HUMAN IMPACT ON BIODIVERSITY IN NATIONAL PARKS DURING SCHOOL HOLIDAYS IN QUEENSLAND, AUSTRALIA
ROOSA PEHKONEN
Master’s thesis
University of Eastern Finland
Department of Environmental and Biological Sciences Biology
2020
UNIVERSITY OF EASTERN FINLAND
Faculty of Science and Forestry, Department of Environmental and Biological Sciences
PEHKONEN, ROOSA: Human impact to biodiversity in national parks during school holidays in Queensland, Australia
M. Sc. Thesis, 25 pp., Appendices 1 October 2020
--- Humans and their actions are causing lot of changes to the environment and living possibilities on Earth. In addition to climate change and habitat destruction, species are experiencing new stressors, such as noise pollution. This has led to changes in the natural acoustic environment and causes difficulties for species that use sound to communicate. Sound has an important role in many animal species' behaviour.
Biodiversity assessment is a demanding task because it is a concept, which includes variability among all living creatures in all ecosystems. Acoustic analysis is proposed to be one way to estimate biodiversity in animal communities. Ecoacoustics investigates environmental sound in large-scale observation, targeting populations, communities and landscapes. It can be used to study animals and their diversity, behaviour and abundance over time and to investigate how anthropogenic noise affects species sound diversity.
In this study, the aim was to examine whether increased number of visitors in national parks, key locations for nature-based tourism, has an impact to biodiversity. The study included two national parks from Queensland, Australia: Lamington and Conondale. The goal was to determine if the biodiversity declines during school holidays (SH), when parks have more visitors, compared to outside school holiday (OSH) seasons. Lamington was considered as ”high visitors" site, with more visitors and Conondale as "low visitor" site. School holiday season chosen for this study were winter, spring and summer. Biodiversity was assessed using Acoustic Complexity Index (ACI) values.
Data for this study was received from previous unpublished study. It included recordings from two locations from each park, during school holidays and outside school holidays. Recordings were analysed in Soundecology R package to produce ACI-values for each recording. Those recordings were sound-truthed and hourly averages were calculated and then analysed using generalised linear mixed models (GLMM) in R statistical environment.
It was discovered that during winter and spring, the biodiversity declined during school holiday but in the summer, the biodiversity was higher during school holiday in all sites. According to GLMM analyses, the results indicated that fixed effects, holiday period and visitor number, were not significant factors. On the contrary, the random effects, time of the day and site, explained most of the variation. However, during summer also the fixed effects explained more of the variation.
Even though there were differences between school holiday and outside school holiday periods, the differences were not caused by the holiday season or the visitor number. The reason appeared to be more site specific. However, during the busiest holiday season, summer, the fixed effect had significant impact and therefore, noise management should be considered in the busiest, most visited areas in national parks. While there were changes in biodiversity in this study, it is impossible to say if the species abundance has changed. In the future it would be ideal to study individual species and differences in species composition.
Key words: biodiversity, ecoacoustics, nature-based tourism, acoustic complexity index ACI
ITÄ-SUOMEN YLIOPISTO
Luonnontieteiden ja metsätieteiden tiedekunta, Ympäristö- ja biotieteiden laitos
PEHKONEN, ROOSA: Ihmisen vaikutus kansallispuistojen monimuotoisuuteen koulujen loma- aikana Queenslandissa, Australiassa
Pro gradu -tutkielma, 25 s., liitteitä 1 Lokakuu 2020
--- Ihmisten vaikutus aiheuttaa paljon muutoksia ympäristöön ja elinolosuhteisiin maapallolla.
Ilmastonmuutoksen ja elinalueiden tuhoutumisen lisäksi lajit kohtaava uusia haasteita, kuten lisääntyneen äänisaasteen. Tämä on johtanut muutoksiin luonnollisessa akustisessa ympäristössä ja aiheuttaa vaikeuksia etenkin niille lajeille, jotka käyttävät ääntä kommunikoimiseen. Äänellä on tärkeä rooli monen eläinlajin käyttäytymisessä.
Monimuotoisuuden määrittäminen on vaativaa, koska se pitää sisällään vaihtelun kaikissa elävissä olennoissa, kaikissa ekosysteemeissä ja niiden välillä. Akustinen analyysi voisi olla yksi tapa havainnoida ja määrittä monimuotoisuutta eläinkunnassa. Ekoakustiikka tutkii ympäristön ääniä suuressa mittakaavassa, keskittyen populaatioihin, eliöyhteisöihin ja elinympäristöön. Sitä voidaan käyttää tutkimaan eläimiä ja niiden käyttäytymistä ja esiintyvyyttä eri aikoina sekä selvittää, miten ihmisen aiheuttama ääni vaikuttaa eläinten ääntelyyn.
