
of biodiversity with the matrix and its characteristics in important conservation landscapes
The compositional elements of a matrix (e.g., habitat type, vegetation structure), and the
spatial configuration of these elements (e.g., proximity to optimal/protected habitat) play an
important role in structuring and determining the composition of biotic communities [
Previous research involving different taxonomic groups ranging from plants to mammals, reveal
differential responses of various taxa to changes in composition and configuration of matrix
]. For birds, the taxon of interest in this study, vegetation structure, in terms of its
composition, plays a significant role in influencing community properties [
structure mediates foraging substrates and resources for birds [
], and can also influence risk
perception through providing cover [
]. Hence, the presence and abundance of different
species is likely to be determined to a large degree by the kind of vegetation structure present. At
the landscape scale, vegetation structure will invariably differ across the matrix, which in turn
can significantly alter bird communities. In addition, spatial configuration of different matrix
elements also impacts the way bird communities use the matrix. An important example is the
proximity of matrix elements to optimal habitat: in many bird communities, increasing
distance from contiguous forest habitat can greatly alter the structure and composition of the
A comprehensive approach to understanding the link between biotic communities and the
matrix require the use of both taxonomic metrics as well as trait-based metrics to measure
community response. Taxonomic metrics such as species richness and composition are highly
useful in highlighting community changes relevant to conservation of important taxa.
However, they fail to take into account species traits such as behavior, diet or ecosystem function/
role that allow generalizations among compositionally different communities [
metrics such as guild occupancy, on the other hand, address shared characters between species
that allow for a mechanistic understanding of how environmental change might affect
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community structure disregarding compositional differences between communities [
However, these, in turn, fall short in their ability to highlight taxonomic changes that may be
of conservation concern. Thus, the use of both types of metrics is highly important for studies
relevant to biodiversity conservation and management to provide a localized as well as a
general understanding of community response to land-use change.
In this study, we first use a guild-based framework to investigate how vegetation structure
and proximity to a PA influence the wintering bird community that uses wooded land-use
types surrounding a contiguous tropical forest in Meghalaya, Northeast India. We use an
occupancy modeling approach to assess vegetation and proximity effects on guilds as it allows the
separation of ecological and sampling processes [
]. In doing so, we also compare the site-use
of individual guilds inside the PA to that in the surrounding matrix, as an evaluation of the
ability of the wooded matrix to maintain community functionality. Second, we compare
estimated species richness, and naïve site-use of detected species, between the PA and the
surrounding wooded matrix to assess changes in taxonomic composition. We define wooded
land-use types here as any land-cover type comprising of closed-canopy or open-canopy
forests situated in the landscape, including, but not restricted to, natural forests, agroforests and
mixed plantations. We conclude with a discussion on the possible mechanisms linking
different guilds to vegetation structure and proximity to a PA, the processes that might explain
siteuse across the landscape by guilds, the relevance of taxonomic changes and the conservation
implications of our study.
Materials and methods
The Forest and Environment Department, Government of Meghalaya, India granted us
research permits to conduct the study within government PAs. We obtained the consent of
various village administrative bodies for fieldwork in village lands.
We conducted our study in the Nongkhyllem landscape (25Ê45'±25Ê52'N, 91Ê44'±91Ê52'E)
located in Meghalaya, Northeast India (Fig 1). The study area covered approximately 100km2
with an elevation range of 300±1000m above mean sea level. The landscape included the
Nongkhyllem Wildlife Sanctuary and Reserve Forest, together comprising the Protected Area
(hereafter, the PA), and a heterogeneous matrix of agricultural lands, community-managed
forests, agro-forests and human habitation. The PA is a contiguous stand of relatively intact
forest. However, the forest was subjected to some amount of selective logging approximately
20 years prior to the study.
We conducted bird surveys between November 2015 and May 2016, from the onset of winter
to early summer to minimize overlap with the breeding and migratory season. We used a
gridbased sampling design, which allowed us to attain spatial coverage of the entire study area.
