Article

Visualization of temporal change in soundscape power of a Michigan lake habitat over a 4-year period

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Abstract

Soundscape Ecology is an emerging area of science that does not focus on the identification of species in the soundscape but attempts to characterize sounds by organizing them into those produced by biological organisms such as birds, amphibians, insects or mammals; physical environmental factors such as thunder, rainfall or wind; and sounds produced by human entities such as airplanes, automobiles or air conditioners. The soundscape changes throughout the day and throughout the seasons. The soundscape components that create the sound occur at different frequencies. A set of metrics termed soundscape power was computed and visualized to examine the patterns of daily and seasonal change in the soundscape.

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... The sounds that emanate throughout the landscape (i.e., soundscape) are, therefore, essential elements of CW. Three components of a soundscape are biological sounds (biophony) (Krause 1998(Krause , 2001(Krause , 2002, geophysical sounds (geophony) (Qi et al. 2008;Pijanowski et al. 2011;Farina 2014), and anthropogenic mechanical sounds (technophony) (Gage and Axel 2014;Mullet et al. 2016). Snowmobiles and other motorized transports (e.g., aircraft, automobiles, motorboats) emit low-frequency technophony that can propagate over long distances and through vegetation (Bashir et al. 2015), thereby creating an acoustic footprint well beyond its source (Barber et al. 2010). ...
... We found that snowmobile noise had substantially higher soundscape power within the 1-2 kHz frequency interval than that of natural quiet sound recordings. This evidence is consistent with the findings of Gage and Axel (2014) and Mullet et al. (2016) who found that a majority of technophony lies within the low frequency range <2000 Hz. Although soundscape power of natural quiet was lower than that of snowmobile noise, a majority of soundscape power was still within the 1-2 kHz interval. ...
... The higher-frequency calls of other winter resident passerines were within the 2-8 kHz frequency intervals, e.g., Black-capped Chickadee and Common Redpoll (Carduelis flammea L.). Gage and Axel (2014) and Mullet et al. (2016) documented similar observations by comparing identified sound sources with their soundscape power within frequency intervals discernable in spectrograms. ...
... An important feature of the landscape is an uninhabited island near the center of the lakes (Fig. 1). Island vegetation consists of 50-60 year old deciduous and coniferous native species including white birch (Betula papyrifera), trembling aspen (Populus tremuloides), balsam fir (Abies balsamea), white cedar (Thuja occidentalis), tamarack (Larix laricina), and white pines (Pinus strobus) (Gage and Axel, 2014). The surrounding forest on public DNR land is natural mixed pine, as well as mixed upland deciduous with conifer. ...
... The soundscapes of the Twin Lakes area are filled with a variety of acoustically communicating species and anthropogenic activities, and these soundscapes have been the focus of multiple studies from the Remote Environmental Assessment Laboratory (REAL) (Kasten et al., 2012;Gage and Axel, 2014;Farina and Gage, 2017). Previous research on the soundscape temporal variability shows the soundscape does not reflect large amounts of vehicular noise during rush hour periods (Gage and Axel, 2014). ...
... The soundscapes of the Twin Lakes area are filled with a variety of acoustically communicating species and anthropogenic activities, and these soundscapes have been the focus of multiple studies from the Remote Environmental Assessment Laboratory (REAL) (Kasten et al., 2012;Gage and Axel, 2014;Farina and Gage, 2017). Previous research on the soundscape temporal variability shows the soundscape does not reflect large amounts of vehicular noise during rush hour periods (Gage and Axel, 2014). Common native amphibians in the area include green frog (Lithobates Clamitans), spring peeper (Pseudacris crucifer), and northern leopard frog (Lithobates pipiens), while some common native avian species include bald eagle (Haliaeetus leucocephalus), osprey (Pandion haliaetus), Caspian tern (Hydroprogne caspia), belted kingfisher (Megaceryle alcyon), great blue heron (Ardea herodias), trumpeter swan (Cygnus buccinator), common loon (Gavia immer), common merganser (Mergus merganser), ruffed grouse (Bonasa umbellus), and many smaller woodland birds (Kasten et al., 2012). ...
Article
Assessing the effects of anthropogenic disturbances on wildlife and natural resources is a necessary conservation task. The soundscape is a critical habitat component for acoustically communicating organisms, but the use of the soundscape as a tool for assessing disturbance impacts has been relatively unexplored until recently. Here we present a broad modeling framework for assessing disturbance impacts on soundscapes, which we apply to quantify the influence of a shelterwood logging on soundscapes in northern Michigan. Our modeling approach can be broadly applied to assess anthropogenic disturbance impacts on soundscapes. The approach accommodates inherent differences in control and treatment sites to improve inference about treatment effects, while also accounting for extraneous variables (e.g., rain) that influence acoustic indices. Recordings were obtained at 13 sites before and after a shelterwood logging. Four sites were in the logging region and nine sites served as control recordings outside the logging region. We quantify the soundscapes using common acoustic indices (Normalized Difference Soundscape Index (NDSI), Acoustic Entropy (H), Acoustic Complexity Index (ACI), Acoustic Evenness Index (AEI)) and Welch Power Spectral Density (PSD) values. We build two hierarchical Bayesian models to quantify the changes in the soundscape over the study period. Our analysis reveals no long-lasting effects of the shelterwood logging on the soundscape as measured by the NDSI, but analysis of H, AEI, and PSD suggest changes in the evenness of sounds across the frequency spectrum, indicating a potential shift in the avian species communicating in the soundscapes as a result of the logging. Further, our analysis confirms previous findings that the ACI does not accurately reflect changes in landscape configuration. Multiple model validation techniques (i.e., comparison of parameter estimates and the widely applicable information criterion (WAIC)) reveal our proposed hierarchical Bayesian models outperform more simple models used for hypothesis testing. Acoustic recordings, in conjunction with this modeling framework, can deliver cost efficient assessment of disturbance impacts on the landscape and underlying biodiversity as represented through the soundscape.
... The biological sound produced by vocalizing animals (e.g., birds and stridulating insects (biophony)), nonbiological sounds such as wind, rain, running stream (geophony) in a forest or any natural habitat (Hildebrand 2009) constitutes the soundscape of that area ( Pijanowski et al. 2011;Gage & Axel 2014). The man-made sounds produced from automobile, machinery (technophony or anthrophony) that dominate in urban settings are rarely detected in forest habitats (Krause 1987;Pijanowski et al. 2011;Gage & Axel 2014). ...
... The biological sound produced by vocalizing animals (e.g., birds and stridulating insects (biophony)), nonbiological sounds such as wind, rain, running stream (geophony) in a forest or any natural habitat (Hildebrand 2009) constitutes the soundscape of that area ( Pijanowski et al. 2011;Gage & Axel 2014). The man-made sounds produced from automobile, machinery (technophony or anthrophony) that dominate in urban settings are rarely detected in forest habitats (Krause 1987;Pijanowski et al. 2011;Gage & Axel 2014). Vocalization of birds (ornithophony) of a terrestrial habitat varies due to the variations in the dominant vocalizers, number of species involved in vocal activity and the time specificity of the birds. ...
... Most of the technophony and a few biophonic sounds (birds) occur in lower frequency range 1-2 kHz. Passerines species' frequency ranges between 3 and 6 kHz, whereas insects occupy a higher range, > 6kHz, and all the geophony are of low frequency ranging from 1-11 kHz (Napoletano 2004;Qi et al. 2008;Joo et al. 2011;Kasten et al. 2012;Gage & Axel 2014). ...
Article
Full-text available
An attempt has been made to understand the extent of ornithophony (vocalization of birds) in the soundscape of Anaikatty Hills. The study was limited to 13 hours of daylight from dawn to dusk (06.00–19.00 h) between January 2015 and October 2016. Six replicates of 5-minute bird call recordings were collected from each hour window in 24 recording spots of the study area. Each 5-minute recording was divided into 150 ‘2-sec’ observation units for the detailed analysis of the soundscape. A total of 78 recordings amounting to 390 minutes of acoustic data allowed a preliminary analysis of the ornithophony of the area. A total of 62 bird species were heard vocalizing during the study period and contributed 8,629 units. A total of 73.75% acoustic space was occupied by birds, among which the eight dominant species alone contributed to 63.65% of ornithophony. The remaining 26% of acoustic space was occupied by other biophonies (12.60%), geophony (5.57%), indistinct sounds (7.66%), and anthropogenic noise (0.41%). Passerines dominated the vocalizations with 7,269 (84.24%) and non-passerines with 1,360 (15.76%) units. Birds vocalized in all 13 observation windows, with a peak in the first three hours of the day (06.00–09.00 h). Vocalizations of non-passerines were prominent in the dusk hours (18.00–19.00 h).
... It is already known that sounds vary according to several environmental features, such as, time of day, seasons (Gage & Axel, 2014;Mullet et al., 2017), terrain features and climatic variables (Farina, 2014), like for example rainfall (Sánchez-Giraldo et al., 2020). In addition, they are also influenced by land use and cover since it interferes directly on sound propagation (Morton, 1975). ...
... A measure of the number of cells in the midfrequency band spectrogram that are identified as being local maxima. Normalized Difference Soundscape Index (NDSI) (Gage and Axel, 2014) Calculates the ratio between biophony and technophony in the spectrogram. The NDSI is known to measure habitat ecological health (Kasten et al., 2012). ...
... The values for Normalized Difference Soundscape Index (NDSI) were not statistically significant among areas but all our values were above 1. High values of this index (around 1) indicate good habitat quality (Gage and Axel, 2014), corroborating that even though it is an area modified by human actions and highly fragmented, it is still used and occupied by wildlife. ...
Article
Full-text available
The ecoacoustics approach for environmental recordings analysis is used to understand and identify big ecological patterns related to different sound sources, like animals, humans and the environment itself. Sounds can vary according to several features that can be on its surroundings or far away, therefore they are very much reliant on scale. Because humans are changing the environment so much and we cannot account for all those changes in the same speed as they happen, we need fast evaluation tools, such as remote sensing and acoustic monitoring (considered the equivalent of spatial remote sensing for sounds). Considering that the scale of effect was never measured for soundscapes before, we aimed to see in what scale different acoustic indices were responsive. Also, we tested how acoustic indices are influenced by natural vegetation cover. We recorded environmental sounds in Atlantic Forest fragments during three months on the rainy season. Then we calculated different acoustic indices and the percentage of natural vegetation cover in different scales. Our results corroborated our initial hypotheses: different indices respond to different scales and their medians varied according to the amount of vegetation cover on the surroundings. More studies are needed with less fragmented areas, to test indices behaviour in a continuum, but we consider this work an important starting point to understand acoustic indices behaviour in tropical areas, especially in such degraded and threatened area as Atlantic Forest.
... An important feature of the landscape is an uninhabited island near the center of the lakes (Figure 1). Island vegetation consists of 50-60 year old deciduous and coniferous species including white birch (Betula papyrifera), trembling aspen (Populus tremuloides), balsam fir (Abies balsamea), white cedar (Thuja occidentalis), tamarack (Larix laricina), and white pines (Pinus strobus) (Gage and Axel, 2014). The surrounding forest on public DNR land is natural mixed pine, as well as mixed upland deciduous with conifer. ...
... The surrounding forest on public DNR land is natural mixed pine, as well as mixed upland deciduous with conifer. The soundscapes of the Twin Lakes area are filled with a variety of acoustically communicating species and anthropogenic activities, and these soundscapes have been the focus of multiple studies from the Remote Environmental Assessment Laboratory (REAL) (Kasten et al., 2012;Gage and Axel, 2014;Farina and Gage, 2017). Previous research on the soundscape temporal variability shows the soundscape does not reflect large amounts of vehicular noise during rush hour periods (Gage and Axel, 2014). ...
... The soundscapes of the Twin Lakes area are filled with a variety of acoustically communicating species and anthropogenic activities, and these soundscapes have been the focus of multiple studies from the Remote Environmental Assessment Laboratory (REAL) (Kasten et al., 2012;Gage and Axel, 2014;Farina and Gage, 2017). Previous research on the soundscape temporal variability shows the soundscape does not reflect large amounts of vehicular noise during rush hour periods (Gage and Axel, 2014). Common amphibians in the area include green frog (Lithobates Clamitans), spring peeper (Pseudacris crucifer), and northern leopard frog (Lithobates pipiens), while some common avian species include bald eagle (Haliaeetus leucocephalus), osprey (Pandion haliaetus), Caspian tern Hydroprogne caspia), belted kingfisher (Megaceryle alcyon), great blue heron (Ardea herodias), trumpeter swan (Cygnus buccinator), common loon (Gavia immer), common merganser (Mergus merganser), ruffed grouse (Bonasa umbellus), and many smaller woodland birds (Kasten et al., 2012). ...
