Extending Species-Area Relationships Into the Realm of Ecoacoustics: The Soundscape-Area Relationship.

The rise in species richness with area is one of the few ironclad ecological relationships. Yet, little is known about the spatial scaling of alternative dimensions of diversity. Here, we provide empirical evidence for a relationship between the richness of acoustic traits emanating from a landscape, or soundscape richness, and island area, which we term the SoundScape-Area Relationship (SSAR). We show a positive relationship between the gamma soundscape richness and island area. This relationship breaks down at the smallest spatial scales, indicating a small-island effect. Moreover, we demonstrate a positive spatial scaling of the plot-scale alpha soundscape richness, but not the beta soundscape turnover, suggesting a direct effect of species on acoustic trait diversity. We conclude that the general scaling of biodiversity can be extended into the realm of ecoacoustics, implying soundscape metrics are sensitive to fundamental ecological patterns and useful in disentangling their complex mechanistic drivers.

Monitoring soil fauna with ecoacoustics.

Ecoacoustics-or acoustic ecology-aids in monitoring elusive and protected species in several ecological contexts. For example, passive acoustic monitoring (PAM), which involves autonomous acoustic sensors, is widely used to detect various taxonomic groups in terrestrial and aquatic ecosystems, from birds and bats to fish and cetaceans. Here, we illustrate the potential of ecoacoustics to monitor soil biodiversity (specifically fauna)-a crucial endeavour given that 59% of species live in soil yet 75% of soils are affected by degradation. We describe the sources of sound in the soil (e.g. biological, geological and anthropogenic) and the ability of acoustic technology to detect and differentiate between these sounds, highlighting opportunities and current gaps in knowledge. We also propose a roadmap for the future development of optimized hardware, analytical pipelines and experimental approaches. Soil ecoacoustics is an emerging field with considerable potential to improve soil biodiversity monitoring and 'soil health' diagnostics. Indeed, early studies suggest soil ecoacoustics can be successfully applied in various ecosystems (e.g. grasslands, temperate, tropical and arid forests) and land uses (e.g. agriculture, viticulture, natural and restored ecosystems). Given the low cost, minimal intrusiveness, and effectiveness in supporting soil biodiversity assessments and biosecurity risks, we advocate for the advancement of soil ecoacoustics for future land management applications.

Sonic restoration: acoustic stimulation enhances plant growth-promoting fungi activity.

Ecosystem restoration interventions often utilize visible elements to restore an ecosystem (e.g. replanting native plant communities and reintroducing lost species). However, using acoustic stimulation to help restore ecosystems and promote plant growth has received little attention. Our study aimed to assess the effect of acoustic stimulation on the growth rate and sporulation of the plant growth-promoting fungus Trichoderma harzianum Rifai, 1969. We played a monotone acoustic stimulus (80 dB sound pressure level (SPL) at a peak frequency of 8 kHz and a bandwidth at -10 dB from the peak of 6819 Hz-parameters determined via review and pilot research) over 5 days to T. harzianum to assess whether acoustic stimulation affected the growth rate and sporulation of this fungus (control samples received only ambient sound stimulation less than 30 dB). We show that the acoustic stimulation treatments resulted in increased fungal biomass and enhanced T. harzianum conidia (spore) activity compared to controls. These results indicate that acoustic stimulation influences plant growth-promoting fungal growth and potentially facilitates their functioning (e.g. stimulating sporulation). The mechanism responsible for this phenomenon may be fungal mechanoreceptor stimulation and/or potentially a piezoelectric effect; however, further research is required to confirm this hypothesis. Our novel study highlights the potential of acoustic stimulation to alter important fungal attributes, which could, with further development, be harnessed to aid ecosystem restoration and sustainable agriculture.

An integrated passive acoustic monitoring and deep learning pipeline for black-and-white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar.