Tässä tutkielmassa tarkoituksena oli selvittää, aiheuttaako kansallispuistojen, jotka ovat tärkeitä kohteita luontoturismissa, lisääntyvät vierailut koulujen loma-aikana muutoksia näiden puistojen monimuotoisuuteen. Tutkielmassa oli mukana kaksi kansallispuistoa Australian Queenslandin osavaltiosta: Lamington ja Conondale. Tavoitteena oli selvittää, laskeeko puistojen monimuotoisuus koulujen loma-aikana (SH), kun puistoissa on enemmän vierailijoita, verrattuna koulujen loma-aikojen ulkopuoliseen (OSH) aikaan. Lamington määriteltiin ”korkean kävijämäärän”
puistoksi, jossa on enemmän vierailijoita, ja Conondale ”matalan kävijämäärän” puistoksi. Tähän tutkielmaan valittiin kolme loma-aikaa, talvi-, kevät- ja kesälomat. Monimuotoisuus määriteltiin käyttäen Acoustic Complecity Index (ACI) -arvoja.
Data saatiin aiemmasta, julkaisemattomasta tutkimuksesta. Se sisälsi äänitteitä kahdesta pisteestä molemmista puistoista, loma-aikana sekä niiden ulkopuolelta. Äänitteet analysointiin Soundecology R-paketilla ACI arvojen laskemiseksi jokaiselle äänitteelle. Nämä äänitteet tarkistettiin kuuntelemassa ja niistä laskettiin keskiarvot tunneittain, mitkä sitten analysoitiin käyttäen generalised linear mixed models (GLMM) R tilasto-ohjelmalla.
Havaittiin, että talvella ja keväällä monimuotoisuus laski koulujen loma-aikana, mutta kesällä monimuotoisuus taas kasvoi loma-aikana. GLMM analyysien mukaan tarkasteltavilla tekijöillä, loma-ajalla ja vierailijamäärillä, ei ollut merkittävää vaikutusta. Satunnaiset tekijät, kellonaika ja alue, selittivät suurimman osan vaihtelusta. Kesällä kuitenkin myös kiinteillä tekijöillä oli merkittävääkin vaikutusta.
Vaikka muutoksia loma-aikoina ja niiden ulkopuolella havaittiin, erot eivät johtuneet itse lomista tai vierailijoiden määristä, vaan syy vaihtelulle riippui ennemmin alueesta ja sen ominaisuuksista. Kaikista kiireisimpänä lomakautena, kesällä, myös tarkasteltavilla tekijöillä oli vaikutusta ja sen vuoksi toimenpiteitä on syytä harkita puistojen vilkkaimmin vierailuilla alueilla vilkkaimpana sesonkina. Tässä tutkielmassa havaittiin muutoksia monimuotoisuudessa, on kuitenkin mahdotonta sanoa, onko lajien esiintyvyydessä tapahtunut muutoksia, koska sitä ei tällä metodilla pysty selvittämään. Tulevaisuudessa olisikin ideaalisempaa tutkia yksittäisiä lajeja ja eroja lajien esiintyvyyksissä.
Avainsanat: monimuotoisuus, ekoakustiikka, luontoturismi, acoustic complexity index ACI
TABLE OF CONTENT
1 INTRODUCTION ... 2
2 NATIONAL PARKS, NATURE-BASED TOURISM AND ECOACOUSTICS... 3
2.1 National parks and nature-based tourism ... 3
2.2 Ecoacoustics and sound ... 5
3 RESEARCH QUESTION AND HYPOTHESIS ... 7
4 MATERIALS AND METHODS ... 7
4.1 Study areas and dates ... 7
4.2 Sound recordings ... 8
4.3 Sound analysis ... 10
5 RESULTS ... 11
5.1. Across sites ... 11
5.2 Across sites during each season ... 11
5.3 Time of day ... 13
5.3.1 Winter ... 13
5.3.2 Spring ... 14
5.3.3 Summer ... 16
5.4 Sound-truthing ... 17
6 DISCUSSION ... 18
6.1 School Holidays vs. Outside School Holidays ... 18
6.2 Night-time / Day Time ... 20
6.3 Seasons ... 21
7 CONCLUSIONS ... 21
ACKNOWLEDEMENTS ... 22
REFERENCES... 23
2 1 INTRODUCTION
Humans and their actions can cause dramatic changes to the environment and resources on Earth (Dahiya 2005). The most important reasons for the changes in environment and biodiversity are growth of human population which have side effects, such as land use and climate change. Species are experiencing new stressors, for which they are not adapted (Dickens & Romero 2013). In addition, among other negative human induced effects, noise pollution has also increased (Pierretti et al. 2011).
This has led to changes in the natural acoustic environment and made it more difficult for vocal species to communicate.
Animal vocalisations are an important part of many species’ behaviour (Farina & Gage 2017). Sounds are used for social interactions, to communicate, to attract mates and to defend territories. Sounds can be divided into three different categories: biophony, geophony and anthrophony (Pijanowski et al. 2011). Sounds that living organisms create are called biophony. This includes sounds that animals use for communication. Sounds made by physical processes, such as rainfall and wind, are called geophony. Lastly, anthrophony refers to sounds from mechanical devices used by humans, such as cars, and aircrafts.
In human-altered areas and landscapes, noise pollution is common (Francis et al. 2009).
The characteristics of noise are high amplitudes and low spectral frequencies, and noise pollution is considered harmful to both humans and wildlife. Noise pollution is considered especially disturbing for birds, but also to other species, that primarily use sound to communicate. Marine organisms are experiencing disturbance and even physical harm from anthropogenic noise (André 2018).