Each sampling unit was a grid-cell of 500m×500m, chosen to minimize within-cell
heterogeneity in vegetation structure while maintaining independence of sampling points across
gridcells. We used the geographic information system (GIS) software QGIS 2.6.0 [
] to overlay
the grid on the study area, as well as for all other GIS-related analyses. We excluded grid-cells
that were completely composed of human habitation. In total, we surveyed 100 grid-cells
covering the landscape.
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Fig 1. Map of study area in the Nongkhyllem Landscape, Meghalaya, India, showing the sampled sites (grid-cells)
within the PA and the matrix. The top right inset shows the location of the study area in India (black rectangle).
We surveyed birds using 10-min, 100-m fixed-radius point counts. We randomly located
five sampling locations per grid cell. We completed five point counts per day, one in each of
five adjacent grid-cells for logistic convenience. In each grid-cell, we sampled the five chosen
point count locations over five consecutive sampling days. Point counts located in completely
open land-use types were shifted to the nearest wooded location since our inferences related to
only wooded land-use types. Each sampling point was located not less than 100m from the
grid-cell boundaries to ensure that birds detected at point counts could be clearly established
as those present within that particular grid-cell. Within a grid-cell point counts were separated
by an average distance of 149.34 m ± 71.30 m (mean ± sd). We used the point count method,
as it is well suited for the detection of both canopy and understory birds, and is a logistically
efficient method to sample across a large and heterogeneous landscape [
]. All bird species
seen or heard during the counts were recorded. Birds that we could not identify to the species
level during the counts, but whose guild identity we could unambiguously establish, were
recorded for guild-level analyses.
At each sampling point, canopy cover and bamboo cover were sampled using a GRS
DensitometerTM (Geographic Resource Solutions, Arcata CA, USA) at 22 points spaced 5m apart,
located at and around the sampling point. Shrub cover was measured as shrub height using a
vertically marked pole at 8 points, spaced 5m apart, around the sampling point (Fig A in S1
File). Trees that were >5m in height within a 10m × 10m plot around the sampling point
were sampled for girth at breast height (GBH), which was then used to compute stand basal
area. The Euclidean distance of each grid-cell centroid from the PA was extracted using QGIS
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While designing the study, we aimed to classify detected bird species into guilds based on (i) size;
(ii) foraging strata; and (iii) diet and (iv) foraging method. Criterion (iv) was exclusive to
insectivorous guilds as it involved behavior such as sallying and gleaning, applicable to only insectivorous
birds. These criteria were chosen as they represent those ecological characteristics of a species that
indicate a mechanistic link to vegetation structure through resource requirements/acquisition (all
three criteria; [
]), and to proximity to PA through dispersal ability (criterion one; ). This
classification scheme could be followed completely for insectivorous species; non-insectivorous
species could only be classified according to a diet guild, as there was little size variation within
these guilds, and there was ambiguity in the foraging strata used by such species. Amongst the
insectivorous species, we classified woodpeckers separately due to their specialized ecology. Small
woodpeckers (including piculets), hornbills, shrikes, raptors and ground-dwelling birds of the
family Phasianidae were detected very rarely. Hence, sample size was too low for guilds of these
species to be included in the guild-level site-use analyses (described below). However, all species
were included in the species level analyses, except for raptors and Phasianidae species as they
required different sampling methods. We used information on different birds from Rasmussen
and Anderton [
], Grimmett et al. [
] and Grimmett et al. [
] to classify them into guilds.
For analysis, we obtained a total of 11 guilds (for species classification into guilds see S5
File): four non-insectivorous guilds and seven insectivorous guilds. We included both forest
specialists and generalists±±even though some species were detected solely in the PA and some
solely in the matrix±±for inference made on maintenance of community functionality, which
is determined by the presence of representative guilds (a starting point to understanding
functional groups [
]) irrespective of their taxonomic composition.