The urgent need for effective wildlife monitoring solutions in the face of global biodiversity loss has resulted in the emergence of conservation technologies such as passive acoustic monitoring (PAM). While PAM has been extensively used for marine mammals, birds, and bats, its application to primates is limited. Black-and-white ruffed lemurs (Varecia variegata) are a promising species to test PAM with due to their distinctive and loud roar-shrieks. Furthermore, these lemurs are challenging to monitor via traditional methods due to their fragmented and often unpredictable distribution in Madagascar's dense eastern rainforests. Our goal in this study was to develop a machine learning pipeline for automated call detection from PAM data, compare the effectiveness of PAM versus in-person observations, and investigate diel patterns in lemur vocal behavior. We did this study at Mangevo, Ranomafana National Park by concurrently conducting focal follows and deploying autonomous recorders in May-July 2019. We used transfer learning to build a convolutional neural network (optimized for recall) that automated the detection of lemur calls (57-h runtime; recall = 0.94, F1 = 0.70). We found that PAM outperformed in-person observations, saving time, money, and labor while also providing re-analyzable data. Using PAM yielded novel insights into V. variegata diel vocal patterns; we present the first published evidence of nocturnal calling. We developed a graphic user interface and open-sourced data and code, to serve as a resource for primatologists interested in implementing PAM and machine learning. By leveraging the potential of this pipeline, we can address the urgent need for effective primate population surveys to inform conservation strategies.

Soundscape Characterization Using Autoencoders and Unsupervised Learning.

Passive acoustic monitoring (PAM) through acoustic recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, acoustic diversity, community interactions, and human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due to the large volumes of ARU data. In this work, we propose a non-supervised framework using autoencoders to extract soundscape features. We applied this framework to a dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based on autoencoder features and represents cluster information with prototype spectrograms using centroid features and the decoder part of the neural network. Our analysis provides valuable insights into the distribution and temporal patterns of various sound compositions within the study area. By utilizing autoencoders, we identify significant soundscape patterns characterized by recurring and intense sound types across multiple frequency ranges. This comprehensive understanding of the study area's soundscape allows us to pinpoint crucial sound sources and gain deeper insights into its acoustic environment. Our results encourage further exploration of unsupervised algorithms in soundscape analysis as a promising alternative path for understanding and monitoring environmental changes.

Efficient Speech Detection in Environmental Audio Using Acoustic Recognition and Knowledge Distillation.

The ongoing biodiversity crisis, driven by factors such as land-use change and global warming, emphasizes the need for effective ecological monitoring methods. Acoustic monitoring of biodiversity has emerged as an important monitoring tool. Detecting human voices in soundscape monitoring projects is useful both for analyzing human disturbance and for privacy filtering. Despite significant strides in deep learning in recent years, the deployment of large neural networks on compact devices poses challenges due to memory and latency constraints. Our approach focuses on leveraging knowledge distillation techniques to design efficient, lightweight student models for speech detection in bioacoustics. In particular, we employed the MobileNetV3-Small-Pi model to create compact yet effective student architectures to compare against the larger EcoVAD teacher model, a well-regarded voice detection architecture in eco-acoustic monitoring. The comparative analysis included examining various configurations of the MobileNetV3-Small-Pi-derived student models to identify optimal performance. Additionally, a thorough evaluation of different distillation techniques was conducted to ascertain the most effective method for model selection. Our findings revealed that the distilled models exhibited comparable performance to the EcoVAD teacher model, indicating a promising approach to overcoming computational barriers for real-time ecological monitoring.

Graph-Based Audio Classification Using Pre-Trained Models and Graph Neural Networks.

Sound classification plays a crucial role in enhancing the interpretation, analysis, and use of acoustic data, leading to a wide range of practical applications, of which environmental sound analysis is one of the most important. In this paper, we explore the representation of audio data as graphs in the context of sound classification. We propose a methodology that leverages pre-trained audio models to extract deep features from audio files, which are then employed as node information to build graphs. Subsequently, we train various graph neural networks (GNNs), specifically graph convolutional networks (GCNs), GraphSAGE, and graph attention networks (GATs), to solve multi-class audio classification problems. Our findings underscore the effectiveness of employing graphs to represent audio data. Moreover, they highlight the competitive performance of GNNs in sound classification endeavors, with the GAT model emerging as the top performer, achieving a mean accuracy of 83% in classifying environmental sounds and 91% in identifying the land cover of a site based on its audio recording. In conclusion, this study provides novel insights into the potential of graph representation learning techniques for analyzing audio data.

