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\begin{document}

\title{Acoustic phenology among tropical resident birds differs between native
forest species and parkland colonizer species}



\author[1]{Laura Berman}%
\author[1]{Wei Xuan Tan}%
\author[2]{Ulmar Grafe}%
\author[1]{Frank Rheindt}%
\affil[1]{National University of Singapore}%
\affil[2]{Universiti Brunei Darussalam}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\selectlanguage{english}
\begin{abstract}
Most birds are characterized by a seasonal phenology closely adapted to
local climatic conditions, even in tropical habitats where climatic
seasonality is slight. In order to better understand the phenologies of
resident tropical birds, and how phenology may differ among species at
the same site, we used \textasciitilde{}70,000 hours of audio recordings
collected continuously for two years at four recording stations in
Singapore and nine custom-made machine learning classifiers to determine
the vocal phenology of a panel of nine resident bird species. We
detected distinct seasonality in vocal activity in some species but not
others. Native forest species sang seasonally. In contrast, species
which have only had breeding populations in Singapore for the last few
decades exhibited seemingly aseasonal or unpredictable song activity
throughout the year. Urbanization and habitat modification over the last
200 years have altered the composition of species in Singapore, which
appears to have influenced phenological dynamics in the avian community.
It is unclear what is driving the differences in phenology between these
two groups of species, but it may be due to either differences in
seasonal availability of preferred foods, or newly established
populations may require decades to adjust to the local phenology. Our
results highlight the ways that anthropogenic habitat modification may
disrupt phenological cycles in tropical regions in addition to altering
the species community.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

Phenology, the timing of biological events within seasonal cycles, is a
vital aspect of the life history of all species (Schwartz 2013).
Knowledge of a species' phenology is necessary to fully understand its
role within the ecosystem. In the case of endangered species,
information on phenology can help target conservation efforts during the
season when potential payoff is highest (e.g. Perkins et al. 2013).
Shifts in phenology are among the most widely documented biotic
responses to global climate change (Scheffers et al. 2016, Oliver et al.
2018), often leading to ecologically disruptive asynchronies. Phenology
is central to both understanding the ecology of ecosystems and to the
conservation of species. Reproductive phenology is a basic aspect of
birds' life history which is well studied in temperate regions, but is a
comparatively younger field of research in the tropics (Abernethy 2018).
Phenological studies of Southeast Asian birds are especially scarce
(Sodhi 2002). The paucity of studies on tropical seasonality is
partially due to historical inequities in the focus of scientific
research, and partially due to the comparative difficulty --
phenological patterns in tropical regions are often less obvious than in
temperate ones (Stutchbury and Morton 2001).

While reproductive seasonality among birds is generally understood to be
the norm, even near the equator (Baker 1939, Snow and Snow 1964, Bell
1982), seasonal fluctuations in food supply are less pronounced in
equatorial regions, leading to greater variability among species in the
timing of the breeding season (Stutchbury and Morton 2001, Stouffer et
al. 2013). Individual species might breed outside the typical season due
to competition for nesting sites (Steward et al. 2013, Sadanandan et al.
2023), particular diets (Ralph and Fancy 1994), or taxon-specific
requirements (Serle 1981). Phenology can sometimes shift dramatically
across small geographic scales due to differences in climate (Wrege and
Emlen 1991, Thomas et al 2001, Moore et al. 2005). As anthropogenic
climate change intensifies, it is becoming increasingly important to
understand phenological cycles of biota in the tropics, where
biodiversity is greatest and under greatest threat, how those cycles are
linked to local climates, and how they might be disrupted as the climate
changes. Moreover, some avian clades may be more resilient to warming
than others (Pollock et al 2021). More detailed research is needed to
understand the phenology of tropical birds on a species-specific level
and at a detailed geographical scale.

