FoxP2 isoforms delineate spatiotemporal transcriptional networks for vocal learning in the zebra finch.

Human speech is one of the few examples of vocal learning among mammals yet ~half of avian species exhibit this ability. Its neurogenetic basis is largely unknown beyond a shared requirement for FoxP2 in both humans and zebra finches. We manipulated FoxP2 isoforms in Area X, a song-specific region of the avian striatopallidum analogous to human anterior striatum, during a critical period for song development. We delineate, for the first time, unique contributions of each isoform to vocal learning. Weighted gene coexpression network analysis of RNA-seq data revealed gene modules correlated to singing, learning, or vocal variability. Coexpression related to singing was found in juvenile and adult Area X whereas coexpression correlated to learning was unique to juveniles. The confluence of learning and singing coexpression in juvenile Area X may underscore molecular processes that drive vocal learning in young zebra finches and, by analogy, humans

Robust sound event detection in bioacoustic sensor networks

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 milliseconds) and long-term (30 minutes) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer. Combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019; revised August 2019; published October 201

Automatic Recognition of Non-Verbal Acoustic Communication Events With Neural Networks

Non-verbal acoustic communication is of high importance to humans and animals: Infants use the voice as a primary communication tool. Animals of all kinds employ acoustic communication, such as chimpanzees, which use pant-hoot vocalizations for long-distance communication. Many applications require the assessment of such communication for a variety of analysis goals. Computational systems can support these areas through automatization of the assessment process. This is of particular importance in monitoring scenarios over large spatial and time scales, which are infeasible to perform manually. Algorithms for sound recognition have traditionally been based on conventional machine learning approaches. In recent years, so-called representation learning approaches have gained increasing popularity. This particularly includes deep learning approaches that feed raw data to deep neural networks. However, there remain open challenges in applying these approaches to automatic recognition of non-verbal acoustic communication events, such as compensating for small data set sizes. The leading question of this thesis is: How can we apply deep learning more effectively to automatic recognition of non-verbal acoustic communication events? The target communication types were specifically (1) infant vocalizations and (2) chimpanzee long-distance calls. This thesis comprises four studies that investigated aspects of this question: Study (A) investigated the assessment of infant vocalizations by laypersons. The central goal was to derive an infant vocalization classification scheme based on the laypersons' perception. The study method was based on the Nijmegen Protocol, where participants rated vocalization recordings through various items, such as affective ratings and class labels. Results showed a strong association between valence ratings and class labels, which was used to derive a classification scheme. Study (B) was a comparative study on various neural network types for the automatic classification of infant vocalizations. The goal was to determine the best performing network type among the currently most prevailing ones, while considering the influence of their architectural configuration. Results showed that convolutional neural networks outperformed recurrent neural networks and that the choice of the frequency and time aggregation layer inside the network is the most important architectural choice. Study (C) was a detailed investigation on computer vision-like convolutional neural networks for infant vocalization classification. The goal was to determine the most important architectural properties for increasing classification performance. Results confirmed the importance of the aggregation layer and additionally identified the input size of the fully-connected layers and the accumulated receptive field to be of major importance. Study (D) was an investigation on compensating class imbalance for chimpanzee call detection in naturalistic long-term recordings. The goal was to determine which compensation method among a selected group improved performance the most for a deep learning system. Results showed that spectrogram denoising was most effective, while methods for compensating relative imbalance either retained or decreased performance.:1. Introduction 