Acoustic restoration: Using soundscapes to benchmark and fast-track recovery of ecological communities.

We introduce a new approach-acoustic restoration-focusing on the applied utility of soundscapes for restoration, recognising the rich ecological and social values they encapsulate. Broadcasting soundscapes in disturbed areas can accelerate recolonisation of animals and the microbes and propagules they carry; long duration recordings are also ideal sources of data for benchmarking restoration initiatives and evocative engagement tools.

Mapping of the Acoustic Environment at an Urban Park in the City Area of Milan, Italy, Using Very Low-Cost Sensors.

The-growing influence of urbanisation on green areas can greatly benefit from passive acoustic monitoring (PAM) across spatiotemporal continua to provide biodiversity estimation and useful information for conservation planning and development decisions. The capability of eco-acoustic indices to capture different sound features has been harnessed to identify areas within the Parco Nord of Milan, Italy, characterised by different degrees of anthropic disturbance and biophonic activity. For this purpose, we used a network of very low-cost sensors distributed over an area of approximately 20 hectares to highlight areas with different acoustic properties. The audio files analysed in this study were recorded at 16 sites on four sessions during the period 25-29 May (2015), from 06:30 a.m. to 10:00 a.m. Seven eco-acoustic indices, namely Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bio-Acoustic Index (BI), Acoustic Entropy Index (H), Normalized Difference Soundscape Index (NSDI), and Dynamic Spectral Centroid (DSC) were computed at 1 s integration time and the resulting time series were described by seven statistical descriptors. A dimensionality reduction of the indices carrying similar sound information was obtained by performing principal component analysis (PCA). Over the retained dimensions, describing a large (∼80%) variance of the original variables, a cluster analysis allowed discriminating among sites characterized by different combination of eco-acoustic indices (dimensions). The results show that the obtained groups are well correlated with the results of an aural survey aimed at determining the sound components at the sixteen sites (biophonies, technophonies, and geophonies). This outcome highlights the capability of this analysis of discriminating sites with different environmental sounds, thus allowing to create a map of the acoustic environment over an extended area.

Local sonic activity reveals potential partitioning in a coral reef fish community.

How vocal organisms share acoustic space has primarily received attention in terrestrial environments. Comparable studies in marine environments, however, remain rare. By recording sounds on a coral reef in French Polynesia for 48 h and 24 h, this study provides first insights on how different sound types are distributed within the acoustic space and may create acoustic niches optimizing acoustic communication within a highly diverse community containing numerous soniferous fish species. Day-time was dominated by two to six sound types, while recordings performed at night revealed a more diverse vocal community made of up to nineteen sound types. Calling activity was distributed over time allowing each sound type to dominate the soundscape sequentially. Additionally, differences in the acoustic features of sounds occurring during the same period were observed. Such partitioning in time and acoustic spaces would reduce potential overlaps of sounds produced by vocal species living in sympatry in coral reefs.

Explosive breeding in tropical anurans: environmental triggers, community composition and acoustic structure.

BACKGROUND: Anurans largely rely on acoustic communication for sexual selection and reproduction. While multiple studies have focused on the calling activity patterns of prolonged breeding assemblages, species that concentrate their reproduction in short-time windows, explosive breeders, are still largely unknown, probably because of their ephemeral nature. In tropical regions, multiple species of explosive breeders may simultaneously aggregate leading to massive, mixed and dynamic choruses. To understand the environmental triggers, the phenology and composition of these choruses, we collected acoustic and environmental data at five ponds in French Guiana during a rainy season, assessing acoustic communities before and during explosive breeding events. RESULTS: We detected in each pond two explosive breeding events, lasting between 24 and 70 h. The rainfall during the previous 48 h was the most important factor predicting the emergence of these events. During explosive breeding events, we identified a temporal factor that clearly distinguished pre- and mid-explosive communities. A common pool of explosive breeders co-occurred in most of the events, namely Chiasmocleis shudikarensis, Trachycephalus coriaceus and Ceratophrys cornuta. Nevertheless, the species composition was remarkably variable between ponds and for each pond between the first and the second events. The acoustic structure of explosive breeding communities had outlying levels of amplitude and unexpected low acoustic diversity, significantly lower than the communities preceding explosive breeding events. CONCLUSIONS: Explosive breeding communities were tightly linked with specific rainfall patterns. With climate change increasing rainfall variability in tropical regions, such communities may experience significant shifts in their timing, distribution and composition. In structurally similar habitats, located in the same region without obvious barriers, our results highlight the variation in composition across explosive breeding events. The characteristic acoustic structure of explosive breeding events stands out from the circadian acoustic environment being easily detected at long distance, probably reflecting behavioural singularities and conveying heterospecific information announcing the availability of short-lived breeding sites. Our data provides a baseline against which future changes, possibly linked to climate change, can be measured, contributing to a better understanding on the causes, patterns and consequences of these unique assemblages.

