MINERAÇÃO, POLUIÇÃO SONORA E IMPACTOS NA...

UNIVERSIDADE FEDERAL DE MINAS GERAIS

PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA,

CONSERVAÇÃO E MANEJO DA VIDA SILVESTRE

MINERAÇÃO, POLUIÇÃO SONORA E IMPACTOS NA

COMUNICAÇÃO ANIMAL

Marina Henriques Lage Duarte

Tese de Doutorado em Ecologia, Conservação e Manejo da Vida

Silvestre / 2015

Orientador: Marcos Rodrigues

Co-orientadores: Robert John

Young e Renata Sousa-Lima

Colaboradora: Nadia Pieretti

Belo Horizonte

2015

Marina Henriques Lage Duarte

MINERAÇÃO, POLUIÇÃO SONORA E IMPACTOS NA

COMUNICAÇÃO ANIMAL

Tese de doutorado apresentada ao

Programa de Pós-graduação em

Ecologia, Conservação e Manejo

da Vida Silvestre da Universidade

Federal de Minas Gerais, como

requisito parcial para obtenção do

título de doutor em Ecologia.

Orientador: Marcos Rodrigues

Co-orientadores: Robert John

Young e Renata Sousa-Lima

Colaboradora: Nadia Pieretti

Belo Horizonte

2015

Esta tese de doutorado foi desenvolvida no Laboratório de Ornitologia, do

Departamento de Zoologia, Universidade Federal de Minas Gerais e no Laboratório de

Bioacústica do Museu de Ciências Naturais, Pontifícia Universidade Católica de Minas

Gerais, em parceria com as seguintes instituições:

Departamento de Ciências Básicas, Universidade de Urbino, Campus Científico

"Enrico Mattei" Urbino - Itália.

Escola de Meio Ambiente e Ciências da Vida, Universidade de Salford

Manchester, Salford - Inglaterra.

Laboratório de Bioacústica (LaB), Departamento de Fisiologia, Universidade

Federal do Rio Grande do Norte.

Apoio financeiro

Dedicado ao meu pai

por guiar todos os meus passos até aqui

“A tarefa não é tanto ver aquilo que

ninguém viu, mas pensar o que

ninguém ainda pensou sobre

aquilo que todo mundo vê.”

Arthur Schopenhauer

Agradecimentos

Esta pesquisa foi concretizada graças ao apoio de diversas pessoas e instituições. O meu

eterno e carinho e gratidão dedico:

Ao meu orientador e amigo, Marcos Rodrigues. Obrigada por me aceitar como sua

aluna, por acreditar em mim e me dar credibilidade para desenvolver esta tese. Nos

momentos de tensão você me transmitiu calma e me ajudou a minimizar os contratempos.

Obrigada pelo apoio e pelas sábias palavras ditas durante estes quase quatro anos. Você foi

parte fundamental deste trabalho.

À Nadia Pieretti. Existem pessoas que surgem inesperadamente em nossas vidas e

iluminam nossos caminhos. Poucas vezes ao longo da minha trajetória profissional conheci

pessoas doces como Nadia Pieretti. Muito obrigada por ter me ensinado grande parte do que

sei hoje sobre os sons naturais. Obrigada por estar disponível a qualquer hora do dia mesmo

quando tínhamos um oceano entre nós. Nos momentos mais difíceis você foi sensível,

compreensiva, otimista, bem humorada, companheira e competente. Obrigada por me

receber com tanto carinho na Itália, por ter me apresentado sua família e seus amigos, me

fazendo sentir acolhida na ausência da minha família. Não tenho palavras para expressar o

quão importante você se tornou em minha vida. Você foi minha inspiração e meu exemplo

profissional! Quero deixar aqui eternizados, meus sentimentos sinceros de admiração,

respeito e afeto por você. Grazie per tutto.

Ao professor Robert John Young. Ainda me lembro com detalhes da primeira vez que

entrei na sua sala, em março de 2005. Eu ainda infantil e imatura, mas cheia de sonhos e

determinação. Você me acolheu com seriedade e me incentivou a desenvolver o projeto que

eu sonhava. Poucos minutos na sua sala e saí com um desenho experimental pronto, um

orientador e um amigo. Hoje, conquisto o título mais alto da carreira de pesquisador e isso só

foi possível porque eu te conheci e porque você acreditou em mim. Serei eternamente grata

pelos oito anos em que trabalhamos juntos no Brasil.

À professora Renata Sousa-Lima. Obrigada por ter participado da idealização deste

projeto, aceitando me co-orientar, sonhando junto comigo e se arriscando a fazer algo nunca

feito antes. Obrigada, porque mesmo estando longe você contribuiu de forma expressiva

nesta pesquisa.

Ao professor Almo Farina, que me recebeu como sua “ragazza brasiliana” e aceitou

dividir sua sala e sua Nadia comigo durante dois meses. Muito obrigada por ter me dado a

oportunidade de te conhecer e aprender com você.

Ao professor Bonifácio, que gentilmente cedeu uma sala no Museu de Ciências

Naturais da PUC para que pudéssemos montar o Laboratório de Bioacústica. Obrigada por

ter me dado condições logísticas e operacionais para o desenvolvimento da minha tese e

também por confiar no meu trabalho e me acolher como parte da equipe do museu.

Ao professor Carlos Augusto e seus alunos, Douglas e Alan, por todo apoio

operacional com os storages, softwares e equipamentos do projeto. Vocês foram essenciais!

Ao professor Nilo Bazzoli, por aceitar ser coordenador do projeto do qual esta tese foi

fruto.

Ao engenheiro Krisdany Cavalcante, pelo apoio e ensinamentos com os medidores de

nível sonoro.

Ao Marcelo Vasconcelos pela ajuda na identificação dos cantos das aves, sempre com

muito entusiasmo.

Aos professores do PPG ECMVS pelas disciplinas ministradas.

Aos professores e pesquisadores que gentilmente aceitaram o convite para participar

da banca e enriquecer meu trabalho.

Aos funcionários do Parque Nacional da Serra do Cipó, especialmente Ivan Campos e

Edward, pelo apoio durante as coletas de dados.

Aos funcionários do Parque Estadual do Rola Moça e da Estação Ambiental de Peti,

especialmente ao Leotacílio da Fonseca.

Aos funcionários do PPG ECMVS, Elídio, Cris, Fred pelo constante apoio.

Aos funcionários do mestrado em Zoologia, da PROPPG e Museu: Clédma, Rosa,

Elane, André e Márcio pelas gentilezas prestadas e especialmente à Ana Cristina pela

amizade.

À FAPEMIG, VALE e CNPq, pelo apoio financeiro concedido para o desenvolvimento

deste projeto e pelas bolsas de doutorado e iniciação científica.

Ao Fabrizio Frontalini, que dividiu comigo sua Nadia e muitas vezes, participou de

discussões importantes sobre o andamento da minha tese.

À Maria Ceraulo, pela doce amizade que fiz no Laboratório de Soundscape Ecology,

durante os dias na Itália.

À Regina Scarpelli por ter trazido ao mundo uma pessoinha que foi fundamental

durante minha pesquisa. Nina, me faltam palavras para expressar o quanto você é

importante para mim. Ao longo dos três anos em que trabalhamos juntas você foi fiel,

paciente, inteligente, competente, dedicada, companheira, amiga, confidente e muito mais!!!

Poucas vezes eu conheci pessoas que tivessem uma sintonia tão grande comigo. Você foi

muito mais que estagiária, porque você fez tudo com muito amor. Muito obrigada!

Às amigas Sara, Marina Nogueira e Isabella Diniz por terem sido as amigas com as

quais eu pude contar a qualquer hora. Eu amo muito vocês!

À Mari e Afiwa pelo apoio durante esta pesquisa e pela companhia no lab de

Bioacústica.

À minha amada família, papai, mamãe e Nang, vocês são o meu mundo e razão pelo

qual cheguei até aqui. Pai, obrigada por ter sempre me guiado em direção à realização dos

meus sonhos! Este título também é seu. Mãe, obrigada por cuidar de mim em todos os

momentos. Nang Pum, meu irmão, meu melhor amigo, você é meu amor maior!

Ao Rafa por ter sido a melodia doce que me muitas vezes aliviou os momentos de

estresse. Durante as adversidades você me transmitiu força. Você foi paciente, amigo,

companheiro e acima de tudo, compreensivo. Você aceitou minha ausência com toda

paciência do mundo, me encorajou e me fez acreditar que eu fosse conseguir. Obrigada pelo

amor, você foi demais!

Por fim, agradeço a Deus e à natureza e seus sons magníficos, que me deram força e

inspiração para chegar até aqui.

- SUMÁRIO -

Introdução geral e apresentação..................................................................................11

Capítulo 1. Determining temporal sampling schemes for passive acoustic studies in

different tropical ecosystems...........................................................................................16

Capítulo 2. The impact of anthropogenic noise from open cast mining on Atlantic

forest biophony................................................................................................................50

Capítulo 3. Mining noise reduces loud call by wild black-fronted titi monkeys….......81

Conclusão.....................................................................................................................105

Referências Bibliográficas..........................................................................................107

11

Introdução geral e apresentação

-Introdução geral e apresentação-

1. Atividades antrópicas e o impacto do ruído

O crescimento da população humana no mundo conduz a exploração acelerada

dos recursos naturais e a invasão das paisagens naturais, causando sérios problemas

ambientais (Cohen, 1995). Atualmente, o impacto humano sobre o meio ambiente atrai

a atenção dos cientistas para abordar os aspectos da pressão sobre os recursos naturais e

populações de animais selvagens (Cohen, 1995; Pijanowski et al., 2011).

Um dos recursos mais explorados no Brasil é o minério de ferro e a mineração é

uma das atividades econômicas mais importantes do país (IBRAM, 2011). A pressão

por extração de minério tem se tornado cada vez mais intensa e muitas minas no Brasil

estão localizadas em biomas considerados hotspots de biodiversidade, como o Cerrado e

a Mata Atlântica (Myers et al., 2000; Estrada, 2009). Entre os impactos gerados por esta

atividade estão: supressão de habitat para construção de estradas e implantação da mina,

vibrações transmitidas aos terrenos e estruturas adjacentes, e produção de ruído

proveniente das etapas de implantação da mina, extração e transporte de minérios

(Donoghue, 2004).

Métodos acústicos proporcionam uma oportunidade de monitorar o ruído

produzido por atividades antrópicas e seus efeitos nos ecossistemas com uma ampla

escala espacial e temporal, fornecendo dados relevantes para decisões sobre manejo e

uso da terra (Brown et al., 2013). Em muitas situações, o ruído antropogênico pode

mascarar sinais acústicos e impedir a capacidade dos animais em compreender,

reconhecer ou detectar sons de interesse (Warren et al., 2006; Clark et al., 2009;

Versace et al., 2008). A comunicação acústica é essencial para a sobrevivência dos

animais, pois dela dependem comportamentos sociais, como defesa de territórios,

comportamentos reprodutivos (a atração e identificação de parceiros sexuais) e também

a percepção de sinais importantes, como chamados de alarme, perigo e vocalizações

relacionadas ao cuidado parental, e detecção de presas ou predadores (Warren et al.,

2006). Além disso, a identificação de sons naturais auxilia na orientação de organismos

e seu deslocamento a locais favoráveis à sua sobrevivência e reprodução (Vermeij et al.,

2010).

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Estudos relatam que o ruído de mineração pode afetar a reprodução das aves e

diminuir a densidade de indivíduos (Smith et al., 2005), diversidade de espécies e

tamanho populacional (Saha e Padhy, 2011). Entretanto, algumas espécies de animais

que vivem em áreas ruidosas são capazes de ajustar seus sinais acústicos para

comunicar nestes ambientes; por exemplo, aumentando a amplitude das vocalizações

(Brumm et al., 2004; Brumm et al., 2009), mudando as frequências (Slabbekorn e Peet

2003; Parks et al., 2007; Nemeth e Brumm, 2009), alterando a taxa e a duração dos

chamados (Brumm et al., 2004; Sun e Narins, 2005) ou o turno de vocalização (Fuller et

al., 2007; Sousa-Lima e Clark, 2008). Outras espécies apresentam mudanças no

comportamento, tais como evitar áreas ruidosas para forragear (Miksis-Olds et al., 2007;

Schaub et al., 2008) e desenvolver outras atividades diárias (Sousa-Lima e Clark, 2009;

Duarte et al., 2011). A evasão de áreas e os mecanismos compensatórios para reduzir os

efeitos do ruído podem alterar a complexidade acústica de uma comunidade e resultar

na diminuição de abundância e da diversidade de espécies em áreas ruidosas (Bayne et

al., 2008; Proppe et al., 2013).

Atualmente, grande parte dos esforços para reduzir a poluição acústica é

destinada a diminuir os efeitos negativos da exposição ao ruído nos seres humanos,

especialmente em comunidades urbanas expostas ao ruído proveniente de estradas e

aeroportos. No entanto, pouca atenção tem sido dedicada à regulamentação da poluição

sonora em relação aos animais (Sousa-Lima, 2007). Diante dos impactos provocados

pelo ruído proveniente das atividades mineradoras e considerando o grau de ameaça dos

biomas e das espécies inseridas em áreas com a presença desta atividade, têm-se a

necessidade de pesquisas envolvendo o impacto do ruído sobre a fauna nestes locais.

2. Monitoramento Acústico Passivo e Ecologia de Paisagem Acústica

O monitoramento acústico passivo (MAP) é uma metodologia inovadora para

ambientes terrestres, que fornece oportunidades de avaliar o grau de conservação de

ambientes e as consequências de diferentes atividades antrópicas na natureza (Blumstein

et al., 2011; Mennit e Fristrup, 2012; Brown et al., 2012, 2013). Através das técnicas de

MAP também é possível avaliar diferenças acústicas entre comunidades que ocorrem

em áreas distintas, monitorar mudanças ao longo do tempo, comprovar a ocorrência de

13


novas espécies em determinado ambiente e entender as relações entre animais e

elementos externos, por exemplo, o ruído (Pijanowski et al., 2011). Esta metodologia é

especialmente importante para ser utilizada em ambientes que são difíceis de acessar ou

monitorar usando métodos convencionais (Mellinger e Barlow 2003; Brandes, 2008).