Preprint
Assessing the effects of anthropogenic disturbances on wildlife is a necessary conservation task. The soundscape is a critical habitat component for acoustically communicating organisms, but the use of the soundscape as a tool for assessing disturbance impacts has been relatively unexplored until recently. Here we present a broad modeling framework for assessing disturbance impacts on soundscapes, which we apply to quantify the influence of a shelterwood logging on soundscapes in northern Michigan. Our modeling approach can be broadly applied to assess anthropogenic disturbance impacts on soundscapes. The approach accommodates inherent differences in control and treatment sites to improve inference about treatment effects, while also accounting for extraneous variables (e.g., rain) that influence acoustic indices. Recordings were obtained at 13 sites before and after a shelterwood logging. Four sites were in the logging region and nine sites served as control recordings outside the logging region. We quantify the soundscapes using common acoustic indices (Normalized Difference Soundscape Index (NDSI), Acoustic Entropy (H), Acoustic Complexity Index (ACI), Acoustic Evenness Index (AEI), Welch Power Spectral Density (PSD)) and build two hierarchical Bayesian models to quantify the changes in the soundscape over the study period. Our analysis reveals no long-lasting effects of the shelterwood logging on the soundscape diversity as measured by the NDSI, but analysis of H, AEI, and PSD suggest changes in the evenness of sounds across the frequency spectrum, indicating a potential shift in the avian species communicating in the soundscapes as a result of the logging. Acoustic recordings, in conjunction with this modeling framework, can deliver cost efficient assessment of disturbance impacts on the landscape and underlying biodiversity as represented through the soundscape.
... As indicators of the characteristics of variation animal communities (Gasc et al., 2016), soundscapes provide acoustic diversity information at individual, population, species and community levels (Gasc et al., 2013). The established positive relationship between acoustic diversity with faunal species richness and diversity serves as the theoretical basis for the application of acoustic indices to ecological monitoring (Do Nascimento et al., 2020;Farina and Gage, 2017;Gage and Axel, 2014;Gasc et al., 2013;Pijanowski et al., 2011b). ...
... Just as the normalized difference vegetation index (NDVI) measures vegetation health, Normalized Difference Soundscape Index (NDSI) (Kasten et al., 2012) is proposed to be a measure of ecosystem health (Gage and Axel, 2014). Like the NDVI, NDSI uses a simple algorithm to compress a large amount of information into an ecological index. ...
... Our studies grouped and visualized different acoustic communities partitioned in the limited bandwidth over time and frequencies, which provide us more information about the peak of activity for urban species to establish the pattern of their activities, as well as possibly to inform ways to protect species, such as those that are sensitive to human disturbance or are too rare to find in conventional field surveys. (Gage and Axel, 2014). ...
Article
Full-text available
Urban foresters are addressing the challenge of urban biodiversity loss through management plans in the context of rapid urbanization. Protecting the integrity of the urban ecosystem requires long-term monitoring and planning for resilience as well as effective management. The soundscape assessment has attracted attention in this field, but applying the soundscape assessment in urban ecological monitoring requires a protocol that links soundscapes to the impact of resource management on biodiversity over time. The effective processing and visualization of large-scale data also remains an important challenge. The aim of this study was to better understand the relationship between soundscape and physical environment, and examine the feasibility of this innovative soundscape approach in highly urbanized areas. Soundscape recordings were collected for 20 urban parks twice on 4 consecutive days in Spring. A total of 691,200 min of sound material were automatically obtained. In order to track the spatio-temporal patterns of a soundscape and determine its potential suitability for ecosystem monitoring, our study characterized soundscape information by adopting 4 widely used acoustic indices: acoustic diversity index (ADI), bioacoustic index (BIO), normalized difference vegetation index (NDSI), and power spectral density (PSD). Daily patterns of PSD have provided a potential connection between soundscapes and bird songs, and 1–2 kHz presented a similar pattern that was linked to human activity. Through further modeling, we tested the relationship of soundscapes to physical environment characteristics. The results showed the importance of habitat vegetation structure for acoustic diversity. More vertical heterogeneity, with an uneven canopy height or multilayered vegetation, was associated with more acoustic diversity. This suggests that clearing ground cover may have a significant negative impact on wildlife. Our results suggest that soundscape approaches provide a way to quickly synthesize large-scale recording data into meaningful patterns that can track changes in bird songs and ecosystem conditions. The proposed approach would enable regular assessment of urban parks and forests to inform adaptive planning and management strategies that can maintain or enhance biodiversity.
... La richesse en espèces peut être étudiée grâce à des indices acoustiques calculés à partir d'enregistrements réalisés en milieu naturel (Harris et al. 2016 ; Lellouch et al. 2014 ;Parks et al. 2014 ;Mammides et al. 2017) ou encore d'identifier les cycles journaliers et/ou annuels de l'activité acoustique Farina et al. 2013 ;Gage & Axel, 2014 ;Towsey et al. 2014b, c). Les indices acoustiques permettent par ailleurs d'obtenir une estimation de la richesse en espèces plus précise que celle issue des comptages réalisés traditionnellement sur le terrain (Towsey et al. 2014a, c). ...
... La richesse spécifique peut ainsi être évaluée et suivie grâce à l'étude des corrélations entre la diversité de la communauté (i.e. ensemble d'espèces) et les valeurs des indices obtenues (Sueur et al. 2008a ;Harris et al. 2016 ;Zhang et al. 2016 ;Mammides et al. 2017) sans même procéder à l'identification des espèces (Gage & Axel, 2014). Par définition, ces indices sont des statistiques qui regroupent et résument plusieurs aspects de la distribution de l'énergie acoustique et de l'information contenues dans les enregistrements (Towsey et al. 2014b, c). ...
Thesis
Le nombre croissant de travaux réalisés ces dernières années a montré que la bioacoustique est particulièrement intéressante pour le suivi d’espèces discrètes. L’émergence de dispositifs d’enregistrement autonomes, associée à de nouvelles méthodes d’analyse, ont récemment participé à l’accroissement des études dans ce domaine. Au cours des 30 dernières années, le Loup gris (Canis lupus), mammifère carnivore aux mœurs discrètes connu pour ses hurlements de longue portée, a fait l’objet de nombreuses études acoustiques. Ces dernières visaient notamment à améliorer son suivi, qui s’avère complexe du fait des grandes capacités de déplacement des loups, de l’étendue de leurs territoires et de la diversité des milieux dans lesquels ils vivent. Cependant, la bioacoustique passive a jusqu’alors très peu été exploitée pour le suivi du Loup. C’est dans ce contexte que la présente thèse s’est organisée autour de trois axes de recherche. Les deux premiers axes portent sur l’apport de la bioacoustique passive pour le suivi du Loup gris en milieu naturel. En combinant des analyses acoustiques, statistiques et cartographiques, le premier objectif a été d’élaborer une méthode pour l’échantillonnage spatial de vastes zones d’étude, afin d’y détecter des hurlements de loups à l’aide de réseaux d’enregistreurs autonomes. Ce même dispositif a ensuite permis, dans un second temps, de tester la possibilité de localiser les loups grâce à leurs hurlements. Les expérimentations conduites en milieu de moyenne montagne (Massif des Vosges) et de plaine (Côtes de Meuse), sur deux zones d’étude de 30 km² et avec un réseau de 20 enregistreurs autonomes, ont permis de démontrer l’intérêt de la bioacoustique passive pour le suivi du Loup gris. En effet, près de 70% des émissions sonores (son synthétique aux propriétés similaires à celles de hurlements de loups) ont été détectés par au moins un enregistreur autonome en milieu de moyenne montagne et plus de 80% en milieu de plaine, pour des distances enregistreurs– source sonore atteignant respectivement plus de 2.7 km et plus de 3.5 km. Grâce à un modèle statistique et à un Système d’Information Géographique, la probabilité de détection des hurlements a pu être cartographiée sur les deux zones. En moyenne montagne, elle était forte à très forte (>0.5) sur 5.72 km² de la zone d’étude, contre 21.43 km² en milieu de plaine. Les sites d’émission ont été localisés avec une précision moyenne de 315 ± 617 (SD) m, réduite à 167 ± 308 (SD) m après l’application d’un seuil d’erreur temporelle défini d’après la distribution des données. Le troisième axe de travail porte quant à lui sur l’application d’indices de diversité acoustique pour estimer le nombre d’individus participant à un chorus et ainsi contribuer au suivi de l’effectif des meutes. Les valeurs obtenues pour les six indices (H, Ht, Hf, AR, M et ACI) étaient corrélées avec le nombre de loups hurlant dans les chorus artificiels testés. De bonnes prédictions de l’effectif ont été obtenues sur des chorus réels avec l’un de ces indices (ACI). L’influence de plusieurs biais sur la précision des prédictions de chacun des six indices a ensuite pu être étudiée, montrant que trois d’entre eux y étaient relativement peu sensibles (Hf, AR et ACI). Finalement, les résultats obtenus avec les enregistreurs autonomes montrent le potentiel des méthodes acoustiques passives pour la détection de la présence de loups mais aussi pour les localiser avec une bonne précision, dans des milieux contrastés et à de larges échelles spatiale et temporelle. L’utilisation des indices de diversité acoustique ouvre également de nouvelles perspectives pour l’estimation de l’effectif des meutes. Prometteuses, l’ensemble des méthodes émergeant de ce travail nécessite à présent quelques investigations complémentaires avant d’envisager une application concrète pour le suivi du Loup gris dans son milieu naturel.
... While a variety of acoustic metrics and indices are available (BradferLawrence et al., 2019), the intention of this study was to utilize the figurative "lens" of soundscape power (Kasten et al., 2012) and NDSI (Gage and Axel, 2014) to describe the current state of the Aialik Bay coastal wilderness. Soundscape power has been successfully used to describe soundscapes in southcentral Alaska (Mullet et al., 2016(Mullet et al., , 2017a and coastal soundscapes in the Pacific Northwest (Ritts et al., 2016). ...
... Computations derived soundscape power after Kasten et al. (2012) as the normalized power spectral density (watts/kHz) at 1 kHz frequency intervals for the frequency ranges of 1000-11,000 Hz for each fiveminute sound recording. The NDSI was calculated as the ratio of soundscape power (normalized watts/kHz) corresponding to frequency intervals commonly associated with technophony (1-2 kHz) and biophony (>2 kHz) as described by Gage and Axel (2014). ...
Article
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I recorded the ambient sounds at three locations in the wilderness of Aialik Bay in Kenai Fjords National Park, Alaska between 25 June and 21 September 2019. My aim was to capture an ecoacoustic snapshot of the coastal soundscape to provide a comparable baseline for evaluating wilderness characteristics defined by the Wilderness Act of 1964. I visually and empirically characterized the Aialik Bay wilderness soundscape using the acoustic metrics of soundscape power (normalized watts/kHz) and Normalized Difference Soundscape Index (NDSI) from 5373 five-minute recordings, combined with visual and aural spectral examination of 4386 recordings. Soundscape power exhibited similar patterns across frequency intervals with sound sources primarily occurring in the low-frequency (1–2 kHz) and mid-frequency (2–5 kHz) intervals. Significant differences within frequency intervals between sites suggested the presence of distinct sonotopes. Low-frequency sounds were dominant across all three sites with peak soundscape power values across study days and 24 h timeframes attributed to wind and occasional periods of technophony emitted from commercial tour boats and private boating activities. Low-frequency geophony from wave action was ever present. Technophony exhibited some predictable patterns consistent with the timing of sightseeing boat tours. Peak values of soundscape power at mid-frequencies were attributed to the geophony of rain. Although biophonies were less common than geophonies, the choruses of songbirds were prevalent in July and promptly occurred daily between 0300 and 0600. Biophonies generally declined over the course of the day. All sites displayed negative NDSI values over most study days and consistently negative values over 24 h time frames, indicating a soundscape primarily influenced by low-frequency geophony and periods of technophony. However, NDSI values showed patterns and peaks similar to biophonies at mid-frequency intervals indicating biophony was still a notable contribution to this geophony-dominant soundscape. Despite the acoustic footprint of motorboat noise detected at all sample sites, the soundscape of the Aialik Bay wilderness was dominated by the natural sounds of geophony, biophony, and occasional periods of natural quiet indicative of a wilderness only partially impacted by technophony.
... In particular, a variety of acoustic indices have been developed over the last decade to act as automatically-calculable proxies for various species richness and biodiversity metrics (Depraetere et al., 2012;Gage and Axel, 2014;Kaufman, 2001;Pieretti et al., 2011). Though lacking in species-specific information, these metrics have been used to study the effect of increasing natural resource extraction activities on the biophony in tropical forests, for example (Alvarez-Berríos et al., 2016;Deichmann et al., 2017;Duarte et al., 2015). ...
