Threshold of Anthropogenic Sound Levels Within Protected Landscapes in Kerala, India for Avian Habitat Quality and Conservation

doi:10.21203/rs.3.rs-1712177/v1

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Threshold of Anthropogenic Sound Levels Within Protected Landscapes in Kerala, India for Avian Habitat Quality and Conservation

https://doi.org/10.21203/rs.3.rs-1712177/v1

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published 01 Feb, 2024

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Anthrophony is an important determinant of habitat quality in the Anthropocene. Acoustic adaptation of birds at lower levels of anthrophony is known. However, threshold anthrophony, beyond which biophony starts decreasing, is less explored. Here, we present empirical results of the relationship between anthrophony and biophony in four terrestrial soundscapes. The constancy of the model predicted threshold vector normalised anthropogenic power spectral density 0.40; at all the study sites is intriguing. The results pave the way to determine permissible sound levels in protected landscapes.

Soundscape

Anthrophony

Biophony

Sound Pressure Level

Acoustic ecology

The prospects of characterising habitats and ecosystems using acoustic data have attracted researchers from various disciplines to acoustic ecology. Over the last few decades, there has been a groundswell of interest in using sound to describe and characterise ecosystems ¹. The meteoric rise in technological capabilities and the plummeting cost of associated hardware^2–4 helped acoustic ecology permeate laboratories worldwide. Ongoing research in acoustic ecology can broadly be classified as studies focusing on the landscape (community) and species levels. While the former is confined predominantly to interpreting acoustic indices ⁵, the latter leverages the analytical powers of the current technology wave ^6,7. Either way, the primary focus is to extract ecological information from sonic data. Acoustic ecology is built-up on the premise that time-stamped soundscape is a signature of a landscape.

An offshoot of landscape ecology ⁸; acoustic ecology is weighed down by the plurality of views and the lack of physical theories of the macroecological significance of soundscape ⁹. The weak consensus on the interpretation of acoustic indices stems from the plurality mentioned above. Intrinsic characteristics of mechanical waves, their propagation, and obstructions in terrestrial landscapes add to the complexity of acoustic ecology ¹⁰. Notwithstanding the advances in field recording techniques that help circumvent some of the challenges posed by the mechanical nature of sound waves ¹¹, the reciprocative nature of communication ¹² of indicator species (here, birds) ^13–17 increases the probability to miss capturing the vocalisation of all bird species present during acoustic data collection in a terrestrial landscape. The lack of scientific consensus on the duration and periodicity of acoustic data measurement also increases the complexity. While strong reasons are put forward as arguments favouring extended and continuous sonic recording^18,19, the counter-arguments are equally strong and multidimensional ^20,21.

The plurality of perspectives in acoustic ecology posits an absorbing canvas. It is still ambiguous whether biophonic heterogeneity can be ascribed to the diversity of vocalising species or to the community diversity¹⁰. Despite the different perceptions, the scientific community of acoustic ecologists is unanimous in advocating the utility of acoustic data in biodiversity studies. The domain is gaining currency as a travelator which we can ill-afford to overlook for biodiversity assessment and conservation ^22,23. The effectiveness and adoption of acoustic metrics for biodiversity monitoring hinge on unravelling underlying physical theories and developing mathematical constructs that aid conservation planning ²⁴.

Here, we present empirical results of the affiliation between vector normalised power spectral density of anthrophony (0–2 kHz) and avian biophony (2–8 kHz) within a wildlife sanctuary, an urban park, and two sacred groves in Kerala, India. The results transform into permissible sound thresholds in terrestrial landscapes for effective conservation.

Relationship between α and β

α and β values derived from the sonic database at SABS, HPM, PK, and IK are presented in Supplementary Table 1. The regression analysis of the α and β components in each soundscape fits into concave down quadratic regression models (Fig. 1). Table 1 summarises the quadratic regression models at the four sites. HPM, PK, and IK show matching patterns with comparable quadratic equation coefficients and standard deviations (Table 1). The consistently low standard deviation indicates fewer Power Spectral Density (PSD) fluctuations in the quadratic fit. While the regression model of SABS was slightly different from the others, the overall trends of the sites are comparable. The statistical significance (p-value < 0.05) and high R² values (0.57, 0.85, 0.66, 0.65 for SABS, HPM, PK, and IK, respectively) of datasets describe sonic powers are fit for the regression models.