O avanço de tecnologias e o consequente desenvolvimento de equipamentos de

MAP possibilitou o surgimento de uma nova linha de pesquisa dentro das disciplinas

ecológicas existentes: a "Ecologia de Paisagem Acústica", ou "Soundscape Ecology".

(Pijanowski et al., 2011). Esta linha de pesquisa é uma das abordagens mais recentes

para estudar o impacto das atividades antrópicas sobre o ambiente. Uma paisagem

acústica é definida como qualquer ambiente acústico natural, urbano ou rural e pode ser

composta por três elementos fundamentais: a biofonia (sons biológicos não humanos,

como vocalizações de anfíbios, aves e estridulações de insetos), a geofonia (sons físicos

da natureza como vento, trovões, cachoeiras, etc) e antropofonia (sons produzidos por

seres humanos) (Krause et al., 2011; Pijanowski et al., 2011).

A Ecologia de Paisagem Acústica ainda é uma linha de pesquisa difícil de ser

investigada devido à ampla variedade de informação disponível em cada ambiente

acústico e à dificuldade que existe na identificação de índices que possam interpretar

rapidamente a grande quantidade de informação contida nos registros de áudio. Como

toda linha de pesquisa recente, a Ecologia de Paisagem Acústica carece do

desenvolvimento de métricas e protocolos que possam otimizar o processo de análise e

interpretação de dados. Apesar do desenvolvimento de novas tecnologias e softwares

nos últimos anos, a análise de sons naturais ainda requer muito tempo para ser realizada,

o que dificulta a extração de dados ecológicos importantes em um amplo banco de

dados. Vários autores têm desenvolvido técnicas baseadas em informações bioacústicas

de uma única espécie (Klinck et al., 2008; Wolf, 2009; Bardeli et al., 2010), enquanto

índices e metodologias para o monitoramento acústico de comunidades de animais são

raramente desenvolvidos. Assim, a Ecologia de Paisagem Acústica oferece novas

perspectivas para investigações no campo da Ecologia de Paisagens, mas a

implementação de novos métodos para otimizar as pesquisas nesta área é extremamente

necessária.

14


3. Apresentação da tese

Esta tese aborda os impactos da poluição sonora na comunicação acústica da

fauna silvestre utilizando métodos de MAP e o Índice de Complexidade Acústica (ACI),

que foi recentemente desenvolvido para medir a complexidade acústica de ambientes

terrestres.

No capítulo 1, um novo método de subamostragem de dados sonoros é

apresentado, com indicações de protocolos de subamostras em diferentes ambientes em

Minas Gerais (Cerrado, Campo Rupestre e Mata Atlântica). O uso de equipamentos de

monitoramento acústico passivo permite a coleta de grande quantidade de dados, o que

faz surgir a necessidade da elaboração de métodos de subamostragem. Estes métodos

são importantes para que seja possível alcançar o compromisso entre um esforço de

amostragem rigoroso e resultados confiáveis, considerando também, questões de

armazenamento e a redução de tempo e recursos consumidos durante o processo de

análise de dados. O protocolo apresentado neste capítulo é feito com base no Índice de

Complexidade Acústica (ACI), um algoritimo criado para produzir uma medida direta

da complexidade de sons biológicos, computando a variabilidade de intensidades

registrada em arquivos sonoros, apesar da presença quase constante de ruído

antropogênico.

No capítulo 2, o ACI é novamente utilizado, porém com objetivo de analisar o

impacto da poluição sonora proveniente de atividade mineradora na biofonia de um

fragmento de Mata Atlântica, localizado próximo à mina de Brucutu, uma das maiores

minas de minério de ferro do mundo. Neste capítulo, duas áreas de mata (uma próxima

e outra distante da mina) do mesmo fragmento são comparadas em termos de ruído e

biofonia e também em riqueza, composição de espécies encontradas nas gravações e

características espectrais dos cantos. Também foi realizada a medição dos níveis

sonoros e a caracterização dos ruídos produzidos pela atividade de mineração com base

nos registros sonoros feitos na área próxima à mina.

No capítulo 3, foi analisado o impacto da poluição sonora da mina de Brucutu

nas vocalizações de guigós (Callicebus nigrifrons), um primata ameaçado de extinção,

que vive em áreas de Mata Atlântica. Neste capítulo, foram quantificadas todas as

vocalizações de guigós encontradas ao longo do dia no período de um ano, em duas

15


áreas sendo uma próxima e outra distante da mina. A duração e a taxa de ocorrência das

vocalizações foram medidas e comparadas entre as duas áreas estudadas. Além disso, o

número de caminhões de mineração que passaram ao longo do dia foi quantificado na

área próxima à mina para que fosse verificada a correlação entre a passagem dos

caminhões e ocorrência de vocalizações.

Finalmente, são apresentadas as principais conclusões do trabalho e

mencionadas as possíveis direções futuras para esta linha de pesquisa.

16

Determining temporal sampling schemes for passive acoustic studies in different tropical ecosystems Capítulo 1

- CAPÍTULO 1-

Determining temporal sampling schemes for passive acoustic studies in

different tropical ecosystems

Artigo aceito no periódico Tropical Conservation Science

Abstract

Among different approaches to exploring and describing the ecological complexity of

natural environments, soundscape analyses have recently provided useful proxies when

it comes to understanding and interpreting dynamic patterns and processes across a

landscape. Nevertheless, the study of soundscapes remains a new field with no

internationally accepted protocols. This work aims to provide the first guidelines for

monitoring soundscapes in three different tropical areas, specifically located in the

Atlantic Forest, Rupestrian fields and the Cerrado. Each area was investigated using

three autonomous devices recording for six entire days during a period of 15 days in

both the wet and dry seasons. The recordings were processed via a specific acoustic

index and successively subsampled in different ways to determine the degree of

information loss when reducing the number of minutes of recording used in the

analyses. We describe for the first time the temporal and spectral soundscape features of

three tropical environments and test diverse programming routines to describe the costs

and the benefits of different sampling designs, taking into consideration the pressing

issue of store and analyze extensive data sets generated by passive acoustic monitoring.

Schedule 5 (recording one minute every five) appeared to retain most of the information

contained in the continuous recordings from all the study areas. Less dense recording

schedules produced a similar level of information just in specific portions of the day.

Substantial sampling protocols such as those presented here will be useful to researchers

and wildlife managers as they will reduce time- and resource-consuming analyses,

whilst still achieving reliable results.

Keywords: environmental monitoring, animal conservation, tropical environments,

soundscape ecology, sampling protocols.

17


Resumo

Entre as diferentes abordagens para explorar e descrever a complexidade ecológica de

ambientes naturais, a análise de paisagem acústica tem fornecido recentemente

ferramentas úteis para o entendimento e a interpretação da dinâmica de padrões e

processos de uma paisagem. Apesar disso, o estudo de paisagens acústicas é uma nova

linha de pesquisa que ainda não possui protocolos e métricas aceitas internacionalmente.

Este estudo tem como objetivo fornecer as primeiras diretrizes para monitorar paisagens

acústicas em três diferentes áreas tropicais localizadas especificamente na Mata

Atlântica, no Campo Rupestre e no Cerrado. Cada área foi investigada usando três

equipamentos autônomos gravando por 6 dias inteiros durante um período de 15 dias

nas estações seca e chuvosa. As gravações resultantes foram processadas utilizando um

índice acústico específico e foram sucessivamente subamostradas para determinar o

grau de informação perdido quando reduzido o número de minutos de gravações usadas

nas análises. Nós descrevemos pela primeira vez, as medidas temporais e espectrais de

três ambientes tropicais e testamos rotinas de programação diversas para descrever os

custos e benefícios de diferentes desenhos de amostragem, considerando questões de

armazenamento e análise de bancos de dados extensos gerados por monitoramento

acústico passivo. A programação 5 (gravação de um minuto a cada 5 minutos) manteve

o maior número de informações contidas nos registros contínuos em todas as áreas de

estudo. Programações de gravação menos intensas produziram um nível similar de

informação apenas em porções específicas do dia. Protocolos de amostragens tais como

os apresentados aqui são úteis para pesquisadores e gestores de meio ambiente, uma vez

que eles podem reduzir tempo e recurso a ser consumido durante análise de dados e

ainda fornecer resultados confiáveis.

Palavras-chave: monitoramento ambiental, conservação animal, ambientes tropicais,

ecologia de paisagens acústicas, protocolos de amostragem.

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Introduction

Nowadays, passive acoustic monitoring (PAM) is considered to be a valuable

tool for both research and management, and involves collecting acoustic data over large

spatial and temporal scales and providing detailed and long-term information on animal

distribution and variations in community dynamics. This wide-scale data collection

inevitably leads to animal populations being better understood and managed more

effectively [1]. However, to avoid time- and resource-consuming analyses, acoustic

surveys need to specifically address general guidelines that can ensure efficient

sampling on the basis of experienced protocols.

Animals produce sounds for diverse biological functions (e.g. communication,

mating, building territories, foraging) [2, 3], which can serve as proxies for estimating

species fitness and individual behavior, especially in environments that are difficult to

access or monitor using conventional methods [4, 5]. In the early 1990s, idiosyncrasies

in the study of marine mammal behavior led researchers in the field to develop

autonomous acoustic devices that enabled them to detect sounds underwater [6].

Successively, the use of acoustic recordings of the natural environment became

gradually an important technique for ecologists for monitoring all ecosystems. In

particular, passive acoustic monitoring has only recently been proposed for terrestrial

environments [7], and the study of the soundscape (soundscape ecology), which is

defined as the aggregation of sounds from physical, biological and human-made

sources, has rapidly gained attention as a potential tool to both evaluate ecosystem

health [8] and the effects of changes in land use and climate at various temporal and

spatial scales [9–11].

Advances in technology over the last decade have revolutionized the potential of

acoustic surveys. Fixed, programmable acoustic recording sensors can sample

continuously for 24 hours a day for prolonged periods of time, allowing for the non-

invasive assessment of changes in the distribution and acoustic behavior of entire

animal communities across a variety of habitats simultaneously. Moreover, all of the

recordings can be permanently stored and serve as an everlasting memory of the sounds

19


of the habitats [1, 5, 12].

The achievement of this temporal and spatial wide-scale application of

soundscape ecology inevitably produces an overwhelming amount of information, with

linked difficulties in data management and analysis [13]. Problems include an ever-

growing requirement for storage space and the need for time-consuming processing,

expensive power supply systems, and field personnel to periodically download data and

reinstall the equipment. Common standards and baseline data collection models could

be useful to limit unnecessary recordings and trips to the field while ensuring targeted

data are collected.

Optimizing the recording schedule by selecting specific portions of active

recording times (ON), which leaves the device off for the rest of the time (OFF),

becomes obligatory when it comes to optimizing basic resources and staff-time,

especially when constrained by limited funding. On the other hand, by reducing ON

periods, the probability of losing important information increases and may result in a

distorted description of the target community. As a consequence, identifying the

appropriate sampling period with which to conduct a study is essential for using

soundscape surveys appropriately to achieve scientific, management and conservation

objectives. To make such a decision, a good understanding of the daily and frequency

patterns of the recorded community is required.

Several acoustic surveys have been conducted in recent years to investigate

animal community dynamics and structure [14, 15], species richness and distribution

[16–18], relationships with vegetational parameters [19, 20], and human or noise impact

[21–23]. However, explicit evaluations of the survey effort required to characterize the

acoustic dynamics of different landscapes are generally lacking. Knowledge about

temporal variations in such acoustic dynamics could improve the design of future

soundscape studies and render soundscape ecology more efficient and applicable for

different categories of users (academics and other stakeholders). Our goal was to

describe the type and extent of soundscape information lost with different recording

schedules in areas located in three tropical ecoregions (Atlantic Forest, Rupestrian fields

20


and the Cerrado). These environments were chosen as they are priority conservation

areas, threatened environments, and contain biodiversity hotspots with high endemism

[24–26]. Additionally, studies of tropical soundscapes are limited [18].

Moreover, on the basis of the obtained results, we tried to both identify a cost-

effective scheme for surveying such areas and suggest the minimum sampling effort

required to meet the goal of characterizing the soundscape features. This was achieved

by identifying when the recording schedule loses acoustic information that is essential

for correctly describing the dynamics of the sound activity of that community and its

circadian rhythms.

Methods

Study area

The study was conducted in three threatened environments in Minas Gerais, in

the southeastern region of Brazil: Atlantic Forest, Rupestrian ferruginous fields and

Cerrado strictu sensu (Fig. 1).

Atlantic Forest – Environmental station of Peti – The Atlantic Forest is a

world biodiversity hotspot with high species richness and high levels of endemism,

which are threatened by the rapid loss of native land-cover types [25]. We collected data

in this biome at the environmental station of Peti in the municipalities of São Gonçalo

do Rio Abaixo and Santa Bárbara (19°53’57’’S and 43°22’07’’W). The reserve is

approximately 605 hectares in size and is located in the upper Rio Doce Basin (altitude

range: 630-806m). The area harbors 29 anuran species [27], 231 bird species [28] and

46 mammal species [29]. A large part of the reserve is covered by secondary arboreal

vegetation, with large trees and a continuous canopy [30].

Rupestrian fields – State Park of Rola Moça - The ecosystems found in

ferruginous outcrops known as ‘Rupestrian ferruginous fields’ or ‘Canga’ are among the

less studied and most endangered areas of Brazil due to restricted geographical

21


distribution and them being associated with the country’s main iron ore deposits [26].