... While audio sampling was continuous for this dataset, in many bioacoustic surveys, one minute or fewer of audio data is collected for every ten minutes (Aide et al., 2013;Aide et al., 2017;Alvarez-Berríos et al., 2016;Deichmann et al., 2017;Depraetere et al., 2012;Duarte et al., 2015;Gage and Axel, 2014;Xie et al., 2016). To examine the effect that intermittent sampling has on the observed bioacoustics trends, the data was reanalyzed using only the first minute from each ten minute interval. ...
Article
The effect of anthropogenic activity on animal communication is of increasing ecological concern. Passive acoustic recording offers a robust, minimally disruptive, long-term approach to monitoring species interactions, particularly because many indicator species of environmental health factors such as biodiversity, habitat quality, and pollution produce distinct vocalizations. Machine learning algorithms have been used in recent decades to automatically analyze the large quantities of audio data that result. In this study, a microphone array was used to collect continuous audio data at a site in the Capital Region of New York State for twelve months, resulting in over 8000 h of recordings. A 19-class database containing a variety of bio- and anthrophony was used to train a convolutional neural network in order to generate a reliable record of species-specific calling activity for the entire study period. These results were used to calculate an acoustics-based pseudo-species richness and abundance distribution. Additionally, heatmap plots were used to visualize (i) the time of day (x), sound category (y), and predicted number of sonic events for an average 30-day period and (ii) the day of the year (x), time of day (y), and predicted number of sonic events for each sound category. The correlations between these sonic events and various abiotic factors such as number of daylight hours, temperature, and weather activity were also examined.
... However, human and biological sounds have been documented crossing the 2 kHz point in the frequency spectrum (Makarewicz and Sato 1996;Napoletano 2004;Bee and Swanson 2007;Can et al. 2010). It is important that we understand the limitations of this boundary, as it is used in studies to infer the relative contribution of biophony and anthrophony to soundscapes (Qi et al. 2008;Gage and Axel 2014;Fuller et al. 2015). ...
... Soundscapes have seasonal patterns (Schafer 1977;Truax 2001;Krause et al. 2011;Gage and Axel 2014). Temporal trends can affect component sounds such as the seasonality of dawn chorus characteristics (Stacier et al. 1996;Brunni et al. 2014) or the thermo-regulated sound power of cicada calls (Fonseca and Revez 2002;Suer and Sanborn 2003). ...
Article
Full-text available
Context It is widely accepted that wildlife is subjected to detrimental human noise within urban landscapes but little is known about how the intensity of land use changes soundscapes. Objectives The objective of this research was to produce quantitative associations between characteristics of ambient soundscapes and land use intensity. These relations were used to examine the 2 kHz demarcation between anthrophony and biophony and compare the impact of different sized contributing areas on ambient soundscape characteristics. Methods This study related the surrounding land use intensity of 67 sites in north central Florida (USA) to several metrics describing their recorded soundscapes. Land use intensity was measured remotely at three scales using the landscape development intensity index (LDI). Results The analysis revealed that the LDI index had a statistically significant effect on soundscape characteristics after controlling for important factors such as climate, season, and attenuation due to hard ground. The trends between LDI and soundscape confirmed that human generated sounds are loud, continuous, and occupy low frequencies. The evenness of the sound distribution decreased with landscape intensity and LDI correlated significantly with sound below 3 kHz. Land use intensity within a 100 and 500-m radius contributing area were most closely related to soundscape metrics. Conclusions LDI is a tool with the potential to predict the extent and intensity of anthropogenic noise disturbance on wildlife from remote sensing data. The utility of this tool allows for widespread application to identify and mitigate conflicts in the acoustic realm between human noise and wildlife.
... Tabela 1 -Valores médios de decibéis equivalentes (dBeq), máximos e mínimos (dB (A)) e classificação do som em cada ponto de coleta e análise estatística das médias (teste t a 95% Segundo a tabela 1, pode-se observar que o ambiente sonoro da trilha é caracterizado por apresentar diferentes tipos de sons em sua composição, sendo os principais tipos os sons de tráfego e os sons naturais ou bióticos de animais. Por se tratar de um fragmento inserido no contexto urbano, já era esperado que os sons provenientes do tráfego de veículos se destacassem, uma vez que especialistas afirmam que o tráfego fornece uma elevada contribuição para o ruído antropogênico nas cidades, sendo considerado o principal elemento da paisagem sonora nesses ambientes (GOSWAMI, 2011;GAGE;AXEL, 2014). ...
... Tabela 1 -Valores médios de decibéis equivalentes (dBeq), máximos e mínimos (dB (A)) e classificação do som em cada ponto de coleta e análise estatística das médias (teste t a 95% Segundo a tabela 1, pode-se observar que o ambiente sonoro da trilha é caracterizado por apresentar diferentes tipos de sons em sua composição, sendo os principais tipos os sons de tráfego e os sons naturais ou bióticos de animais. Por se tratar de um fragmento inserido no contexto urbano, já era esperado que os sons provenientes do tráfego de veículos se destacassem, uma vez que especialistas afirmam que o tráfego fornece uma elevada contribuição para o ruído antropogênico nas cidades, sendo considerado o principal elemento da paisagem sonora nesses ambientes (GOSWAMI, 2011;GAGE;AXEL, 2014). ...
Conference Paper
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Resumo A paisagem sonora é composta pelo total de sons presentes em um ambiente, podendo ser naturais ou artificiais, incluindo-se a capacidade física dos componentes presentes em transmiti-la. Este trabalho teve como objetivo caracterizar a paisagem sonora de uma trilha situada em um fragmento florestal urbano (Capão do Tigre) de Curitiba, Paraná. A coleta dos dados foi realizada durante o outono/2018, em 14 pontos diferentes na floresta, na parte interna e externa. Foi utilizado um decibelímetro modelo DEC-470 para a avaliação da pressão sonora. Foram coletados os níveis do som ambiente com compensação em A (simulando a recepção humana) (dB (A)) e equivalente (dBeq) e realizada a classificação do som em cada ponto de coleta. Foi observada diferença significativa das condições do som, sendo os maiores níveis de pressão sonora observados nos pontos P1 e P11 com 75,5 e 76,5 dB (A), respectivamente. Nesses pontos também foram encontrados os maiores valores de dBeq (56,2 e 56,1 dBeq), os quais encontram-se acusticamente poluídos. Os valores mínimos registrados foram nos pontos P4 (36,2 dB (A)) e P10 (36,7 dB (A)). Os menores valores de dBeq foram registrados nos pontos P3, P4, P7 e P8 com 46,4, 46,9, 47,1 e 47,6 dBeq. Também foi observada diferença entre os valores medidos externa e internamente, com valor médio de 5,9 dBeq. O ambiente sonoro da trilha apresenta diferentes tipos de sons em sua composição, com predominância de sons artificiais provenientes de tráfego (uma rodovia próxima a floresta) e sons bióticos de animais, na sua maioria pássaros. Palavras-chave: Fragmento florestal urbano; Ruído sonoro; Vegetação. 1. Introdução A paisagem sonora é composta pelo total de sons presentes em um ambiente, podendo ser naturais ou artificiais, incluindo-se a capacidade física dos componentes presentes em transmiti-los (GOSWAMI, 2011; FARINA; PIERETTI, 2012; MOLINA et al., 2013). Nos ambientes naturais, a paisagem sonora pode ser constituída unicamente por sons naturais ou pela sobreposição desses com os de atividades humanas (MOLINA et al., 2013), compostos principalmente por sons desagradáveis, os quais constituem os ruídos sonoros. Os elementos que compõe uma paisagem sonora são estritamente conectados, uma vez que os padrões de vegetação, as infraestruturas e a distribuição de animais influenciam na produção e propagação dos sons (FARINA; PIERETTI, 2012). A vegetação é o elemento mais eficiente na atenuação do ruído nas áreas urbanas, devido a capacidade que as folhas das plantas possuem de absorver o ruído e de transmitir o som para outras superfícies, alterando a sua direção e misturando sons indesejados com outros mais agradáveis (YANG et al., 2011; CHOUDHURY, 2013). A paisagem sonora das cidades é um indicativo da qualidade de vida nesses ambientes (GOSWAMI, 2011). Brambilla et al. (2013) acrescentam que os benefícios à saúde devem ser preservados e melhorados, especialmente nas áreas verdes urbanas, pois constituem ilhas de atenuação de ruído sonoro cercadas por áreas poluídas sonoramente. Além disso, as áreas verdes podem fornecer conforto sonoro, ao promover sensação de relaxamento e bem-estar à população (YANG et al., 2011). Segundo Curitiba (2002), o som é a vibração acústica capaz de provocar sensações auditivas, enquanto que o ruído é o som que pode causar perturbação ao sossego público ou efeitos psicológicos e fisiológicos negativos em seres humanos e animais. Curitiba (2002) ainda define a poluição sonora
... Although sound complexity and biodiversity are higher in the tropics, developing countries in these regions may allocate few resources to conservation actions (Myers et al., 2000). The study of soundscapes can be used as an acoustic monitoring tool, which can help to sample large geographical areas in less amount of time (Sueur et al., 2014;Gage & Axel 2014;Krause & Farina 2016), helping conservation efforts in the tropics, especially in developing countries. The bias in scientific research can also be a problem when considering the allocation of resources -especially financial. ...
... For example, ACI is used to evaluate the sonic complexity of the soundscape in the research of the relationship between sonic environment and vegetation structure [14]. NDSI helps visualise the temporal change in soundscape power of a habitat and reveal the acoustic patterns [17]. 14 acoustic indices are calculated to facilitate sampling avian species [41]. ...
Article
This research explores the data selection problem in acoustic recognition of two co-existing sibling frog species from long-duration field recordings. This study explores the data selection problem in species recognition with machine learning, including instance selection and acoustic index feature selection. Our target species are two co-existing frog species. The Wallum Sedgefrog (Litoria olongburensis) is the most threatened acid frog species, facing habitat loss and degradation across much of their distribution, in addition to further pressures associated with anecdotally-recognised competition from it's sibling species, the Eastern Sedgefrog (Litoria fallax). Monitoring the calling behaviours of these two species is essential for informing L. olongburensis management and protection, and for obtaining ecological information about the process and implications of their competition. In order to recognise them from recordings, automated recognisers, instead of manual surveys, are required due to the overwhelmingly large volume of acoustic data. However, it costs much time and effort to annotate acoustic data, which results in a lack of labelled data for training recognisers. In addition, the composition of field audio recordings is complex and varies according to weather conditions, seasonal changes and animal activities. In this case, the chorusing behaviours of these two frog species can be greatly affected by weather conditions, for example, rains. Since we can only select limited amount of data from a very diverse data pool to annotate, it is important to explore how the selection of instances and features affects the performance of recognisers. In this paper, we selected two 24-h audio datasets from different weather conditions as our dataset. We first give a detailed visual comparison of them with false-colour spectrograms. Then we use datasets from individual days, their combination and a synthetic set constructed from them to evaluate the impact of different training instances on the recognition performance. We also analyses the effectiveness of acoustic index features. The experimental results show that models trained on data of a vocalisation-intense day or a normal day do not work well on each other. However, extra data from a normal-day dataset does help the recognition on a vocalisation-intense day, especially when the composition of the training set is consistent with the real-life weather condition ratio. Generally, including more data in training set is helpful, but limiting vocalisation-intense data as their ratio in real life help optimise the performance of recognisers. As for the selection of acoustic index features, a good recognition performance on different chorusing behaviours requires different sets of features.
... Human impacts on our environment are causing a dramatic reduction in ecosystem functionality resulting in a growing silencing of biophony (Carson, 1962;Krause and Farina, 2016;Sueur et al., 2019). Many studies have shown that the impoverishment of taxonomic, phylogenetic, genetic, and functional diversity as a consequence of human impact (Naeem et al., 2012) can be effectively detected using ecoacoustic indices (Laiolo, 2010;Depraetere et al., 2012;Gage and Axel, 2014;Sueur et al., 2014;Fuller et al., 2015;Harris et al., 2016). In particular, human disturbance in forest landscapes of temperate regions is such a common and recurrent process that all forests, even those perceived as well preserved, exhibit some signs of anthropogenic influence on species composition and structure (Caviedes and Ibarra, 2017). ...