Noise pollution can decrease the effectiveness of an animal’s calling and therefore, eventually cause changes in behaviour (Schroeder et al. 2012). Some species of birds and dolphins can alter the signal frequency or timing to avoid overlapping with other noises, but foraging may become more difficult when additional noise complicates their ability to find prey (Schaub et al.
2008). It can eventually cause changes in species abundance and reproduction success: environments without roads and traffic are becoming rare and it has been discovered that traffic noise is one reason that decreases the breeding success of birds. In addition, continuous mining noise has been discovered to change the behaviour and abundance of certain bird species (Duarte et al. 2015). During the observation period, some birds were only found at sites that were further away from the mine, suggesting that they were affected by the noise.
Biodiversity assessment is a demanding task for ecologists because, it is a concept which includes variability among all living creatures in all ecosystems (Dahiya 2005). Biodiversity is the diversity within individuals (genetic diversity), between individuals and species, and between
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ecosystems (Sueur et al. 2008). Acoustic analysis is proposed to be one way to estimate biodiversity in animal communities. Ecoacoustics is a new science that investigates environmental sound, natural and anthropogenic sound, their relationships and tries to find answers to questions related to biodiversity and ecology (Sueur & Farina 2015). Compared to bioacoustics, that studies animal behaviour and individual signalling, ecoacoustics does large-scale observation targeting populations, communities and landscapes. It can be used to study animals and their diversity, behaviour and abundance over time, but also to investigate how species sound diversity is affected by anthropogenic noise. However, it only works for species that use sound to communicate.
In recent years, nature-based tourism has increased (Bushell 2003, Puhakka & Saarinen 2013). This means that formerly undisturbed areas are now tourist attractions and experiencing urbanisation (Huhta & Sulkava 2014). As these national parks and other protected areas often include disturbance-sensitive and endangered species, this growth of nature-based tourism in protected areas can be very harmful to the biodiversity. For instance, visitors make more noise than is common to the area (Reid & Olson 2013). In the United States it has been discovered that many national parks experience an increased amount of daily noise (Barber et al 2009). For vocal animals, additional noises can block their own sounds and therefore, additional noise disturbance can cause a park to lose its natural heritage (Reid & Olson 2013). Even quiet activities like hiking and skiing can affect species in national parks (Barber et al. 2009). Huhta and Sulkava (2014) discovered that in Pallas-Yllästunturi National Park in Finland, the bird abundance had changed in areas near active hiking and skiing tracks.
2 NATIONAL PARKS, NATURE-BASED TOURISM AND ECOACOUSTICS
2.1 National parks and nature-based tourism
The International Union for Conservation of Nature (IUCN) (2019) defines national park as natural or almost natural large areas that have been reserved to protect ecological processes, species and ecosystem characteristics. National park should represent local ecosystems, including local species and habitats but also, they can provide protection for bird migration routes. In addition, they should be located in areas with high conservation value. National parks also provide cultural, scientific and educational services and are tourist attractions, providing income to the local economy. National parks often do not have strict limitations on visitor use and numbers.
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National parks have always had two purposes: conservation and recreation (Puhakka &
Saarinen 2013). The recent growth of nature-based tourism and ecotourism have affected the management of the parks. Nature-based tourism can be defined as a large-scale tourism in which the nature is the main attraction, and nature’s recreational use means spending time and exercising in nature (Sievänen et al. 2017). Other definitions exist as well, but the common factor often includes pure nature (O’Neill et al. 2010). It is now considered that combining conservation and utilisation of nature is beneficial to regional development and economic growth, as many national parks are located in rural areas (Puhakka & Saarinen 2013). Visiting nature can also be educational experience (Hardiman & Burgin 2016). Direct nature experience affects visitor attitude and behaviour towards nature, for both adults and children.
The tourism industry has changed over the past few decades (O’Neill et al. 2010). People are not as interested in the typical, mainstream tourism and destinations. This has caused the tourism industry and governments to consider alternative tourist attractions, such as nature-based tourism.
The industry has grown in recent years. In the year 2000, up to 15% of global tourism activity came from nature-based tourism and in the year 2015 it was estimated that globally protected areas have 8 billion visits yearly (Balmford et al. 2015). National parks and other protected areas are key locations for nature-based tourism (Stokke & Haukeland 2017).
National parks have their own soundscape (Reid & Olson 2013). In addition to the biophony made by the animals of the park, facilities, maintenance work and vehicles produce noise.
Trail maintenance, overflights, snow removal and transporting humans and equipment generate noise levels, which can also affect biodiversity. Population density of some species has been noticed to decline close to roads, and it is suggested that noise is an important factor. Huhta and Sulkava (2014) discovered, that in the popular Pallas-Yllästunturi National Park in Finland, bird abundance was affected by human disturbance. In highly urbanised areas (skiing tracks, accommodation etc.), the bird species found were ones that were less sensitive to human disturbance, like corvid and edge species. In this case, nature-based tourism had an effect on bird communities. In addition to conservation, managers now need to identify the activities that would serve future visitors as well (Moyle et al. 2017). Parks need to provide a good variety of experiences, not only inside the park but also on the borders, in order to keep current visitors but also, to attract new people to visit.