Rupestrian fields have a relatively continuous herbaceous stratum of sclerophyllous

plants, which are small evergreen shrubs located between rocky outcrops that occur at

altitudes between 800 and 2000m. This ecosystem is highly diversified, with more than

4000 plant species along the Espinhaço Range and one of the highest levels of

endemism in Brazil [31]. We collected data in the Rupestrian fields at the State Park of

Rola Moça, which is located in the northwest of ‘Quadrilátero Ferrífero’ (20°03'60"S,

44°02'00"W) at an altitude of approximately 1450m.

The Cerrado – National Park of Serra do Cipó - The Cerrado is a biodiversity

hotspot and a highly threatened environment [25]. The Cerrado sensu strictu is

characterized by the presence of small trees with thick and twisted trunks and branches,

while grasses characterize the understory [32]. We collected data in an isolated area of

the Cerrado strictu sensu at the core of the national park of Serra do Cipó, which is

approximately 34,000 hectares in size and is situated at 19°12’19’’S and 43°30’43’’W.

This area provides habitat for 226 bird species [33] and 26 medium-large mammalian

species [34].

22


Figure 1. Location map of the study areas: Atlantic Forest (AF), Rupestrian fields

(RF), and Cerrado strictu sensu (CE). The photographs represent the typical

surroundings of the three habitats where the acoustic measurements were taken.

Acoustic recordings and data analyses

The climate of southeastern Brazil can be divided into two macro-climatic

seasons: a hot wet season, running from October to March, and a cooler dry season from

April to September [35]. The soundscape of the three study areas was collected by

recording for six non-consecutive days during a period of 15 days during the dry

(Cerrado: 9-23 September 2012; Rupestrian fields: 17-30 April 2013; Atlantic Forest: 4-

23


19 June 2013) and wet seasons (Cerrado: 15-30 March 2013; Rupestrian fields: 6-21

October 2012; Atlantic Forest: 17 October-1 November 2012). In each sampling area,

three SongMeter Digital Field Recorders (SM2) (Wildlife Acoustics, Inc.,

Massachusetts) were set to record from 00:00 to 23:59h continuously. Accordingly,

each area was recorded for 432 hours (24h * six days * three recorders) each season,

making 2592 hours in total. 06:00 and 18:00 were considered as the approximate times

of dawn and dusk, which were slightly varying among the different months. One of the

three recorders stopped recording during the wet season in the Rupestrian fields after

three days, while another recorder in the Atlantic Forest stopped working after 17 h on

the last recording day during the dry season.

The recorders were placed at a distance of approximately 300m from each other

to avoid double sampling the same sounds and intend each recorder as an independent

sampler per area. They were mounted on a tree at approximately 1.5m from the ground,

and ensured any nearby vegetation would not interfere with recordings. The SM2s

recorded at a sampling rate of 44,100Hz, set at 16 bits.

The Kaleidoscope converter utility (Wildlife Acoustics, Inc., Massachusetts) was

used to split the collected data into files of one minute in length, which were further

processed via the Wavesurfer software [36] powered by the SoundscapeMeter plug-in

[37]. One minute resolution was chosen since most of the recent literature used this time

lapse for sound assessments [18, 19, 38, 39], and to compare with previous research.

Among the variety of the available acoustic indices to directly summarize the

information in a recording (i.e. [16, 17, 22, 38, 40]), the Acoustic Complexity Index

(ACI) [11, 41] was selected for this study. The ACI was chosen since it is an algorithm

designed to measure the spectral complexity of soundscapes and was recently used to

track the dynamics of animal acoustic communities [15] and compare it with

vegetational parameters [19]. Moreover, in the recent study of Towsey et al. [38], it was

found to be one of the best indicator of the biodiversity of a bird community among a

list of 14 different acoustic indices, with weaknesses due to the sensitivity to wind

gusts. To analyze the collected acoustic data, a Fast Fourier Transform (FFT) of 512

points was applied, obtaining from every recorded minute a matrix made by 256

24


frequency bins of 86.13Hz and 5167 temporal intervals of 0.012s. This matrix was used

to calculate the Acoustic Complexity Index, with the following formula:

where |Ik-Ik+1| is the absolute difference between two adjacent values of amplitude along

a frequency bin, n represents the total number of temporal steps (k) contained in every

interval of time in which the calculation is made (in this study, 1s). The sum of the

results for all of the frequency bins and temporal intervals is then calculated. To avoid

bias due to background ambient noise that is inevitably present in every recording (even

if soft), we set an a priori filter on the power spectral density (SoundscapeMeter

settings: Noise filter =3000 μV2/Hz) operating on all the frequency bins, so that the ACI

did not apply to values under the selected threshold. This filter was appositely verified

for the type of recording used in order to not filter biophonies but just background noise

and to increase the signal to noise ratio.

Five different recording schedules were then chosen to be simulated: (i)

Schedule 5: recording one minute over five minutes; (ii) Schedule 10: one minute over

10 minutes; (iii) Schedule 20: one minute over 20 minutes; (iv) Schedule 30: one

minute over 30 minutes; and (v) Schedule 60: one minute over 60 minutes.

These schedules were obtained by selecting the corresponding minutes of each

simulated configuration from the continuous recording, thus simulating a recording

routine whereby the recorder was not running continuously, but intermittently, at

respectively one minute every five, 10, 20, 30 and 60 minutes. A mean of the ACI

values was then calculated for each recording hour for both the continuous recordings

and the simulated samplings in order to compare the different schedules with the

original and complete sampling. These comparisons were conducted for both the

temporal and spectral dimensions.

Rain and wind were found to be recognizable in abnormal ACI results [15, 38],

especially at lower frequencies. Consequently, when the ACI values highlighted

discrepancies with the normal acoustic behavior of the local community, the sound files

25


were aurally checked to verify if the anomaly was due to some atypical biophony or

anthrophony (such as insect buzzes on the microphone or transits of airplanes) or to the

influence of stormy weather. This allowed us to generate a table of the adverse weather

conditions during the recording days that was filtered from the analyses in selected

statistical tests.

Statistics

All of the statistical tests were performed using Statistica v.8.0. A non-

parametric approach was utilized, since the variables did not present a normal

distribution pattern, even after transformation of the data values. Non-parametric

correlation analyses (Spearman’s rho, p < 0.01) were conducted to investigate the

relationship between the continuous data set and the simulated recording schedules.

To quantify the relative non-conformity of the sampling schedules with the real

distribution of the ACI levels along the different hours of the day and the different

frequency bins, the percent deviation [42] was calculated using the following formula:

(1) % deviation = (actual value – expected value)/expected value x100

in which the ‘actual value’ was the ACI value calculated for a simulated configuration

(expressed as an hourly mean) and the ‘expected value’ is the ACI resulting from the

continuous recordings. Successively, the percent deviation was grouped by temporal

slots (hours of the day) and frequency bins (1kHz-wide) to determine specifically where

results from the simulated schedules differed from those from to the continuous data set.

For both the correlation and percent deviation tests, only frequencies above 500Hz were

processed since, under that threshold, the ACI could not well filter the background noise

from the environment, which, if included, could have affected the final results. At the

Rupestrian fields it was windy all year round, and so the cutoff frequency for the

temporal analyses was 1500Hz to avoid the inclusion of soft wind noise. Rain and wind

produce sounds and add complexity to soundscape analyses. As a result, to enable us to

26


both consider the consequences of adverse weather when searching for the best

sampling approach and focus on the acoustic behavior of the animal community, it was

decided to treat the temporal and the frequency analyses differently.The entire data set

was used for the temporal analyses in order to include realistic limitations caused by

weather conditions. On the other hand, the hours affected by rain and strong wind were

left out of the data set when considering the differences reported with respect to the

spectral distribution of sounds (the frequency footprint, sensu Farina et al. 2011a),

enabling us to reliably track the acoustic community dynamics and identify which

frequencies were most affected when the sampling was less intense.

Results

The singing community

The ACI values varied greatly from the wet to the dry season in all our study

areas, with a pronounced change between daytime and nighttime recordings. Figures 2,

3 and 4 show, respectively, the seasonal, temporal and spectral acoustic complexity

variations of the investigated environments based on the complete data set. A summary

of their main soundscape features resulting from the ACI is set out in Table 1.

Table 1. Summary of the principal soundscape features of the three environments.

Wet season

Dry season Higher

acoustic

activity Peaks of

activity (kHz)

Peaks of activity

(hours)

Peaks of activity

(kHz)

Peaks of

activity (hours)

Atlantic forest 4-6 kHz

15-16 kHz

18:00 - 01:00

07:00-08:00

1 kHz, 4 kHz

15-16 kHz 18:00 - 19:00

Wet season

Rupestrian field 3-5 kHz

9-13 kHz 19:00 - 20:00

2-4 kHz

5- 7 kHz 12:00 - 16:00

Wet season

Cerrado 5-6 kHz

10-17 kHz

18:00 - 03:00

12:00

15:00 -17:00

3 kHz - 5-6 kHz

10-14 kHz 07:00 - 17:00 Wet season

27


recording period, nonetheless, when filtering out the windy hours from the data set, it

was possible to register narrow peaks from 3 to 5kHz and from 9 to 13kHz in the wet

season, which switched into peaks from 2 to 4kHz and 5 to 7kHz in the dry season.

The sampled areas in Cipó (the Cerrado) had the highest ACI values, especially

in the wet season. In the wet season, most of the acoustic complexity was registered

above 10kHz, with a narrow peak from 5 to 6kHz; the ACI presented high values

preferentially during the night hours (18:00 to 03:00). In the dry season, a higher

acoustic complexity was registered during daylight hours (07:00 to 18:00) mostly

between the 10 and 14kHz frequency bands. Others peaks of ACI were found from 3 to

6kHz.

28


Figure 2. Box-Whisker plot of the hourly means of the ACI values in

the three environments.

29


Figure 3. Temporal trends of the acoustic complexity recorded in the three

environments. Each graph represents the mean pattern resulting from sampling on six

days at three recording points. The dark lines show the ACI trends when not deleting

the files with adverse weather conditions; this highlights discrepancies in the hours of

the day in which rain and wind mainly occurred. The green highlight shows the period

of the day comprised between the approximate times of dawn and dusk.

30

Determining temporal sampling schemes for passive acoustic

studies in different tropical ecosystems Capítulo 1

Figure 4. Frequency distribution of the ACI in the three biomes. The dark lines

show the ACI trends when not deleting the files that present adverse weather

conditions; this highlights discrepancies, especially at the lower frequencies in

which the energy of the sounds produced by rain and wind are mainly comprised.

31



Statistical analyses of the sampling schemes

All of the correlations between the ACI values from the scheduled and

continuous recordings were significant and positive (Fig. 5). An expected inverse

relationship between time OFF and the value of the correlation was found for both the

frequency and time analyses. The correlation coefficients were very high (r>0.90;

p<0.01) for the more intense sampling period (Schedules 5 and 10) and fell with

increasing OFF minutes, especially when considering Schedule 60. The Rupestrian field

correlations generally had the lowest values. The frequency correlations were always

found to be higher than the temporal correlations.

The percent deviations were low for the intense sampling schedules and tended

to increase when enlarging the OFF period (Fig. 6). As for the correlations, the temporal

analyses tended to diverge away from the continuous recordings more strongly than the

spectral analyses (Fig. 6). When categorized by hour of the day or 1kHz-frequency

bands, interesting trends on the possible major losses of information of the subsampled

recording schedules became clearly visible (Figs. 7 and 8). In particular, Schedule 5

assumed values that deviated by a maximum of 10%. Schedule 60 registered substantial

deviations of 90% and 80% at specific hours of the day (Cerrado, wet and dry seasons,

respectively), and deviations over 30% in the frequency analyses (Rupestrian fields and

Cerrado, wet season).

In the wet season, both the Atlantic Forest and the Rupestrian fields seemed to

experience a greater loss of information during daylight hours, while in the dry season

the deviations were more evenly distributed. In the Cerrado, we found peaks in the

deviations at 17-18:00 (both seasons) and 01:00 (wet season). The highest frequency

bands registered null deviations in the Atlantic Forest (dry season) and Rupestrian fields

(wet season). In the Cerrado during the dry season, low variations were found in the

frequencies around 11-13kHz.

32


Figure 5. Spearman Rank correlations of the ACI results according to the temporal

and spectral comparisons of the different schedules (p< 0.01). The ACI results were

grouped by hour, comparing the mean value registered each hour by the different

recording schedules, or by the frequency bin (1kHz), comparing the mean value

registered for every frequency band by the different recording schedules.

33


Figure 6. Percent deviations of each subsampling category with respect to the

continuous recording. The unfiltered data set and the recordings with the

weather perturbations removed (i.e. optimum weather) are shown.

34


Figure 7. Percent deviations of the five recording routines from continuous

recordings aggregated by the time of the day (hours).

35


Figure 8. Percent deviations of the five recording routines from continuous

recordings aggregated by frequency (1kHz).

36


Discussion

Soundscape studies can be particularly useful for exploring fragile and

endangered ecosystems that need special attention for their conservation [18]. Our

results from simulating recording schedules provided information useful for suggesting

the first guidelines for soundscape studies in three tropical areas considered as

threatened environments. These suggestions are based upon our overall description of

the main dynamics recorded in the three study areas, evaluation of the different

sampling schedules as representative of the real acoustic dynamics, and the percentage

of information lost when reducing the recording time.

Soundscape characterization

Temporal and spectral characteristics of the soundscape for each study area were

unique and largely specific to the climate season. Generally we found a comparatively

higher ACI in the wet season which, in Brazil, coincides with the breeding season for

most species [33, 43, 44], when anurans, birds, and insects produce sounds to achieve

mating success. Acoustic complexity differences were clearly noticeable from the

diverse trend across the temporal domain (Fig. 3), and by delineating habitat and

season-specific frequency footprints (Fig. 4) (sensu [11]) depending on the singing

behavior of the emitters acting in each season and environment. Evidence of habitat

type acoustic signatures was also found in temperate environments in four forest and

two grassland habitat types in Northern Greece [45].