Article
Full-text available
The Sasso Fratino Integral Natural Reserve (Central Italy), a rare example of climax Mediterranean forest, provides an extraordinary opportunity to create an important soundscape reference of old-growth forest. In this study, we describe the soundscape of three localities (Lama, Sasso 950, Sasso 1400) representative of a gradient of variety and complexity of habitats, recorded during the period 10 May to 9 June 2017. Our results reveal temporal partitioning into acoustically homogeneous periods across 24 h suggesting that soniferous species (mainly birds) adopt ecological routines in which their acoustic activity is organized according to specific transient physiological needs. We processed multi-temporal aggregates of 1, 5, 10, and 15 s recordings and calculated the Acoustic Signature (AS) with four new indices: Ecoacoustic Events (EE), Acoustic Signature Dissimilarity (ASD), and their fractal dimensions (D EE and D ASD), derived from the Acoustic Complexity Index (ACI). The use of the EE and ASD greatly improved the AS interpretation, adding further details such as the emergence of a clear sequence of patterns consistent with the daily evolution of the overall soundscape. D EE and D ASD confirm the patterns observed using the AS, but provide more clarity and detail about the great acoustic complexity that exists across temporal scales in this old-growth forest. The temporal turnover of different acoustic communities occurs as a result of a gradual shift of different homogenous acoustic properties. We conclude that soniferous species use distinct, species-specific temporal resolutions according to their physiological and ecological needs and that the fractal approach used here provides a novel tool to overcome the difficulties associated with describing multi-temporal acoustic patterns.
... Though there are different interpretations of the concept, the soundscape can be defined as ''the collection of biological, geophysical and anthropogenic sounds that emanate from a landscape" [8]. The soundscape is, therefore, divided into three main-class active acoustic sources [8,9,10]: biophony -biological sounds produced by living organisms such as birds, insects, amphibians or mammals within a landscape; geophony -natural sounds created by geophysical processes, such as wind, water flow, thunder, and rainfall; and anthrophony -the sounds created by man-made stationary (e.g. fans and air conditioners) and mobile (e.g. ...
... Recent technological advances have facilitated the development of several methods of biodiversity or wildlife monitoring that generate continuous or near-continuous datasets. GPS tracking, camera trapping, and passive acoustic monitoring (PAM) are several examples of such promising emerging technologies that will be enhanced by refined analytical approaches with a greater focus on the temporal aspects of the data they produce (Cushman, 2010;Frey et al., 2017;Gage and Axel, 2014). ...
Article
Unprecedented rates of biodiversity loss and intensifying human attempts to rectify the biodiversity crisis have heightened the need for standardized, large-scale, long-duration biodiversity monitoring at fine temporal resolution. While some innovative technologies such as passive acoustic monitoring are well suited for such monitoring challenges, many questions remain as to how they should be scaled out and optimally implemented across ecosystems. Our research questions center on temporal sampling regimes—how frequently and how long one should collect data to represent biodiversity conditions over a given timeframe. Addressing this concern in the context of passive acoustic monitoring, we investigated whether temporal soundscape variability—the characteristic short-term acoustic change in an environment—is consistent across ecosystems and times of day, and we considered how various temporal subsampling schemes affect the representativeness of resultant acoustic index values, relative to continuous sampling. We quantified soundscape variability at eight sites across four continents based on temporal autocorrelation ranges and standard deviations of acoustic index values, and we created a heuristic model to classify types of soundscape variability based on those two variables. Drawing on values derived from three distinct acoustic indices, we found that the characteristic temporal variability of soundscapes varied between sites and times of day (dawn, daytime, dusk, and nighttime). Some sites exhibited little difference in variability between times of day whereas other sites exhibited greater within-site differences between times of day than many inter-site differences. Daytime soundscapes generally tended to exhibit more temporal variability than nighttime soundscapes. We also compared potential subsampling schemes that could be advantageous in terms of power, data storage, and data analysis costs by modeling subsample error as a function of total analysis time and number of subsamples within a larger block of time. Greater numbers of evenly distributed subdivisions drastically increased the representativeness of a sampling scheme, while increases in subsample duration yielded fairly minimal gains in representativeness between 33 and 67% of the full time one wishes to represent. Generally, our results show that for a long-term, fine temporal resolution monitoring program, one should record in evenly distributed durations at least as short as 1 min while only recording up to a third of the time one wishes to represent. While more continuous monitoring can be advantageous and necessary in many cases, current economic and logistical limitations in power, data storage, and analysis capabilities will often warrant optimized subsampling designs.
... Thanks to the analysis of acoustic data, it is possible to obtain a picture of the soundscape, which is the acoustic expression of the local ecosystem, composed of the sounds produced by the animals (biophony, mainly concentrated in the range 2000-8000 Hz, but including frequencies down to 200 Hz and up to 120 kHz), the sounds produced by atmospheric and physical events (geophony: wind, rain, water, etc., wide frequency range) and by human activity (anthropophony that includes the widely invasive technophony: road traffic, airplanes, railways, generally occupying low frequencies of 20-2000 Hz) Gage and Axel 2013;Pavan 2017). ...
Article
Ecoacoustics investigates natural and anthropogenic sounds and their relationship with the environment. It is a powerful tool for biodiversity monitoring, management and conservation and also with regards to the global climate change issue. This study, based on data collected in 2017, describes for the first time the soundscape of the Sasso Fratino Integral Nature Reserve (INR) in Italy, an area characterised by the almost absence of anthropogenic noise, where we selected three recording sites within and adjacent the reserve. We adopted a double approach: one qualitative, based on visual screening of compact daily spectrograms; the other quantitative, by generating acoustic indices. In general, all sites are characterised by quiet nights and very acoustically dense daylight hours, with a composite biophony occupying the range 1500–9000 Hz. Moreover, the principal component analysis shows that the sites inside and outside the reserve are well differentiated and distinctly clustered, which could be due to their spatial heterogeneity and to the biophony’s different contributions. In this case, our results agree with the recognition of sonic patterns, or sonotopes, related to the different overlapping of biotic and abiotic sonic agents. The long-term acoustic data collection allows a reference repository to be built for monitoring the INR’s biophony status and evolution: as any human presence or intervention is currently prohibited, only external global changes could be considered as possible factors influencing any shift in the species’ presence and distribution inside the reserve.
... For example, ACI is used to evaluate the sonic complexity of the soundscape in the research of the relationship between sonic environment and vegetation structure [12]. NDSI helps visualise the temporal change in soundscape power of a habitat and reveal the acoustic patterns [13]. 14 acoustic indices are calculated to facilitate sampling avian species [14]. ...
Conference Paper
Full-text available
This research explores the recognition of choruses of two co-existing sibling frog species in long-duration field recordings using false-colour spectrograms and acoustic indices. Acid frogs are a group of endemic frogs that are particularly sensitive to habitat change and competition from other species. The Wallum Sedgefrog (Litoria olongburensis) is the most threatened acid frog species facing habitat loss and degradation across much of their distribution, in addition to further pressures associated with anecdotally-recognised competition from their sibling species, the Eastern Sedgefrogs (Litoria fallax). Monitoring the calling behaviours of these two species is essential for informing L. olongburensis management and protection, and for obtaining ecological information about the process and implications of their competition. Considering the cryptic nature of L. olongburensis and the sensitivity of their habitat to human disturbance, passive acoustic monitoring is a suitable method for monitoring this species. However, manually processing the large quantities of acoustic data collected using these methods is time-consuming and not feasible in the long-term. Therefore, there is a high demand for automated acoustic recognition tools to efficiently search months of recordings and identify target species. Our research provides more insight on how to choose acoustic features that efficiently recognise species from large-scale field-collected recordings at a larger scale. The experimental results show that these techniques are useful in identifying choruses of the two competitive frog species with an accuracy of 76.7% on identifying four acoustic patterns (whether the two species occurred).
... Yet, as with most areas of ecology, the transition from the terrestrial to the marine poses a new range of obstacles to overcome (Giorli, 2016;Radford et al., 2011;Ricci et al., 2017). Many different indices have been produced to quantify marine biological processes, such as Acoustic Richness, Acoustic Entropy Index and Acoustic Complexity Index (Gage and Axel, 2014;Lillis et al., 2014;McWilliam and Hawkins, 2013;Staaterman et al., 2014). Their use as proxies for marine biodiversity has been assessed (Harris et al., 2016), with the Acoustic Complexity Index being the most favoured (Lindseth and Lobel, 2018) both alone and in combination with other acoustic indices (Gordon et al., 2018). ...
... acoustic communities in different environments (Farina et al. 2011;Joo et al. 2011;Gage and Axel 2014;Rodriguez et al. 2014;Desjonquères et al. 2015;Ruppé et al. 2015;Erbe et al. 2015;Haver et al. 2017), as well as to get an insight into human impact on the environment (Dumyahn and Pijanowski 2011;Pieretti and Farina 2013;Merchan et al. 2014;Mullet et al. 2016;Rossi et al. 2016). Ecoacoustics emerged as a discipline using environmental sounds as an indicator of ecological processes (Sueur and Farina 2015) and has been applied among others to studies associated with biodiversity and habitat assessment (Harris et al. 2016;Rankin and Axel 2017), community ecology (Gasc et al. 2013a) and conservation biology (Krause and Farina 2016). ...
Chapter
In nature, vibrational communication takes place in an ecological context and in a complex vibrational environment that can be a major driver of evolution. Vibroscape is a collection of biological, geophysical and anthropogenic vibrations emanating from a given landscape to create unique vibrational patterns across a variety of spatial and temporal scales. Here, we discuss basic concepts and propose some basic terminology in this field of research. Vibroscape is virtually unexplored so far and we also provide some guidelines on how to approach fieldwork associated with vibroscape studies, as well as analyses of recordings obtained in the field. Vibroscape research is still facing technical challenges; however, we urge further studies in this area in order to provide much needed information on natural vibrational communities and sources of biotic, as well as anthropogenic vibratory noise.
... Although sound complexity and biodiversity are higher in the tropics, developing countries in these regions may allocate few resources to conservation actions (Myers et al., 2000). The study of soundscapes can be used as an acoustic monitoring tool, which can help to sample large geographical areas in less amount of time (Sueur et al., 2014;Gage & Axel 2014;Krause & Farina 2016), helping conservation efforts in the tropics, especially in developing countries. The bias in scientific research can also be a problem when considering the allocation of resources -especially financial. ...
Article
There has been a body of research examining the sounds produced in landscapes. These sounds are commonly defined as soundscapes, however, the term is often used in different contexts. To understand how the various meanings attributed to soundscapes, we identified how soundscapes are represented in the scientific literature and identified current knowledge gaps in soundscape research focusing on terrestrial environments. We conducted a quantitative review of published papers with the keyword soundscape available at Web of Science and Scopus databases. A total of 1,309 abstracts and a subset of about 5% (N=68) complete papers and reviews published from 1985 to 2017 were read and analysed, identifying types of sound, types of environment and focal species studied, as well as study regions and climates. By identifying the current focus of research, we also identified gaps and research opportunities. Research was biased towards temperate regions, terrestrial environments, and the impacts on humans in urban areas. Although most of the world’s biodiversity is concentrated in tropical wilderness areas, these regions had fewer studies attributed to them. Given the importance of tropical landscapes for biodiversity conservation, we strongly suggest that more research should be undertaken in the tropics, with a particular focus on wildlife in these regions. Furthermore, soundscape research (methods and tools) should increasingly target the anthropogenic impacts on wildlife, including behavioural and physiological changes, alongside the current focus on human-sound interactions and the approach used by bioacoustics methods.
... Interestingly, given a certain sound unit shape, NDSI was not affected by the vocalization intensity and frequency of vocalization occurrence (Figs. 6 and 7). This could be due to the fact that NDSI is calculated by a simple ratio using biophony (e.g., 2-12 kHz) and anthrophony (e.g., 1-2 kHz) (Gage and Axel, 2014;Kasten et al., 2012). However, this simple division in frequency to distinguish between biophony and anthrophony renders NDSI not suitable for acoustic assessment in complex habitats where acoustic events with various frequency ranges may exist. ...
Article
Passive acoustic monitoring of biotic sounds is increasingly popular in conservation biology. Among vocalizing animals, birds are most frequently studied and multiple acoustic indices have been proposed for rapid acoustic assessment of avian diversity. Preliminary results suggest that several indices can be used as proxies for bird species richness, however it is still unclear to what extent different conditions of bird vocalizations affect the relationship between indices and bird species richness-should it matter if the bird vocalizations contain different sound unit shapes, or if the frequency of vocalization occurrence differs, or if the vocalizations are made with various intensities? In this work, seven commonly used acoustic indices were tested using three controlled computational experiments with real-world recordings to provide an objective measure of each index's performance for answering the aforementioned questions. In the experiments, different options of sound unit shape and frequency of vo-calization occurrence were precisely controlled and intensity variations were expressed as different signal-to-noise ratios (SNR) of the vocalizations. The first experiment showed that three indices (the acoustic entropy index (H), acoustic diversity index (ADI), and acoustic evenness index (AEI)) performed better than the other four, showing moderate correlations with avian species richness. The second experiment revealed that ADI for each sound unit shape tended towards a constant value with increasing frequency of vocalization occurrence while the influence from frequency of vocalization occurrence on H and AEI varied with different sound unit shape options. Our third experiment showed that the vocalization intensity affected the values of these three indices while the performance disparity among different sound unit shapes for only ADI explicitly appeared a decreasing tendency with increasing vo-calization intensities. We conclude that ADI, among the tested indices, is relatively more robust with regard to bird species richness surveys when sound unit shape, frequency of vocalization occurrence, and vocalization intensity are considered. Meanwhile, since multiple indices are usually applied together to provide a comprehensive observation, the above acoustic dimensions should be taken into account especially in comparative research of different bird communities.