Assessing covariate effects and probability of site-use
We used the single-season occupancy model [
] to assess the influence of covariates on the
probability of each guild using the landscape, and quantify probability of use inside the PA and
the surrounding matrix. The occupancy modeling approach accounts for the imperfect
detection of study animals, i.e., the probability of guilds being present within a grid-cell (hereafter,
`site'), but not being recorded during surveys. Such non-detection has been shown to
substantially impact both estimated probabilities of site-use, as well as species±or guild±habitat
]. Estimation of detection probability, or the probability of detecting a guild in a
site, given its presence, is achieved through repeated sampling of sites; our sampling included
5 spatial replicates for each site.
The occupancy model is a hierarchical model comprising of two key parameters, for
occupancy (C) and detection (p) probabilities respectively, modeled as a function of ecological
covariates. In this study, we estimate the probability of site-use (referred to only as `site-use')
rather than occupancy [
] as our site was smaller in area than bird home-ranges in general.
Hence, our model estimates two parameters: (i) site-use (C) and (ii) detection probability (p).
We selected covariates for site-use C to represent composition and spatial configuration of
a site. Composition was defined by vegetation structure±±characterized by the covariates
canopy cover, bamboo cover, stand basal area and shrub cover. Spatial configuration of each site
was measured with respect to the PA, expressed as the distance of the site to the PA. We
selected the covariates time from sunrise, canopy cover and shrub cover to model detection
probability p as affected by both imperfect aural and visual detections. Covariates were
standardized (mean of 0, standard deviation of 1) to improve convergence of models (see Table A
and Table B in S1 File for untransformed values). Additive effects of these covariates were
included after testing for correlations among covariates.
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We used an information theoretic approach, the Akaike's Information Criterion (AIC), for
model selection [
]. This approach assesses the fit of a particular model to the observed data
while penalizing for model complexity. For each guild, we first identified model structures that
best describe variation in p. To do this, we ran different models for p (Table C in S1 File) while
fixing C to a general model (first model of Table D in S1 File). We then selected all covariate
structures for p that corresponded to models where ΔAIC < 2 to run different models for C
(Table D in S1 File). Models that did not converge were removed from the model set. In
general, models with ΔAIC < 2 were considered as those with sufficient support to make inference
on the effects of covariates on sites-use of guilds. However, we clarify that we did not use this
threshold in a very strict sense as we also examined models for uninformative parameters [
models with uninformative parameters were not included in the chosen model set.
The estimated covariate coefficients for each guild were not model-averaged across models
]. We present estimated covariate coefficient values from the models in the chosen model
set with the most parameterized covariance structure for site-use. Estimated covariate
coefficients for detection probability are reported from the same models that are used to report
siteuse coefficients. If the spatially invariant model (null model) also figured in the chosen model
set, other models in the chosen model set±±and by extension, the covariate effects included in
these models±±were presented as having only weak support.
Guild site-use was quantified by model averaging the predictions across the model set [
We compared average estimated guild site-use for grid-cells that fell within the PA, to that in
the surrounding matrix by calculating the mean of model-averaged predictions for site-use of
PA grid-cells and matrix grid-cells separately. The occupancy analysis was carried out using
the `unmarked' package in the statistical software R version 3.3.0 [
]. Additional packages
used were `ggplot2', `gridExtra' and `gtable [32±34].
Estimating species richness within and outside the PA
To compare species richness within and outside the PA, we estimated species richness
separately for sites that fell within, and those that fell outside the PA. Raw counts of detected
species can be a misleading indicator of species richness as there are likely to be species that go
undetected during surveys. Capture±recapture analyses successfully address this issue by
accounting for heterogeneous detection probabilities across species [
]. For the purpose of
this analysis, we considered each site as a replicate. We thus obtained a detection history for
each species, indicating detections and non-detections in each site for the PA, and the matrix
outside the PA. We used the program SPECRICH2 [
] for this analysis.