Nature-based tourism can have both direct and indirect effects on the biodiversity (Huhta
& Sulkava 2014), but on the other hand, parks that have high biodiversity are more popular among visitors (Siikamäki et al. 2015). Therefore, monitoring and preserving biodiversity is important for both conservation and tourism. Acoustics provides an easy way to study biodiversity in large areas
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(Sueur et al. 2008) and thus, ecoacoustics is a useful tool for national park managers to study changes in biodiversity.
Tourism in Australia has been growing yearly (Figure 1) and was the fourth largest export industry in the year 2018-2019 (Tourism Australia 2020). Nature based tourism is an important part of tourism in Australia (Hardiman & Brugin 2016). Unique scenery and biodiversity are the main attractions and Australia has multiple national parks which attracts both local and global visitors (Packer et al. 2014). The most famous national parks are Uluru-Kata Tjuta and the Great Barrier Reef.
Nature-based tourism brings annually over $20 billion to Australia’s economy.
Figure 1. The graph shows the growth of tourism in Australia in the past decade. It includes all tourism. The drop in 2020 is due to coronavirus (The Bureau of Statistics, Tourism Australia 2020 https://www.tourism.australia.com/en/markets-and-stats/tourism-statistics/international-market- performance.html)
2.2 Ecoacoustics and sound
Sound is vibration in the air (Farina & Gage 2017). Sound is measured in frequencies (hertz), which are the number of vibrations produced. Hearing is a key sense, along with four others (vision, smell,
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taste and touch), and important for animal life, including humans. Sound and hearing are used to send and receive signals from other animals and also from the environment. Sound is vital for survival, because it often means danger or food. Sound has been used as tool for decades, especially for monitoring birds. Furthermore, birds can also be used as indicators to discover how disturbed an area is (Huhta & Sulkava 2014). Ecoacoustics can also be used to evaluate the complexity of animal community (Farina & Pieretti 2017).
Animals use sound for multiple reasons, such as hunting and finding a suitable resting or breeding area (Reid & Olson 2013). Migratory birds use sound to decide whether a location is a good place to stop. Furthermore, amphibians use other wildlife calls to determine breeding sites. As some animals rely on sound more than others, these can be more disturbed by increased and chronic noise.
Increased noise can cause changes to communities, when more sensitive species leave (Francis et al.
2009). Even for noise tolerant species, it can reduce reproductive success. Schroeder et al. (2012) discovered that the offspring of house sparrow (Passer domesticus) females nesting in chronic noise affected areas were smaller and fewer in number. These females had less food for their offspring when they lived in noisy areas. For marine animals, chronic exposure to noise can affect to development of normal activities and survival of individuals and populations (André 2018).
However, for some species, excess noise can be beneficial (Yorzinski & Herman 2016).
Prey species can prefer noisy areas and find them safer because predators that are sensitive to additional noise avoid these areas. In addition, some species can adapt well to noise pollution.
There are many benefits for using sound to monitor species and ecosystems including its affordability, and recordings can be made remotely (Sueur & Farina 2015). Recording devices can be used both in wilderness and in human-modified areas, and they can monitor in the air, in water and on land. It is possible to monitor species continuously and for a long period of time and across various spatial scales, without harming the environment or the species (Farina & Pieretti 2017). Ecoacoustics provides fast results for assessing biodiversity. However, it requires good planning and consideration to use passive acoustic procedures. The surroundings affect the propagation of sound and in addition, the amount and type of vegetation can affect to the received audio. Furthermore, it may be difficult to determine the effects of noise because noise usually comes with other environmental changes (Blickley et al. 2012). Yearly changes affect to the results as well: bird communities are more active during migratory and breeding seasons and therefore, there can be more variation in the acoustic community at this time (Farina & Pieretti 2017). Furthermore, large volumes of data are generated which can be time and cost prohibitive for manual species identification. Various acoustic computer analysis tools (individual species recognisers and acoustic indices) have been developed to address this issue.
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One such acoustic index has been used widely: The Acoustic Complexity Index (ACI).
The ACI was created by Pieretti et al. (2011) and, is an algorithm that produces quantifications of the biotic sounds by counting the variabilities of sound intensities. It can separate biophony from anthrophony, making it easier to investigate the soundscape, which contains a large amount of information. ACI uses the observation that biophony sounds have variability of intensities whereas human generated noises tend to be more constant. Therefore, it is a useful tool for noisy areas (Farina
& Pieretti 2017). It has been used to study animal behaviour and changes in response to impacts. ACI has also been used as a tool for monitoring landscape transformations that human development or other issues can cause. ACI values calculated from sound recordings can be used as a proxy of biodiversity: the higher the value, the higher biodiversity. Bradfer-Lawrence et al. (2020) have discovered in their study, that ACI is a useful tool to monitor avian species richness and abundance.