... We used the NDSI to determine the dominance of these 2 sound types on the landscape. We considered acoustic signals detected from frequencies between 0.100 and 2 kHz as most likely to be anthropogenic noise (technophony) in this landscape, while signals above 2 kHz were considered most likely to be biological sounds (biophony) emitted by organisms such as grassland birds, amphibians, and insects (Gage and Axel 2014). Concurrent work in this landscape demonstrated that wind turbine noise overlaps with the 0.200-2 kHz spectrum (Walsh et al. 2015, Raynor et al. 2017b, Whalen et al. 2018. ...
Article
Full-text available
Over the last century, increasing human populations and conversion of grassland to agriculture have had severe consequences for numbers of Greater Prairie-Chicken (Tympanuchus cupido). Understanding Greater Prairie-Chicken response to human disturbance, including the effects of anthropogenic noise and landscape modification, is vital for conserving remaining populations because these disturbances are becoming more common in grassland systems. Here, we evaluate the effect of low-frequency noise emitted from a wind energy facility on habitat selection. We used the Normalized Difference Soundscape Index, a ratio of human-generated and biological acoustic components, to determine the impact of the dominant acoustic characteristics of habitat relative to physical landscape features known to influence within–home range habitat selection. Female Greater Prairie-Chickens avoided wooded areas and row crops but showed no selection or avoidance of wind turbines based on the availability of these features across their home range. Although the acoustic environment near the wind energy facility was dominated by anthropogenic noise, our results show that acoustic habitat selection is not evident for this species. In contrast, our work highlights the need to reduce the presence of trees, which have been historically absent from the region, as well as decrease the conversion of grassland to row-crop agriculture. Our findings suggest physical landscape changes surpass altered acoustic environments in mediating Greater Prairie-Chicken habitat selection.
... As well as providing cost-effective monitoring methods, ecoacoustics offers a valuable conceptual framework to integrate biospheric and anthropogenic perspectives. Following Odum's (1953) classification of broad ecosystem components, elements of the soundscape are described according to their source: Geophony denotes the sounds made by abiotic processes (wind, rain etc.) in the landscape; biophony the sounds of animals; and anthrophony, the sounds of humans (Pijanowksi et al., 2011) We find the term technophony to be more useful in order to refer specifically to the noises of man-made powered machinery which are distinct in terms of their acoustic signals and resulting impact on soniferous species communication (Gage and Axel 2014). The soundscape is therefore a site of rich interaction between processes of the lithosphere, biosphere, hydrosphere and anthroposphere: machine listening provides a means to listen to and interpret these interactions. ...
Article
The critical importance of wilderness areas (WAs) for biodiversity conservation and human well-being is well established yet mapping criteria on which WA management policies are based take neither into account. Current WA mapping methods are framed in terms of absence of anthropogenic influence, and created using visual satellite data, obviating consideration of the ecological or anthropogenic value of WAs. In this paper we suggest that taking the acoustic environment into account could address this lacuna. We report the first investigation into the potential for ecoacoustic methods to complement existing geophysical approaches. Participatory walks, including in situ questionnaires and ecoacoustic surveys were carried out at points along transects traversing urban-wilderness gradients at four study sites in the Scottish Highlands and French Pyrenees. The relationships between a suite of six acoustic indices (AIs), wilderness classifications and human subjective ratings were examined. We observed significant differences between five out of six AIs tested across wilderness classes, demonstrating significant differences in the soundscape across urban-wild gradients. Strong, significant correlations between AIs, wilderness classes and human perceptions of wildness were observed, although magnitude and direction of correlations varied across sites. Finally, a compound acoustic index is shown to strongly predict mapped wildness classes (up to 95% variance explained MSE 0.22); perceived wilderness and biodiversity are even more strongly predicted. Together these results demonstrate that the acoustic environment varies significantly along urban-wild gradients; AIs reveal details of environmental variation excluded under current methods, and capture key facets of the human experience of wildness. An important next step is to ascertain the ecological and anthropogenic relevance of these differences, and develop new automated acoustic analysis methods suited to mapping the environmental characteristics of WAs. Taken together, our results suggest that future management of WAs could benefit from ecoacoustic methods to take the biosphere and anthroposphere into account.
... Recent technological advances have facilitated the development of several methods of biodiversity or wildlife monitoring that generate continuous or near-continuous datasets. GPS tracking, camera trapping, and passive acoustic monitoring (PAM) are several examples of such promising emerging technologies that will be enhanced by refined analytical approaches with a greater focus on the temporal aspects of the data they produce (Cushman, 2010;Frey et al., 2017;Gage and Axel, 2014). ...
Article
Unprecedented rates of biodiversity loss and intensifying human attempts to rectify the biodiversity crisis have heightened the need for standardized, large-scale, long-duration biodiversity monitoring at fine temporal resolution. While some innovative technologies such as passive acoustic monitoring are well suited for such monitoring challenges, many questions remain as to how they should be scaled out and optimally implemented across ecosystems. Our research questions center on temporal sampling regimes—how frequently and how long one should collect data to represent biodiversity conditions over a given timeframe. Addressing this concern in the context of passive acoustic monitoring, we investigated whether temporal soundscape variability—the characteristic short-term acoustic change in an environment—is consistent across ecosystems and times of day, and we considered how various temporal subsampling schemes affect the representativeness of resultant acoustic index values, relative to continuous sampling. We quantified soundscape variability at eight sites across four continents based on temporal autocorrelation ranges and standard deviations of acoustic index values, and we created a heuristic model to classify types of soundscape variability based on those two variables. Drawing on values derived from three distinct acoustic indices, we found that the characteristic temporal variability of soundscapes varied between sites and times of day (dawn, daytime, dusk, and nighttime). Some sites exhibited little difference in variability between times of day whereas other sites exhibited greater within-site differences between times of day than many inter-site differences. Daytime soundscapes generally tended to exhibit more temporal variability than nighttime soundscapes. We also compared potential subsampling schemes that could be advantageous in terms of power, data storage, and data analysis costs by modeling subsample error as a function of total analysis time and number of subsamples within a larger block of time. Greater numbers of evenly distributed subdivisions drastically increased the representativeness of a sampling scheme, while increases in subsample duration yielded fairly minimal gains in representativeness between 33 and 67% of the full time one wishes to represent. Generally, our results show that for a long-term, fine temporal resolution monitoring program, one should record in evenly distributed durations at least as short as 1 min while only recording up to a third of the time one wishes to represent. While more continuous monitoring can be advantageous and necessary in many cases, current economic and logistical limitations in power, data storage, and analysis capabilities will often warrant optimized subsampling designs.
... Soundscapes can represent the health of an environment if acoustical niches correlate with ecological niches of vocal animals (Farina et al., 2011;Kasten et al., 2012;Gage and Axel, 2014;Fuller et al., 2015). Soundscapes can be used to detect early signs of bird stress related to changes in habitat or climate (Sueur and Farina, 2015). ...
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Climate change is increasing aridity in grassland and desert habitats across the southwestern United States, reducing available resources and drastically changing the breeding habitat of many bird species. Increases in aridity reduce sound propagation distances, potentially impacting habitat soundscapes, and could lead to a breakdown of the avian soundscapes in the form of loss of vocal culture, reduced mating opportunities, and local population extinctions. We developed an agent-based model to examine how changes in aridity will affect both sound propagation and the ability of territorial birds to audibly contact their neighbors. We simulated vocal signal attenuation under a variety of environmental scenarios for the south central semi-arid prairies of the United States, ranging from contemporary weather conditions to predicted droughts under climate change. We also simulated how changes in physiological conditions, mainly evaporative water loss (EWL), would affect singing behavior. Under contemporary and climate change induced drought conditions, we found significantly fewer individuals successfully contacted all adjacent neighbors than did individuals in either the contemporary or predicted climate change conditions. We also found that at higher sound frequencies and higher EWL, fewer individuals were able to successfully contact all their neighbors, particularly in the drought and climate change drought conditions. These results indicate that climate change-mediated aridification may disrupt the avian soundscape, such that vocal communication no longer effectively functions for mate attraction or territorial defense. As climate change progresses increased aridity in current grasslands may favor shifts toward low frequency songs, colonial resource use, and altered songbird community compositions.
... As well as providing cost-effective monitoring methods, ecoacoustics offers a valuable conceptual framework to integrate biospheric and anthropogenic perspectives. Following Odum's (1953) classification of broad ecosystem components, elements of the soundscape are described according to their source: Geophony denotes the sounds made by abiotic processes (wind, rain etc.) in the landscape; biophony the sounds of animals; and anthrophony, the sounds of humans We find the term technophony (Gage and Axel, 2014) to be more useful in order to refer specifically to the noises of man-made powered machinery, which are distinct in terms of their acoustic signals and resulting impact on soniferous species communication. The soundscape is therefore a site of rich interaction between processes of the lithosphere, biosphere, hydrosphere and anthroposphere: machine listening provides a means to listen to and interpret these interactions. ...
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Background The concept of nature-based solutions (NBS) has evolved as an umbrella concept to describe approaches to learning from and using nature to create sustainable socio-ecological systems. Furthermore, NBS often address multiple societal challenges that humans are facing in the medium to long-term and as such can enhance human well-being (HWB). This study was commissioned to fulfil the need for a targeted systematic evidence map on the linkage between NBS and HWB to support focused research going forward that addresses the key knowledge needs of policy makers in the UK and beyond. Methods A consultation with policy makers and government agency staff (n = 46), in the four component parts of the UK (England, Wales, Scotland, Northern Ireland) was conducted in spring 2019. This identified four key societal challenges of operational experience lacking a scientific evidence base. Three of these challenges related to management issues: NBS cost-efficacy, governance in planning, environmental justice. The fourth challenge related to the acoustic environment (soundscape). Using systematic methods, this study searched for and identified studies that assessed NBS on HWB with regard to these four selected societal challenges. Review findings A total of 7287 articles were returned from the systematic search and screened for suitability at the level of title and abstract. A total of 610 articles passed screening criteria to warrant full text screening. Of these, 115 studies met the full text criteria for eligibility in the final systematic map database. Included studies were coded for twelve NBS interventions and ten HWB related outcome categories. Most of the evidence reviewed referred to natural, blue or green infrastructure in the urban environment and focused on economic, material and health aspects of HWB. Less than 2% of studies identified in the searches robustly reported the role of NBS actions or interventions on HWB compared with non-NBS actions or interventions Conclusion This systematic map found the evidence base is growing on NBS-HWB linkages, but significant biases persist in the existing literature. There was a bias in favour of the urban environment and restoration studies focused on conservation aspects, with only a few studies investigating the full suite of advantages to HWB that can be delivered from NBS actions and interventions. The soundscape was the least studied of the societal challenges identified as being of key importance by policy makers, with cost-efficiency the most reported. There was a lack of robust long-term studies to clearly test the potential of NBS regarding the HWB outcomes compared with non-NBS alternatives. This lack of robust primary knowledge, covering all four key societal challenges identified, confirms that the knowledge gaps identified by the policy makers persist, and highlights a clear research need for long-term, transdisciplinary studies that focus on comparisons between NBS and non-NBS alternatives
... Recently these acoustic indices have been used to look at local habitats (e.g., Lake Michigan [Gage and Axel 2014]), community diversity (Gasc et al. 2013), landscape configuration (Fuller et al. 2015), and avian phenology (Buxton et al. 2016), but more work is needed across ecosystem types with varied levels of human interaction and at different spatial and temporal scales (Fuller et al. 2015). While there is no objectively correct scale at which to study and manage ecosystems (Levin 1992), including the soundscape, consideration of multiple scales has proven valuable in research and practice (e.g., Quinn et al. 2014). ...
... Events Per Second was correlated with Low Frequency Cover and Temporal Entropy and showed a clear peak during the day, but no peaks at dawn and dusk. This might have related to an increase in geophony and anthropophony during the day recordings (Figure 2), which should both be related to Low Frequency Cover (Gage and Axel, 2014;Shaw et al., 2021). The two-species mixtures were a result of establishment failure of Cordia alliodora. ...