2. Foundations in Automatic Recognition of Acoustic Communication 3. State of Research 4. Study (A): Investigation of the Assessment of Infant Vocalizations by Laypersons 5. Study (B): Comparison of Neural Network Types for Automatic Classification of Infant Vocalizations 6. Study (C): Detailed Investigation of CNNs for Automatic Classification of Infant Vocalizations 7. Study (D): Compensating Class Imbalance for Acoustic Chimpanzee Detection With Convolutional Recurrent Neural Networks 8. Conclusion and Collected Discussion 9. AppendixNonverbale akustische Kommunikation ist für Menschen und Tiere von großer Bedeutung: Säuglinge nutzen die Stimme als primäres Kommunikationsmittel. Schimpanse verwenden sogenannte 'Pant-hoots' und Trommeln zur Kommunikation über weite Entfernungen. Viele Anwendungen erfordern die Beurteilung solcher Kommunikation für verschiedenste Analyseziele. Algorithmen können solche Bereiche durch die Automatisierung der Beurteilung unterstützen. Dies ist besonders wichtig beim Monitoring langer Zeitspannen oder großer Gebiete, welche manuell nicht durchführbar sind. Algorithmen zur Geräuscherkennung verwendeten bisher größtenteils konventionelle Ansätzen des maschinellen Lernens. In den letzten Jahren hat eine alternative Herangehensweise Popularität gewonnen, das sogenannte Representation Learning. Dazu gehört insbesondere Deep Learning, bei dem Rohdaten in tiefe neuronale Netze eingespeist werden. Jedoch gibt es bei der Anwendung dieser Ansätze auf die automatische Erkennung von nonverbaler akustischer Kommunikation ungelöste Herausforderungen, wie z.B. die Kompensation der relativ kleinen Datenmengen. Die Leitfrage dieser Arbeit ist: Wie können wir Deep Learning effektiver zur automatischen Erkennung nonverbaler akustischer Kommunikation verwenden? Diese Arbeit konzentriert sich speziell auf zwei Kommunikationsarten: (1) vokale Laute von Säuglingen (2) Langstreckenrufe von Schimpansen. Diese Arbeit umfasst vier Studien, welche Aspekte dieser Frage untersuchen: Studie (A) untersuchte die Beurteilung von Säuglingslauten durch Laien. Zentrales Ziel war die Ableitung eines Klassifikationsschemas für Säuglingslaute auf der Grundlage der Wahrnehmung von Laien. Die Untersuchungsmethode basierte auf dem sogenannten Nijmegen-Protokoll. Hier beurteilten die Teilnehmenden Lautaufnahmen von Säuglingen anhand verschiedener Variablen, wie z.B. affektive Bewertungen und Klassenbezeichnungen. Die Ergebnisse zeigten eine starke Assoziation zwischen Valenzbewertungen und Klassenbezeichnungen, die zur Ableitung eines Klassifikationsschemas verwendet wurde. Studie (B) war eine vergleichende Studie verschiedener Typen neuronaler Netzwerke für die automatische Klassifizierung von Säuglingslauten. Ziel war es, den leistungsfähigsten Netzwerktyp unter den momentan verbreitetsten Typen zu ermitteln. Hierbei wurde der Einfluss verschiedener architektonischer Konfigurationen innerhalb der Typen berücksichtigt. Die Ergebnisse zeigten, dass Convolutional Neural Networks eine höhere Performance als Recurrent Neural Networks erreichten. Außerdem wurde gezeigt, dass die Wahl der Frequenz- und Zeitaggregationsschicht die wichtigste architektonische Entscheidung ist. Studie (C) war eine detaillierte Untersuchung von Computer Vision-ähnlichen Convolutional Neural Networks für die Klassifizierung von Säuglingslauten. Ziel war es, die wichtigsten architektonischen Eigenschaften zur Steigerung der Erkennungsperformance zu bestimmen. Die Ergebnisse bestätigten die Bedeutung der Aggregationsschicht. Zusätzlich Eigenschaften, die als wichtig identifiziert wurden, waren die Eingangsgröße der vollständig verbundenen Schichten und das akkumulierte rezeptive Feld. Studie (D) war eine Untersuchung zur Kompensation der Klassenimbalance zur Erkennung von Schimpansenrufen in Langzeitaufnahmen. Ziel war es, herauszufinden, welche Kompensationsmethode aus einer Menge ausgewählter Methoden die Performance eines Deep Learning Systems am meisten verbessert. Die Ergebnisse zeigten, dass Spektrogrammentrauschen am effektivsten war, während Methoden zur Kompensation des relativen Ungleichgewichts die Performance entweder gleichhielten oder verringerten.:1. Introduction 2. Foundations in Automatic Recognition of Acoustic Communication 3. State of Research 4. Study (A): Investigation of the Assessment of Infant Vocalizations by Laypersons 5. Study (B): Comparison of Neural Network Types for Automatic Classification of Infant Vocalizations 6. Study (C): Detailed Investigation of CNNs for Automatic Classification of Infant Vocalizations 7. Study (D): Compensating Class Imbalance for Acoustic Chimpanzee Detection With Convolutional Recurrent Neural Networks 8. Conclusion and Collected Discussion 9. Appendi

