DOI: 10.5061/dryad.t1g1jwt5x
title: Acoustic phenology of tropical resident birds differs between native forest species and parkland colonizer species

description: 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 ~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 had
 breeding populations in Singapore only for the last few decades exhibited
 seemingly aseasonal or unpredictable song activity throughout the year.
 Urbanization and habitat modification over the last 100 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 local environmental conditions. Our results highlight the ways
 that anthropogenic habitat modification may disrupt phenological cycles in
 tropical regions in addition to altering the species community.
description: This is an acoustic phenology dataset. Soundscape recordings were
 collected 24/7 in Singapore over the course of 2 years. The machine
 learning software Kaleidoscope Pro was used to make species classifiers
 for 9 species of birds. Species classifiers are able to automatically
 detect all occurrences of the target species' song within the
 2-year-long dataset. Automatic outputs were manually verified to ensure
 accuracy. In addition to the cleaned species detection
 dataset, we also provide the Kaleidoscope species classifiers. These
 classifiers can be used to detect the 9 focal species in your own audio
 data.
description: Each .csv file contains the results of one species classifier
 summarised by input file. Each row represents one unique audio file with a
 ~30min (29:55) duration and tells the number of times the target species
 can be heard within that file.
 SeasonalityAnalysisExampleCode.R generates a calendar plot, radar
 plot, and vector-based seasonality index for Little Spiderhunters.
 Changing the loaded .csv file can generate these plots for the other
 species.
description: Raw species detection data, R code for data processing, and Kaleidoscope
 species classifiers are available. ## Species Detection Data Each .csv
 file contains the results of one species classifier summarised by input
 file. Each row represents one unique audio file with a ~30min (29:55)
 duration and tells the number of times the target species can be heard
 within that file. **Site Names:** CCNR - Central Catchment Nature Reserve,
 Singapore (1.355488, 103.804549) DAFA - Dairy Farm Nature Park, Singapore
 (1.358419, 103.777492) SBWR - Sungei Buloh Wetland Reserve, Singapore
 (1.441586, 103.735308) NUS - National University of Singapore Campus,
 Singapore (1.295020, 103.779385) **Species Names:** LSpider - Little
 Spiderhunter - Arachnothera longirostra DCuck - Drongo-cuckoo - Surniculus
 lugubris RTTail - Rufous-tailed Tailorbird - Orthotomus sericeus PSTB -
 Pin-striped Tit-babbler - Mixornis gularis STBab - Short-tailed Babbler -
 Pellorneum malaccense BNO - Black-naped Oriole - Oriolus chinensis LinBarb
 - Lineated Barbet - Psilopogon lineatus SHBul - Straw-headed Bulbul -
 Pycnonotus zeylanicus Koel - Asian Koel - Eudynamys scolopaceus
 **Variables:** fileName - input file recording. Naming convention is
 SITE_yyyymmdd_hhmmss.WAV FOLDER - folder where the input file is located
 DURATION - duration of the input file in seconds (max 1795 seconds, aka 29
 minutes and 55 seconds) DATE - date of the recording TIME - start time of
 the recording, using a 24 hour clock. There should be one file every half
 hour. HOUR - Hour of the day when audio was recorded. Used to make hourly
 activity plot. SERIAL.NO - not used. Serial number of the audio recorder.
 autoID_target - number of automatic detections of the species'
 vocalization within the audio file manualID_target - number of manually
 verified (correct) detections of the species' vocalization
 manualID_otherSounds - not used. number of manually identified sounds that
 were NOT the species' vocalization **.csv files:** BNO.csv -
 Black-naped Oriole detections DCuck.csv - Drongo-cuckoo detections
 Koel.csv - Asian Koel detections LinBarb.csv - Lineated Barbet detections
 LSpider.csv - Little Spiderhunter detections PSTB.csv - Pin-striped
 Tit-babbler detections RTTail.csv - Rufous-tailed Tailorbird detections
 SHBul.csv - Straw-headed Bulbul detections STBab.csv - Short-tailed
 Babbler detections ## R Code SeasonalityAnalysisExampleCode.R generates a
 calendar plot, radar plot, and vector-based seasonality index for Little
 Spiderhunters. Changing the loaded .csv file can generate the plots for
 the other species. ## Species Classifiers Each classifier is zipped into a
 folder. Classifier folders are named according to the species they detect.
 To use a classifier, load the 'settings.ini' file in
 Kaleidoscope to set up the proper signal parameters, then use the
 'cluster.kcs' file to scan and cluster new audio. **Files within
 each species classifier:** cluster.kcs - the trained clustering algorithm
 settings.ini - Kaleidoscope settings used for this classifier **Species
 Classifiers:** Black-naped Oriole.zip Drongo-cuckoo.zip Koel.zip
 LineatedBarbet.zip LittleSpiderhunter_SG.zip Pin-striped Tit-babbler.zip
 RufousTailedTailorbird.zip StrawHeadedBulbul.zip ShortTailedBabbler_SG.zip
 For questions, comments, or additional details, feel free to contact Laura
 Berman [lauraberman@u.nus.edu](mailto:lauraberman@u.nus.edu)
 [lberman6@wisc.edu](mailto:lberman6@wisc.edu)
 [lauraberman11@gmail.com](mailto:lauraberman11@gmail.com)