Long term soundscape recordings can be used to determine the breeding
phenology of birds (Brumm and Zollinger 2013, Jahn et al. 2017,
P\selectlanguage{ngerman}érez-Granados and Schuchmann 2020). New bioacoustic technologies have
made it possible to explore questions which were difficult or impossible
to address with traditional methods (Pijanowski et al. 2011, Shonfield
and Bayne 2017). Autonomous recording units (ARUs), which can be
deployed outdoors to record long-term continuous soundscape data, have
become increasingly reliable, user friendly, and inexpensive in recent
years, making the widespread use of this technology more feasible for
large scale projects and more common as an aspect of long-term
ecological monitoring regimens (Hill et al. 2018, Manzano-Rubio et al.
2022, Bota et al. 2023). Long-term autonomous bioacoustic monitoring has
many advantages over traditional survey methods: it is less labor
intensive, less expensive, less prone to observer bias, can be used at
difficult-to-access locations, and creates an archival record which
enables the comparison of vocalizations and soundscapes across time
(Frommolt et al. 2008, Borker et al. 2015, Shonfield and Bayne 2017).
While acoustic monitoring is becoming more common, continuous multi-year
datasets are still rare. This study makes use of an exceptionally large
and complete soundscape dataset: two full years of continuous 24/7
soundscape recordings from four ARUs deployed in the forests of
Singapore, a small Southeast Asian country only 1\selectlanguage{english}@ north of the equator.

Recent advances in machine learning technology have made it possible to
efficiently process the terabytes of data produced by each monitoring
station over the years (Stowell et al. 2019). Long term acoustic
monitoring in combination with machine learning analysis has a wide
utility in ecological research: it can be used for assessment of
population dynamics, activity patterns, and human impacts on a site over
time, for rapid site inventories, to detect and map the habitats of rare
and endangered species, and to determine the phenological cycles of
individual species (Bardeli et al. 2010, Shonfield and Bayne 2017,
Deichmann et al. 2018). Software like Kaleidoscope Pro (Wildlife
Acoustics) and BirdNET (Cornell Lab of Ornithology) are able to
automatically detect and identify species' vocalizations with a high
degree of accuracy (Manzano-Rubio et al. 2022) and can be trained to
identify new species.

The objectives of this study were to elucidate the species-specific
breeding seasons of some of the birds of Singapore. To meet this
objective, we used two years of continuous soundscape recordings and
nine tailor-made species-specific machine learning classifiers to
determine the seasonal vocal activity of nine focal species. There were
two emergent properties in the preliminary results which warranted
further analysis. First, several species did not appear to follow any
seasonal pattern at all in their vocal activity. To quantitatively
measure this pattern, we developed a novel seasonality index which
measures the extent to which vocal activity is seasonal or aseasonal.
While a number of excellent R functions exist for detecting and
extracting the seasonal component of a trend (e.g. decompose), none
measure the strength of seasonality. Our novel seasonality index fills
this gap. Second, the least seasonal species appeared to be those which
had not historically occurred at the study site. To quantitatively
assess this apparent pattern, focal species were
categorized\emph{post-hoc} into native forest species versus parkland
colonizer species and the degree of seasonality of each species' vocal
activity was measured.

\textbf{METHODS}

\textbf{Study area}

Data were collected in Singapore, a small Southeast Asian island nation
(1.3@\selectlanguage{english}N, 103.7@\selectlanguage{english}E; 734 km\textsuperscript{2}) located within the Sundaic
biogeographic region close to the equator with a tropical rainforest
climate. It is hot and rainy year-round. Climatic seasonality is weak,
but on average it is slightly sunnier in February-March and rainier in
November-December (Figure 1, Berman et al. 2023). Historically this
island was dominated by mixed dipterocarp everwet forest. Singapore,
however, has undergone dramatic urbanization in the last 200 years: only
0.28\% of its original primary forest cover remains (Yee et al. 2011)
and about 30\% of the native bird species have gone nationally extinct
(Chisholm et al. 2016, Chisholm et al. 2023). Currently, 20\% of the
country's land cover is secondary forest (Yee et al. 2011), which ranges
from native-dominated old secondary forest within nature reserves to
open woodlands dominated by primarily exotic tree species.