Using Self-Organizing Maps to Recognize Acoustic Units Associated with Information Content in Animal Vocalizations

Kohonen self-organizing neural networks, also called self-organizing maps (SOMs), have been used successfully to recognize human phonemes and in this way to aid in human speech recognition. This paper describes how SOMS also can be used to associate specific information content with animal vocalizations. A SOM was used to identify acoustic units in Gunnison’s prairie dog alarm calls that were vocalized in the presence of three different predator species. Some of these acoustic units and their combinations were found exclusively in the alarm calls associated with a particular predator species and were used to associate predator species information with individual alarm calls. This methodology allowed individual alarm calls to be classified by predator species with an average of 91% accuracy. Furthermore, the topological structure of the SOM used in these experiments provided additional insights about the acoustic units and their combinations that were used to classify the target alarm calls. An important benefit of the methodology developed in this paper is that it could be used to search for groups of sounds associated with information content for any animal whose vocalizations are composed of multiple simultaneous frequency components

Acoustic sequences in non-human animals: a tutorial review and prospectus.

Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise - let alone understand - the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, 'Analysing vocal sequences in animals'. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.This review was developed at an investigative workshop, “Analyzing Animal Vocal Communication Sequences” that took place on October 21–23 2013 in Knoxville, Tennessee, sponsored by the National Institute for Mathematical and Biological Synthesis (NIMBioS). NIMBioS is an Institute sponsored by the National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Department of Agriculture through NSF Awards #EF-0832858 and #DBI-1300426, with additional support from The University of Tennessee, Knoxville. In addition to the authors, Vincent Janik participated in the workshop. D.T.B.’s research is currently supported by NSF DEB-1119660. M.A.B.’s research is currently supported by NSF IOS-0842759 and NIH R01DC009582. M.A.R.’s research is supported by ONR N0001411IP20086 and NOPP (ONR/BOEM) N00014-11-1-0697. S.L.DeR.’s research is supported by the U.S. Office of Naval Research. R.F.-i-C.’s research was supported by the grant BASMATI (TIN2011-27479-C04-03) from the Spanish Ministry of Science and Innovation. E.C.G.’s research is currently supported by a National Research Council postdoctoral fellowship. E.E.V.’s research is supported by CONACYT, Mexico, award number I010/214/2012.This is the accepted manuscript. The final version is available at http://dx.doi.org/10.1111/brv.1216

A collection of best practices for the collection and analysis of bioacoustic data

The field of bioacoustics is rapidly developing and characterized by diverse methodologies, approaches and aims. For instance, bioacoustics encompasses studies on the perception of pure tones in meticulously controlled laboratory settings, documentation of species’ presence and activities using recordings from the field, and analyses of circadian calling patterns in animal choruses. Newcomers to the field are confronted with a vast and fragmented literature, and a lack of accessible reference papers or textbooks. In this paper we contribute towards filling this gap. Instead of a classical list of “dos” and “don’ts”, we review some key papers which, we believe, embody best practices in several bioacoustic subfields. In the first three case studies, we discuss how bioacoustics can help identify the ‘who’, ‘where’ and ‘how many’ of animals within a given ecosystem. Specifically, we review cases in which bioacoustic methods have been applied with success to draw inferences regarding species identification, population structure, and biodiversity. In fourth and fifth case studies, we highlight how structural properties in signal evolution can emerge via ecological constraints or cultural transmission. Finally, in a sixth example, we discuss acoustic methods that have been used to infer predator–prey dynamics in cases where direct observation was not feasible. Across all these examples, we emphasize the importance of appropriate recording parameters and experimental design. We conclude by highlighting common best practices across studies as well as caveats about our own overview. We hope our efforts spur a more general effort in standardizing best practices across the subareas we’ve highlighted in order to increase compatibility among bioacoustic studies and inspire cross-pollination across the discipline

A collection of best practices for the collection and analysis of bioacoustic data

