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, Pérez-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˚ 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 categorizedpost-hoc into native forest species versus parkland colonizer species and the degree of seasonality of each species’ vocal activity was measured.