While the urbanization of Singapore has led to the local extinction of
many forest interior species, it has also led to the colonization of
regionally local species adapted to more open habitat. The four
recording stations in Singapore were in Sungei Buloh Wetland Reserve
(1.441586 N, 103.735308 E), Dairy Farm Nature Park (1.358419 N,
103.777492 E), Central Catchment Nature Reserve (1.355488 N, 103.804549
E), and National University of Singapore Campus (1.295020 N, 103.779385
E). These recording stations were 3-10 km apart from one another. All
recorders were placed in mixed dipterocarp tropical rainforest habitat.
The recording stations in Central Catchment Nature Reserve, Sungei Buloh
Wetland Reserve and Dairy Farm Nature Park were all in mature secondary
mixed dipterocarp tropical rainforest, while the recording station on
the National University of Singapore campus was located in early-stage
successional forest. These four recording sites were close enough to one
another, and located in sufficiently similar habitat, that we would not
expect phenology to differ among them. Data from these four recording
stations were amalgamated into a single dataset to maximize sample size
for each species.

\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/761505/articles/737555-acoustic-phenology-among-tropical-resident-birds-differs-between-native-forest-species-and-parkland-colonizer-species}

\emph{\textbf{Figure 1}. Seasonal climate of Singapore. Data obtained
from weatherspark.com.}

\textbf{Audio recordings}

Audio data was collected continuously for two years at each recording
station for a total of about 70,000 hours. Each audio file begins on the
half hour and has a duration of 29 min 55 sec, with a 5 second rest
period between files for data writing. The study period ran from June
2020 -- May 2022. Data was recorded on AudioMoth acoustic recorders
(Open Acoustic Devices) modified for long-term deployment: eight
supplementary battery packs were wired in parallel to each AudioMoth,
increasing the potential battery life eight-fold, and each unit was
placed inside a custom-built waterproof 15x20x10 cm enclosure, with an
aperture for the microphone protected by a waterproof acoustic membrane
and a wire mesh. Units were deployed at shoulder height and attached to
a tree via python lock. Units were checked once every 5 weeks to replace
batteries, SD cards and desiccant, and to collect data. Every 5 weeks,
units were given enough battery power and SD memory to collect data
continuously for another 6 weeks, to provide a one-week buffer.
AudioMoth units were set to collect data with a 16 kHz sampling rate,
medium gain, and no subsampling or filtering. All recording units were
deployed inside forest and at least 50 m from the nearest publicly
accessible trail to minimize human disturbance.

\textbf{Handling missing data}

There were a few instances when recorder failure led to missing data,
most notably in the first half of February 2022. Recorder failure was
typically caused by low battery voltage and resulted in premature
termination of recordings, i.e., files with a duration of less than 29
minutes and 55 seconds. A complete record of all dates and times with
missing data can be found in the Supporting Information. When reporting
number of vocalizations per hour or per month, the true recording
duration was used as the denominator to account for missingness in the
time series and variability in the duration of months. Hours with
greater than 60\% missingness were omitted from the time series.

\textbf{Manual identification}

Manual identification was used to identify candidate focal species and
collect vocalizations to be used in species classifiers. A random
selection of 1,000 audio files from the complete audio dataset were
manually assessed and a record was made of all species that could be
positively identified within each file in order to determine which
species were heard most often and would therefore make the best
candidate focal species. Files with positive identifications were then
used as part of the training datasets for the single species
classifiers. These manual identifications were used again later in the
workflow to measure the recall rate of the completed species
classifiers.