The lower acoustic complexity of the Rupestrian fields is probably related to

their high altitude, which directly influences vegetation structure (fewer trees, open

areas and strong winds). This leads to lower species richness and, consequently, lower

acoustic diversity (Figure 2).

37


General considerations on the different sampling schedules

All study areas were characterized by falling Spearman Rank correlation

coefficients with increasing OFF minutes, showing that there was a gradual loss of

correspondence with the real soundscape. This testifies that, in these environments, it is

difficult to provide a perfect picture of the acoustic dynamics of the community if the

sampling becomes too sparse. It is therefore likely that there will be a loss of important

data that could be essential for conservation issues.

This decreasing trend is shown by both temporal and spectral correlations, even

if the correlation coefficients are always very high in the latter. We hypothesize that this

is probably because the frequency bins have a lower degree of freedom than the

temporal analyses, since the spectral emissions were strictly linked to the acoustical

organs of indigenous species. Accordingly, animals cannot vary the spectral properties

of their emissions, which over the entire day are likely to be registered by less intense

sampling, but they can vary the moment and the length of a singing period. In other

words, the presented temporal analyses depend on what was singing across all of the

spectrum at a certain temporal interval, while the frequency analyses depend on what

was singing in the 24 hours of one day in a fixed frequency band. The frequency

footprint is thus less variable than the temporal trend across time. The Rupestrian fields

were the most critical environment, since the lowering of the sampled files

corresponded to very low correlations with the continuous recordings. The main reason

probably lies in the lower acoustic activity presented by the area (Fig. 2), which has a

higher risk of not being recorded and, thus, needs a greater sampling effort to be

captured and measured.

More detailed evidence about the loss of acoustic information is given by the

percent deviation analyses. In general, lower percent temporal deviations were found

where the sound emissions were more constant and prolonged in time, such as during

the night in the wet season in the Atlantic Forest and Rupestrian fields. At these times,

insects are the main protagonists of the acoustic performances, and tended to produce a

38


longer sonic performance than other taxa (mammals, birds). This makes them more

easily detectable in all of the sampling schedules, thus minimizing the percent

deviations. In contrast, during the day, birds also sung abundantly, but were less

constant in their acoustic emissions and more variable over time than the insects,

meaning that they may or may not be detected by less intense sampling. This

consideration suggests the need for more cautionary sampling during specific hours of

the day and a less intense effort at other times, which are typically characterized by the

greater constancy of sounds produced by the community.

The percent spectral deviation was found to be at a minimum where the

frequency bins were unoccupied (or rarely occupied) by some species, such as in the

Atlantic Forest (dry season) and Rupestrian fields (wet season). The narrow peaks

visible on the lower frequencies all referred to insects, most likely crickets, while

cicadas presented a broader frequency band. In the Cerrado (dry season), the reduction

in variation from 10 to 15kHz is related to the continuous and abundant sound

emissions of cicadas.

Which sampling routine is better?

The choice of the type of sampling will always depend on the principal focus of

the investigation, and so these results may help researchers to opt for the best sampling

protocol according their principal goals. Our findings show that there are preferential

recording schedules for each of the three investigated ecosystems. When the mean

soundscape of the community across the six recording days shows a high and

continuous presence of sounds, it may be preferable to use less dense recording

schedules, since the acoustic information is going to be captured anyway and will be

representative of the community. On the other hand, when the acoustic emissions are

occasional or intermittent and impossible to predict, the sampling should be more

intense to ensure a reliable representation of the soundscape.

39


Schedule 5 seemed to most reliably depict the soundscapes captured by

continuous recordings in all of our study areas. This schedule, which is the most

conservative, results already in an 80% storage space and battery power reduction with

respect to the continuous sampling. Schedule 10 seems often to represent a good

compromise, which will correspond to a 90% reduction in respect to the continuous

sampling, and to a 50% reduction of energy and storage space occupied by Schedule 5.

Following these considerations, and with the intention of creating an effective

reproduction of the soundscapes, it could be possible to design robust sampling for the

Atlantic Forest from 06:00 to 17:00, such as Schedule 5 (to avoid important gaps in the

6-8kHz frequency band) for the wet season, while an even less dense sampling routine

could be used from 18:00 to 03:00 without registering a major loss of information

(Schedule 30). In the dry season, it could be enough to record one minute in every 10,

or even every 20, although this risks losing some sounds at 7kHz at dawn.

For the Rupestrian fields in the wet season, a similar solution to that for the

Atlantic Forest wet season should be applied, with the exception being the early

morning hours when it is necessary to record one minute in every 30. In the dry season,

Schedule 10 should be adopted, which would be a good compromise in both the

temporal and spectral domains.

In the Cerrado, schedules 5 or 10 will provide reliable insights into the acoustic

diversity of the community, both for the dry and wet seasons. Schedules 60 and 30

should be avoided, especially when recording at 18:00 (dusk).

Additional insights

Soundscape information can sometimes be misleading and interpreted

incorrectly. Where the weather intervenes significantly in the soundscape of the

environment, as in the Rupestrian fields or the Cerrado (wet season), sounds produced

by the rain and wind mask and interrupt the soundscape of the community, meaning that

weather condition is an additional variable to take into account, with all of its

unpredictability. Moreover, Towsey et al. [38] found that ACI was responsive to wind

40



gusts, and that it was not inherent to the biological community in adverse

meteorological conditions. Certainly, before conducting brief acoustic surveys, which

rely on only recording for a few days, it is advisable to select the days that may be less

demanding from this point of view. Nevertheless, in the case of long-term investigations

(as in the case of fixed stations detection of acoustic dynamics), a precautionary

schedule should be adopted. In the Atlantic Forest in both the dry and wet seasons, we

found a lower degree of bias due to sounds from adverse weather conditions. Moderate

or strong wind was not noticed in this location, and the rain was easily detectable

because of its natural broadband and dominance signal across the spectrogram. In

contrast, the Rupestrian fields were always very windy, consequently having an

influence on the distribution of sounds across time.

Moreover, we need to underline that it is not possible to extend the results of this

study for all locations in all weather conditions. Soundscape dynamics vary enormously

from an ecosystem to another, and they even tend to differ between two recording

points with the same macroscopic vegetation features on the base of the therein

established animal community. Thus, the analyses here proposed can be representative

of just the three localities taken under investigation and cannot necessarily be extended

to all ecosystems.

Despite of this limitation in the present methodology and the relatively small

number of studied days per season, we believe that our results can provide useful

insights in how to approach the problem of choosing the correct sampling of the sounds

of an ecosystem. Moreover, we trust that the three recording points randomly chosen in

each area were so spaced to be independent and to be good representatives of the

variability of those selected environments.

Clearly more work could be done with other acoustic indices besides the ACI.

Adding further elaborations including a number of other indices could certainly improve

the herein presented results and add more information, so that researchers and field

technicians can have a better understanding of the impact of a particular sampling

41


strategy. This could lead to a better support to establish an international accepted

sampling methodology.

As a final consideration, in the present study we tested different sampling

schedules keeping the ON duration fixed to one minute, and varying the OFF period

from four to 59 minutes. It would also be interesting to test whether the one minute

resolution is the best setting for soundscape investigations or if a shorter/longer

recording interval could be more cost-effective.

Implications for conservation

Sounds are valuable scientific specimens that provide an indirect source of

information with which to survey animal dynamics and diversity in particular regions of

interest [3, 7]. The assessment of acoustic temporal and spectral changes offers a new

way to interpret the dynamics of animal communities and, consequently, understand or

address spatio-temporal variations in community structure across space and time [8, 11,

22]. Given the urgency of the issue of climate change and the loss of habitats,

understanding normal levels of variation in acoustic complexity could be fundamental

for conservation efforts, enabling managers to decide whether changes in acoustic

dynamics warrant further investigation.

Herein, we have produced a starting point for what could be a series of research-

guidelines to improve the efficiency of acoustic surveys using analytical methods, by

suggesting the sampling effort needed for planning biologically robust investigations of

animal communities in three tropical environments.

This could be especially useful for wildlife managers who have their choices

linked to economic and staff constrictions. If non-optimal sampling schedules were to

be adopted, our results will help to identify the most critical points, both temporal and

spectral, when the risk of the loss of information is highest.

Future research may focus on the sampling efforts required in temperate areas or

in different tropical ecosystems. Additional insights could be provided by the use of

other indices besides the ACI, or by testing variations in length of the ON period (here

42


kept constant to one minute). Suggesting the ideal number of days needed to represent

the acoustic community reliably in different seasons throughout the year would also be

another important step when it comes to designing the best protocol for soundscape

investigation.

These kinds of study are particularly important at this early stage of soundscape

ecology research, since this discipline is demonstrating its suitability to both interpret

the state of health of environments and monitor the anthropogenic challenges that

natural environments face today.

Acknowledgements

We would like to thank the editor, Dr. Alejandro Estrada, and one anonymous

referee for useful comments and constructive suggestions on this manuscript. We

warmly thank all of the staff at the national park of Serra do Cipó, the environmental

station of Peti and the state park of Rola Moça who assisted with our study. We are also

grateful to Marina Scarpelli, Mariane Kaizer and Renan Duarte for their help during the

data acquisition. This study was funded by FAPEMIG and VALE S.A. We would also

like to thank CNPq for their continuing support. R.J.Y. and M.R. were financially

supported by CNPq and FAPEMIG (PPM). The authors declare that there are no

conflicts of interest, financial or otherwise.

43


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49

The impact of anthropogenic noise from open cast mining on Atlantic forest biophony Capítulo 2

- CAPÍTULO 2-

The impact of anthropogenic noise from open cast mining on Atlantic

forest biophony

Artigo submetido ao periódico Biological Conservation

Abstract

Anthropogenic noise (anthropophony) is known to cause negative impacts on animal

communication and wellbeing. Mining is an important economic activity in Brazil,

which is often conducted close to forested areas and produces a diffuse noise. In this

study we investigated the impact of such noise on biophony (biological sounds) by

characterizing and comparing the soundscapes of two different sites (close versus far

from an open cast mine) in the same Atlantic forest fragment matched for habitat type in

Southeast Brazil. Six Song Meters (SM2) were installed in each site and programmed to

record continuously during seven continuous days every two months from October 2012

to August 2013. Anthropophony and biophony values were derived from power spectra

and the Acoustic Complexity Index (ACI). As predicted, anthropophony was

significantly higher closer to the mine site. Biophony was significantly higher in the wet

season at both sites. Anthropophony was significantly higher in the wet season close to

the mine. The soundscape of the site close to the mine presented higher biophony during

the day and higher anthropophony levels at night whereas the site far from the mine

showed higher biophony during the night. Potential species richness was higher at the

site far from the mine. The animal community composition and the spectral

characteristics of the calls were different between the two sites. Thus, here we have

shown that mining noise can affect biophony dynamics by modifying the temporal

distribution and daily patterns of animal sounds. These results provide important

information to be taken into consideration during the regulation of the use of natural

areas for mining.

Keywords: Acoustic Complexity Index, Atlantic forest, biophony, mining activity.

50


1. Introduction

Opencast mining can produce high sound pressure levels by exploratory and

production drilling, blasting, cutting, handling of materials, ventilation, crushing,

conveying and ore processing (Donoghue, 2004). This massive noise pollution can

negatively impact wildlife. Mining has been shown to impact breeding birds by

reducing their density (Smith et al., 2005), their species diversity, and their population

sizes (Saha and Padhy, 2011). Ant species richness also decreases due to mining activity

(Queiroz, 2013). Despite the evidence that noise pollution negatively affects wildlife

reproduction and longevity (Warren et al., 2006; Slabbekoorn and Ripmeester, 2008;

Barber et al., 2009; Francis et al 2011; Kight and Swaddle 2011), sound pollution from

mining activity is still poorly regulated around the world (Hessel and Sluis-Cremer,

1987; Frank et al., 2003).

Many animal species depend on acoustic signals for intraspecific communication

(Catchpole and Slater, 2008). Several studies have shown that noise may reduce habitat

quality for many species (Bayne et al., 2008) by masking sound signals and decreasing

the efficiency of animal communication (Langemann, et al 1998; Lohr et al., 2003;

Brumm, 2004; Bee and Swanson, 2007). Noise can also decrease reproductive success

(Halfwerk et al., 2011), alter mating systems (Swaddle and Page, 2007; Habib et al.,

2007) and parental care in bird species (Schroeder et al., 2012). Nonetheless, some

animal species are capable of adjusting their acoustic signals to communicate in noisy

environments, for example, increasing their amplitude (Brumm et al., 2004; Brumm et

al., 2009), shifting frequencies (Slabbekorn and Peet 2003; Parks and Clark 2007;

Nemeth and Brumm, 2009), calling rates (Sun and Narins, 2005), call duration (Brumm

et al., 2004) or time of calling (Fuller et al., 2007; Sousa-Lima and Clark, 2008). Other

species present changes in behavior by avoidance of noisy areas for foraging (Miksis-

Olds et al., 2007; Schaub et al., 2008), and other daily activities (Sousa-Lima and Clark,

2009; Duarte et al., 2011). Area avoidance and acoustic compensatory mechanisms to

reduce or offset the effects of noise may alter the acoustic complexity of a community

in a given location and result in a decrease in species’ abundance (Bayne et al., 2008)

51


and/or diversity (Proppe et al., 2013) in noise polluted sites.

Anthropogenic noise has become omnipresent in natural soundscapes (Barber et

al., 2011) and despite evidence showing negative impacts on animals, there is still a lack

of official regulation of noise produced by industrial and exploratory activities in

terrestrial natural areas. The Atlantic forest in Brazil is one of the richest and most

endangered biomes of the world (Myers et al., 2000) where much mining activity takes

place. Despite this, there are no laws regulating sound pollution levels allowed in this

biome. In many countries of the world, noise monitoring from industrial activities is

required only in respect to its impacts on human health. Thus, the already known

impacts of noise on wildlife should drive efforts to develop environmental legislation to

protect wildlife (Brown et al., 2013).