The α_maxβ and β_max of the fitted curves are given in Table 1. α_maxβ values describe the changing behaviour of soundscapes across anthrophonic (α) and biophonic (β) components. The highest β_max (2.26 Watts/Hz) was observed at SABS. The four sites showed almost identical α_maxβ (0.39–0.40) values.

Table 1 α - β regression model summary of the four sites

Site	α	α²	(Constant)		β_max (Watts/Hz)	α_maxβ (Watts/Hz)
Salim Ali Bird Sanctuary (SABS)	4.11 (± 0.16)	-5.1 (± 0.17)	1.43 (± 0.02)	0.57	2.26	0.40
Hill Palace Museum (HPM)	3.45 (± 0.22)	-4.29 (± 0.17)	1.29 (± 0.06)	0.85	1.98	0.40
Poyil Kavu (PK)	3.42 (± 0.28)	-4.31 (± 0.25)	1.31 (± 0.07)	0.66	1.99	0.40
Iringole Kavu (IK)	3.35 (± 0.20)	-4.34 (± 0.19)	1.42 (± 0.04)	0.65	2.06	0.39

Significance of α_maxβ

PSD represents average sonic power during a specific time in a certain frequency range. It is a physical measure of information that leads to understanding the spatio-temporal dynamics of soundscapes. Mechanical and biological sounds are prevalent between 1–2 kHz and 2–8 kHz, respectively^25,26. The frequency ranges are divided into 1 kHz frequency bins, and α and β estimate the vector normalised power spectral density of the anthrophony and biophony components by the sum of the power in these frequency bins. Since the prevalent anthrophonic range contains only one bin (1–2 kHz), the maximum α yields a power of 1 watt/Hz²⁷. It explains the convergence of α at 1 in Fig. 1. The magnitude of β represents the intensity of the biophony and thus reveals specific characteristics of vocal organisms in a soundscape.

The Ordinary Least Square regression analysis of α and β components across all the sites fits into concave down quadratic functions. Biophony (β) increases with increasing anthrophony (α) to reach a maximum before decreasing. The empirical results presented here correspond well with the Lombard effect ^28,29. The positive relationship of α and β at lower levels of α at all sites (Fig. 1) explicates biophonic adaptive resilience of birds to changing soundscapes. However, the biophonic resilience collapses to zero after β_max with increasing α (Fig. 1). Higher β is indicative of the intensified bird vocalisations at the study sites and indicates their presence.

Anthrophonic level in a landscape (α) is a proxy for the degree of disturbance and stress to non-human vocalising species therein. The notion of α_maxβ introduced in this paper is identical to the point corresponding to the vertex in Functional Calculus (corresponds to β_max). It is the critical point where a curve changes direction from increasing to decreasing. Geometrically α_maxβ is the point at which the axis of symmetry through the vertex of the quadratic curve cuts the x-axis (α). The identical α_maxβ observed at all the sites (Table 1) open the prospect of defining acoustic limits in protected terrestrial landscapes. Elevated anthrophonic levels disturb indicator species like birds. They either become alarmed and keep silent or move to another soundscape with lower anthrophony^21,30. Constant α_maxβ at the four sites point to similarities in their soundscapes. We presume α_maxβ to be dependent on geography. All sites in the present study are located in the tropical monsoon region. We recommend αmaxβ as an indicator of anthropogenic tolerance level of sonic pressure in the sites.

Although several indices are available to study the presence and diversity of acoustic communities ^18,31,32, we are yet to arrive at a standard metric to denote the sonic characteristic of natural soundscapes. We propose estimating α_maxβ from the α - β regression model of the soundscapes as a pragmatic way to define the threshold anthrophonic sound in protected landscapes. The α - β regression model can also be used to monitor habitat quality and act as a baseline for landscape conservation planning.

The α - β regression model of soundscape is a characteristic attribute of the terrestrial landscape. The estimated α_maxβ of protected landscapes (soundscapes) provides us with a metric that can be used as the permissible threshold of anthrophony in conservation planning. Reconfirmed in different locations and times, the α - β regression model of soundscape will lead to a quantitative framework to protect terrestrial habitats and species conservation.