\textbf{Classifier training}

After nine focal species had been identified, custom-made single-species
classifiers were trained for each focal species using the machine
learning clustering software Kaleidoscope Pro v 5.4.8 (Wildlife
Acoustics). Each classifier was used to detect and identify
vocalizations from one species. Kaleidoscope Pro uses a two-step process
for species identification: scanning and clustering. During the scanning
step, Kaleidoscope scans the dataset of audio files for sounds that
match a set of signal parameters tuned to pick up the focal species.
Those matching target sounds are extracted, and Kaleidoscope then
clusters those sounds based on similarity, and those clusters can be
trained to accurately place vocalizations from the focal species into
one cluster, while noise and similar sounding species go in other
clusters. Appropriate signal parameters were chosen by measuring the
minimum frequency, maximum frequency, minimum motif duration, maximum
motif duration, and maximum inter-syllable gap of five unattenuated
recordings of each focal species. Unattenuated vocalizations were
collected from among the site recordings and found during the manual
identification step. Absolute minima and absolute maxima, rather than
averages, were used to ensure the edges of detected vocalizations were
not lost during the scanning step. Spectrogram measurements were taken
in Raven Pro v 1.6.4 (Cornell Lab of Ornithology, Ithaca, NY, USA).
Signal detection parameters for each species classifier can be found in
Table 1.

Once appropriate signal parameters had been identified, a training
dataset was compiled for each species. For each classifier, the training
dataset contained a) examples of vocalizations from the focal species at
the study site, b) randomly selected audio files from the study site
which did not include vocalizations from the focal species to be used as
negative examples, c) clean recordings of the focal species obtained
from the online sound library Xeno-Canto (xeno-canto.org), and d)
example audio recordings from the cohort of non-focal species which
occur at the study site, also from Xeno-Canto. Setting up the training
datasets this way ensured that there were clear examples of the focal
species' vocalization as well as realistic examples containing the type
of interfering background noise experienced at the site. Recordings of
non-focal species and of random audio from the study site were used as
negative examples, i.e., potentially similar sounding vocalizations and
background noise which the classifier was trained to identify as
non-target sounds. The amount of data for each category can be found in
Table 1. Using non-bat analysis mode, Kaleidoscope was then employed to
scan and cluster the recordings from the training dataset, using 2.0 max
distance from cluster center to include inputs, a 5.33ms FFT window, 12
max states, 0.5 max distance from cluster center for building clusters,
and 500 max clusters. Among all the resulting clusters which contained
at least one detection of the focal species, detections were manually
re-classified as either the focal species or noise. Clusters which did
not contain the focal species were left as is. These edited cluster IDs
were then used to create a new clustering algorithm. The training and
re-clustering steps were repeated for multiple iterations until the
accuracy reached 80\% or improvement plateaued, resulting in the final
trained classifiers.

\textbf{Automatic detection and results cleaning}

Trained classifiers for each focal species were used to scan through the
entire two-year audio dataset for each site and automatically detect all
occurrences of the target vocalizations. As compared with other acoustic
clustering software, Kaleidoscope tends to produce false positives
(Knight et al. 2017), so all automatic positive detections were manually
verified for accuracy and false positives were removed (Table 1).
Because all positive detections were verified by a trained human
observer, the accuracy of the detections used in the results was
estimated at 100\%. The presence or absence of each focal species was
manually assessed in 1,000 audio files and compared with classifier
outputs to estimate the false negative rate and the recall rate (Table
1). False negative rate was the percentage of audio recordings where the
focal species was present but not detected by the classifier. Recall
rate is the proportion of all vocalizations that were successfully
detected by the classifier (true positives/(true positives + false
negatives)).

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/761505/articles/737555-acoustic-phenology-among-tropical-resident-birds-differs-between-native-forest-species-and-parkland-colonizer-species}

\emph{\textbf{Table 1}. Parameters and input data used to train each
species classifier. Training parameters: parameters input into
Kaleidoscope Pro to determine whether or not a sound could potentially
refer to the focal species and should be extracted from the
\textasciitilde{}70,000 hour acoustic dataset. Training data: audio
recordings used to train the species classifiers; each training dataset
includes vocalizations from the focal species (focal), vocalizations
from other similar sounding species found at the same site (non-focal),
audio recorded at the study site, and clear recordings from the sound
library Xeno-Canto (www.xeno-canto.org). Verified detections: total
number of times the focal species was accurately detected in the
\textasciitilde{}70,000 hour audio dataset, manually verified to remove
false positives. The presence or absence of each focal species was
manually assessed in 1,000 audio files and compared with classifier
outputs to estimate the false negative rate and the recall rate.}

\textbf{Seasonality Index}

A novel seasonality index (S) was developed for this dataset to assess
how strongly seasonal the vocal activity was for each species. This
index defines seasonal behavior as behavior concentrated during a
particular time of year, occurring at the same time across years. The
index is made up of two components: signal strength (R) and directional
agreement (D) (Formula 1).