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In this ecoacoustic study we used the setting of a tropical tree diversity planted forest to analyze temporal patterns in the composition of soundscapes and to test the effects of tree species richness on associated biodiversity measured as acoustic diversity. The analysis of soundscapes offers easy, rapid and sustainable methods when assessing biodiversity. During the last years the quantification of regional or global acoustic variability in sounds and the analysis of different soundscapes has been evolving into an important tool for biodiversity conservation, especially since case studies confirmed a relationship between land-use management, forest structure and acoustic diversity. Here we analyzed soundscapes from two seasons (dry and rainy season) and aurally inspected a subset of audio recordings to describe temporal patterns in soundscape composition. Several acoustic indices were calculated and we performed a correlation analysis and a non-metric multidimensional scaling analysis to identify acoustic indices that: (i) were complementary to each other and such represented different aspects of the local soundscapes and (ii) related most strongly to differences in acoustic composition among tree species richness, season and day phase. Thus, we chose “High Frequency Cover,” “Bioacoustic Index,” and “Events Per Second” to test the hypothesis that acoustic diversity increases with increasing tree species richness. Monocultures differed significantly from polycultures during night recordings, with respect to High Frequency Cover. This index covers sounds above 8 kHz and thus represents part of the orthopteran community. We conclude that increasing tree species richness in a young tropical forest plantation had positive effects on the vocalizing communities. The strongest effects were found for acoustic activity of the orthopteran community. In contrast to birds, orthopterans have smaller home ranges, and are therefore important indicator species for small scale environmental conditions.
... ;Bormpoudakis, Sueur, & Pantis, 2013;Gage & Axel, 2014;Fuller et al., 2015;Grant & Samways, 2016).Different acoustic indices express different behavior and highlight different aspects of a soundscapeTowsey, Zhang, Cottman-Fields et al., 2014). For example: the acoustic complexity index (ACI)(Pieretti, Farina, & Morri, 2011) showed high sensitivity to the dynamics of bird choruses, while an index of acoustic cover (CVR) was most sensitive in response to continuous cicada choruses. ...
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Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas. To overcome these limitations we propose a network of Automated Multisensor stations for Monitoring of species Diversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution.
... Developments in data acquisition, storage, and processing have led to Passive Acoustic Monitoring (PAM) approaches-so called because recorders can collect data autonomously-increasingly being adopted for a wide array of ecological applications and conservation management (Lomolino et al. 2015;Deichmann et al. 2018;Gasc et al. 2018;Burivalova et al. 2019aBurivalova et al. , 2019bElise et al. 2019;Sethi et al. 2020aSethi et al. , 2020bDuarte et al. 2021;Keitt & Abelson 2021). Despite its increasing popularity, there remains considerable debate surrounding methodological best practice in PAM studies, for example in terms of survey design Sugai et al. 2020), data visualisation (Gage & Axel 2014;Towsey et al. 2014), and the utility of a suite of acoustic indices for rapidly summarising the soundscape when applied in a management context Bradfer-Lawrence et al. 2019;Ross et al. 2021b). Regardless, PAM is now widely accepted as a useful addition to the environmental monitoring toolkit (Gibb et al. 2019;Guan et al. 2021). ...
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Through global environmental change, humans are modifying the planet at an unprecedented rate and scale, triggering the ongoing biodiversity and climate crises. Ecological stability and the consistency of nature’s contributions to people are fundamental to the continued sustainability of human societies. Stability is a complex and multidimensional concept including components such as variability in time and space and the resistance to and recovery from disturbances. Global change has the potential to destabilise ecosystems, but the form and strength of the relationship between different global change drivers and dimensions of stability remains understudied, precluding general or mechanistic understanding. Here, I combine theory, a field experiment, and observational data from a high-resolution acoustic monitoring network to reveal the potential for multiple global change drivers to erode multidimensional ecological stability. Critically, I also show how biodiversity and natural habitats can buffer the destabilising effects of global environmental change on ecosystems and soundscapes, providing vital insurance against disturbance. In an era characterised by unrelenting global change and intensifying disturbance regimes, my results provide a key step towards a generalisable understanding—and ultimately management—of the stability of ecosystems and their contributions to human wellbeing.
... For example, the acoustic diversity index is based on the Shannon diversity index (Shannon and Weaver, 1964). NDSI (Gage and Axel, 2014) measures the ratio between biophony (biological sounds) and anthrophony (human and technological sounds) and is derived from NDVI, an index used in the remote sensing analysis of vegetation (Pettorelli, 2013). Acoustic indices have been used in different contexts such as to evaluate the differences in faunal beta-diversity between forests and plantations (Hayashi et al., 2020), to detect rainfall in acoustic recordings (Sánchez-Giraldo et al., 2020), to examine differences among indices representing taxonomic groups (e.g., birds, anurans, mammals and insects) (Ferreira et al., 2018), to relate indices with bird diversity (Tucker et al., 2014), and to identify frog species (Brodie et al., 2020). ...
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High rates of biodiversity loss caused by human-induced changes in the environment require new methods for large scale fauna monitoring and data analysis. While ecoacoustic monitoring is increasingly being used and shows promise, analysis and interpretation of the big data produced remains a challenge. Computer-generated acoustic indices potentially provide a biologically meaningful summary of sound, however, temporal autocorrelation, difficulties in statistical analysis of multi-index data and lack of consistency or transferability in different terrestrial environments have hindered the application of those indices in different contexts. To address these issues we investigate the use of time-series motif discovery and random forest classification of multi-indices through two case studies. We use a semi-automated workflow combining time-series motif discovery and random forest classification of multi-index (acoustic complexity, temporal entropy, and events per second) data to categorize sounds in unfiltered recordings according to the main source of sound present (birds, insects, geophony). Our approach showed more than 70% accuracy in label assignment in both datasets. The categories assigned were broad, but we believe this is a great improvement on traditional single index analysis of environmental recordings as we can now give ecological meaning to recordings in a semi-automated way that does not require expert knowledge and manual validation is only necessary for a small subset of the data. Furthermore, temporal autocorrelation, which is largely ignored by researchers, has been effectively eliminated through the time-series motif discovery technique applied here for the first time to ecoacoustic data. We expect that our approach will greatly assist researchers in the future as it will allow large datasets to be rapidly processed and labeled, enabling the screening of recordings for undesired sounds, such as wind, or target biophony (insects and birds) for biodiversity monitoring or bioacoustics research.
... type or indicators Classify or map habitat type or indicators (e.g., land cover, functional group) from terrestrial laser imageryRehush et al. (2018) Autonomous camera-trap images Individual animal recognition Identify or count individual animals using individual recognition (e.g., Facial recognition) from camera-trap imagesSchofield et al. (2019) Terrestrial animal species Classify species of terrestrial animals (often medium-to largesized mammals such as primates, ungulates, and felids) from terrestrial camera-trap imagesYu et al. (2013) Aquatic animal species Classify aquatic animal species from underwater camera-trap imagesZhang et al. (2016) Non-autonomous camera images in field or laboratory Microorganism Classify the taxonomic group of a microorganism from closerange or microscopic images Buetti-Dinh et al.(2019),Cheng et al. (2019) models for identifying bat species are welldeveloped and commercially available (e.g., Kaleidoscope Pro, SonoBat). To our knowledge, no pre-trained models are widely available for other taxa aside from the recently released BirdNET model(Kahl et al. 2021), although models for bird species, and for anurans to a certain degree, are an active area of development and improvement(Acevedo et al. 2009, Aide et al. 2013, Bedoya et al. 2014, Gage and Axel 2014, Priyadarshani et al. 2018, Lapp et al. 2021). ...
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A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human‐centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long‐term costs. In this manuscript, we first review the current status of instrument‐based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound‐producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground‐based measurements for plant biodiversity, and laboratory‐based imaging for physical specimens and samples in the NEON biorepository. Through its data science‐literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human‐driven surveys.
... For example, entry 2.3 of ISO 12913-1:2014 [118] defines "soundscape" as "acoustic environment as perceived or experienced and/or understood by a person or people, in context." However, the same term is used without this implication in contexts other than human hearing, in air [119]- [121], and in water [122]- [124]. For this reason, the definition of "soundscape" according to ISO 18405:2017 [46] (entry 3.1.1.3) ...
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Applications of underwater acoustics include sonar, communication, geophysical imaging, acoustical oceanography, and bioacoustics. Specialists typically work with little interdisciplinary interaction, and the terminology they employ has evolved separately in each discipline, to the point that transdisciplinary misunderstandings are common. Furthermore, increasing societal concern about possible detrimental effects of underwater noise on aquatic animals has led national and international regulators to require monitoring of underwater noise, with a consequent need for interdisciplinary harmonization of terminology. By adopting a common language, we facilitate the effective communication of concepts and information in underwater acoustics, whether for research, technology, or regulation. In the words of William H. Taft, “Don't write so that you can be understood, write so that you can't be misunderstood.” Clear definitions of widely used terms are needed, such as those used for the characterization of sound fields (e.g., “soundscape” and “ambient noise”), sound sources (“source level” and “source waveform”), sound propagation (“transmission loss” and “propagation loss”), and sound reception (“hearing threshold” and “frequency weighting function”). Terms that are used synonymously in one application have different meanings in another (examples include “hearing threshold” versus “detection threshold” and “transmission loss” versus “propagation loss”). Distinct definitions for these and many other acoustic terms are provided in a standard published in April 2017 by the International Organization for Standardization, ISO 18405. This article summarizes ISO 18405 and the process that led to the published definitions, including the reasons for omitting some terms.
... The development of soundscape indices (also referred to as acoustic indexes) has allowed for unique, rich, and informative quantitative measurement and characterization of diverse landscapes Pijanowski et al. 2011;Farina 2013, Villanueva-Rivera et al. 2011;Zhao et al. 2019). For example, researchers have used soundscape indices to characterize change in ecosystems, phenology, circadian rhythms, and biological diversity (Sueur et al. 2008b, Gasc et al. 2013Gage and Axel 2014;Pieretti et al. 2015;Buxton et al. 2017;Quinn et al. 2018;Whalen et al. 2019;Gerber et al. 2020, Schindler et al. 2020. The specifics of each index are discussed in the aforementioned citations, reviewed by (Bradfer-Lawrence et al. 2019), and summarized in the methods below. ...
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ContextMost data collection and analyses in soundscape ecology have focused on summer or breeding seasons in urban or protected landscapes, missing important acoustic dynamics in winter, non-breeding periods and in agricultural landscapes, a land-use that constitutes 39% of ice-free surface globally.Objectives To address these gaps, we examined the variation of winter soundscapes across a rural agricultural landscape of Nebraska, USA. We compared high and low traffic sites, testing if traffic levels affected soundscape structure.Methods We recorded sound over two winters at 19 sites located adjacent to major and minor roadways. We calculated eight unique soundscape indexes to quantify the soundscape over time as a function of traffic and land cover. We applied filters at 80, 1000, and 2000 Hz.ResultsWe found clear statistical differences between high and low traffic sites in 7 of 8 soundscape indexes. Soundscape varied throughout the day, but not throughout the season. There was a clear negative correlation between technophony (human-derived sounds) and biophony (ecologically derived sounds) across sites. We found that not all indices may be suitable for all ecosystems.Conclusions We quantified the effects of noise pollution on the soundscape of understudied habitats during winter months. By using soundscape indexes as surrogates for biodiversity, acoustic sampling could be an effective method for monitoring biodiversity when traditional methods may be ineffective or too costly. However, caution needs to be taken when choosing indices.
... Recent studies indicate that acoustic metrics respond to different spatial scales, with the magnitude of their associations with landscape elements being affected by how the scale delimiting the soundscape is defined and in which such attributes are estimated (Dooley and Brown, 2020;Dein and Rüdisser, 2020). Likewise, the connection between acoustic metrics and landscape elements may respond to the temporal dynamics of the soundscape, varying in magnitude across seasons or even emerging only at specific daily periods (Gage and Axel, 2014;Mullet et al., 2016). ...
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tSoundscape research has acquired a paramount role in biodiversity conservation as it may provide timelyand reliable information about the ecological integrity. The relationship between soundscape complexityand ecological integrity in highly biodiverse environments, as well as the factors affecting this relation-ship require a thorough understanding. We determined how the soundscape relates to the landscapeecological integrity at different spatial and temporal scales in a montane forest in the northern Andes ofColombia. Between May–July 2018 we obtained acoustic recordings from 31 sampling sites in the pro-tected area of a hydropower plant, and estimated nine acoustic indices and an ecological integrity index(EII) derived from fragmentation, connectivity, and habitat quality. Five of the acoustic indices, linked tothe evenness of the acoustic signals and levels of the biophonic signals, were associated with changesin the EII and indicated the presence of more even, saturated, and acoustically rich soundscapes in siteswith higher integrity. Relationships between acoustic indices and the EII were stronger at a smaller spa-tial scale (100 m) and responded to daily variation of the soundscape, with the strongest associationsoccurring mainly from sunrise to noon. We show that acoustic indices measuring the evenness of theacoustic activity distribution and the number of frequency peaks reliably reflect the changes in the eco-logical integrity, and can be integrated with remote sensing as a tool for landscape management. Ourresults highlight the soundscape analysis as a feasible approach for the monitoring and conservationplanning of acoustically unknown and threatened Andean landscapes.