Vessels and aircraft are chronic sources of anthropogenic noise in coastal marine and terrestrial soundscapes on Long Island, New York.

Passive acoustic data collected during 2020 and 2021 were used to monitor changes in both terrestrial and underwater soundscapes, as well as human activity from aircraft and vessels. Passive acoustic data were collected at two artificial reefs south of Long Island, as well as along ocean beaches in Southampton, NY. At the artificial reefs, vessel noise was recorded more frequently during 2020 than in 2021. Commercial vessels and multi-user charter fishing vessels were more abundant during 2020. Peaks in power spectral density occurred at 60, 90 and 120 Hz in 2020 and 2021, which are frequencies consistent with noise generated by commercial vessels, suggesting that vessels are a significant contributor to the soundscape of the artificial reefs. In the terrestrial environment, noise generated by aircraft was more common during 2021. Peaks in power spectral density were measured around 160 and 290 Hz at one of the ocean beach sites. These frequencies are consistent with noise generated by aircraft. This study documents the chronic extent of anthropogenic noise in both the underwater and terrestrial environments of Long Island, NY, as well as quantifies the occurrence of various noise sources in these habitats.

The potential of acoustic monitoring of aquatic insects for freshwater assessment.

Aquatic insects are a major indicator used to assess ecological condition in freshwater environments. However, current methods to collect and identify aquatic insects require advanced taxonomic expertise and rely on invasive techniques that lack spatio-temporal replication. Passive acoustic monitoring (PAM) is emerging as a non-invasive complementary sampling method allowing broad spatio-temporal and taxonomic coverage. The application of PAM in freshwater ecosystems has already proved useful, revealing unexpected acoustic diversity produced by fishes, amphibians, submerged aquatic plants, and aquatic insects. However, the identity of species producing sounds remains largely unknown. Among them, aquatic insects appear to be the major contributor to freshwater soundscapes. Here, we estimate the potential number of soniferous aquatic insects worldwide using data from the Global Biodiversity Information Facility. We found that four aquatic insect orders produce sounds totalling over 7000 species. This number is probably underestimated owing to poor knowledge of aquatic insects bioacoustics. We then assess the value of sound producing aquatic insects to evaluate ecological condition and find that they might be useful despite having similar responses in pristine and degraded environments in some cases. Both expert and automated identifications will be necessary to build international reference libraries and to conduct acoustic bioassessment in freshwaters. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets.

Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suited for characterizing marine acoustic environments. Here, we describe the application of multiple machine-learning techniques to the analysis of two PAM datasets. We combine pre-trained acoustic classification models (VGGish, NOAA and Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine acoustic environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labeled sounds in the 8 kHz range; however, low- and high-frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow establishing a link between ecologically relevant information and PAM recordings at multiple scales, ranging from large-scale changes in the environment (i.e., changes in wind speed) to the identification of marine mammal species.

An exploration of ecoacoustics and its applications in conservation ecology.