Comparing naïve site-use of species within and outside the PA
We calculated naïve site-use, i.e., the proportion of sites within and outside the PA used by
different species but uncorrected for detection probability. Calculation of this metric was based
on detection/non-detection data of individual species obtained from the five sampling
replicates per site described earlier. For each species at each site, detection histories obtained from
replication was converted into a binary form for calculation of naïve site-use. Detection in at
least one sampling replicate was taken as 1 and non-detection in any of the replicates was
considered as 0.
Covariate effects on detection probability and site-use are presented for 11 guilds: nectarivores,
granivores, omnivores, frugivores, large high-canopy gleaning insectivores, large understory
gleaning insectivores, large high-canopy sallying insectivores, small mid-canopy gleaning
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insectivores, small understory gleaning insectivores, small mid-canopy sallying insectivores
and large woodpeckers. The model selection table for each guild is given in S2 File.
Effect of covariates on detection probabilities of guilds
Covariate effects on detection probability varied across guilds. In general, detection probability
was higher in the early morning hours for seven guilds. Increase in canopy cover lowered the
detection probability of two guilds, whereas it increased the detection probability of three
guilds. Increase in shrub cover decreased the detection probability of two guilds, while it
increased the detection probability of two guilds. There was weak support for a negative
influence of shrub cover on the detection probability of frugivores. However, the spatially invariant
model was also supported among the chosen models. Covariate effects on detection probability
for the chosen model set of each guild are summarized in Table 1. Detailed coefficients for all
models are given in S4 File.
Effect of covariates on site-use of guilds
Increasing canopy cover reduced the site-use of granivores (±0.36 ± 0.39) and omnivores (±
0.70 ± 1.17). In addition, there was weak support for a negative impact of canopy cover on
large high-canopy gleaning insectivores and small understory gleaning insectivores. However,
for both guilds, the spatially invariant model was also among the chosen models. Bamboo
cover had a negative effect on granivores (±0.85 ± 0.43) and omnivores (±0.65 ± 0.47) and a
positive effect on small mid-canopy sallying insectivores (3.41 ± 3.33).
There was no strong support that stand basal area affected site-use of any guild. However,
there was weak support for a negative impact of this covariate on small understory gleaning
insectivores, with the spatially invariant model also amongst the chosen models.
Shrub cover was supported in the top model set of three guilds: nectarivores, granivores,
large high-canopy sallying insectivores. Shrub cover benefitted nectarivores (3.00 ± 1.90)
and granivores (0.42 ± 0.39) but was detrimental to large high-canopy sallying insectivores
(±1.09 ± 0.37) and small mid-canopy sallying insectivores (-0.63 ± 0.37). There was also weak
support for shrub cover positively affecting large woodpeckers.
Distance from PA had no strong support in the chosen model set of any guild, but had
weak support for a positive effect on large high-canopy gleaning insectivores and small
midcanopy gleaning insectivores.
Large high- Small mid- Small
canopy canopy understory
sallying gleaning gleaning
insectivore insectivore insectivore
±0.17 (0.10) ±0.37 (0.10) ±0.30 (0.12)
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The spatially invariant model for site-use was the best supported model for two guilds:
frugivores and large understory gleaning insectivores. The frugivore guild initially had a naïve
site-use of 0.99; hence most models with covariates did not converge. The non-convergence
was driven in part by the most abundant species, the black-hooded oriole Oriolus xanthornus,
which comprised 23.45% of detections in this guild (Fig B in S1 File). However, even after the
removal of this species naïve site-use only reduced to 0.98 due to the presence of other
abundant species. The models converged after the removal of this species, and in accordance with
the high naïve site-use, the spatially invariant model was the best supported model for site-use
of this guild.
Covariate effects on site-use for each guild are summarized in Table 2. Fig 2 shows the
effects of individual covariates on model-averaged predictions of site-use for each guild.
Detailed coefficients for all models are given in S3 File.
Site-use of guilds inside and outside PA
Mean site-use was very high (>0.80) for most guilds both within the PA and in the matrix.