3 RESEARCH QUESTION AND HYPOTHESIS
The aim of this study was to examine visitor impact on park biodiversity in two national parks in Australia: Lamington and Conondale. The goal was to determine whether biodiversity declines during school holiday seasons, when parks have more visitors, compared to outside (before or after) the school holiday season. In Australia, all school holidays are relatively around the same time (Hadwen et al. 2011). An ecoacoustics approach was used to monitor biodiversity. The hypotheses were:
- H1: Visitors have an impact on biodiversity
- H0: Visitors do not have an impact on biodiversity
4 MATERIALS AND METHODS
4.1 Study areas and dates
This study carried out in subtropical national parks of Lamington and Conondale, which are located in the subtropical zone of Australia (Australian Bureau of Meteorology 2000) (Figure 2). Lamington is located in southern Queensland, approximately 110km south of Brisbane (Anon. 2019a). It has different hiking tracks and camping areas. Lamington is included in the Gondwana Rainforests of Australia World Heritage Area (Anon. 2019b). It has highly important ecology and diversity, as many threatened and endemic species, both animals and plants, are found there. For many species,
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Lamington provides a refuge and protection essential for their survival. In addition, Lamington has an important meaning for the Yugambeh Aboriginals.
Conondale is also located in Queensland, but it is approximately 130km north of Brisbane (Anon. 2019c). It has walking tracks from short day walks to longer, four-day hikes as well as camping and caravan possibilities and scenic drive roads. Conondale is rainforest with eucalypt trees and waterfalls. Also, Conondale has large number of threatened species, especially plants that occur there are at the limit of their distribution.
Visitor numbers were only available from Lamington. Recording locations in Conondale were only accessible by visitors that own cars, which limits the accessibility and visitation in the park.
Lamington has more daily visitors, and some of the tourists are delivered to the park by bus from the Gold Coast and therefore, it is more accessible for all people. Therefore, for the purpose of this study, Conondale was considered a “low visitor” site and Lamington a “high visitor” site.
Figure 2. Locations of the two national parks used in this study (Google Maps).
4.2 Sound recordings
Recordings were obtained from previous unpublished study that was conducted in 2015. Recordings were made using Song Meter SM2+ automatic sound recorders (Figure 3). These were installed on trees at eye level, away from tracks. The data collection period was from September 2014 to
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December 2015 and the recording schedule was one-minute recording, every five minutes during both day and night. The devices were configurated to record monoaural 16-bit recordings at a frequency of 22,050 Hz, and the batteries were changed once per month.
Figure 3. Picture of the Song Meter SM2+ which were used to collect the recordings (http://www.faunatech.com.au/products/songmeterdetails.html)
For this study, two recording locations from each park were chosen. Approximately 15 000 one-minute recordings were used for each site. Three school holidays were chosen for this study:
summer, winter and spring holidays and the aim was to get approximately 14 days of recordings during each school holiday (SH) and outside school holiday (OSH) period (Table 1). Recordings were sound-truthed in order to remove recordings consisting of noises that could affect results, e.g. rain, storms and human noises. Human noises (talk) were present in very few recordings. Due to the removal of noisy recordings, the number of days analysed varied between 5 and 12, and some days were not consecutive.
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Location Code Date (2015) SH OSH
Lamington: BBN2 16.-27.1. X
Binna Burra Carpark 7.-9.2., 14.-22.2. X
2.-12.7. X
20.-27.7., 5.-8.8. X
1.-5.10. X
13.-25.10. X
Lamington: BBN3 16.-27.1. X
Coomera Falls Track 7.-9.2., 14.-22.2. X
2.-12.7. X
20.-27.7., 5.-8.8. X
1.-5.10. X
19.-26.10., 7.-10.11. X
Conondale: CON2 15.-26.1. X
Booaloumba Falls
Carpark 7.-8.2., 13.-22.2. X
3.-12.7. X
20.-27.7., 6.-9.8. X
2.-5.10. X
19.-26.10., 6.-9.11. X
Conondale: CON3 15.-26.1. X
Bundaroo Creek 7.-8.2., 13.-22.2. X
3.-12.7. X
20.-27.7., 6.-9.8. X
1.-5.10. X
19.-26.10., 6.-9.11. X
Table 1. Recording locations and chosen study dates during school holiday (SH) and outside school holiday (OSH).
4.3 Sound analysis
Recordings were analysed in the Soundecology R package, developed by Villanueva-Rivera et al.
(2011), to produce ACI values for each one-minute recording. Hourly averages were calculated and statistically analysed using generalised linear mixed models (GLMM) implemented in the R statistical environment. GLMM was used because it allowed the analysis of fixed effects (school holiday and visitor number) and random effects (site and time of the day).
Further sound truthing was undertaken to examine the predominant sounds in recordings at different times of the day across sites. There were five categories: bird, insect, frog, weather (wind/rain etc.) and other, which included miscellaneous noises, such as walking. Sounds were scored 0 (no sound), 1 (some sound) and 2 (lot of sound).
11 5 RESULTS
In total approximately 73 000 one-minute recordings were analysed in this study. Recordings mostly consisted bird vocalisation, some amphibian calls and insect stridulations, and weather noises.
Recordings were graphed to show daily variation and visualisation enabled detection of outliers (e.g.
high points outside morning and at night, or low points during) These outliers were sound-truthed, in order to determine the cause of the sounds. After removal of the noisy recordings (e.g. that consisted rain, storm and other voices), hourly averages were calculated for each day and analysed.
5.1. Across sites
ACI-values were not different between school holidays and outside school holidays within each site.
However, BBN2 had higher ACI-values than all other sites (Figure 4).
Figure 4. Average ACI-values across seasons by location and according to SH and OSH period.