Our planet is facing unprecedented adversity due to the global impacts of climate change and an emerging sixth mass extinction. These impacts are exacerbated by population and industrial growth, where increased resource extraction is required to meet our insatiable demands. Yet, the tangible elements of our lone inhabited planet in the solar system are not the only things disappearing or being modified. The sounds of Earth are being altered in ways that may never be recovered. Indeed, we occupy a noisier world in this age of machines that comes at a great expense in the form of sonic extinctions. It is profoundly apparent, yet not widely recognized, that conservation efforts must consider the importance of the sonic environment (i.e., sonosphere). Although sound has been integral to life for millions of years, our understanding of its ecological role has only just begun. Sounds are one of the most important extensions of the organismic inner world, becoming testimonials of environmental complexity, integration, and relationships between apparently separated parts. From a semiotic perspective, sounds are signals utilized by many organisms to save energy in patrolling, defending, exploring, and navigating their surroundings. Sounds are tools that establish dynamic biological and ecological competencies through refined partitioning in the natural selection process of evolution. Ecoacoustics is a recent scientific discipline that aims to investigate the role of sound in ecological processes. Despite its youth, Ecoacoustics has had rapid theoretical and applied growth, consolidating a diverse array of research on the ecology of sounds across many disciplines. Here, we present how Ecoacoustics plays a significant role in conservation ecology by exploring the discipline's theoretical framework, new descriptors of sonic complexity, and innovative methods for supporting conservation efforts from singular species to entire landscapes across local and global scales. The combination of automated recording units and ecoacoustic indices present a very promising approach to the study of remote areas, rare species, and data rich analyses. While Ecoacoustics scientists continue to explore this new scientific horizon, we encourage others to consider Ecoacoustics in their conservation agendas because of its application to the study and management of terrestrial, marine, and freshwater habitats.

Fish responses to underwater sounds depend on auditory adaptations: An experimental test of the effect of motorboat sounds on the fish community of a large fluvial lake.

Freshwater fishes exhibit a wide range of auditory adaptations and capabilities, which are assumed to help them navigate their environment, avoid predators, and find potential mates. Yet, we know very little about how freshwater environments sound to fish, or how fish with different auditory adaptations respond to different soundscapes. We first compiled data on fish hearing acuity and adaptations and provided a portrait of how anthropogenic sounds compare to natural sounds in different freshwater soundscapes. We then conducted a sound-enrichment field experiment at Lake Saint Pierre, a large fluvial lake in Canada, to evaluate the effect of motorboat sound exposure on the fish community by looking at the extent to which changes in species abundances were linked to auditory adaptations. Data compilation showed that the hearing acuity of most species overlaps with a wide range of ambient and anthropogenic underwater sounds while the field experiment showed that species with more specialized auditory structures were captured less often in sound-enriched traps, indicating avoidance behavior. Our findings highlight the importance of considering species' sensorial adaptations when evaluating the community-scale effects of anthropogenic sounds on the fish community, especially at low levels of anthropogenic activity.

Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network.

Passive Acoustic Monitoring (PAM), which involves using autonomous record units for studying wildlife behaviour and distribution, often requires handling big acoustic datasets collected over extended periods. While these data offer invaluable insights about wildlife, their analysis can present challenges in dealing with geophonic sources. A major issue in the process of detection of target sounds is represented by wind-induced noise. This can lead to false positive detections, i.e., energy peaks due to wind gusts misclassified as biological sounds, or false negative, i.e., the wind noise masks the presence of biological sounds. Acoustic data dominated by wind noise makes the analysis of vocal activity unreliable, thus compromising the detection of target sounds and, subsequently, the interpretation of the results. Our work introduces a straightforward approach for detecting recordings affected by windy events using a pre-trained convolutional neural network. This process facilitates identifying wind-compromised data. We consider this dataset pre-processing crucial for ensuring the reliable use of PAM data. We implemented this preprocessing by leveraging YAMNet, a deep learning model for sound classification tasks. We evaluated YAMNet as-is ability to detect wind-induced noise and tested its performance in a Transfer Learning scenario by using our annotated data from the Stony Point Penguin Colony in South Africa. While the classification of YAMNet as-is achieved a precision of 0.71, and recall of 0.66, those metrics strongly improved after the training on our annotated dataset, reaching a precision of 0.91, and recall of 0.92, corresponding to a relative increment of >28 %. Our study demonstrates the promising application of YAMNet in the bioacoustics and ecoacoustics fields, addressing the need for wind-noise-free acoustic data. We released an open-access code that, combined with the efficiency and peak performance of YAMNet, can be used on standard laptops for a broad user base.