Exceptions to this were the granivores and omnivores which had lower mean site-use in the
PA as compared to the matrix. Mean site-use for granivores in the PA was 0.30 ± 0.12
(mean ± SE) as compared to 0.62 ± 0.12 in the matrix; mean site-use for omnivores in the PA
was 0.69 ± 0.19, and in the matrix was 0.85 ± 0.15. Mean site-use for the all guilds is visualized
in Fig 3. See Table E in S1 File for detailed values.
Avian community species richness in the landscape
In total, we detected 94 species across 100 sites during the entire sampling period. Of these, 11
species were exclusive to sites within the PA, 20 species were exclusive to sites in the matrix
outside the PA, and 63 species were detected both within the PA and the matrix (For list of
species see Table G in S1 File). PA sites had 74 detected species. The matrix had 83 detected
species. Estimates of species richness was 90 ± 7(mean ± SE) species for the PA and 107± 11
(mean ± SE) for the matrix (Fig 4).
Standard errors are shown in parentheses. Guilds where covariate effect is unsupported are marked with a dash (`±`). Guilds where covariate effect has weak support are
marked with an asterisk (` ').
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Fig 2. Predicted site-use of a) non-insectivorous guilds and b) insectivorous guilds as a function of vegetation and
proximity effects as estimated through single-season occupancy models. Predictions were model-averaged across all
converged models; shaded regions represent associated standard errors. Covariates were standardized to have a mean
of 0 and standard deviation of 1. Guilds where covariate effect has weak support are marked with an asterisk (` ').
Guilds where covariate effect is unsupported are not included. Guild abbreviations: NEC (Nectarivores), GRN
(Granivores), OMN (Omnivores), FRG (Frugivores), LHG (Large high-canopy gleaning insectivores), LUG (Large
understory gleaning insectivores), LHS (Large high-canopy sallying insectivores), SMG (Small mid-canopy gleaning
insectivores), SUG (Small understory gleaning insectivores), SMS (Small mid-canopy sallying insectivores), LWP
Naïve site-use by species inside and outside the PA
Of the 94 species detected during our study, 49 species had a higher naïve site-use in the PA
than in the matrix while 45 species had a higher naïve site-use in the matrix than in the PA.
The magnitude of naïve site-use change between PA and the matrix for each species is
visualized in Fig 5 (see also Table F in S1 File).
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Fig 3. Mean site-use of guilds in the PA and in the matrix. Error bars represent standard errors. Guilds shown
towards the left show higher estimated site-use in the matrix, while those towards the right show higher estimated
siteuse in the PA. Guild abbreviations: GRN (Granivores), OMN (Omnivores), SUG (Small understory gleaning
insectivores), LHG (Large high-canopy gleaning insectivores), LWP (Large woodpeckers), NEC (Nectarivores), LUG
(Large understory gleaning insectivores), LHS (Large high-canopy sallying insectivores), SMS (Small mid-canopy
sallying insectivores), SMG (Small mid-canopy gleaning insectivores), FRG (Frugivores).
Our study brings to light two interesting findings. First, in our assessment of vegetation and
proximity effects on the site-use of various guilds of the wintering bird community in wooded
Fig 4. Estimates of species richness for sites located in the PA and in the matrix in the study area at Meghalaya.
Estimates were obtained from capture±recapture sampling of species that accounted for heterogeneous detection
probabilities across species.