... However, they have been mostly used in research focused on specific aspects of the environment (oftentimes the information about biodiversity that could be obtained with the biophony, or environmental information in general), paying less attention to other components (human generated sound but also rain or wind, for example) and also often filtering part of the frequency spectrum accordingly (Gasc et al., 2015;. Other research focused on specific indices, like acoustic complexity (Pieretti et al., 2011;Farina et al., 2018Farina et al., , 2021 or the Normalized Difference Soundscape Index (Gage and Axel, 2014;Ritts et al., 2016), showing how they reveal a variety of environmental information. In most of the cases (with the exception of Farina et al. who use a common acoustic event library for all sites), each recording site is processed separately in these studies, and the samples are not gathered in a single database, which does not allow for the direct comparison of sonic or acoustic patterns across sites. ...
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Soundscapes are increasingly used as innovative entry doors in environmental studies. Facing huge libraries of sound files which cannot be processed manually, acoustic indices provide an overview to the information contained in them, as well as to allow for automatic processing. Studies dealing with such indices have, however, focused more on specific topics or indices than on the overall characteristics of the soundscapes they were analyzing. The aim of this paper is to propose a holistic approach to soundscapes. Our hypothesis is that sufficient number and variety of indices can help frame the characteristics of sound environments and that the use of clustering algorithms allows us to group them in families and study the distribution of those across space and time, revealing a geography that will not necessarily coincide with the obvious landscape/visual geography. To demonstrate this point, we have run indices analysis and classification on a soundscapes database recorded in the Sonoran Desert region (Southeastern Arizona, USA). The results show that sound indices reveal temporal variations and patterns of soundscapes and point out to sometimes surprising similarities between otherwise different environments. As sound indices capture a wealth of information which characterizes the environment at a given place and time, they could be used as proxies to continuous monitoring without having to store extreme amounts of data.
... The oceans abound with natural physical sounds (from wind, rain, polar ice, and seismic activity), biological sounds (from crustaceans, fishes, and marine mammals), and anthropogenic sounds (from transport, construction, offshore exploration, and mining). Soundscapes naturally change over time because of temporal cycles in weather (e.g., cyclones and annual monsoon [1,2]) and animal behaviour (e.g., diurnal foraging patterns, lunar spawning, seasonal mating, and annual migration [3][4][5][6]). However, in many habitats, soundscapes further change with patterns of human presence (e.g., temporary construction or summer recreation [7]) and some have changed steadily over time with increasing intensity of anthropophony (e.g., due to shipping [8]). ...
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Marine soundscapes consist of cumulative contributions by diverse sources of sound grouped into: physical (e.g., wind), biological (e.g., fish), and anthropogenic (e.g., shipping)—each with unique spatial, temporal, and frequency characteristics. In terms of anthropophony, shipping has been found to be the greatest (ubiquitous and continuous) contributor of low-frequency underwater noise in several northern hemisphere soundscapes. Our aim was to develop a model for ship noise in Australian waters, which could be used by industry and government to manage marine zones, their usage, stressors, and potential impacts. We also modelled wind noise under water to provide context to the contribution of ship noise. The models were validated with underwater recordings from 25 sites. As expected, there was good congruence when shipping or wind were the dominant sources. However, there was less agreement when other anthropogenic or biological sources were present (i.e., primarily marine seismic surveying and whales). Off Australia, pristine marine soundscapes (based on the dominance of natural, biological and physical sound) remain, in particular, near offshore reefs and islands. Strong wind noise dominates along the southern Australian coast. Underwater shipping noise dominates only in certain areas, along the eastern seaboard and on the northwest shelf, close to shipping lanes.
... Audio recordings, however, are comparably effective at capturing changing patterns in wildlife presence or behavior Pijanowski et al., 2011a) and deliver data with millisecond-level temporal resolution. With recent advances in storage capacity and energy efficiency, acoustic recorders can now capture months or years of data between servicing periods (Gage and Axel, 2014;Gibb et al., 2019;Hill et al., 2018), making this technology highly useful for assessing the animal impacts of unpredictable pulse disturbances. ...
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Disturbance regimes and biodiversity—two factors that govern the stability of ecosystems—are changing rapidlydue to anthropogenic forces including climate change. Determining whether ecosystems retain their structure and function through intensifying disturbance regimes is an urgent task. However, quantitatively assessing the resilience of natural systems is a complex and challenging endeavor, especially for animal communities, for which datasets around disturbance events are scarce. Here, we apply an emerging remote sensing tech-nology—the recording and analysis of soundscapes—to quantify the resilience of Puerto Rican coral reef and dry forest animal communities in relation to Hurricane Maria, which struck the island in September 2017. Using recordings collected between March 2017 and January 2018 at three terrestrial and three marine sites, we measured three dimensions of resilience—the magnitude of the impacts (resistance), the spatial pattern of the impacts (heterogeneity), and the diversity and timeline of functional responses (recovery)—across eight sound types representing different broad taxonomic groups. While the coral reef communities exhibited high resistance to the storm, all sound types within the dry forest were significantly impacted, with two of the three insect choruses and bird vocalizations at dawn declining approximately 50% in the weeks following Hurricane Maria. The mid-frequency insect sound type returned to pre-storm levels after 56 days, while bird vocalizations returned after 67 days, though seasonal and lunar patterns underscored the importance of long-term data for accurately measuring trajectories of recovery. This study demonstrates that soundscape methodologies can help to quantify elusive dimensions of animal community resilience in order to better understand how biodiversity and ecosystem functioning will change under novel disturbance regimes.
... Acoustic indices have been used as biodiversity surrogates (Depraetere et al., 2011;Sueur et al., 2008) and have facilitated our understanding of ecosystems and how they are structured and change across spatiotemporal scales (Gage and Axel, 2014;Priyadarshani et al., 2018;Towsey et al., 2014). However, some contention surrounds the use of acoustic indices as surrogates for biodiversity where they can be limited in their utility in certain environments (e.g. ...
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As the rate of urbanisation continues to increase, widespread habitat clearing within peri-urban landscapes contributes to significant environmental impacts, including loss of biological diversity. Acoustic recording has recently been identified as an effective tool for monitoring biodiversity and ecosystem health. With increasing pressure from urbanisation, it is critical that spatial and temporal variability in biodiversity is mapped across future development sites to enable sound decision-making and to deliver ecological urban design outcomes. This study used ecoacoustic monitoring to map biodiversity patterns in space and time to identify hot spots and hot moments of biodiversity activity across a peri-urban landscape in south-east Queensland, Australia. In this study, a hot spot represents an increase in acoustic activity at a given spatial location, whereas hot moments represent an increase in acoustic activity at a given time point. An acoustic index (Acoustic Complexity Index, ACI) was used as a proxy for biodiversity and visualised through spatial interpolation. The acoustic data were statistically modelled using Boosted Regression Trees (BRT). This approach enabled predictors related to acoustic complexity to be identified, including vegetation and landform. Results of this study have shown that ecoacoustic data can be used to map hot spots and hot moments of biodiversity and support more informed conservation decision-making in future urban planning frameworks, to avoid or mitigate negative impacts on biodiversity.
... It targets single sound sources (i.e., single species or noise) or all the sound sources in a given environment (i.e., the soundscape). The marine soundscape is shaped by three components: biophony (signals and vocalizations produced by marine mammals, fishes, invertebrates), geophony (i.e., sounds from waves and rain, undersea earthquakes) and anthropophony (human-produced noise from shipping, industrial activities, oil and gas explorations) (Pieretti, 2017;Putland et al., 2017;Gage and Axel, 2014;Mullet, 2016;Farina, 2013;Pijanowski, 2011;Krause, 1987). PAM technologies allow the collection of large acoustic datasets (including species' presence/absence and their behavioral alterations due to external factors (Burivalova et al., 2019), consequently enabling the investigation of ecological patterns at different spatial and temporal scales (Erbe, 2015;Haver, 2018;Merchant, 2016). ...
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The analysis of temporal trends and spatial patterns of marine sounds can provide crucial insights to assess the abundance, distribution, and behavior of fishes and of many other species. However, data on species-specific temporal and seasonal changes are still extremely limited. We report here the result of the longest recording ever conducted (five years, from 2014 to 2018) on fish vocalization. Findings from the Eastern Taiwan Strait (ETS) revealed a periodic fish chorusing pattern, with peaks in summer and almost complete silence, for ~2 months, during winter. Chorusing pattern was influenced by abiotic parameters, including temperature, tides and moon phase. We also report, for the first time, that extreme weather events (e.g., typhoons, storms with sediment resuspension) caused the cessation of the chorusing. The chorusing pattern explored in this long-term study provides important baseline data to understand the impact of climate change and of climate-driven extreme/episodic events on the phenology of fishes; this work also provides evidence that changes in the ambient conditions might significantly alter the phenology of vocalizing marine species.
... A significant research effort has been expended on the development and validation of proxies for biodiversity, or level of environmental integrity and degradation (Grant and Samways 2016;Harris et al. 2016;Krause and Farina 2016), and on environmental monitoring and environmental quality assessment (Tucker et al. 2014). These developments in computational ecoacoustics run in parallel with a body of research in computational bioacoustics which focuses on automated species recognition (Sueur et al. 2008;Gage and Axel 2013;Gasc et al. 2013a, b;Sueur et al. 2014). Many studies, some hybridising bioacoustic and ecoacoustic approaches, focus on the impact of anthropogenic noise on the acoustic performances of individual species (Brumm and Todt 2003;Brumm and Slabbekoorn 2005;Brumm and Slater 2006;Brumm 2010;Luther and Gentry 2013) or on community dynamics (Gil et al. 2015). ...
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Biosemiotics to date has focused on the exchange of signals between organisms, in line with bioacoustics; consideration of the wider acoustic environment as a semiotic medium is under-developed. The nascent discipline of ecoacoustics, that investigates the role of environmental sound in ecological processes and dynamics, fills this gap. In this paper we introduce key ecoacoustic terminology and concepts in order to highlight the value of ecoacoustics as a discipline in which to conceptualise and study intra- and interspecies semiosis. We stress the inherently subjective nature of all sensory scapes (vivo-, land-, vibro- and soundscapes) and propose that they should always bear an organismic attribution. Key terms to describe the sources (geophony, biophony, anthropophony, technophony) and scales (sonotopes, soundtopes, sonotones) of soundscapes are described. We introduce epithets for soundscapes to point to the degree to which the global environment is implicated in semiosis (latent, sensed and interpreted soundscapes); terms for describing key ecological structures and processes (acoustic community, acoustic habitat, ecoacoustic events) and examples of ecoacoustic events (choruses and noise) are described. The acoustic eco-field is recognized as the semiotic model that enables soniferous species to intercept core resources like food, safety and roosting places. We note that whilst ecoacoustics to date has focused on the critical task of the development of metrics for application in conservation and biodiversity assessment, these can be enriched by advancing conceptual and theoretical foundations. Finally, the mutual value of integrating ecoacoustic and biosemiotics perspectives is considered.
... It relies on the investigation of the soundscape, which can be defined as the set of sounds produced in a given environment (Farina, 2014). The different components of a soundscape are described as geophony (sounds produced by abiotic agents such as wind, rain, eruptions, falling water), biophony (sounds produced by special organs of soniferous species or produced incidentally while feeding or moving), anthropophony (human vocalizations transmitted and magnified by technological devices) and technophony (sounds related to the functioning of machines) (Gage and Axel, 2014). ...
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Wildfire is a natural process in Brazilian savannas, but human activities alter fire regimes and threaten biodiversity. In this study, weused an ecoacoustics approach to assess fauna responses and recovery after wildfire in a Brazilian savanna. Six passive acoustic monitoring devices were used to record soundscapes before and after a wildfire a at burned and non-burned sites for one year and one month (September 2012 to September 2013). Power Spectral Density and the Acoustic Complexity Index were used to track biophony. Before the fire, the two sites had similar biophonic patterns (PSD: T=1136, Z=1.52, P=0.12; ACI: T=1117, Z=1.10 , P=0.26) and soniferous species richness (Site 1=52 and Site 2=49). However, in the first two sessions of recordings after the fire, biophony became higher at the burned site during the day (PSD: T=211 and 233; Z=4.13 and 6.41; ACI: T=120 and 469, Z=5.14 and 7.07; all P<0.00). During the night, biophony was usually higher at the non-burned site until May 2013 (PSD: T=0 to 453; Z=3.30 to 5.90; ACI: T=333 to 491, Z=3.80 to 4.93; all P<0.00). Biophony became similar (P=0.17 to 0.38) at the two sites or higher (P=0.00 to 0.01) at the burned site from July to September 2013 (PSD: T=55 to 1167; Z=1.35 to 6.89; ACI: T=719 to 1365, Z=0.87 to 3.04). After the fire, a reduction of soniferous species at the burned site was observed for insects and bats. Both biophonic activity and soniferous species showed a tendency to recover one year after the fire, but there were still less species in September 2013 (non-burned=43 and burned=37) when compared to September 2012 at both sites (Site 1= 52 and Site 2=49). Our results showed that changes in the natural regimes of fire can negatively impact the biodiversity and reinforce the need for monitoring protocols and inspection of wildfires.