Passive acoustic monitoring (PAM) methods provide opportunities to evaluate

the consequences of different land use decisions (Blumstein et al., 2011; Mennit and

Fristrup 2012; Brown et al., 2012 and 2013), especially in environments such as mines,

that are difficult to access or monitor using conventional methods (Mellinger and

Barlow 2003; Scott Brandes, 2008). PAM devices can record data during several days

continuously and hence, a large amount of information can be collected from the

acoustic environment. As a result, special software and indices to rapidly and efficiently

process audio files are required (Sueur et al., 2014). In this context, Pieretti et al. (2011)

introduced the Acoustic Complexity Index (ACI), which allows an indirect and rapid

measure of the complexity of the soundscape. The ACI has been proven to be a useful

tool in tracking the dynamics of the sounds produced by animal communities (Farina et

al., 2013) by describing the spectral complexity of the biophony of soundscapes,

through the intrinsic variability of biotic sounds. This index has already been used in

noisy environments (Pieretti et al., 2011; Pieretti and Farina, 2013) and Towsey et al.

(2014) indicate ACI as one of the best indicators of bird biodiversity among 14 different

acoustic indices.

There are no studies investigating how anthropogenic noise affects soundscapes

and biophony in mining areas. The aim of this study was to investigate noise effects on

52


Atlantic forest soundscape dynamics by comparing biophony and anthropophony in a

site close to an active opencast mine and at a habitat matched site far from any mine or

anthropogenic activities.

2. Methodology

2.1 Study area

Data were collected at the Environmental station of Peti in the municipalities of

São Gonçalo do Rio Abaixo and Santa Bárbara, Minas Gerais state, Brazil (centered at

19°53’57’’S and 43°22’07’’W). The climate of southeastern Brazil can be divided into

two macro-climatic seasons: a hot wet season, from October to March, and a cooler dry

season from April to September (Minuzzi et al., 2007).

The reserve is an Atlantic forest fragment of approximately 605 ha located in the

upper Rio Doce Basin (altitude range: 630-806m). It is estimated that the area harbors

approximately 29 species of anurans (Bertoluci et al., 2009), 231 species of birds (Faria

et al., 2006) and 46 species of mammals (Paglia et al., 2005). A large part of the reserve

is covered by secondary arboreal vegetation of continuous canopy and large trees

(Nunes and Pedralli, 1995).

Peti is surrounded by small farms and is contiguous with the Brucutu Mine,

which occupies an area of 8km2 and produces noise through road traffic, sirens and

explosions during the day and night (Roberto, 2010). Brucutu’s iron ore extraction

started in 1992 and currently is one of the largest mines of the world (Roberto, 2010).

2.2 Acoustic recordings and data analysis

Sensor arrays comprised by six Song Meter Digital Field Recorders (SM2)

(Wildlife Acoustics, Inc., Massachusetts) distributed in two triangles were installed in

two sites and programmed to record continuously during seven days every two months

53


from October 2012 to August 2013 (six recording sessions). Both sites were matched by

habitat and located in the same Atlantic forest fragment. The 6-SM2 array close to the

active opencast mine was installed at a distance of 500m from the mine and 25m from

the closest mining road. The 6-SM2 array far from the mine was installed at a distance

of approximately 2 500m from the mine and 25m from a rarely used road in order to

control for a potential border effect due to the physical structure of the road (Fig 1).

Figure 1. Position of the passive acoustic monitoring devices close (1) an far (2) to the

mine site at Peti Environmental station, Southeast Brazil.

To avoid overlap of sounds recorded, each SM2 within each sensor triangle was

placed 80m from each other. This distance between recorders was established during a

pilot study conducted in the area. The distance between two SM2 triangles was at least

100m in order to have two independent recording samples in each site (close and far

from the mine). The distance between arrays (far and close sites) was approximately 2

300m (Fig 1). The triangular array geometry was chosen to have one SM2 at the forest

border and two located 80m towards the interior of the forest.

54


Each SM2 was fixed on a tree at 1.5m above the ground and placed in order to

have the two lateral microphones clear of any surface that could be an obstacle to

incoming sound waves. They were configured to record in wave format at a sampling

rate of 44.1 kHz, at 16 bits. One SM2 was stolen during the 5th session (at the site close

to the mine), and the 2nd session was not considered for one SM2 installed in the site

far from the mine because the geophony produced by a flooded river masked all

incoming sounds.

The collected data were sub sampled by analyzing the first two minutes of

recordings every hour. The resulting 23 520 minutes (392 hours) were further processed

using the Wavesurfer software (Sjölander and Beskow 2000) powered by the

SoundscapeMeter plug-in (Farina et al., 2012). A Fast Fourier Transform (FFT) of 512

points was applied to obtain from every two-minute file a matrix made by 256

frequency bins of 86.13Hz and 10 335 time intervals of 0.012s. The resulting database

of power spectra (i.e., the sound energy values along a frequency axis in each temporal

interval) was used to analyze and describe two sonic components of the soundscape in

each site: anthropophony and biophony.

All files were separated into two frequency bands: 1) 0–1.5 kHz (predominantly

occupied by noise or anthropophony) and 2) 1.5–22.05 kHz (mainly occupied by

biophony). The lower frequency band was used to characterize noise by considering the

raw values expressed in the power spectrum and the second band was further processed

to extract values of the Acoustic Complexity Index (ACI) (Farina et al., 2011; Pieretti et

al., 2011). The threshold of 1.5 kHz was chosen since most of the energy of the

anthropogenic noise is mainly concentrated under 2kHz (Warren at al., 2006), and

lowered 500Hz to prevent the exclusion of some important biophonies from the ACI

calculations that were just above 1.5 kHz (Pieretti and Farina, 2013). Nonetheless, in the

site closest to the mine, noise produced by truck transits often covered frequencies up to

7-8 kHz, sometimes reaching upwards of 21 kHz. To avoid bias from these specific

events in the ACI estimations of the biological sound expression, a specific routine was

elaborated in JustBasic v.1.01 to recognize and eliminate from the recordings every

recorded truck passing. This allowed us to focus only on the features of biophony

55

The impact of anthropogenic noise from open cast mining

on Atlantic forest biophony Capítulo 2

above the threshold of 1.5 kHz.

Sound recording files dominated by rain or wind, which can also influence ACI

estimations, were eliminated from the analysis.

Noise levels at the sites close and far from the mine were compared by

conducting two 20 minutes measurements of the background sound pressure levels at

each SM2 recording point using a A-weighted B&K2270 sound level meter. We

excluded from the recordings all the animal sounds close to the microphone using the

BZ5503 software. The standard sound pollution measurements of minimum (Lmin),

maximum (Lmax) and equivalent sound levels (Leq) were then extracted from the

recordings (Rossing 2007). The number of passings of the mining trucks per day was

determined by listening to the recordings made at the site close to mine during 24 hours

in two days in each recording session using Raven Pro 1.5. Recurrent sounds produced

by the mine were classified and characterized. Different types of anthropogenic sounds

were selected from two days of recordings (48 hours) from one SM2 at the site close to

the mine in each recording session. The two most frequent types of noise were truck

passing noise and reverse warning sound of trucks. Twenty noise events were randomly

selected per day, totalizing 240 truck traffic events and 240 reversing sound events. For

the less frequent noises, such as explosions, horns, and sirens, all events heard in two

days were selected. These noise events were described using Raven Pro 1.5 by

measuring their Min, Max, Peak frequency and Duration.

Along with noise, variables such as species richness, species composition and

abundance could have influenced the acoustic dynamics of the two sites. To account for

such differences between the sites close and far from the mine, species richness was

obteined for each site by the aural identification of animals’ sounds using Raven Pro 1.5

software. A single day of recording per session in four points (two in the close and two

in the far site) were randomly selected for species identification surveys. Sounds

emitted by amphibians, birds, mammals, and insects were identified by specialists in the

two minute files from 0500 to 0700 hours, 1000 to 1200 hours, and from 1800 to 2200

hours, totalizing 528 analyzed minutes.

56


The above time slots were chosen to include: the dawn and dusk choruses of

birds and the midday and night activity of insects. It was not possible to determine

species abundance aurally due to the huge number of calls recorded. Insects and bats

sounds were classified as soundmorphs (different sound emissions or codas). This

procedure was essential to identify potential species since biodiversity in the Atlantic

forest is so high that it is impossible to identify every species by aural census – and

there is a chance that some species recorded are not yet taxonomically classified.

We extracted from the recordings the bandwidth, minimum and maximum

frequencies of bird vocalizations and insect stridulations (most representative groups),

that were identified only at one of the two sites (close or far from the mine) to compare

the acoustic niche occupation of the singing community between the two sites.

2.3 Statistical analysis

Data analyses were separated into wet and dry seasons, and into day (5 am to 5

pm) and night (6 pm to 4 am). We conducted two analyses considering the time of the

day: (1) comparison of ACI day x ACI night in each site separately; and (2) comparison

between sites of ACI day and ACI night.

Preliminary analyses showed that sample points on the border (closer to the

roads), at both sites, were noisier than the other points, therefore data analyses also

included the groups: border and forest points.

All the statistical tests were performed using Statistica v.8.0. A non-parametric

approach was used, since the variables were not normally distributed, even after

attempted transformation of the data values. Mann Whitney U tests were conducted to

test for differences in ACI and noise values between: sites (close and far from the

mine); seasons (wet and dry); and time period (day and night).

57


3 Results

3.1 Mining noise characterization

Sites close and far from the mine differed significantly in terms of background

noise. The site close to the mine showed levels 3 to 36 dB(A) higher in comparison to

the site far from the mine (T=2.94, DF=5, p<0.05). Mean Leq, Lmax and Lmin of each

type of soundscape are shown in Table 1. The noise measured using power spectral

density confirmed the results of the noise level measurements, showing that noise was

significantly higher at the site close to the mine both on the border and on the interior of

the forest (Border: U=118, Z= 20.70, p<0.01, Nclose=288, Nfar=288; Forest: U=3556,

Z=28.4, p<0.01, Nclose=552, Nfar=575).

Table 1. Mean noise levels measurements in sites close and

far from an opencast mine at Peti environmental station,

Minas Gerais, Brazil.

Site Leq dB(A) L max dB(A) L min dB(A)

Close (border) 51.7 78.0 30.9

Close (forest) 41.2 64.1 30.1

Far (border) 35.5 41.3 27.4

Far (forest) 33.2 48.9 23.3

The five most frequent mining noise sources identified in the site close to the

mine were: trucks passing, reversing alarm of trucks, work sirens, horns, and explosions

(Fig. 2). The most frequent noise was truck transiting. A mean of 700±43.8 (mean±SD)

trucks passed daily (29.91±1.82 trucks/hour) in the wet season and 244.6±57

(10.91±2.37 trucks/hour) in the dry season. The descriptive statistics of the acoustic

parameters of each noise event type are presented in Table 2.

58


Table 2. Acoustic variables of most frequent noise sources from mining activity at Peti

environmental station, Minas Gerais, Brazil.

Noise source Duration (s)

Average ±SD

Peak Freq.(Hz)

Average ±SD

Max Freq.(Hz)

Average ±SD

Min Freq.(Hz)

Average ±SD

Truck

N=240

20.2±8.9 553.9±38.6 15291.0±43.8 0.0±0.0

Reversing

N=240

10.56±2.54 1314.6±91.4 1373.9±90.6 1255.1±91.4

Siren

N=75

17.9±4.6 1229.4±136.1 1393.6±101.4 872.3±144.9

Horn

N=19

6.7±14.5 1219.0±98.5 4747.0±55.4 781.0±75.9

Explosion

N=19

5.9±3.2 197.2±117.8 4353.0±206.0 90.4±30.7

59


Figure 2. Spectrograms of the noise produced by: A- transit of trucks, B- explosion, C-

work sirens, D-horns, E-reversing alarm of trucks on a mining road at Peti

environmental station, Southeast Brazil. In the background of the spectrograms there is

also biophony.

60


Considering that a mean of 700 trucks passed per day in wet season with a mean

duration of 20.2 seconds, this means 16.2% of the time of the day was occupied by

truck transiting noise. The mean maximum frequency of this type of noise event was

15.2 kHz, meaning that the noise occupied 68% of the full spectrogram bandwidth

(22.05 kHz).

3.2 Soundscape dynamics

3.2.1 Wet versus dry season

The ACI was significantly higher in the wet season than in the dry season at both

sites (Close border: U=4824, Z= 7.84, p<0.01; Nwet=144; Ndry=144; close forest: U=

20759, Z= 8.04, p<0.01; Nwet=264; Ndry=264; Far border: U= 5915, Z= 6.3, p<0.01;

Nwet=144, Ndry= 144; Far forest: U= 23689, Z= 7.65, p<0.01; Nwet= 288, Ndry=264).

Noise showed a similar trend except for the interior of the forest at the site close the

mine (Close border: U=6531, Z= 5.42, p<0.01; Nwet=144, Ndry 144; Close forest:

U=37359, Z=-0.35, p= 0.72; Nwet=288, Ndry=264).

3.2.2 Day versus night

In the wet season, at the site far from the mine, the ACI values were significantly

higher during the night (Border: U=1220, Z=5.42, p<0.01; Nday=78 Nnight=66; Forest:

U=3386, Z=8.51, p<0.01; Nday= 143, Nnight=121). Contrastingly, there was no difference

between the ACI values of day and night at the site close to the mine (Border: U=2481,

Z=0.37, p=0.70; Nday=78, Nnight=66; Forest: U=8498, Z=-0.24, p=0.80; Nday=143,

Nnight=121). Noise values were significantly higher during the night at the site close to

the mine (Border: U=1884, Z=2.76, p<0.01; Nday= 78, Nnight=66; Forest: U=7304,

Z=4.24, p<0.01; Nday=156, Nnight=132).