PLOS ONE | https://doi.org/10.1371/journal.pone.0201657
Fig 5. Comparison of naïve site-use by species in the matrix and in the PA. Estimates of site-use were based on the
proportion of sites in the matrix and the PA where a species was detected. Expanded species abbreviations are given in
Table F in S1 File.
land-use types of the matrix, we found that tree cover variables were not the limiting factors
for most guilds. Interestingly though, shrub cover and bamboo cover were found to be
important factors for some of the guilds that were not limited by tree cover. Second, our study
highlights the ability of wooded land-use types in the matrix to maintain high-levels of site-use by
multiple guilds, and hence the functionality, of the wintering community. However, the
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maintenance of community functionality did not correspond with the maintenance of
Canopy cover not the limiting factor for guild site-use in wooded land-use types
Our finding that canopy cover was not a factor limiting site-use for the majority of the guilds is
consistent with our study focus on wooded land-use types, where tree cover variables are
expected to have higher values. The overall canopy cover in all but three of our sites was over
20%, which may have been adequate to maintain site-use by these guilds (see [
this, we also found that all guilds not limited by canopy cover were woodland-associated
guilds, except for small understory gleaning insectivores. Moreover, the guilds where canopy
cover had a strong negative effect were guilds containing generalist species (granivores and
omnivores).This finding in our study is further reflected on the high estimated site-use by
these guilds across the landscape, which we discuss in more detail in the sections below.
Notwithstanding the lack of effect of tree cover variables, the effects of shrub cover and
bamboo cover on two woodland-associated guilds suggests that other components of
vegetation structure are also important in governing site-use in wooded land-use types. Miller and
] also found shrub density to be an important factor predicting the number of foraging
guilds in woodland patches in Australia. However, Raman et al. [
], found that bamboo
affected mainly non-forest bird species in India. Many related studies account for different
components of vegetation structure by combining them with other variables into one or two
integrated vegetation axes (e.g., [
]). While this analytical method is highly useful and
necessary in many cases, we feel that separately testing for different components of vegetation
structure, whenever feasible, can also help capture nuanced relationships that may otherwise go
unexplained. Moreover, it can also help us understand the processes operating when those
components become limiting factors. For example, when tree cover is not limiting, the
negative effect of shrub cover on large high-canopy sallying insectivores, and small mid-canopy
sallying insectivores, help point to several different hypotheses: spatial restrictions on foraging
method imposed by shrub cover [
], or variations in insect prey abundance/diversity [
access to prey [
] with changing shrub cover, to suggest a few. Separately testing for different
components of vegetation structure becomes especially important when we consider the fact
that shrub cover and bamboo cover can undergo dramatic change with human-use even when
tree cover is left intact.
While we accounted for the effects of the different components of vegetation structure in
our current study, we further suggest that testing the floristic effects of vegetation, and their
associated resources would be an important second step [
] to better our understanding of
the mechanistic relationship of vegetation with guilds of the bird community.
The weak support for the effects of proximity to PA in our models suggest that site-use by
guilds are not limited with increasing distance from the PA. However, differences in species
naïve site-use between the PA and the matrix might suggest that proximity to PA is more
important a factor when it is considered at the species level.
Maintenance of guilds across the landscape
Our study found a high use of the landscape by all analyzed guilds both inside the PA, and in
wooded land-use types of the matrix outside the PA. This suggests that the matrix surrounding
the PA in our study area was capable of supporting site-use by multiple guilds of the wintering
bird community. Since our focus was on wooded land-use types only, we add that our
inference does not extend to open land-use types such as paddy fields and human habitation.