VGGish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern China.

Passive acoustic monitoring technology is widely used to monitor the diversity of vocal animals, but the question of how to quickly extract effective sound patterns remains a challenge due to the difficulty of distinguishing biological sounds within multiple sound sources in a soundscape. In this study, we address the potential application of the VGGish model, pre-trained on Google's AudioSet dataset, for the extraction of acoustic features, together with an unsupervised clustering method based on the Gaussian mixture model, to identify various sound sources from a soundscape of a subtropical forest in China. The results show that different biotic and abiotic components can be distinguished from various confounding sound sources. Birds and insects were the two primary biophony sound sources, and their sounds displayed distinct temporal patterns across both diurnal and monthly time frames and distinct spatial patterns in the landscape. Using the clustering and modeling method of the general sound feature set, we quickly depicted the soundscape in a subtropical forest ecosystem, which could be used to track dynamic changes in the acoustic environment and provide help for biodiversity and ecological environment monitoring.

Systematic review of machine learning methods applied to ecoacoustics and soundscape monitoring.

Soundscape ecology is a promising area that studies landscape patterns based on their acoustic composition. It focuses on the distribution of biotic and abiotic sounds at different frequencies of the landscape acoustic attribute and the relationship of said sounds with ecosystem health metrics and indicators (e.g., species richness, acoustic biodiversity, vectors of structural change, gradients of vegetation cover, landscape connectivity, and temporal and spatial characteristics). To conduct such studies, researchers analyze recordings from Acoustic Recording Units (ARUs). The increasing use of ARUs and their capacity to record hours of audio for months at a time have created a need for automatic processing methods to reduce time consumption, correlate variables implicit in the recordings, extract features, and characterize sound patterns related to landscape attributes. Consequently, traditional machine learning methods have been commonly used to process data on different characteristics of soundscapes, mainly the presence-absence of species. In addition, it has been employed for call segmentation, species identification, and sound source clustering. However, some authors highlight the importance of the new approaches that use unsupervised deep learning methods to improve the results and diversify the assessed attributes. In this paper, we present a systematic review of machine learning methods used in the field of ecoacoustics for data processing. It includes recent trends, such as semi-supervised and unsupervised deep learning methods. Moreover, it maintains the format found in the reviewed papers. First, we describe the ARUs employed in the papers analyzed, their configuration, and the study sites where the datasets were collected. Then, we provide an ecological justification that relates acoustic monitoring to landscape features. Subsequently, we explain the machine learning methods followed to assess various landscape attributes. The results show a trend towards label-free methods that can process the large volumes of data gathered in recent years. Finally, we discuss the need to adopt methods with a machine learning approach in other biological dimensions of landscapes.

Soundscape phenology: The effect of environmental and climatic factors on birds and insects in a subtropical woodland.

Climate change and biodiversity loss are significant global environmental issues. However, to understand their impacts we need to know how fauna respond to environmental and climatic variation over time. In this study, remote sensing techniques (satellite imagery and passive acoustic recorders) were used to investigate the variation in biophony over different timescales, ranging from one day to one year, in a sub-tropical woodland in eastern Australia. The prominent sources of biophony were birds at dawn and during the day, nocturnal insects at dusk and during the night, and diurnal birds and insects (mainly cicadas) over the summer period of December, January, and February. While different environmental factors were found to be key drivers of phenological response in different faunal groups, temperature, humidity and the interactions between temperature, humidity, moon illumination and vegetation greenness were most important factors overall. Using observed temperatures relative to the historical mean for each day of the year, we evaluated the impact of higher-than-average temperatures on calling activity. We found that nocturnal insects call less frequently on days when the temperature was hotter than average in winter months (June, July, and August), and birds call less frequently in hot spring days (September, October, and November) meaning these groups can be susceptible to temperature increase as consequence, for example, of climate change. This study demonstrates how animal calling behaviour is affected by different environmental variables over different temporal scales. This study also demonstrates the utility of remote sensing techniques for assessing the impacts of climate change on biodiversity. It is highly recommended that monitoring schemes and impact assessments account for phenological changes and environmental variability, as these are complex and important processes shaping animal communities.