61


In the dry season, the ACI values were significantly higher during the night at

both sites except on border points at the site close to the mine (Far border U=1987,

Z=2.35, p<0.01; Nday=78, Nnight=66; far forest: U=6840, Z=4.9, p<0.01; Nday=156,

Nnight=132; close border U= 2279, Z= 1.18, p=0.23; Nday=78, Nnight=66; close forest:

U=5595, Z=4.94, p<0.01; Nday=143, Nnight=121). At the site close to the mine, noise

values were also significantly higher during the night, but only on the forest points

(Forest: U=6764, Z=3.05, p<0.01; Nday=143, Nnight=121; Border: U=2276, Z=1.19,

p=0.23; Nday=78, Nnight=66).

3.2.3 Close versus Far site

In the wet season during the night the ACI was significantly higher at the site far

from the mine (Fig.3, A,B) (Border: U=1510, Z=-3.04, p<0.01; Nclose=66, Nfar=66;

Forest: U=5145 Z=-3.99, p<0.01; Nclose=121, Nfar=121) and during the day at the site

close to the mine (Border: U=2032, Z=3.57, p<0.01; Nclose=78, Nfar=78; Forest: U=

6191, Z=5.76, p<0.01, Nclose=143, Nfar=143).

In the dry season, there was no significant difference in ACI values at night

between the two sites (Fig 4, A,B) (Border: U=2070, Z=-0.49, p=0.62; Nclose=66

Nfar=66; Forest: U= 7156, Z=-1.42, p=0.15; Nclose=121, Nfar=132), while during the day

ACI values were significantly higher at the close site except at the border points

(Border: U= 2633, Z= -1.44, p=0.14; Nclose=78, N far=78; Forest: U= 8989, Z= -2.89,

p<0.01; Nclose=143, Nfar=156).

62


Figure 3. Temporal distribution of the ACI values at border (A) and forest (B) points at sites

close and far from an opencast mine during the wet season at Peti Environmental Station,

Brazil.

Figure 4. Temporal distribution of the ACI values at border (A) and forest (B) points at sites

close and far from an opencast mine during the dry season at Peti Environmental Station,

Brazil.

3.3 Characterization of fauna

A total of 91 bird species (16 were classified as "not identified" due to the short

duration of the song or long distance from the microphone, which prevented

identification), 84 different soundmorphs of insects, 9 of bats, 3 species of frogs and 2

species of primates were identified. In both sites, the insect community was particularly

acoustically active during the night although cicadas were highly active during the day.

63


The bird community showed greater acoustic activity during the day, mainly

during the dawn chorus, with few species singing during the night. Primates were

especially vocal early in the morning. Insect species were estimated to be higher in the

wet season and especially during the night. Bat calls were detected only at night. The

richness of species was higher: (1) during the wet season on both sites; (2) during the

day in both seasons and sites; and (3) on the site far from the mine, especially in wet

season. Species richness results from the aural survey are shown in Table 3. Insects

species detected only at the site far from the mine presented stridulations with

significantly larger bandwidth (bandwidthclose=1777Hz±378, bandwidthfar=3233Hz±517;

U=3391.5, Z=-3.88, p<0.01, Nclose=80, Nfar=125), higher maximum (max.

freq.close=6117Hz±360, max.freq.far=8560Hz±615; U=3893, Z=-2.67, p<0.01, Nclose=80,

Nfar=125) and minimum frequencies (min. freq.close=4340Hz±213 min.freq.far=5326Hz ±

262; U=3877, Z=-2.71 p<0.01, Nclose=80, Nfar=125) than species which occurred only at

the site close to the mine. The opposite occurred with bird species. Species recorded

close to the mine presented significantly larger bandwidth (bandwidthclose=2189±164,

bandwidthfar=2088±212; U=11723, Z=2.16, p<0.05, Nclose=160, Nfar=170), higher

maximum (max. freq.close=5832Hz ±243, max.freq.far=3425Hz ± 249, U=6039, Z=8.72,

p<0.01, Nclose=160, Nfar=170) and minimum frequencies than species from the site far

from the mine (min. freq.close= 3643Hz ±172, min.freq.far =3425H ± 249, U=11110,

Z=2.87, p<0.01, Nclose=160, Nfar=170).

64


Table 3. Potential number of species at close and far sites from an opencast mine at the Peti Environmental Station, Brazil by season (wet

versus dry) and time of day (day versus night).

CLOSE FAR

WET DRY WET DRY

Taxonomic

Group Day Night Total Day Night Total Day Night Total Day Night Total

Species

in

common

Species

only in

close

Species

only in

far

Insects 25 36 49 15 23 33 26 42 54 12 27 36 43 16 25

Amphibians - 1 1 - - - - 2 2 - 1 1 1 - 1

Birds 40 - 40 34 - 34 43 - 43 34 1 35 25 32 34

Bats - 4 4 - 7 7 - 5 5 - 2 2 6 2 1

Primates 2 - 2 2 - 2 2 - 2 2 - 2 2 - -

Total species 67 41 96 51 30 76 71 49 106 48 31 76 77 50 62

65


4 Discussion

Large scale anthropogenic activities can have a considerable impact on the

accomplishment of daily ecological functions of a community (Francis et al 2011);

especially on acoustic communication processes (Rabin et al., 2003, Slabbekoorn and

Ripmeester, 2008). Noise is one of the most common threats to landscapes around the

world due to its established negative impacts on fauna (Brown et al., 2013, Pieretti and

Farina, 2013). Although mining is an important economic activity in many parts of the

world, its subtle effects on animal ecosystems are still poorly understood. The approach

taken in this study of investigating acoustic dynamics has recently been considered as a

proxy for biodiversity measurement (Krause, 1987; Sueur et al., 2008) and can also

provide additional information related to species’ adaptation and the well being of

animal communities (Farina et al., 2011).

Our results showed that there were significant differences between the sites close

and far from the mine in terms of anthropogenic noise levels. Considering that the

mining activity has been ongoing in the study area for decades, changes in the behavior

of the animal community could be interpreted as long term responses to mining impacts.

Noise sources in our study area were diverse, continuous and occupied a wide

frequency bandwidth potentially masking many animal sounds and affecting their

behavior and distribution. For example, some birds with low frequency vocalizations

were only recorded far from the mine such as Patagioenas plumbea, Leptotila sp.,

Leptotila verreauxi, and Ramphastos toco (see supplementary material - table 1).

During the wet season almost 70% of the frequency bandwidth (up to 22kHz) was

completely occupied by the truck transit noise for 16% of the day time, interfering with

the acoustic space used by the animal community.

As expected, the acoustic complexity registered during the wet season was much

higher than in the dry season. The wet season in Brazil coincides with breeding season

of insects, amphibians and birds; thus, animals are more acoustically active (Aichinger,

1987; Haddad et al., 1992; Rodrigues et al., 2005). This result was confirmed by the

66


species count, since a higher number of species from all of the animal groups was

detected during the wet season.

Our results showed a change in soundscape dynamics resulting from proximity

to the mine. In the wet season, the site close to the mine lost the diel pattern of having

higher acoustic activity at night found at the site far from the mine (Fig. 3), presenting a

tendency of higher acoustic complexity along the day and lower along the night. This

phenomenon can be explained by two possible reasons: (1) noise levels are lower during

daytime and the acoustic community can be more acoustically active during the day

when there is more available acoustic space and less competition with anthropogenic noise; (2) acoustic activity might be higher than expected during the day simply because

there are more singing species, which affects the ACI values. The first explanation is

most likely because at the site far from the mine, species richness was higher during the

day and ACI was higher during the night. Greater acoustic activity during the night was

expected at both sites because the majority of biophony was due to the insects. This

animal group is mostly active during the night and produces long songs with high

amplitude that results in high ACI values (Farina et al. 2011).

Comparison of the same period of the day between sites (close day versus far day and

close night versus far night) yielded similar results. Higher acoustic activity at the site

close to the mine during the day could also be explained by anthropogenic noise. During

the day the number of species recorded was higher at the site far from the mine and we

expected a higher ACI value as well. However, we observed the opposite. We suggest

that this result might be related to compensatory mechanisms of individuals trying to

propagate their signals with greater emphasis (higher amplitude or repetition of the

strophes or syllables) to override the masking effect of anthropogenic noise. The

number of individuals singing might have an effect on ACI values, but unfortunately,

species abundance was impossible to assess. Other studies have found similar results in

different environments. Pieretti and Farina (2013) found that both ACI values resulting

from birds and noise were significantly higher with greater proximity to a road,

indicating a more active singing/calling community where noise is more intense.

67


The animal community in an urban forest in Brazil also presented higher activity

in noisy sites (Santos, 2013). Birds and mammals can present a behavior known as the

Lombard Effect in which animals increase the amplitude of their calls in the presence of

high levels of competing environmental noise (Cynx et al., 1998; Brumm et al., 2004,

Brumm and Slater, 2006). Additionally, many species are capable of increasing the rate

and duration of their vocalizations to ensure the efficiency of their communication

(Brumm et al., 2004, Sun and Narins, 2005). Greater amplitudes, higher emission rates

and longer call duration could all lead to an increase in ACI values and explain our

results.

The higher ACI values at night in the site far from the mine in comparison with the

values from the same period in the site close to the mine may be a direct effect of a

higher number of species vocalizing, especially insects. Noise can affect species

diversity and population density of birds in areas close to mining activity (Saha and

Padhy 2011). Our results show that the number of species was lower at the site close to

the mine and species composition was different between the two sites. Many studies

concerning the effects of road noise on animals show that there is a strong negative

relationship between traffic intensity and species richness with changes in composition

and density of individuals (Forman et al., 2002; Rheindt, 2003). Bayne et al.

(2008) showed that near noiseless energy facilities passerine density was 1.5 times

higher than areas near noise-producing energy sites. Other factors can contribute to a

low species richness, abundance and diversity in noisy environments; for example,

quality of habitat, vegetation characteristics, air and chemical pollution, soil vibration

and others (Summers et al., 2011). Therefore, due to the importance of acoustic

communication, which animals use to find food (Elowson et al., 1991; Slabbekoorn and

Ripmeester, 2008) and reproductive partners (Patricelli et al., 2002); to escape from

predators (Greig-Smith, 1980; Chan et al., 2010); to defend resources (Zuberbuehler et

al., 1997) just to name the main functions, it is expected that noise will affect species

richness, abundance and community composition. The acoustic measurements of the

insect stridulations and bird vocalizations showed other possible effects of

anthropogenic noise on the animal communication.

68


The greater bandwidth of the insect stridulations in the site far from the mine

could be interpreted as the natural pattern that evolved in the absence of anthropogenic

noise. In contrast, the noise present in the site close to the mine could be selecting for

species with narrowband stridulations, since a considerable part of the local acoustic

space is occupied by anthropogenic noise. Species that produce sounds which occupy

less acoustic space can better cope with the competition from noise given that the

probability of overlap would be reduced, especially if the spectral occupancy pattern of

these animals’ sounds overlaps with less intense bands of noise. Bird vocalization

analyses showed different results. Species with sounds with larger bandwidths were

recorded in the site close to the mine. Additionally, bird calls at the site close to the

mine presented higher maximum and minimum frequencies in comparison with species

from the site far from the mine. Hence, it could be speculated that the pervasive noise in

the site close to the mine could be selecting species which vocalize at higher frequencies

and are less masked by the noise. This can be confirmed by the absence of species,

which vocalize at very low frequencies in that site. Rheindt (2003) has shown that there

is a significant relationship between dominant frequency of bird vocalizations and

decline in abundance towards the motorway, indicating that having a higher pitched

song with frequencies above those of traffic noise makes birds less susceptive to

anthropogenic noise. Hence, our result supports the hypothesis that noise can affect the

animal community by changing its singing dynamics.

5. Conclusion

Many studies have shown the negative impact of noise pollution on animal

acoustic communication and other studies have shown negative impacts on species

diversity, richness and abundance. Nevertheless, studies about the impact of

anthropogenic noise on the biophony in the terrestrial soundscapes in tropical

environments are still inexistent. Here we have shown that sound pollution from

opencast mining activities has a significant impact on the biophonical soundscape of a

neighboring tropical forest. Differences found in soundscape complexity were probably

69


related to lower species richness at the site close to the mine, changes in animal

community composition, spectral characteristics of calls between the two sites and

possible animal adaptive responses to noise. Given that opencast mining is a major

global economic activity, which frequently occurs in natural areas, these results show

the need for its noise impact to be taken into consideration during the elaboration of

conservation and management strategies of natural areas close to mining activity.

Beside this, we provide data to enhance the importance of the elaboration of laws and

regulations to monitor and control noise close to natural areas.

6. Acknowledgements

We would like to thank all of the staff at the environmental station of Peti who

assisted with our study, especially Leotacílio da Fonseca. We are also grateful to Marina

Scarpelli, Mariane Kaizer and Renan Duarte for their help during data acquisition and

the engineer Krisdany Cavalcante for help with the noise level measurements.

7. Role of the funding source

We would like to thank CNPq for their continuing support. RJY and MR were

financially supported by CNPq and FAPEMIG (PPM). MHLD was supported by a

FAPEMIG postgraduate scholarship during this research. This study was funded

competitively by FAPEMIG from a financial donation made by VALE, but VALE did

not in any way restrict our research or contribute to its design, execution or publication.

70


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Supplementary material:

Table S1. Potential species richness in sites close and far from an opencast mine

at Peti Environment Station, Brazil by season and time of day.