5.2 Across sites during each season
Figure 5 shows that ACI-values in winter and spring were higher outside school holidays, compared with during school holidays for all sites, except BBN2. In summer, ACI-values were higher at all sites during school holidays. In BBN2 the ACI-values during school holiday were higher in every season.
1520 1560 1600 1640 1680 1720
BBN2 BBN3 CON2 CON3
High_Visitor Low_Visitor
ACI
OSH SH
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Figure 5. Average ACI-values of OSH and SH during each season.
1500 1520 1540 1560 1580 1600 1620 1640 1660 1680
BBN2 BBN3 CON2 CON3
High_Visitor Low_Visitor
ACI
Winter
OSH SH
1560 1580 1600 1620 1640 1660 1680 1700
BBN2 BBN3 CON2 CON3
High_Visitor Low_Visitor
ACI
Spring
OSH SH
1450 1500 1550 1600 1650 1700 1750 1800
BBN2 BBN3 CON2 CON3
High_Visitor Low_Visitor
ACI
Summer
OSH SH
13 5.3 Time of day
5.3.1 Winter
ACI-values during the day were higher in both high and low visitor sites outside school holidays (Figure 6). During the night there was not consistent pattern as on high visitor sites the SH values were higher but on low visitor site the OSH values were higher. While there was a significant difference in winter ACI values for both day and night periods (p<0.0001) however, fixed effects only explain 2.8% and 10.6% of the variation (R2m day and night, respectively) (Table 2). Random effects explain 72% and 60% (R2c) of the variation in day and night, respectively. Therefore, site specific effects and hour of the day/night explain most of the variation and the visitor number was not an important factor. During winter, CON3 had a peak around 2-3am at night, but further sound- truthing revealed it to be only wind.
DAY NIGHT
Figure 6. Average ACI-values between high and low visitor sites and within SH and OSH periods during day and night periods in winter.
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Winter Day
Fixed effects Estimate Std. Error df t-value P-value Area: Low Visitor -6,592 30,83 2,001 -0,214 0,850496 Treatment: SH -10,705 1,474 721,714 -7,263 9,84E-13
Marginal R2 0,0279
Conditional R2 0,719
Winter Night
Fixed effects Estimate Std. Error df t-value P-value Area: Low visitor -30,438 35,699 1,999 -0,853 0,483703 Treatment: SH -13,257 2,764 572,222 -4,797 2,06E-06
Marginal R2 (R2m) 0,1056
Conditional R2 (R2c) 0,5967
Table 2. Table showing results of the GLMM analysis during day and night for winter, with fixed effects of high and low visitation and SH and OSH periods, and random effects of site and day.
5.3.2 Spring
Spring results were similar to winter; ACI-values were higher outside school holidays (Figure 7). In addition, the ACI-values were on average higher than during winter. There was a significant difference in spring ACI-values for both day and night periods (p<0.0001) however, fixed effects only explain 2.7% and 9.1% of the variation (R2m, day and night, respectively) (Table 3). Random effects explain 25% and 65% of the variation in day and night, respectively. Therefore, site specific effects and hour of the day/night explain most of the variation, just like in winter, rather than visitor number.
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DAY NIGHT
Figure 7. Average ACI-values between high and low visitor sites and within SH and OSH periods during day and night periods in spring.
Spring Day
Fixed effects Estimate Std. Error df t-value P-value
Area: Low visitor -4,841 3,833 2,21 -1,263 0,323
Treatment: SH -11,424 2,584 577,496 -4,422 1,17E-05
Marginal R2 (R2m) 0,0273
Conditional R2 (R2c) 0,252
Spring Night
Fixed effects Estimate Std. Error df t-value P-value Area: Low visitor -39,179 36,814 2,001 -1,064 0,399 Treatment: SH -19,472 4,32 466,567 -4,508 8,29E-06
Marginal R2 (R2m) 0,0916
Conditional R2 (R2c) 0,5599
Table 3. Table showing results of the GLMM analysis during day and night for spring, with fixed effects of high and low visitation and SH and OSH periods, and random effects of site and day.
16 5.3.3 Summer
In contrast to all other seasons and the hypothesis, during summer the ACI-values were higher during school holidays at all sites and, both during day and night (Figure 8). In addition, ACI-values were higher (greater than 1700) than during other seasons and higher during the night. There was a significant difference in summer ACI-values for both day and night periods (p<0.0001). Fixed effects (visitors and holiday) explain 29.2% and 54.1% of the variation (R2m day and night, respectively) (Table 4). Random effects explain 42% and 65%. Therefore, site specific effects and hour of the day/night explain most of the variation, but also school holidays have some impact, especially during night-time.
DAY NIGHT
Figure 8. Average ACI-values between high and low visitor sites and within SH and OSH periods during day and night periods in summer.
17
Summer Day
Fixed effects Estimate Std. Error df t-value P-value Area: Low visitor -47,137 7,029 2,036 -6,706 0,0206
Treatment: SH 23,117 2,736 692,315 8,449 2,00E-16
Marginal R2 (R2m) 0,2926
Conditional R2 (R2c) 0,4244
Summer Night
Fixed effects Estimate Std. Error df t-value P-value Area: Low visitor -133,665 15,219 2,02 -8,783 0,0123
Treatment: SH 33,18 4,867 510,142 6,817 2,63E-11
Marginal R2 (R2m) 0,5415
Conditional R2 (R2c) 0,6484
Table 4. Table showing results of the GLMM analysis during day and night for summer, with fixed effects of high and low visitation and SH and OSH periods, and random effects of site and day.