Signal strength (R) is a measure of how concentrated vocal activity is
within a single time of year. To calculate this, the number of vocally
active hours per day is averaged by month and each monthly value is
treated as a vector, where the direction of the vector corresponds with
the month of the year, placed radially on a circle (Figure 2). The
magnitude of the sum of all 12 vectors is divided by the total magnitude
of all vectors to get signal strength (R) (Formula 2, Figure 2iii).
Subscript \emph{i} indexes the month. Signal strength (R) has a maximum
value of 1 when all activity is concentrated within a single month, and
a minimum value of 0 when activity is equally distributed throughout the
year.

Directional agreement (D) is a measure of the similarity between years.
To calculate directional agreement (D), all 12 monthly vectors are added
together for each year to find the direction of the resultant vector \selectlanguage{greek}θ.
\selectlanguage{english}Directional agreement is the inverse of the difference between
\selectlanguage{greek}θ\selectlanguage{english}\textsubscript{year 1} and \selectlanguage{greek}θ \selectlanguage{english}\textsubscript{year 2} (Formula 3, Figure
2ii). Directional agreement (D) has a maximum value of 1 when peak vocal
activity occurs at the same time in both years, and a minimum value of 0
when peak activity happens at opposite times of year between the two
years; for example, if peak activity occurred on January
1\textsuperscript{st} of the first year and July 1\textsuperscript{st}of
the second, then D=0.

The seasonality index (S) will have a maximum value of 1 when vocal
activity is concentrated entirely in the same single month in both
years. S will have a minimum value of zero either if activity is evenly
distributed throughout the year, or if peak activity happens at opposite
times of year in the two years. R code used to calculate the seasonality
index is available in the Supporting Information.

Formula 1:

\begin{equation}
S=R*D=Seasonality\ index\nonumber \\
 \end{equation}

Formula 2:

Formula 3:

\begin{equation}
\theta_{i}=direction\ of\ the\ sum\ of\ all\ vectors\ [\overrightarrow{v_{1}\ }:\overrightarrow{v_{12}}]\ from\ \text{year}_{i}\nonumber \\
 \end{equation}\begin{equation}
dd=\ {|\theta}_{1}-\theta_{2}|=directional\ disagreement\nonumber \\
 \end{equation}\begin{equation}
\text{if\ }\left(\left|\theta_{1}-\theta_{2}\right|>\pi\right),\ \ then\ dd=(2\pi-\left|\theta_{1}-\theta_{2}\right|)\nonumber \\
 \end{equation}

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/761505/articles/737555-acoustic-phenology-among-tropical-resident-birds-differs-between-native-forest-species-and-parkland-colonizer-species}

\emph{\textbf{Figure 2}: Panel (i) Visual representation of vectorized
monthly activity. All units are radians, not degrees. Months are placed
radially on a circle, with January at 0 radians. For each monthly
vector} \({\overrightarrow{v}}_{i}\)\emph{, the magnitude of the vector is the
number of vocally active hours per month, and the direction of the
vector is dictated by the month's identity. These monthly vectors are
used to calculate R. Panel (ii) Visual representation of the directional
agreement between years. Panel (iii) Visual representation of the
magnitude of the sum of all vectors versus the total magnitude of all
vectors.}