Taxonomic group CLOSE FAR

Insects WET DRY WET DRY

Day Night Day Night Day Night Day Night

Insect 1 x x x

Insect 2 x x

Insect 3 x x x x x x

Insect 4 x x x

Insect 5 x x x x x

Insect 6 x x x

Insect 7 x x x x x x x x

Insect 8 x x x x x x x x


Insect 10 x x

Insect 11 x x x

80

Insect 12 x x

Insect 13 x x

Insect 14 x

Insect 15 x x

Insect 16 x x

Insect 17 x

Insect 18 x x x x

Insect 19 x x x x

Insect 20 x

Insect 21 x x x x

Insect 22 x x

Insect 23 x x x

Insect 24 x x x


Insect 26 x x x

Insect 27 x x x x x

Insect 28 x x x

Insect 29 x x x

81

Insect 30 x x

Insect 31 x

Insect 32 x

Insect 33 x x

Insect 34 x x x

Insect 35 x x x

Insect 36 x x x

Insect 37 x x x

Insect 38 x x

Insect 39 x

Insect 40 x

Insect 41 x

Insect 42 x

Insect 43 x x

Insect 44 x x x x

Insect 45 x


Insect 47 x

82

Insect 48 x

Insect 49 x x

Insect 50 x x

Insect 51 x x

Insect 52 x

Insect 53 x x x

Insect 54 x

Insect 55 x

Insect 56 x x x x x x x

Insect 57 x x x x

Insect 58 x x

Insect 59 x x x

Insect 60 x x

Insect 61 x x x x

Insect 62 x

Insect 63 x x x

Insect 64 x x

Insect 65 x x

83

Insect 66 x x x

Insect 67 x

Insect 68 x

Insect 69 x

Insect 70 x x

Insect 71 x

Insect 72 x

Insect 73 x

Insect 74 x

Insect 75 x x

Insect 76 x

Insect 77 x x x

Insect 78 x x

Insect 79 x

Insect 80 x

Insect 81 x

Insect 82 x

Insect 83 x

84

Insect 84 x

Amphibians x x

Hypsiboas faber x

Hypsiboas lundii x x x

Birds

Arremon sp. x

Amazilia lactea x

Automolus

leucophthalmus

x

Basileuterus

culicivorus

x

Camptostoma

obsoletum

x

Capsiempis

flaveola

x

Casiornis rufus x

Chiroxiphia

caudata

x x x

Conopophaga

lineata

x x

Crypturellus

obsoletus

x x

Drymophila

ochropyga

x

Dysithamnus

mentalis

x

85

Dysithamnus sp. x

Elaenia sp. x x

Euphonia

chlorotica

x

Formicivora

serrana

x

Hemithraupis

ruficapilla

x x x

Herpsilochmus

atricapillus

x x x x

Hylophilus

amaurocephalus

x x x x

Lanio melanops x x x

Lanio pileatus x

Legatus

leucophaius

x

Leptotila sp. x

Leptotila verreauxi x x

Manacus manacus x x x

Micrastur

semitorquatus

x

Milvago

chimachima

x

Myiarchus ferox x

Myiarchus sp. x

Myiodynastes x

86

maculatus

Myiopagis

viridicata

x x

Myiornis

auricularis

x

Myiotlhypis

flaveola

x x x x

Nyctiphrynus

ocellatus

x

Patagioenas

plumbea

x x

Phaeomyias

murina

x x

Phyllomyias

fasciatus

x x x

Picumnus cirratus x x

Platyrinchus

mystaceus

x x

Psittacara

leucophtalma

x

Pyriglena

leucoptera

x x

Ramphastos toco x

Saltator similis x x x

Schiffornis

virescens

x

Sporophila

nigricollis

x x

Synallaxis

cinerascens

x

87

Syndactyla

rufosuperciliata

x

Tachyphonus

coronatus

x

Tangara cayana x x x

Tangara

cyanoventris

x

Tangara sp. x x

Thamnophilidae x

Thamnophilus

caerulescens

x x

Thlypopsis sordida x

Thraupidae 1 x x x

Thraupidae 2 x x x x

Thraupidae 3 x x x

Thraupidae 4 x x x x

Thraupidae 5 x x x

Thraupidae 6 x

Thraupidae 7 x

Thraupidae 8 x

Thraupidae 9 x x

Thraupidae 10 x

88

Thraupis sp. x

Tolmomyias

sulphurescens

x x x

Trochilidae x x

Trogon surrucura x x x x

Turdus albicollis x x x

Turdus leucomelas x

Turdus rufiventris x

Turdus sp. x

Tyrannidae x x x

Vireo chivi x x

Zonotrichia

capensis

x

NI1 x x

NI2 x x

NI3 x

NI4 x

NI5 x

NI6 x

NI7

89

NI8 x

NI9 x

NI10 x

NI11 x

NI12 x

NI13 x

NI14 x

NI15 x

NI16 x

Bats

Bat 1 x

Bat 2 x x x

Bat 3 x x

Bat 4 x x x

Bat 5 x x

Bat 6 x x

Bat 7 x x

Bat 8 x x

90

Bat 9 x

Primates

Callithrix

penicillata

x x x x

Callicebus

nigriffrons

x x x x

91

Mining noise reduces loud call by wild black-fronted titi monkeys Capítulo 3

- CAPÍTULO 3-

Mining noise reduces loud calls by wild black-fronted titi monkeys

Artigo submetido ao periódico International Journal of Primatology

Abstract

Human activity has resulted in increased anthropogenic noise on soundscapes. Noise

pollution can constrain acoustic communication and prevent animals to effectively

communicate. Our aim was to investigate how the black-fronted titi monkey (Callicebus

nigrifrons) is affected by noise produced by mining activity in a fragment of Atlantic

forest in Brazil. We installed two passive acoustic monitoring devices to record

24h/day, 7 days every two months, for a year, one unit close to an opencast mine and

the other 2.5km away. Both sites presented similar habitat structures and were inhabited

by multiple groups of C. nigrifrons. Sound pressure levels measurements were

undertaken six times for 20 minutes on different days at both sites. The number of

Callicebus loud calls was quantified at both sites by analyzing the recorded files. The

site close to the mine presented higher noise levels than the one further away. More

black-fronted titi loud calls were detected at the far site and many vocalisations

(20.32%) from the site close to the mine were masked by noise. Duration of loud calls

was longer at the site far from the mine and the diel pattern of vocalisations was

different between the two sites. Our results indicate that mining noise can constrain

Callicebus long distance vocal activity, probably because their loud calls occupy a

similar frequency band of the noise. Given that vocalisations are important regulators of

social behavior in primates, consideration should be given to the impact of mining noise

on their behavior in impact evaluations and mitigation recommendations.

Keywords: Animal communication, anthropogenic activity, primates, social behavior,

sound masking

92


Introduction

Beyond the effect of deforestation caused by mining, one less obvious impact on

wildlife is the noise produced by such activity. Mining noise, especially if produced at

the same frequencies that animals use in their vocalisations can mask important calls

and, consequently, greatly reduce the efficiency of animal communication (Foote et al.

2004; Bee and Swanson 2007).

Acoustic communication is essential in the lives of many species as they use

such signals to transmit biologically relevant information; for example, to find

reproductive partners (Brumm et al. 2009), to escape from predators (Greig-Smith 1980;

Chan et al. 2010) and defend resources (Zuberbuehler et al. 1997). However,

anthropogenic noise has become a common impact on animal communication systems

(Slabbekoorn and Ripmeester 2008; Barber et al. 2009; Laiolo 2010). Noise can

interfere with the propagation and detection of signals by masking animal sounds and

thus, preventing effective species communication (Foote et al. 2004; Bee and Swanson

2007).

Noise pollution can also affect the behaviour of many species. Studies have

shown that animals avoid foraging in noisy areas (Schaub et al. 2008), increase their

vigilance behaviour in presence of noise (Delaney et al. 1999; Karp and Root 2009),

select quiet areas to perform their daily activities (Sousa-Lima and Clark 2009, Duarte

et al. 2011) and can be distracted by noise, thereby increasing the risk of predation

(Chan et al. 2010). Noise can also cause physiological stress (Campo et al. 2005, Kight

and Swaddle 2011) and impact on ecological aspects of animals lives such as population

distribution (Reijen et al. 1998; Bejder et al. 2006), species abundance (Bayne et al.

2008) and diversity (Proppe et al. 2013).

Animal livelihoods can be severely impaired by anthropogenic noise,

nonetheless many studies have documented a range of adaptive responses to minimize

the immediate impact of noise of communication systems including: changes in

frequency (Slabbekoorn and Peet 2003; Parks et al. 2007; Nemeth and Brumm 2009),

93


amplitude (Brumm 2004; Brumm et al. 2009; Hage et al. 2013), calling rate (Sun and

Narins 2005) number of notes (Slabbekoorn and Boer-Visser 2006), timing (Fuller et al.

2007) and duration of the calls (Brumm et al. 2004). The direct impact of noise on

animal behaviour and ecology and incidental costs of maintaining an efficient

communication system through compensatory mechanisms can impose fitness costs on

affected individuals (senders and receivers) and consequently on their survival and

reproduction (Chan et al. 2010; Schroeder et al. 2012).

The effects of mining noise on animals have been poorly documented, especially

in the Neotropical region. Smith et al. (2005) showed that diamond mines affect tundra

birds by lowering breeding bird densities. In India, stone mining and crushing affected

bird species diversity and population density in the areas adjacent to crushers (Saha and

Padhy 2011). Thus, studies involving mining noise impact in terrestrial mammals and

their communication systems are still lacking.

Species of titi monkeys (genus Callicebus) exchange loud calls (duets) to either

defend territories or food resources in their home-ranges; thus, these vocalisations are

important regulators of their social behaviour (Robinson 1979, 1981; Kinsey and

Robinson 1983; Prince and Piedade 2001; Caselli et al. 2014). Many of the forests in

South America, where titi monkeys live suffer from large scale mining (Estrada 2009).

In the state of Minas Gerais, Brazil, mining is an important economic activity and is

commonly conducted close to Atlantic forest region, one of the world’s richest

biodiversity hotspots (Myers et al. 2000). The Atlantic forest is one of the most

impacted habitats of the world retaining only 7% of its primary vegetation (Myers et al.

2000) and is home to the black-fronted titi monkey (Callicebus nigrifrons), an endemic

primate classified as Near Threatened on the IUCN’s Red List (Veiga et al. 2008).

Primates of the genus Callicebus live in monogamous family groups, consisting

of a reproductive pair and up to four generations of offspring (Kinzey and Becker 1983;

Mendoza and Mason 1986; Valeggia et al. 1999). Titi monkeys are morphologically

cryptic primates, which hinders surveying them using traditional methods such as linear

94


transects (Aldrich et al. 2008). Mated pairs of Callicebus species regularly emit loud

and coordinated calls (duets), which permit researchers to use an alternative and

potentially more accurate method to monitor populations based in call surveys (Melo

and Mendes 2000; Aldrich et al. 2008). Duetting is commonly used by many bird and

primate species for both within and between group communication (Hall 2004; Oliveira

and Ades 2004). Studies of Callicebus species show that their duets have a role in group

location and avoidance of intergroup aggressive encounters (C. lucifer, previously C.

torquatus, Kinzey and Robinson 1983; C. personatus, Kinzey and Becker 1983; Price

and Piedade 2001), in territory establishment and probably mate defense (C. ornatus,

previously C. moloch, Mason 1968; Robinson 1979, 1981). Black-fronted titi monkeys

(C. nigrifrons) loud calls are used during intergroup communication to regulate access

to important food resources, such as fruits. There is also some evidence that loud calls

are used for mate defence (Caselli et al. 2014). Typically, titi monkeys vocalise mostly

at dawn, but also during the day when another group is sighted or heard (Kinzey et al.

1977, Kinzey and Robinson 1983; Melo and Mendes 2000).

Due to spectral characteristics of titi monkey loud calls such as high amplitude

and low frequency, these calls can be heard over long distances (Melo and Mendes

2000; Caselli et al. 2014). Unfortunately, the same acoustic characteristics that were

adaptive for long distance communication are now bringing these sounds into

competition with mining noise.

In this study, we investigated how the noise produced by one of the largest

opencast mines of the world affects acoustic communication of C. nigrifrons in an

Atlantic forest fragment in Southeast Brazil. Here we tested the following hypotheses:

(1) noise levels are different in the sites close and far from the mine; (2) temporal

acoustic parameters, duration and diel pattern of titi monkey loud vocalisations would

change between the areas due to noise exposure.

95


Material and Methods

Study area

This study was conducted at Peti environmental station, which is located in an

Atlantic forest fragment of approximately 605 hectares. The reserve is located in the

upper Rio Doce Basin (altitude range: 630-806m) in the municipalities of São Gonçalo

do Rio Abaixo and Santa Bárbara, Minas Gerais state, Brazil (19°53’57’’S and

43°22’07’’W), one of the most fragmented Atlantic forest regions of Brazil (Machado

and Fonseca 2000). Peti environmental station harbors approximately 46 species of

mammals (Paglia et al. 2005), 231 species of birds (Faria et al. 2006) and 29 species of

anurans (Bertoluci et al. 2009).

A large part of the reserve is covered by secondary arboreal vegetation, and is

surrounded by a matrix mainly composed by Eucalyptus, small farms and areas of

exposed soil due to the activities of the Brucutu mine. Mining activity occupies an area

of approximately 8km2 and produces noise through road traffic, sirens and explosions

along the day and night (Roberto 2010). Brucutu’s iron ore extraction started in 1992.

To increase the capacity of iron production, expansion projects started in 2004 placing

Brucutu among the largest opencast mines in the world (Roberto 2010).

Data collection

To record black fronted titi monkey loud calls, one song meter (SM2, Wildlife

Acoustics) was installed in the forest fragment close to the mine site at a distance of

100m from the closest mining road (Fig.1). Another song meter was installed far from

the mine at a distance of 2,500m and 100m away from a low traffic (‘quiet’) road (to

control for a potential border effect at both sites in the same Atlantic forest fragment).

Both sites were habitat matched; they presented similar floristic compositions and

96

Mining noise reduces loud call by wild black-fronted

titi monkeys Capítulo 3

habitat structures and were inhabited by multiple groups of C. nigrifrons. Groups of titi

monkeys were sighted many times at both sites during fieldwork. At both sites the

passive acoustic monitoring devices were programmed to record 24h/day during seven

days every two months from October 2012 to August 2013, in a total of six sessions and

2,016 hours of recordings. Each SM2 was fixed on a tree 1.5m above the ground,

leaving the two lateral microphones free from any surface that could be an obstacle to

incoming sound waves. They were configured to record in wave format at a sampling

rate of 44,100Hz, at 16 bits, and with a 36% microphone gain. This configuration had

been found in pilot studies to be optimal for recording the soundscape of the Atlantic

forest (Pieretti et al. in press). The sound pressure levels at both sites were characterized

by using B&K2270 (Denmark) sound level meter configured on the A curve to conduct

6 measurements of 20 minutes at each site on different days and time.