Study Area

Acoustic data were collected from Salim Ali Bird Sanctuary (SABS), Hill Palace Museum (HPM), Poyil Kavu (PK), and Irigole Kavu (IK), in Kerala, India, in 2018 and 2019, respectively. SABS is an International Bird Area (IBA) ³³ spread over 25.16 km² and lies between 10°7′ and 11° N latitudes and 76°40′ and 76°45′ E longitudes. Out of the 284 bird species observed at SABS, 03 are vulnerable, 08 are near-threatened, and 11 are endemic. SABS also provides seasonal refuge to 72 migratory bird species.

HPM is an urban park spread across 22.8 ha, bound by 9°57'10.27" N latitude and 76°21'49.94" E longitude in the Ernakulam district. PK and IK are sacred groves in urban settings. PK is located at 11° 24' 35.05" N latitude and 75° 42' 58.55" E longitude in the Kozhikode district and sandwiched in 12 ha of land between the National Highway (NH 66) and Kappad beach. IK is situated at 10°6' 30.95" N latitudes and 76°30' 1.81" E longitude in the Ernakulam district and is spread across 20 ha.

Acoustic Data Collection

Acoustic data of SABS were recorded from 6.00 AM to 6.00 PM on 19 April, 07 September, 11 December 2018, and 18 April 2019, 09 September 2019, and 10 December 2019. Acoustic data of HPM and IK were collected for the same duration one day before and after that of the measurement at SABS in April, December 2018, and 2019, respectively. An identical framework was used to record the acoustic data of PK on 14 December 2018, and 21 April 2019. All recordings were carried out at preselected locations within the study sites. Ten sound clips of 1 minute each were recorded during each hour. The measurements were carried out using Marantz PMD 661 MK III sonic recorder with an omnidirectional boundary microphone at 44.1 kHz/16-bit sampling rate. Acoustic data was stored in .wav format as signals in two channels (left and right). We focused on avian sounds, as they are indicator species ^34,35

Data Analysis

Acoustic Data from each site was pooled separately for analysis. The Welch Power Spectral Density (PSD) (Watts/Hz) in the frequency range between 1–2 kHz (α), and 2–8 kHz (β), of SABS, HPM, PK and IK soundscapes at the two time periods were extracted as the average of the left and right channels using Tune R®³⁶ and ndsi() function in soundecology ®³⁷ packages in R v.3.1.2 ³⁸.

The relationship between α and β was estimated using Ordinary Least Square regression. We determined the α_maxβ, where the fitted α function changes from increasing to decreasing (points correspond to maximum β, β_max) by calculating the axis of symmetry of a quadratic function:

$${\alpha }_{max\beta }=\frac{-b}{a}$$

where a and b are the coefficients of the quadratic fit function$\beta \left(\alpha \right)=a{\alpha }^{2}+b\alpha +c$

Ethics Declaration

The acoustic data collection method is non-intrusive and hence does not violate any regulations. In this study no birds/animals were handled by the authors.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Acknowledgements

Authors thank Dr. R. Sugathan, Ornithologist and Mr. Mani, Forest Watcher, Salim Ali Bird Sanctuary, Thattekkad, and the Charge Officers, Hill Palace Museum, Ernakulam for providing assistance and support for the field survey. We are thankful to Ms. Dominic L for the support in the initial stages of the data analysis. We also thank Prof. Elizabeth Sherly, Director, IIITM-K for supporting this study.

Author contributions

Sajeev C Rajan – Responsible for field data collection, pre-processing, data analysis and writing.

Vishnu, M: Data analysis and writing.

Ahalya Mitra: Statistical analysis and preparation of figures and tables.

Sooraj, N.P: Acoustic data collection and writing.

Athira, K: Assisted with field data collection, supported bird species identification and pre-processing and preparation of figures.

M S Pillai: Statistical analysis.

R. Jaishanker: (corresponding author): Conceptualised the study, supervised the work and writing.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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You are reading this latest preprint version

Threshold of Anthropogenic Sound Levels Within Protected Landscapes in Kerala, India for Avian Habitat Quality and Conservation

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Abstract

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Materials And Methods

Study Area

Acoustic Data Collection

Data Analysis

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