\textbf{Focal species: historically native versus parkland colonizers}

The number of focal species was limited to nine due to logistical
constraints; training a species classifier takes a degree of time and
effort. These nine species were selected because theirs were the
vocalizations which appeared most frequently in the audio dataset. The
focal bird species from Singapore were divided \emph{post-hoc} into two
main categories: recent parkland colonizers and historically native
forest species. Parkland colonizers are not what would typically be
considered invasive species; these parkland species are native to the
broader Sundaic region, but did not historically occur within Singapore
because they are not adapted to closed canopy forest. Much of the
Sundaic bioregion is climatically everwet and has historically been
dominated by rainforest. However, deciduous woodland or coastal scrub,
with their distinct avifauna, exist in scattered pockets throughout the
area, or have been the predominant habitat type in some peripheral
regions of the Sundaic region, such as eastern Java and northern
peninsular Malaysia. Parks and gardens in urbanized Singapore more
closely resemble woodland than rainforest and have been colonized by
such woodland species over the past few decades. While all the recording
stations in this study were inside forest, not parkland, focal species
recorded included both historically native species and more recent
colonizers. The focal species in this study include four recent
colonizers, all of which have had breeding populations in Singapore for
less than 100 years, and five forest species historically native to
Singapore. The recent colonizers in Singapore are Black-naped Oriole
(\emph{Oriolus chinensis,} established \textasciitilde{}1925), Lineated
Barbet (\emph{Psilopogon lineatus,} established \textasciitilde{}1997),
Straw-headed Bulbul (\emph{Pycnonotus zeylanicus,} established on the
main island \textasciitilde{}1980), and Asian Koel (\emph{Eudynamys
scolopaceus,} established \textasciitilde{}1950) (Gibson-Hill 1950,
Wells 2010, Lim 2019). The historically native forest species in
Singapore are Little Spiderhunter (\emph{Arachnothera longirostra} ),
Drongo Cuckoo (\emph{Surniculus lugubris} ), Rufous-tailed Tailorbird
(\emph{Orthotomus sericeus} ), Pin-striped Tit-babbler (\emph{Mixornis
gularis} ), and Short-tailed Babbler (\emph{Pellorneum malaccense} ).

\textbf{RESULTS}

\textbf{Historically native species were more vocally seasonal than
recent parkland colonizers} . The seasonality indices of native forest
species ranged from 0.25--0.69. The seasonality indices of parkland
colonizers were lower, ranging from 0.01--0.25 (Figure 3, Figure 4).
Only the most seasonal species, Little Spiderhunters and Drongo-cuckoos,
were entirely silent for months at a time. Species with slightly weaker
seasonal trends in vocal behavior, including Rufous-tailed Tailorbirds
and Pin-striped Tit-babblers, had a distinct season of heightened vocal
activity but could still be heard to a lesser extent during the off
season. In contrast, parkland colonizers like Straw-headed Bulbuls and
Asian Koels did not follow any seasonal pattern -- peak vocal activity
occurred in different months between the two years and concentrated
irregularly throughout the year.

\textbf{The peak vocal season for most species was around June.} Of the
five focal native forest species, all but one had their peak seasons in
May-June. Little Spiderhunters, the only nectarivore, had a peak vocal
season earlier in the year, in March (Figure 3, Figure 5, Table 2).
Lineated Barbets did not show a seasonal trend but did have an
interannual trend -- they were heard more often in year two than in year
one of the study.

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\emph{\textbf{Figure 3.} Vocal activity of focal species in Singapore
throughout the seasons: (a) Little Spiderhunter, (b) Drongo-cuckoo, (c)
Rufous-tailed Tailorbird, (d) Pin-striped Tit-babbler, (e) Short-tailed
Babbler, (f) Black-naped Oriole, (g) Lineated Barbet, (h) Straw-headed
Bulbul, and (i) Asian Koel. Sub-panels: (i) spectrogram of the focal
species' main vocalization, i.e., the specific motif detected by the
species classifier; (ii) radar plot, lines indicate number of vocally
active hours per month, direction and magnitude of arrows indicate the
directionality ? and signal strength R of year 1 (red) and year 2
(black) respectively. Year 1 extends from June 2020-May 2021. Year 2
extends from June 2021-May 2022; (iii) hourly vocalizations detected
with Kaleidoscope Pro using single species classifier, manually verified
to remove false positives. White space indicates missing data. Each
pixel represents one hour, the color of the pixel is the number of times
the focal species' vocalization was accurately detected during that hour
on a logarithmic scale. Species illustrations from Eaton et al. (2021)
with permission.}