5.4 Sound-truthing
ACI-values during summer were higher at all sites. In order to investigate further, random minutes of summer recordings, both night-time and daytime, were sound-truthed. Mostly the sounds were made by birds and insects (Figure 8). In addition, activity was higher during the night and these sounds were mostly made by birds, and insects (data not shown).
18
Figure 8. Comparison of the source of sound or noise in sound-truthed summer recordings.
6 DISCUSSION
6.1 School Holidays vs. Outside School Holidays
While there was a trend during winter and spring for lower average ACI-values during school holidays but higher ACI-values during summer school holidays, this was not found to be significant according to the GLMM analyses. The results indicated that fixed effects of holiday period and visitor numbers were not significant factors and that random effects, which in this case were time of the day and night and site, explained most of the variation. However, during summer the fixed effects explained more of the variation. In particular, the high visitor sites of BBN2 and BBN3 had higher average ACI-values during the summer school holidays. This could be due to the influx of tourists and campers in this peak holiday period, resulting in the generation of large amount of human noise.
While early sound-truthing was used to identify recordings consisting of high levels of human noise, some of these times would likely have been missed and furthermore, and a list of noisy
0 10 20 30 40 50
BBN2
0 10 20 30 40 50
BBN3
0 10 20 30 40 50
CON2
0 10 20 30 40 50
CON3
19
recording times was not generated. It would have been useful for analysing the results to mark the times and places that consisted of these high human noises.
Interestingly, ACI-values were higher during summer school holidays in all sites. Further sound-truthing revealed that the main vocalising animals were birds. Birds were active both during the day and the night but in addition to birds, insect were more active during the night than during the day. During summer school holidays the night-time ACI-values were higher than during any other season. Brumm (2004) discovered, that in noisy environments, birds use acoustic windows to vocalise and furthermore, they sing louder. In addition, some bird species, especially passerines, can change the minimum frequency of their songs and calls in urban environment (Hu & Cardoso 2010). Urban noises that are higher in frequency, are lower in volume and therefore, if birds sing and call at high frequency, those songs and calls can be heard. Therefore, it can be assumed that during the busiest summer holiday season, some birds, that can change the frequency of their singing, can sing also during the day. During the night, other birds use that acoustic window to vocalise and therefore, resulting in higher ACI-values.
BBN2 was the only location, that did not match with the hypothesis and ACI-values during school holidays were higher in all seasons. BBN2 is located nearby to a major carpark and campground, where all the tracks start and end, and so it is very high traffic area. Even though recordings were sound-truthed, the greater amount of human traffic in that area may partly explain the higher values found at this site, especially because it has been discovered that activity is distributed differently around national parks (Kim et al. 2018). Young families with small children would prefer shorter walks and stay closer to carpark, and small children are noisy. Anthropogenic noise can affect species, especially avian species abundance (Blickley et al. 2012), and in this study it was impossible to say whether avian species abundance around Binna Burra carpark had changed, as density and abundance cannot be determined from a recording; there could be one bird calling many times, or many birds calling one time.
BBN2 is also the only edge site in this study, located on the edge of forest and carpark. It could be more diverse and have more species than other areas further in the park (Ries & Sisk 2004), as it offers more habitat complexity. As it is an edge site, it is also possible that BBN2 has high abundance of forest species but also edge species that are tolerant of human presence and exploit these environments. In addition, humans often leave food waster behind and animals can use those resources (Huhta & Sulkava 2014), which has a positive effect on species abundance. This may explain the higher ACI-values in BBN2.
As mentioned earlier, holidays and visitors do not have a great impact on the biodiversity measured using ACI-values. Huhta and Sulkava (2014) discovered that in highly disturbed areas in
20
national parks, the bird abundance changed, and species found in these areas were edge species and those which are more accustomed to human presence. Therefore, even if the overall activity is not affected by increased visitor noise, it may have some effect on the species composition. Those which are more sensitive to additional noise and cannot adapt, need to move further into the park, to those areas that have less visitors. Therefore, noise has impact on a larger area (Schaub et al. 2008).
Behavioural response to noise is more likely because it is linked to possible threat, not because the level of noise itself (Barber et al. 2009). Also, the number of passing vehicles can lead to habitat degradation and fitness consequences, as Schaub et al. (2008) discovered while studying noise effects on bats. Therefore, noise management is important and needed, especially in highly visited sites of the park (Barber et al. 2009).
6.2 Night-time / Day Time
There was not a consistent difference in ACI-values between day and night across sites, except in summer in high visitor sites, when night-time values were higher than daytime values. In addition to earlier observations, birds are flexible in their singing schedule and can change it if the environment is experiencing a lot of noise (Fuller et al. 2007). Humans tend to be more active during the day and so in the current study there would be more anthropogenic noise, visitor and camping noises. If the area is noisy during the day, then birds may sing more during the night (Fuller et al. 2007), which in this case might explain why night-time values were higher during summer. This may happen either because birds sing less when there is competing noise (anthropogenic noise) or because they are using the quieter time for additional signalling (Fuller et al. 2007). This is suggested to be only a temporal change in behaviour, which in this case would mean that birds are not drastically affected by additional visitor noise, as holidays are shorter periods. This supports the discovery that holidays and visitors were not the main reason for declined activity in winter and in spring. Furthermore, Larsen et al. (2014) reported that captive koalas exhibited a behavioural reaction and increased vigilance to additional noise and to close visitors, but not total daily visitor number. It is possible that wild animals may exhibit a similar response and increase vigilance when noisy visitors come too close.