The field of bioacoustics is rapidly developing and characterized by diverse methodologies, approaches and aims. For instance, bioacoustics encompasses studies on the perception of pure tones in meticulously controlled laboratory settings, documentation of species’ presence and activities using recordings from the field, and analyses of circadian calling patterns in animal choruses. Newcomers to the field are confronted with a vast and fragmented literature, and a lack of accessible reference papers or textbooks. In this paper we contribute towards filling this gap. Instead of a classical list of “dos” and “don’ts”, we review some key papers which, we believe, embody best practices in several bioacoustic subfields. In the first three case studies, we discuss how bioacoustics can help identify the ‘who’, ‘where’ and ‘how many’ of animals within a given ecosystem. Specifically, we review cases in which bioacoustic methods have been applied with success to draw inferences regarding species identification, population structure, and biodiversity. In fourth and fifth case studies, we highlight how structural properties in signal evolution can emerge via ecological constraints or cultural transmission. Finally, in a sixth example, we discuss acoustic methods that have been used to infer predator–prey dynamics in cases where direct observation was not feasible. Across all these examples, we emphasize the importance of appropriate recording parameters and experimental design. We conclude by highlighting common best practices across studies as well as caveats about our own overview. We hope our efforts spur a more general effort in standardizing best practices across the subareas we’ve highlighted in order to increase compatibility among bioacoustic studies and inspire cross-pollination across the discipline.Publisher PDFPeer reviewe

Sperm whale foraging behaviour: a predicted model based on 3D movement and acoustic data from Dtags