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Three explanations appear to work together to maintain the high site-use of the matrix by
the analyzed guilds in our study area. 1) We found that within seven guilds, there were
ubiquitous species that had relatively high site-use in both the matrix and the PA. Species such as
the little spider hunter Arachnothera longistra, black-crested bulbul Pycnonotus flaviventris,
black-hooded oriole Oriolus xanthornus, scarlet minivet Pericrocotus speciosus, black drongo
Dicrurus macrocercus, yellow-browed warbler Phylloscopus inornatus and grey-headed canary
flycatcher Culicicapa ceylonensis had relatively high site-use in both the matrix and the PA. 2)
There was some level of redundancy of species within guilds. This buffered against a decrease
in guild site-use due to loss of species and/or decrease in site-use as the habitat changes from
the PA to the matrix. For example, within the large woodpecker guild, although all species
decreased in site-use from the PA to the matrix±±with two species, greater yellownape Picus
flavinucha and great slaty woodpecker Mulleripicus pulverulentus, completely disappearing±±
guild site-use was still comparable between the PA and the matrix. 3) A turnover in guild
composition and/or abundances to more generalist species as well as addition of new species in the
matrix was found in a few guilds. For example, within the frugivore and small low-canopy
gleaning insectivore, there was a noticeable site-use increase in the matrix, as compared to the
PA, of the red-vented bulbul Pycnonotus cafer and common tailorbird Orthotomus sutorius
respectively; these species are known generalists. Similarly, within the small mid-canopy
gleaning insectivore guild, there was a noticeable site-use decrease in two species±yellow-bellied
warbler Abroscopus superciliaris and velvet-fronted nuthatch Sitta frontalis, with an increase in
two other species±oriental white-eye Zosterops palpebrosus and common iora Aegithina tiphia
in the matrix. The comparable site-use by guilds between the matrix and the PA could also
suggest that movement of species belonging to those guilds was possible through the matrix,
i.e., the sampled wooded land-use types were either structurally or functionally connected,
even those furthest from the PA. Such movement has been found to occur in other tropical
agricultural matrices; the movement of birds either depended on the intervening land-use type
] or on traits of the birds±forest-specialist versus generalist [
]. The movement of species
through the matrix could also operate in conjunction with the turnover in guild composition
and abundances, whereby generalist species are better adapted to move through more open
An important point to note in our study is that although we only looked at site-use by guilds
and not occupancy, and hence cannot ascertain which areas contain resident bird populations,
it makes ecological sense to reason that the high site-use by guilds especially far away from the
PA is due to populations that are resident within the matrix itself.
Taxonomic changes across the landscape
Although we found that the species richness was higher in the matrix than in the PA, many of
the additional species found in the matrix were also generalist species, with a few exceptions.
For example, red-vented bulbul, oriental magpie robin Copsychus saularis, jungle babbler
Turdoides striata, Siberian rubythroat Luscinia calliope, rufescent prinia Prinia rufescens and
greybreasted prinia Prinia hodgsonii, all of them generalists, were additional detections in the
matrix. However, the great hornbill Buceros bicornis a large seed-dispersing bird of high
conservation value was only encountered within the PA. In addition, two large woodpecker
species±the greater yellownape and great slaty woodpecker, and two piculet
species±whitebrowed piculet Sasia ochracea and speckled piculet Picumnus innominatus, were also detected
only within the PA. This suggests that while the matrix can support higher species richness,
due to the addition of generalist species, some specialized species such as hornbills,
woodpeckers and piculets may lose out.
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Guild-specific detection probability
Our study found that detection probability varied between guilds both spatially and
temporally. Except for nectarivores, our site-use estimates for all guilds would have been biased if we
had not accounted for factors such as time, canopy cover and shrub cover that could affect our
ability to see or hear birds during sampling. These findings point to the importance of
accounting for detection probability in ecological studies of birds in order for a more robust
inference. For this same reason, we state that our interpretation of species level site-use is a
naïve estimate in the sense that it is uncorrected for detection probability, and only provides
speculations to possible ecological trends.