This research adhered to the Brazilian legal requirements and to the American

Society of Primatologists (ASP) Principles for the Ethical Treatment of Non Human

Primates.

Fig.1 Sites close to and far from the Brucutu mine at Peti Environmental station,

southeast Brazil. Red lines represent the limit of each site by considering the geographic

barriers.

97


Data analyses

To test for a difference in noise levels between the sites close and far from the

mine we extracted data from the sound level pressure measurements and analyzed them

using BZ 5503 software (Bruel and Kjaer). To avoid bias in the measured levels we

excluded all recordings, which included loud animal sounds (i.e., animals close to the

microphone).

The rate of occurrence and the duration of black-fronted titi monkey loud calls

were measured in both sites during seven days by session from 0500 to 1700 hours

totalizing 1,092 analyzed hours. This procedure was done using the band limited energy

detector in Raven Pro 1.5 but resulted in a large number of false positives and misses.

Therefore all sound files used for analyses had to be visually and aurally checked in

Raven. We also manually detected all the loud calls, which were partially masked by

anthropogenic noise at the site close to the mine.

To verify a possible association between the noise produced by mining truck traffic at

the site close to the mine and the occurrence of the loud calls, we quantified all trucks

passing from 0500 to 1700 hours at the road in front of the sampling site. This

procedure was done by audio and visual identification of the trucks’ noise pattern in

spectrograms. An FFT size of 1024 points was used for all analyses in Raven. We used

a nonparametric statistical approach with our data analyses since data did not meet the

requirements for parametric statistics even after data transformations. All the statistical

analyses were performed in Statistica version.8.0.

Results

Sound pressure (noise) levels were significantly higher at the site close to the

mine (Mann-Whitney U-test: U=1, Z=2.72, N=6, p<0.01), as expected (Table 1).

98



Table 1. Equivalent sound pressure levels (Leq) at sites close to and far from an

opencast mine site near Peti environmental station, southeast Brazil.

Close

Leq dB(A)

Far

Leq dB(A)

42.6 33.8

38.7 30.3

42.0 30.1

60.9 37.2

42.9 38.8

41.2 33.3

Black fronted titi monkeys emitted more loud calls than expected at the site far

from the mine (Chi-squared test: X2= 339.96, df=1, P<0.001 Nclose=187, Nfar=752). A

considerable part (20.32%) of the vocalisations found in the site close to the mine was

partially masked by noise from mining activity. Duration of loud calls were also

significantly longer at the site far from the mine (Mann-Whitney U test: U= 29142.5,

Z= 12.40, P<0.01; Medianclose=1.77, Medianfar=16.33).

The temporal distribution pattern of the vocalisations was also different between

the two sites (Fig. 2). At the site far from the mine, titi monkeys were more vocally

active early in the morning (from 0600 to 1000 hours, with peak vocal activity around

0700 hours), while at the site close to the mine they presented a constant but very low

activity from 0700 to 1000 hours with peak vocal activity occurring around 1300 hours.

The time period of highest truck passing activity coincided with the time period

of the lower number of loud vocalisations at the site close to the mine and the peak of

loud calls also occurred when there was a decrease in trucks passing (Fig 3). Despite

this, a Spearman rank test showed no significant correlation between the number of

trucks and number of vocalisations (rs = -0.21, t= -0.71, P>0.05).

99


Fig 2. Daily distribution of the mean (±SD) number of loud calls emitted by black

fronted titi monkeys at sites close to and far from an opencast mine site near Peti

environmental station, southeast, Brazil.

100


Fig 3. Daily distribution of mean mining truck activity (number passing a fixed point)

and mean frequency of loud calls of black-fronted titi monkey close to an opencast

mine site near Peti Environmental station, southeast Brazil.

101


Discussion

The higher rate of loud calls found at the far site could be explained by several

non-exclusive hypotheses, such as: (1) more titi monkey groups are present at the far

site; (2) more encounters between titi groups at the far site; (3) titi monkeys from the

close site reducing their emission of calls due to masking caused by mining noise; (4)

call emissions masked by noise decreasing detection of vocal activity at the site close to

the mine. All of these hypotheses are possible; however, habitat matching means that

there should be very similar numbers of groups at both sites. The area monitored by the

passive acoustic monitoring devices was the same at both sites. Home range sizes of C.

nigrifrons ranged from 8.35 to 9.23 ha in an Atlantic forest field site approximately

25km far from our study site (Santos 2012). The positions of geographic barriers such

as roads and a river that surround close and far sites suggest that groups, which inhabit

the close site are isolated from the groups that inhabit the far site. Thus, while there will

be some differences between sites, these are unlikely to be the major factors affecting

differences in the rate of loud call emissions. Another noteworthy factor is the longer

duration of calls at the far site. This fact supports the third hypothesis and not the first

two: as it indisputably demonstrates the impact of mining noise on titi monkey’s loud

vocalisations.

A decrease of animal call rate in presence of noise has already been established

in other studies and can be interpreted as a response to avoid interference from

anthropogenic noise (Miksis-Olds and Tyack 2009; Sun and Narins 2005; Parks et al.

2007; Sousa-Lima and Clark 2008). This pattern may indicate that animals wait until it

is quiet to vocalize, exhibiting only minimal vocalisation effort during periods of

masking noise (Miksis-Olds and Tyack 2009; Sousa-Lima and Clark 2008). In this

study, at the close site many loud calls (20%) were partially masked by noise, thereby

potentially disturbing the exchange of acoustic information and preventing titi monkeys

from communicating effectively (Lohr et al. 2003; Foote et al. 2004; Bee and Swanson

2007). One particularly important factor driving vocalisation effort is the range over

102



which the signaller and receiver must effectively communicate (Miksis-Olds and Tyack

2009). In this context, when noise masks the vocalisations there is a decrease in the

acoustic space over which the information can reach.

The longer duration of titi monkey loud calls at the far site is further evidence of

the noise impact from mining. Researches have already documented that some species

adjust their vocal behaviour to compensate for anthropogenic noise by increasing or

decreasing the duration of the calls. Studies with Saguinus oedipus showed a decreasing

in the average call duration to avoid overlapping the noise (Egnor et al. 2007).

However, common marmosets Callithrix jacchus increase the duration of their calls in

presence of noise, but they use higher vocal frequencies (Brumm et al. 2004). Our

results, suggest that there is more available acoustic space at the far site, especially in

the lower frequencies, which are naturally occupied by titi monkeys. At the close site,

noise from the mine could be excluding titi monkeys from an acoustic niche. Thus, they

probably are emitting calls with shorter duration to save energy since acoustic

communication is an energetically expensive behaviour and vocalisation effort is

increased by increasing call duration (Miksis-Olds and Tyack 2009).

The difference in the diel pattern of loud calls between the two sites can be also

a consequence of the mining noise disturbance on titi monkeys’ vocal behaviour. C.

nigrifrons are vocally active mainly during the first hours of the day (Melo and Mendes

2000) and this natural pattern was observed only at the far site. At the site close to the

mine, animals presented very low vocal activity in the first hours of the day and peak of

activity at 1300 hours, which coincides with the lower traffic of trucks and mining

activities due to lunch time of mine employees. Although there was no significant

negative correlation between the number of trucks passing and the number of loud calls

at the site close. Titi monkeys vocalised less in the first hours of the day when there

were a higher number of trucks and presented a peak of vocal activity around 1300

when the number of trucks passing was lower. Many mammals affected by

anthropogenic noise have limited developmental capacity to change the acoustic

103



parameters of their calls to avoid the masking by noise such as some birds can do

(Weiss et al. 2014). On the other hand, mammals may avoid noise with other

behavioural modifications, such as vocalizing during periods of low noise (Rabin et al.

2003) or moving to quieter areas (Duarte et al. 2011).

Loud vocalisations are key factors involved in the regulation of titi monkey

social behaviour. One consequence of the masking of such calls can be increased

territory invasion by neighbouring group and consequently increased rates of inter-

group agonistic encounters. Such changes could impact on the survival and reproductive

success of the affected individuals. Future studies should investigate the other

behavioural effects of sound pollution on primates, for example, do they try and

compensate by using more ‘exaggerated’ visual signals, and/or do they may alter their

acoustics parameters, such as amplitude or the frequency of their calls when it is noisy?

Overall our results suggest that although several factors can influence the rate

and duration of loud calls – the principal cause in difference between sites close and far

from an opencast mine was the noise being emitted by mining activities.

Conclusion

In the last decade, many studies have documented the impact of anthropogenic

noise on animal communication systems and wellbeing. Most of the studies have

focussed on bird communication and there are no studies about noise impact from

mining activity on primates communication. Here, we have shown for the first time how

a noise disturbance affects black fronted-titi monkey communication. Our results

provide important information to be considered during the elaboration of conservation

strategies in natural areas affected by mining activity. Furthermore, we suggest that

noise monitoring plans for wildlife should be part of the process of licensing large scale

anthropogenic activities such as mining.

104



Acknowledgements

We thank all of the staff at the environmental station of Peti, especially

Leotacílio da Fonseca. We are also grateful to Marina Scarpelli and Renan Duarte for

their help during data acquisition and the engineer Krisdany Cavalcante for help with

the noise level measurements. MHLD and MCK were supported by a Fundação de

Amparo à Pesquisa de Minas Gerais (FAPEMIG) postgraduate and technical

scholarship respectively. MR, RJY and RSL received financial support from FAPEMIG

and Conselho Nacional de Pesquisa (CNPq).

105


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Conclusão

A atividade de mineração gera diversos tipos de ruído que atuam como fator

estressor e cria uma nova pressão seletiva sobre as espécies que usam sinais acústicos

para se comunicar. Um sistema efetivo de monitoramento acústico de populações

animais se faz necessário em áreas com a presença desta atividade, devido às mudanças

que o ruído pode causar na dinâmica de comunicação das espécies.

Nesta tese de doutorado, a abordagem da Ecologia de Paisagem Acústica foi

utilizada para monitorar ambientes naturais e ruidosos. Em particular, as pesquisas

mostradas aqui, apresentam uma nova forma de coleta, análise e interpretação de dados

acústicos para monitorar as alterações antrópicas no ambiente, avaliar diferenças entre

comunidades e monitorar a dinâmica acústica no espaço e no tempo.

O estudo de paisagens acústicas ainda é um tópico difícil de investigar,

especialmente em áreas tropicais, devido à quantidade de informações contidas em cada

ambiente e seu estudo requer o desenvolvimento de técnicas de subamostragem, a fim

de otimizar o processo de análise e interpretação de dados. Neste contexto, a

metodologia apresentada no capítulo 1 fornece critérios para subamostragem de dados

em alguns biomas tropicais e sugere rotinas de programação baseadas nas características

de cada ambiente estudado. Em conclusão, ambientes que mostraram alta e contínua

presença de sons podem ser amostrados de forma menos intensa, enquanto aqueles que

apresentam emissões acústicas ocasionais ou imprevisíveis devem ser amostrados de

forma mais intensa para garantir uma representação confiável da paisagem acústica.

O capítulo 2 apresentou resultados significativos sobre o impacto da poluição

sonora proveniente de atividade mineradora na dinâmica de comunicação acústica dos

animais. A maior complexidade acústica durante o dia na área próxima à mina pode

estar relacionada com os níveis de ruído desta área. Além disso, a menor riqueza de

espécies na área próxima à mina, as diferenças na composição de espécies e nas

características espectrais dos cantos registrados nas duas áreas são outros indicadores do

impacto da poluição sonora. Assim, é possível concluir que o ruído pode contribuir para

alterar aspectos ecológicos de comunidades e a dinâmica da comunicação acústica dos

animais.

No capítulo 3 foi encontrada uma menor taxa de ocorrência das vocalizações de

guigós na área próxima à mina, maior duração dos chamados na área distante e uma

115

Conclusão diferença na distribuição diária das vocalizações ao longo do dia entre as duas áreas

estudadas. Estes resultados também são indicadores de que o ruído altera o

comportamento natural da espécie e afeta diretamente na sua comunicação acústica.

Futuras pesquisas deveriam focar na elaboração de metodologias e índices para

análise de paisagens acústicas em ambientes tropicais, uma vez que todas as ferramentas

existentes atualmente foram elaboradas em ambientes temperados, onde o ambiente

acústico é consideravelmente menos complexo. O estudo da paisagem acústica mostrou

ser uma ferramenta eficaz para monitoramento acústico em áreas afetadas por ruído

antrópico, mas pesquisas envolvendo cada espécie individualmente também podem

fornecer dados extremamente relevantes para estratégias de conservação e manejo. Por

exemplo, uma informação importante seria a identificação das espécies mais sensíveis

ao ruído e daquelas que conseguem se adaptar e desenvolver estratégias de comunicação

para sobreviver em áreas ruidosas. Outro dado que pode direcionar ações mais efetivas

de manejo é a identificação dos níveis de ruído acima dos quais cada espécie começa a

ter sua comunicação afetada.

Esta tese de doutorado apresenta uma contribuição inédita sobre o impacto da

poluição sonora proveniente da atividade mineradora na comunicação acústica da fauna

silvestre, um impacto ainda não estudado no Brasil. Os resultados desta tese fornecem

informações importantes para fomentar políticas públicas durante a elaboração de

estratégias de conservação e manejo de áreas naturais. Desta forma, o monitoramento do

ruído em áreas naturais afetadas por mineração deveria ser incluído como uma das

exigências dos órgãos ambientais durante o processo de licenciamento desta atividade.

116

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