High-resolution sound and movement recording tags (e.g. Dtags, Acousonde tags, Atags) offer unprecedented views of the fine-scale foraging behaviour of cetaceans, especially those that use sound to forage, such as the sperm whale (Physeter macrocephalus). However, access to these tags is difficult and expensive, limiting studies of sperm whale foraging behaviour to small sample sizes and short time periods, preventing inferences at the population level. The development of accurate foraging indices from relatively inexpensive time-depth recorder (TDR) data would allow obtaining data from a larger number of individuals, and capitalizing on datasets already available, providing long-term analyses of foraging activity. In this study, data from high-resolution acoustic and movement recording tags from 8 sperm whales was used to build predictive models of the number of buzzes (i.e, indicative of prey capture attempts (PCA)) for dive segments of different lengths, using dive metrics calculated from timedepth data only. The number of buzzes per dive segments of 180s and 300s was best predicted by the average depth, depth variance, vertical velocity variance and number of wiggles. Model performance was best for 180s segments, accurately predicting the number of buzzes in 63% of the segments used to construct the model and in 58% of the segments for new individuals. Predictive accuracy reached 81%, when only presence or absence of buzzes in segments was assessed. These results demonstrate the feasibility of finding a reliable index of sperm whale foraging activity for time-depth data, when combining different dive metrics. This index estimates the number of buzzes over short dive segments (of 180s), enabling investigating and quantifying PCAs at very finescales. Finally, this work contributes to leverage the potential of time-depth data for studying the foraging ecology of sperm whales and the capacity of applying this approach to a wide range of cetacean species.O cachalote (Physeter macrocephalus) é um dos mais conhecidos predadores marinhos, passando mais da metade da sua vida abaixo dos 500m de profundidade, onde se alimenta principalmente de lulas meso e bentopelágicas, embora também possam consumir outros cefalópodes, peixes profundos e invertebrados. Apresenta uma distribuição mundial e pode ser encontrado no arquipélago dos Açores durante todo o ano, perto da costa, razão pela qual os Açores foram uma das regiões baleeiras mais importantes. O som desempenha um papel fundamental na vida dos cachalotes. Eles produzem sons enquanto estão a socializar e na procura e captura de alimento. Foram identificados pelo menos quatro tipos de cliques (cliques usuais, “buzzes”, codas e “slow clicks”), dos quais os cliques usuais e os “buzzes” estão envolvidos no comportamento de alimentação. Os cliques usuais têm níveis sonoros elevados e são altamente direcionais, servindo como um biosonar para navegar pelo ambiente e eco-localizar presas. Os “buzzes”, consistem em cliques de alta frequência e baixa amplitude, produzidos em intervalos rápidos. Por esta razão, têm um alcance mais curto do que os cliques usuais, fornecendo uma resolução mais alta e, portanto, informações mais detalhadas sobre o seu ambiente próximo e presas. A observação direta é uma das ferramentas mais poderosas para estudar o comportamento animal, não obstante, no caso dos cachalotes é altamente limitada, consequência dos longos períodos que passam em profundidade. Por este motivo, os estudos sobre o comportamento do cachalote, e de outros predadores marinhos de mergulho profundo, dependem da utilização de diferentes ferramentas que permitem obter informações sobre o seu comportamento subaquático. Os hidrofones e as marcas colocadas em animais estão entre as ferramentas mais importantes para estudos sobre o comportamento dos cetáceos, permitindo o registo contínuo de sons produzidos debaixo de água e o seguimento, também contínuo, de movimento e outras variáveis de mergulho. A incorporação de hidrofones em marcas para colocação em animais, como as marcas acústicas digitais (“Dtags”), marcas “Acousonde” ou “A-tags” revolucionou o estudo do comportamento dos cetáceos. Estas marcas fornecem dados de movimento tridimensional e acústicos de alta resolução, simultaneamente registando informação sobre o comportamento do animal, possibilitando, por exemplo, a compreensão de como os cachalotes usam o som durante a alimentação. Estudos baseados na análise de dados de “Dtags” revelaram que a presença de picos de velocidade na parte mais profunda do mergulho e movimentos rápidos da mandíbula estavam relacionados com a produção de “buzzes”. Consequentemente, foi sugerido que os “buzzes” são emitidos durante a fase terminal de captura de presas, a fim de obter informação de alta resolução sobre o alvo. Desde então, a produção de cliques tem sido usada como um indicador de esforço de alimentação e a produção de “buzzes”, considerada como o melhor indicador de tentativa de captura de presas. Não obstante, o acesso a estas marcas de alta resolução acústica e movimento é extremamente difícil e caro, limitando o estudo do comportamento de alimentação do cachalote a amostras pequenas e curtos períodos de tempo. Por esta razão, o desenvolvimento de um índice de esforço de alimentação exato, a partir de dados de mergulho 2D de dados de tempo-profundidade como os “time-depth recorders” (TDR), permitiria capitalizar um conjunto de dados de mergulho já disponíveis, analisando séries temporais de atividade de alimentação e avaliando alterações ligadas a mudanças climáticas ou antropogénicas. No presente estudo, dados de alta resolução com informação acústica de oito cachalotes marcados com “Dtags” no arquipélago dos Açores foram usados para construir um modelo preditivo do número de “buzzes”, baseado exclusivamente em dados profundidade-tempo e com resolução máxima de 1m de profundidade, correspondendo, portanto, às capacidades de registo de um TDR. O número total de “buzzes” por segmento foi modelado a partir de um conjunto de variáveis que descrevem a média e variabilidade de profundidade, tempo passado na fase profunda do mergulho, velocidade vertical, aceleração vertical e número de excursões verticais, usando um modelo linear generalizado misto (GLMM), com o indivíduo como um efeito aleatório. De um total de 816 “buzzes” analisados, 95% apresentaram uma duração de 2 a 14 segundos. Portanto, inicialmente os mergulhos foram divididos em segmentos de curta duração. Porém, as primeiras análises demonstraram fracas capacidades preditivas e finalmente optou-se por usar segmentos de 180s e 300s. Os melhores modelos de número de buzzes por segmento de 180s e 300s incluíram a profundidade média, a variância de profundidade, a variância da velocidade vertical e o número de “wiggles” por segmento. Os segmentos de mergulho com “buzzes” apresentaram uma maior profundidade média, menor variância de profundidade, maior variância de velocidade e maior presença de “wiggles”, sendo a profundidade média a métrica mais relevante do modelo. Estes resultados confirmam que os “buzzes” ocorrem nas partes profundas do mergulho e sugerem que as várias tentativas de captura podem ocorrer numa extensão de profundidade limitada, demonstrado pela pequena variação de profundidade, maior variação de velocidade e presença de “wiggles”. O desempenho do modelo foi melhor para segmentos de 180s, resultando em deteções corretas do número de “buzzes” em 63% dos segmentos usados para construir o modelo e em 58% dos segmentos para novos indivíduos usados para testar o modelo. Assim mesmo, o modelo resultou em 81% de deteções corretas quando avaliada apenas a presença ou ausência de “buzzes” nos segmentos. Apesar do nosso modelo ter algumas deficiências preditivas, os resultados preditivos são similares àqueles obtidos com modelos desenvolvidos anteriormente, para prever tentativas de captura de presas em conjuntos de dados 2D de baixa resolução em outras espécies. Porém, ao contrário desses modelos que previram tentativas de captura de presas na escala de mergulho ou, na melhor das hipóteses, em escalas de 30 minutos e de uma hora, o modelo desenvolvido neste estudo previu tentativas de captura de presas a cada 3 minutos. Este é o primeiro estudo a desenvolver um modelo que prevê o número de tentativas de captura de presas e, consequentemente, o esforço de alimentação em cachalotes a partir de perfis de mergulho 2D. O presente método poderá ser aplicado a conjuntos de dados de profundidade de tempo já disponíveis, a fim de conduzir análises retrospetivas do comportamento de alimentação. Porém, o aumento do tamanho da amostra e uma análise de dados mais detalhada permitiria obter previsões mais precisas. Finalmente, a presente abordagem de estimativa de alimentação é baseada na previsão do número de “buzzes” e, portanto, poderia ser potencialmente aplicada a uma série de espécies de odontocetes, potencialmente permitindo estimativas mais precisas do esforço de alimentação, do que os índices grosseiros e gerais tipicamente derivados de perfis de mergulho 2D