\textbf{Hosted file}

\verb`image13.emf`  available at \url{https://authorea.com/users/761505/articles/737555-acoustic-phenology-among-tropical-resident-birds-differs-between-native-forest-species-and-parkland-colonizer-species}

\emph{\textbf{Figure 4}. Species' seasonality indices by native versus
colonizer status. The seasonality index reaches a maximum of 1 when all
vocal activity occurs within a single month of the year and peak
activity occurs at the same time in both years. The seasonality index
reaches a minimum of 0 when vocal activity is equally distributed
through all 12 months, or if peak vocal activity occurs at opposite
times during the two years.}

\textbf{Hosted file}

\verb`image14.emf`  available at \url{https://authorea.com/users/761505/articles/737555-acoustic-phenology-among-tropical-resident-birds-differs-between-native-forest-species-and-parkland-colonizer-species}

\selectlanguage{greek}\emph{\textbf{Table 2}. Seasonality indices by species. R is a measure
of signal strength, or how concentrated vocal activity is within a
specific time of year. R=1 when all vocal activity is concentrated into
a single month, and R=0 when vocal activity is equally distributed
throughout the year. θ is the time of year when vocal activity was at
its peak, measured in radians; month names are included in parenthesis
for convenience. R\textsubscript{Overall} is R of the full 2-year time
series, and θ\textsubscript{Overall} is θ of the full 2-year time
series.}\selectlanguage{english}

\textbf{Hosted file}

\verb`image15.emf`  available at \url{https://authorea.com/users/761505/articles/737555-acoustic-phenology-among-tropical-resident-birds-differs-between-native-forest-species-and-parkland-colonizer-species}

\selectlanguage{greek}\emph{\textbf{Figure 5.} Vocal seasonal directionality of all nine
species. Vector magnitude is Seasonality Index (S). Vector direction is
θ\textsubscript{overall} , the mean direction of the full two-year time
series. Green: Historically native forest species; Orange: Recent
parkland colonizer.}\selectlanguage{english}

\textbf{Hosted file}

\verb`image16.emf`  available at \url{https://authorea.com/users/761505/articles/737555-acoustic-phenology-among-tropical-resident-birds-differs-between-native-forest-species-and-parkland-colonizer-species}

\emph{\textbf{Figure 6.} Seasonality index versus approximate date when
breeding population was established, shown across the four parkland
colonizer species in Singapore.}

\textbf{DISCUSSION}

\textbf{Urbanization and phenology}

Phenology is an aspect of a species' ecological niche which is
dynamically associated with both habitat and climate, and vocal
seasonality is typical of most birds globally, even in equatorial
regions where the climate is nearly aseasonal (Thomson 1950, Hau et al.
1998, Beebe et al. 2005, Berman et al. 2023). Among the panel of nine
focal species in this study, some sang seasonally as expected, while
others did not. Historically native species adapted to forest interiors
sang seasonally, while species which have only established breeding
populations within the last few decades, and which are adapted to more
open habitats, sang aseasonally. Singapore has undergone dramatic
urbanization over the last 200 years. That urbanization has led to
changes in land cover, including the introduction of open parkland
habitat resembling woodlands, which in turn has led to a shift in
species composition and the colonization of woodland-adapted birds. Our
results suggest that this conversion of forest into parkland and urban
areas has not only altered the species composition of Singapore, but in
doing so may have also led to an altered community phenology. While it
is clear from the data that historically native forest species are more
seasonal in their song than the more recently established species, it is
less obvious why these two groups of species should have distinct
phenologies. Some potential explanations include dietary differences, or
an initial transitional period of aseasonality for newly established
populations that may last for decades. In the following paragraphs we
discuss the potential factors driving these phenological differences.