In addition to avoiding anthropogenic noise, birds tend to avoid overlapping with other species (Brumm 2006). This means that birds sing alternately, when first one stops the other one starts, as there often is a silent moment between signals. This is the moment for others to start singing and thus avoid overlapping. This would suggest that in louder environments, every second counts.
Frogs also avoid overlapping with anthropogenic noise and with other species (Zelick & Narins 1985 cited in Brumm 2006). In addition to avoid overlapping, in noisy environments birds can cope by
21
singing at higher volume. Both of these, avoiding overlapping and singing at higher volume, increases the signal transmission.
6.3 Seasons
ACI-values were higher outside school holidays in winter and in spring however, in summer the values were higher during school holidays in the high visitor sites (BBN2 and BBN3). Visitation in protected areas is highly seasonal and timing of major holidays is one cause of seasonality and the other being climate (Hadwen et al. 2011). In Queensland, the climate is mild and therefore, holiday periods are more likely to affect park visitation rates than climate. In addition to humans being more active, animals are also more active during warmer seasons. In particular, ectotherms (such as frogs and insects) are temperature dependent and more active during warmer seasons (Sperry et al. 2010).
ACI-values were higher during the summer in school holidays, which was opposite to what was expected. Further sound-truthing only revealed that birds and insects were main source of sound, but how that causes the values to be higher, especially in BBN2, is unclear. Being a busy carpark and camping ground, values could be affected by increased human noises in the background in the area, even though during the first sound-truthing the majority of close (loud) human noises were removed. Summer school holiday period is the longest holiday of the year and the season when parks get more daily visitors. Presence of humans can also have positive effect on some species. They might even prefer close contact to humans, as it reduces predation risk especially outside winter (McLoughin et al. 2011).
In the busiest seasons and days, mainly weekends (Hadwen et al. 2011), parks may need better management, especially in the most visited sites (Barber et al. 2009). Managing national parks is a difficult task, balancing between conservation and enhancing the experience of visitors (Kim et al. 2018). Human activity will affect large areas and eventually, this will have an impact on the fitness of natural populations (Schaub et al. 2008).
7 CONCLUSIONS
In this study, it was discovered that the biodiversity, represented by ACI, was different during school holidays and outside school holidays. However, the difference was not caused by school holidays or visitor number, but the reasons appeared to be more site specific. However, during the busiest holiday season (summer), the holidays and visitors had significant impact, too, and noise management should be considered in the busiest, most visited areas of the park. Even if the changes in were not
22
significantly impacted by humans, it is impossible to say whether the species abundance in the parks has changed. In addition, some site-specific environmental measurements (rainfall, temperature etc.) should have been made, as most of the variance appears to be due to site specific reasons.
In the future, it would be best to focus on single species identification and differences in species composition rather than an index which could represent many different sounds. Furthermore, potentially a combination of indices which are linked to different acoustic components (e.g. signal to noise ratio, background noise, entropy) may reveal more information.
ACKNOWLEDEMENTS
Thank you to Susan Fuller and Mervi Kunnasranta for being my instructors and helping me during this process. Especially huge thank you to Susan for the idea, data and all your knowledge and help during planning and writing. Thank you, Briana Holgate, for helping me with R and Soundecology in the beginning and Brendan Doohan for all the help with statistics and teaching me how GLMM works. I really needed it and your help was priceless. Thank you to Samu, Lucija and Kaisa for listening me during the writing process that, in some point, was challenging in many ways, and helping me to continue.
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26 Attachment 1
Winter DaySpring DaySummer Day Fixed effectsEstimateStd Errordft-valueP-valueEstimateStd, Errordft-valueP-valueEstimateStd, Errordft-valueP-value Area: Low Visitor -6,59230,832,001-0,2140,850496-4,8413,8332,21-1,2630,323-47,1377,0292,036-6,7060,0206 Treatment: SH-10,7051,474721,714-7,2639,84E-13-11,4242,584577,496-4,4221,17E-0523,1172,736692,3158,4492,00E-16 R2m0,0279R2m0,0273R2m0,2926 R2c0,719R2c0,0252R2c0,4244 Winter NightSpring NightSummer Night Fixed effectsEstimateStd, Errordft-valueP-valueEstimateStd, Errordft-valueP-valueEstimateStd, Errordft-valueP-value Area: Low visitor-30,43835,6991,999-0,8530,483703-39,17936,8142,001-1,0640,399-133,6715,2192,02-8,7830,0123 Treatment: SH-13,2572,764572,222-4,7972,06E-06-19,4724,32466,567-4,5088,29E-0633,184,867510,1426,8172,63E-11 R2m0,1056R2m0,0916R2m0,5415 R2c0,5967R2c0,5599R2c0,6484