Anthropogenic Noise is Associated with Reductions in the Productivity of Breeding Eastern Bluebirds (Sialia sialis)

Although previous studies have related variations in environmental noise levels with alterations in communication behaviors of birds, little work has investigated the potential long-term implications of living or breeding in noisy habitats. However, noise has the potential to reduce fitness, both directly (because it is a physiological stressor) and indirectly (by masking important vocalizations and/or leading to behavioral changes). Here, we quantified acoustic conditions in active breeding territories of male Eastern Bluebirds (Sialia sialis). Simultaneously, we measured four fitness indicators: cuckoldry rates, brood growth rate and condition, and number of fledglings produced (i.e., productivity). Increases in environmental noise tended to be associated with smaller brood sizes and were more strongly related to reductions in productivity. Although the mechanism responsible for these patterns is not yet clear, the breeding depression experienced by this otherwise disturbance-tolerant species indicates that anthropogenic noise may have damaging effects on individual fitness and, by extraction, the persistence of populations in noisy habitats. We suggest that managers might protect avian residents from potentially harmful noise by keeping acoustically dominant anthropogenic habitat features as far as possible from favored songbird breeding habitats, limiting noisy human activities, and/or altering habitat structure in order to minimize the propagation of noise pollution

Melanotan-II reverses autistic features in a maternal immune activation mouse model of autism

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by impaired social interactions, difficulty with communication, and repetitive behavior patterns. In humans affected by ASD, there is a male pre-disposition towards the condition with a male to female ratio of 4:1. In part due to the complex etiology of ASD including genetic and environmental interplay, there are currently no available medical therapies to improve the social deficits of ASD. Studies in rodent models and humans have shown promising therapeutic effects of oxytocin in modulating social adaptation. One pharmacological approach to stimulating oxytocinergic activity is the melanocortin receptor 4 agonist Melanotan-II (MT-II). Notably the effects of oxytocin on environmental rodent autism models has not been investigated to date. We used a maternal immune activation (MIA) mouse model of autism to assess the therapeutic potential of MT-II on autism-like features in adult male mice. The male MIA mice exhibited autism-like features including impaired social behavioral metrics, diminished vocal communication, and increased repetitive behaviors. Continuous administration of MT-II to male MIA mice over a seven-day course resulted in rescue of social behavioral metrics. Normal background C57 male mice treated with MT-II showed no significant alteration in social behavioral metrics. Additionally, there was no change in anxiety-like or repetitive behaviors following MT-II treatment of normal C57 mice, though there was significant weight loss following subacute treatment. These data demonstrate MT-II as an effective agent for improving autism-like behavioral deficits in the adult male MIA mouse model of autism