\textbf{Seasonal availability of preferred foods may differ between
species}

One potential explanation for why colonizer species are less seasonal
than native forest species may be differences in food availability.
Translocation of birds to sites with high year-round food availability
can lead to a prolonged or year-round breeding season (Komdeur 1996).
While all audio recorders, and therefore all detected birds, were in
forested sites, colonizer species are generally more abundant nearer the
forest edge, in more open habitat patches and in parkland. Man-made
parks have a different floral species composition as compared to natural
forests, and it is possible that food availability does not correlate as
closely with seasonal cycles in manicured parklands. Many genera of
bird-dispersed fruit-bearing plants which are common in Singaporean
forests, including \emph{Litsea, Myristica, Santiria} ,
and\emph{Timonius} , follow a masting pattern of reproduction (Corlett
1990). Mast fruiting most often occurs in June -- August (Corlett 1990,
Chong et al. 2016, Corlett 2019), coinciding with the fledging period of
the native breeding season (Berman et al. 2023). In contrast, fruiting
figs, another staple of the frugivorous diet, fruit continuously on the
population level and provide a stable year-round food supply (Cannon et
al. 2007; Corlett 2019). Figs are common in forest, but are especially
abundant in parklands. It is possible that a lack of masting species in
parklands may reduce the benefits of seasonal breeding among
parkland-adapted birds. Two of the four parkland colonizers among our
target species are primarily frugivorous (Straw-headed Bulbul, Lineated
Barbet), and the other two are known to take fruit at least seasonally
or occasionally (Asian Koel, Black-naped Oriole), supporting the
potential importance of fruit availability for the phenology of these
species.

\textbf{Aseasonality may be adaptive in newly established populations}

Recent parkland colonizers may be less seasonal because they have not
yet adapted their phenology to the local cycle. Breeding seasonally is
adaptively advantageous (Ims 1990), but introduced species in tropical
habitats sometimes do breed aseasonally (Rodda and Savidge 2007, Beard
et al. 2009, Brodie et al. 2021). Introduced populations which initially
bred year-round have been known to become seasonal breeders after
several generations in a new habitat (Hengeveld 1994).

Year-round breeding may be adaptive for newly established species
expanding into an unoccupied niche (Hengeveld 1994), but for populations
at carrying capacity, the inability to synchronize breeding with the
time of peak food availability may lead to lower survivorship of young
and excessive metabolic stress on parents (Thomas 2001). A lack of
synchronized breeding may also reduce the chance of finding a suitable
partner that is at the same reproductive and hormonal stage in the
seasonal cycle. For newly established populations, it may take time to
attune the biological clock to a new set of environmental signals,
especially if those signals are weak (Baker and Ranson 1938). If this
hypothesis is true, we might expect the most recent colonizers to be the
least seasonal, and for populations to become more seasonal over time.
The data from our limited panel of species only partially supports this
pattern (Figure 6). Among the four parkland colonizers in our panel,
Black-naped Orioles have been present in Singapore the longest, first
establishing a breeding population in 1925 due to the conversion of
forests into plantations (Wells 2010). At the same time, Black-naped
Orioles exhibit the strongest seasonality among our four parkland
colonizers (Figure 4, Figure 6), suggesting that their extended
residency may have provided them with sufficient time to converge on a
new seasonal rhythm. However, there is no clear linear pattern between
seasonality and establishment dates among the other three species
(Figure 6), suggesting that a larger species panel may be needed to
confirm this relationship.

\textbf{Conclusions}

Recent parkland colonizers were less seasonal than historically native
forest species in their vocal phenologies, suggesting that either a)
man-made parklands may promote aseasonal phenologies among
parkland-adapted birds, or b) newly arrived species may undergo an
initial period of transitional aseasonality. In either case, the
conversion of forest into parkland and urban areas which has occurred in
Singapore over the last 200 years has altered not only the community of
species present, but also the phenology of that community. This altered
phenology is yet another way that human habitat modification may disrupt
tropical communities. While continuous or aseasonal breeding may be
advantageous to species expanding their ranges, it is expected to lead
to reduced reproductive success for species already in decline.

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