Forest conversion due to the expansion of the matrix seems inevitable under current trends of
land-use change, often with negative impacts on the composition, structure and function of
the resident biotic communities. The need to better manage the matrix in order to mitigate
these impacts becomes ever increasing as more of the world's biodiversity gets lost with each
passing day. Our study highlights the value of wooded land-use types in the matrix
surrounding forests for maintaining the functionality of a wintering bird community. In the context of
our study landscape, i.e., the Ri-Bhoi District of Meghalaya, Northeast India, we recommend
land managers to promote the prevailing wooded land-use types such as recovering secondary
forests, community-managed forests and betel leaf cultivation forests due to their high
predicted value for supporting multiple bird guilds. We expect that a decrease in such wooded
land-use types across the landscape will disrupt the retention of multiple bird guilds. Although,
there is a lack of scientific information on land-use change in the region, there has been an
observable increase in more open land-use types±±such as broom-grass Thysanolaena maxima
plantations±±over the past decade, which may be a cause of concern. In other parts of India as
well, the conservation value of agricultural matrices for birds has been shown to be enhanced
by the presence of wooded land-use types such as shade-coffee plantations, rubber plantations
and other agro-forests [
]. However, we caution that prevailing wooded land-use types in the
matrix may not be enough to retain certain species that appear to be found, or have a high
naïve site-use, only in the PA. Species such as hornbills, large woodpeckers and piculets may
need a management approach similar to that of the PA to have continued presence in the
matrix. Nonetheless, the agricultural matrix has a conservation role to play in
human-dominated heterogeneous landscapes; our study provides scientific findings on community
functionality and individual species distribution in the matrix that can inform landscape-scale
planning to better enhance the conservation value of these lands.
S1 File. Supporting informationÐTables and figures.
S2 File. Model selection of site-use and detection probability for different guilds using an
occupancy-based approach. Model selection was conducted using AIC, while removing all
uninformative models. Chosen models for inference are highlighted in bold.
S3 File. Covariate coefficients of site-use for all the models of each guild. Standard errors
are shown in parentheses. Model selection was conducted using AIC, while removing all
uninformative models. Chosen models for inference are highlighted in bold. Covariate coefficients
14 / 17
for site-use are reported from models marked ` '.
S4 File. Covariate coefficients of detection probability for all the models of each guild.
Standard errors are shown in parentheses. Model selection was conducted using AIC, while
removing all uninformative models. Chosen models for inference are highlighted in bold.
Covariate coefficients for detection probability are reported from models marked ` '.
S5 File. List of bird species recorded during the study period, along with their assigned
guilds. Nomenclature follows Grimmett et al. (2011).
S6 File. Species detection±non-detection data and covariate data used in our study. Sepa
rate worksheets contain: i) Species detection±non-detection data, ii) Site covariate data and iii)
Sampling occasion covariate data for time (minutes) from sunrise.
This study was undertaken towards partial fulfillment of a post-graduate degree in wildlife
biology and conservation. We would like to thank the National Centre for Biological
Sciences, Wildlife Conservation Society India Program and Centre for Wildlife Studies for
institutional support; the Department of Science and Technology, Govt. of India and Wildlife
Conservation Society, India Program for financial support. We would like to thank the
Forest and Environment Department, Govt. of Meghalaya, India and various village
administrative bodies around the Nongkhyllem Wildlife Sanctuary and Reserve Forest for granting us
permission to work in the landscape. We thank A. Kumar and J. Ratnam for support; J. D.
Nichols, R. M. Dorazio, D. Jathanna, M. Gangal and B. Borah for inputs and discussions. We
also thank Mr. P. Doonai for his support. We thank the xeno-canto team who maintain the
rich diversity of bird calls and songs at www.xeno-canto.org that helped species
identification in the field. We are grateful to B. Nongrum for assistance with fieldwork. Finally, we
would like to thank Led Zeppelin for inspiration; a line from their song ªStairway to Heavenº
we quote in our title.
Conceptualization: Biang La Nam Syiem, Varun R. Goswami, Divya Vasudev.
Data curation: Biang La Nam Syiem.
Formal analysis: Biang La Nam Syiem, Varun R. Goswami, Divya Vasudev.
Investigation: Biang La Nam Syiem.
Methodology: Biang La Nam Syiem, Varun R. Goswami, Divya Vasudev.
Project administration: Biang La Nam Syiem, Varun R. Goswami, Divya Vasudev.
Supervision: Varun R. Goswami, Divya Vasudev.
Writing ± original draft: Biang La Nam Syiem, Varun R. Goswami, Divya Vasudev.
Writing ± review & editing: Biang La Nam Syiem, Varun R. Goswami, Divya Vasudev.
15 / 17
16 / 17
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