... This corresponds to the structure of most insect songs, which sound like a repetitive pulse signal (Cocroft and Rodríguez, 2005). The difference in the sound frequency of vocalizing species reflects the exploitation and distribution of the sound environment by different species and minimizes the overlap of sound signals between different species (Gage and Axel, 2014), which reflects the stability of the forest ecosystem (Sueur, 2002). Different sound frequencies and unique sound behavior of vocalizing species determine the variance between the forest sound environment during day and night. ...
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Bioacoustic methods analyze sounds to monitor wildlife in forests, providing novel perspectives to understand the relationship between forest vegetation structure and wildlife species. Bioacoustic studies differ from traditional field surveys that are often more visual and require work in the daytime. A sampling of acoustic signals is possible to explore the diurnal variation of the forest ecosystem. Normalized difference soundscape index (NDSI), acoustic diversity index (ADI), and power spectral density (PSD) were chosen as acoustic indices to quantify the dominance, diversity, and composition of the biophony of forest settings. Biophony was recorded in ten south subtropical evergreen broad-leaved forest sites in Shenzhen, China. Composition structure (species richness), horizontal structure (vegetation density and canopy cover), and vertical structure (tree height diversity and each layer height) were used to evaluate the structure of vegetation. Linear Mixed Models (LMMs) were applied to examine the effects of different vegetation structures on biophony between day and night. The relationships between various biophonic frequency bands and vegetation structural variables during day and night were estimated by redundancy analysis (RDA). Results showed that different sound frequencies and vocalization behavior of vocalizing species determine the variance between the forest sound environment during day and night. Forest canopy and understory have a significant influence on biophonic diversity and dominance separately. The vocalizing species' preferences for different habitats were affected by the same vegetation structure variables across diurnal variation. Canopy cover and understory species richness had a significant positive effect on low-frequency biophony, and tree height diversity was found to have a significant positive impact on high-frequency biophony. Finally, tree height diversity was the core variable in the forest acoustic environment, and higher tree height diversity resulted in a more dominant and diverse biophony. This study shows the potential of bioacoustic methods to reveal the day-night dynamics of vocalizing species, using easily measured acoustic variables that can help with the timely evaluation of potential impacts of vegetation structure on forest animal communities.
... These temporal patterns have also been detected at the acoustic community level in terrestrial (Lellouch, Pavoine, Jiguet, Glotin, & Sueur, 2014;Rodriguez et al., 2014) as well as in aquatic environments (Linke, Decker, Gifford, & Desjonquères, 2018;Ruppé et al., 2015). Acoustic diversity not only varies diurnally but also seasonally (Amoser & Ladich, 2010;Gage & Axel, 2014;Jansson, 1974;Risch et al., 2014). It is therefore crucial to choose a recording schedule appropriate to the ecological question being addressed. ...
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This study aims to recognise frog choruses using false-colour spectrograms and machine learning algorithms with acoustic indices. This can be a useful solution for improving the efficiency of long-term acoustic monitoring. Acid frogs, our target species, are a group of endemic frogs that are particularly sensitive to habitat change and competition from other species. The Wallum Sedgefrog (Litoria olongburensis) is the most threatened acid frog species facing habitat loss and degradation across much of their distribution, in addition to further pressures associated with anecdotally-recognised competition from their sibling species, the Eastern Sedgefrogs (Litoria fallax). Monitoring the calling behaviours of these two species is essential for informing L. olongburensis management and protection, and for obtaining ecological information about the process and implications of their competition. Considering the cryptic nature of L. olongburensis and the sensitivity of their habitat to human disturbance, passive acoustic monitoring is a suitable method for monitoring this species. However, manually processing this overwhelmingly large quantities of acoustic data collected is time-consuming and not feasible in the long-term. Therefore, there is a high demand for automated acoustic recognition methods to efficiently search long-duration recordings and identify target species. In this study, we propose a two-step scheme for quickly identifying frog choruses, which is first narrowing down the search scope by inspecting long-duration false-colour spectrograms and then recognising target acoustic signals using machine learning and acoustic indices. This method is efficient, time-saving and general, which means it can easily adopted to other species. Our research also provides insights on how to choose acoustic features that efficiently recognise species from larger scale field-collected recordings. The experimental results show that these techniques are useful in identifying choruses of the two competitive frog species with an accuracy of 76.7% on identifying four acoustic patterns (whether the two species occurred).
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Tallgrass prairies are rapidly vanishing biodiversity hotspots for native and endemic species, yet little is known regarding how spatial and temporal variation of prairie soundscapes relates to seasonal changes, disturbance patterns and biological communities. Ecoacoustics, the study of environmental sounds using passive acoustics as a non-invasive tool for investigating ecological complexity, allows for long-term data to be captured without disrupting biological communities. Two studies were carried out by employing ecoacoustic methodology to study grassland carrion food webs and to capture the phenology of a grassland soundscape following a prescribed burn. Both studies were conducted at the Nature Conservancy’s Tallgrass Prairie Preserve (36°50’N, 96°25’W) and used six acoustic indices to quantify the ratio of technophony to biophony, acoustic complexity, diversity, evenness, entropy, and biological acoustic diversity from over 70,000 sound recordings. Acoustic index values were used to determine the relationship between Nicrophorus burying beetle species composition and the prairie soundscape (Chapter 1) and to determine if prescribed burning changes the composition of the soundscape over time (Chapter 2). In Chapter 1, I found that associations between Nicrophorus burying beetles and the soundscape were unique to particular species, acoustic indices and times of day. For example, N. americanus trap rates showed a positive correlation to areas of increased acoustic complexity specifically at dawn. In addition to positive associations with the soundscape, we found that N. marginatus was consistently negatively correlated to higher levels of biophony, while N. tomentosus was consistently positively correlated to places with higher levels of biophony. Although reproduction of all species examined is dependent upon securing small carrion for reproduction, I found that known habitat and activity segregation of five Nicrophorus beetle species may be reflective of the soundscape. Finally, I show that favorable habitat for a critically endangered necrophilous insect, the American burying beetle (Nicrophorus americanus) can be identified by the acoustic signature extracted from a short temporal window of its grassland ecosystem soundscape. Using the same suite of acoustic indices from Chapter 1, in Chapter 2 I examined acoustic recordings at a much larger time scale to determine distinctive acoustic events driven by biophony and geophony across a 23-week period. In addition to examining acoustic changes over time, I examined differences between 11 burned and unburned pastures. Results from this study indicate that prescribed burning does alter the soundscape, especially early in the post-burn period, but the effects are ameliorated by a significant increase in biophony as the growing and breeding season progressed into the warmer summer months. Both studies demonstrate that passive acoustic recording is a reliable method to assess relationships to acoustic communities over space and time.
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Continuous audio recordings are playing an ever more important role in conservation and biodiversity monitoring, however, listening to these recordings is often infeasible, as they can be thousands of hours long. Automating analysis using machine learning algorithms requires a feature representation. In this paper we propose a technique for learning a general feature representation from unlabelled audio using auto-encoders, which can be used for analysing environmental audio on a small timescale. We start by segmenting the audio data into non-overlapping 1-s long chunks and generating audio spectrograms. These audio spectrograms are then used to train a basic auto-encoder, with the output of the encoder network being used to generate the feature representation. We have found that at a 1-s timescale, our feature representation offers marginal improvements over “acoustic indices”, a common representation for analysing environmental audio.
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The plot of the frequencies that build soundscapes (power spectrum) has been used to show some of their distinctive characteristics. The application of a discretized sample space is proposed to describe, in a concise manner, the contribution of different frequency signals in a soundscape on a yearly scale. A sample space composed of frequency and intensity combinations was used to describe the shapes of power spectrums and their recurrence. The number of elements of the sample space was set so that each element had a low probability of being found in random samples. In this way, the statistical confidence in the occurrence and recurrence of the elements is increased, making it possible to validate traits in the soundscape. According to their occurrence, it was established that there are “base”, “temporary” and “sporadic” combinations of frequency-intensity. When the relative frequencies of the frequency-intensity combinations were plotted versus the number of analyzed power spectrums, it was observed that few combinations predominate in the soundscape. Further, the distribution of the relative frequencies of the combinations tends to stabilize with time; a potential function described the relative frequency of the combinations. It was found that a logarithmic accumulation curve described the relationship between the analyzed power spectrums and the detected combinations in each of the proposed frequency sections; such a function was useful to validate the sampling method.
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Context One mainstay of soundscape ecology is to understand acoustic pattern changes, in particular the relative balance between biophony (biotic sounds), geophony (abiotic sounds), and anthropophony (human-related sounds). However, little research has been pursued to automatically track these three components. Objectives Here, we introduce a 15-year program that aims at estimating soundscape dynamics in relation to possible land use and climate change. We address the relative prevalence patterns of these components during the first year of recording. Methods Using four recorders, we monitored the soundscape of a large coniferous Alpine forest at the France-Switzerland border. We trained an artificial neural network (ANN) with mel frequency cepstral coefficients to systematically detect the occurrence of silence and sounds coming from birds, mammals, insects (biophony), rain (geophony), wind (geophony), and aircraft (anthropophony). Results The ANN satisfyingly classified each sound type. The soundscape was dominated by anthropophony (75% of all files), followed by geophony (57%), biophony (43%), and silence (14%). The classification revealed expected phenologies for biophony and geophony and a co-occurrence of biophony and anthropophony. Silence was rare and mostly limited to night time. Conclusions It was possible to track the main soundscape components in order to empirically estimate their relative prevalence across seasons. This analysis reveals that anthropogenic noise is a major component of the soundscape of protected habitats, which can dramatically impact local animal behavior and ecology.
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It employs state-of-the art remote sensing technology and methodologies to delimit Madagascar’s vegetation. It represents an all-inclusive collaboration between specialists from a wide range of botanical and conservation institutions, which has ensured the most thoroughly ground-truthed vegetation map ever compiled for Madagascar. Finally, through a series of workshops, it incorporates detailed consultations with the conservation community to ensure that the final products are of maximum relevance and utility to conservation planners and managers. An accurate and updated vegetation map is imperative for conservation planning and natural resource management in Madagascar. It is also essential that the data on which such a map is based be made freely available, so that conservation organisations, government departments, academic institutions and other stakeholders can use them as an up-to-date standard dataset on which to base their activities. The electronic version of this atlas is available on Kew’s website (www.vegmad.org), and local experts were invited to continually improve and update the map. In order for a vegetation map to fulfil its intended role it must accurately delimit areas with various vegetation types as they currently exist, and assign those areas to objective categories that can be easily recognised in the field and that reliably reflect fundamental biological differences (primarily structural features, for example, physiognomy). Madagascar is becoming increasingly aware of the need to protect its biodiversity. The most immediate use of this vegetation map in conservation is likely to be by protected area managers who wish to understand the flora of their designated areas. It will also provide a valuable baseline for monitoring longer-term changes in vegetation inside and outside protected areas. However, Madagascar also provides an exceptionally high rate of species discovery and description, and this atlas will be used by field biologists attempting to identify potential sampling sites for biodiversity surveys, which will in turn yield data that is critical for biogeographic research and conservation planning. At the 2003 World Parks Congress, Madagascar’s President Marc Ravalomanana emphasised his country’s commitment to conservation by announcing its intent to triple the size of its existing protected area network. This admirable effort to prevent the extinction of many of Madagascar’s endemic species has become known as the ‘Durban Vision’. In order to ensure effective preservation of Madagascar’s biodiversity, the identification of sites for these new protected areas should follow a systematic process. A recent workshop on systematic conservation planning (November 2005, Antananarivo) highlighted the importance of using habitat types and indicators of habitat quality in addition to species distribution data when conducting conservation prioritisation analyses, concluding that this is the best way to produce robust conservation solutions. Because only a small proportion of Madagascar’s species have had their distributions documented, the vegetation types identified by this mapping project are good surrogates for habitat diversity and for the majority of the biota, which is so little known. In addition, conservation practitioners, including NGOs and donors, need information on trends in natural vegetation cover and quality in order to assess the outcomes of their conservation work. The Convention on Biological Diversity includes trends in the extent of habitats among its headline indicators for tracking progress towards the 2010 target (SBSTTA, 2004). The immediate focus of the Durban Vision group will be on establishing new protected areas (map 1) in remaining native vegetation, although subsequent attention could productively turn to managing that vegetation, and the habitat quality categories in the atlas provide valuable information. The atlas also provides important up-to-date information on native vegetation cover and quality, which maximises its potential to aid planning for future habitat restoration activities.
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