UNIVERSIDADE ESTADUAL PAULISTA “JÚLIO DE MESQUITA FILHO” INSTITUTO DE BIOCIÊNCIAS – RIO CLARO unesp PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA E BIODIVERSIDADE NEW INSIGHTS ON URBAN ECOLOGY FOR MORE SUSTAINABLE CITIES integrating nature-based-solutions, human perception and the knowledge on how the urban landscape influence bird communities GABRIELA ROSA Rio Claro – SP 2022 UNIVERSIDADE ESTADUAL PAULISTA “JÚLIO DE MESQUITA FILHO” INSTITUTO DE BIOCIÊNCIAS – RIO CLARO unesp PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA E BIODIVERSIDADE NEW INSIGHTS ON URBAN ECOLOGY FOR MORE SUSTAINABLE CITIES integrating nature-based-solutions, human perception and the knowledge on how the urban landscape influence bird communities GABRIELA ROSA Tese apresentada ao Instituto de Biociências do Câmpus de Rio Claro, Universidade Estadual Paulista, como parte dos requisitos para obtenção do título de Doutora em Ecologia e Biodiversidade. Orientador: Prof Dr. Milton Cezar Ribeiro Co-orientador: Dr. João Carlos Pena Rio Claro – SP 2022 R788n Rosa, Gabriela New insights on urban ecology for more sustainable cities : integrating nature-based-solutions, human perception and the knowledge on how the urban landscape influence bird communities / Gabriela Rosa. -- Rio Claro, 2022 192 p. : il., tabs., fotos, mapas Tese (doutorado) - Universidade Estadual Paulista (Unesp), Instituto de Biociências, Rio Claro Orientador: Milton Cezar Ribeiro Coorientador: João Carlos Pena 1. Land use, Urban Planning. 2. Landscape ecology. 3. Urban ecology (Biology) DLC. 4. Conservation of birds. 5. Sustainability. I. Título. Sistema de geração automática de fichas catalográficas da Unesp. Biblioteca do Instituto de Biociências, Rio Claro. Dados fornecidos pelo autor(a). Essa ficha não pode ser modificada. UNIVERSIDADE ESTADUAL PAULISTA Câmpus de Rio Claro NEW INSIGHTS OF URBAN ECOLOGY FOR MORE SUSTAINABLE CITIES integrating nature-based-solutions, human perception and the understudying about how urban landscape characteristics effects coloration and taxonomic and functional diversities of birds TÍTULO DA TESE: CERTIFICADO DE APROVAÇÃO AUTORA: GABRIELA ROSA ORIENTADOR: MILTON CEZAR RIBEIRO COORIENTADOR: JOÃO CARLOS DE CASTRO PENA Aprovada como parte das exigências para obtenção do Título de , área: Biodiversidade pela Comissão Examinadora: Instituto de Biociências - Câmpus de Rio Claro - Avenida 24-A, n. 1515, 13506900, Rio Claro - São Paulo http://www.rc.unesp.brCNPJ: 48.031.918/0018-72. Prof. Dr. MILTON CEZAR RIBEIRO (Participaçao Virtual) Departamento de Biodiversidade / Unesp - IB Rio Claro Profa. Dra. SIMONE RODRIGUES DE FREITAS (Participaçao Virtual) Centro de Ciências Naturais e Humanas / Universidade Federal do ABC Prof. Dr. MARCO AURÉLIO PIZO (Participaçao Virtual) Departamento de Biodiversidade / Unesp - IB Rio Claro Profa. Dra. KARLLA VANESSA DE CAMARGO BARBOSA (Participaçao Virtual) Pós-Doutoranda do Departamento de Biodiversidade / IB Rio Claro Profa. Dra. ÉRICA HASUI (Participaçao Virtual) Instituto de Ciências da Natureza / Universidade Federal de Alfenas Rio Claro, 18 de novembro de 2022 Digitally signed by Milton Cezar Ribeiro: 12704118884 DN: CN=Milton Cezar Ribeiro:12704118884, OU=UNESP - Universidade Estadual Paulista Julio de Mesquita Filho, O=ICPEdu, C=BR Reason: I am the author of this document Location: Rio Claro Date: 2022.11.18 18:23:10-03'00' Foxit PDF Editor Version: 11.2.2 Milton Cezar Ribeiro: 12704118884 Felipe Máquina de escrever Título alterado para: "NEW INSIGHTS ON URBAN ECOLOGY FOR MORE SUSTAINABLE CITIES: integrating nature-based-solutions, human perception and the knowledge on how the urban landscape influence bird communities" ... Eu fui aparelhado para gostar de passarinhos. Tenho abundância de ser feliz por isso ... (O apanhador de desperdícios, Manoel de Barros) AGRADECIMENTOS Essa tese é fruto de um Mestrado que se transformou em Doutorado Direto. O primeiro capítulo foi financiado com uma bolsa de Mestrado, pelo processo 2018/ 06773-5, Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), o segundo é fruto de uma pesquisa realizada durante o estágio BEPE, processo 2019/07534-7, Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP). O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Código de Financiamento 001, nos seus capítulos 3 e 4. Para que o presente resultado fosse obtido, foram necessários anos de intenso trabalho, com várias noites mal dormidas e sacrifícios principalmente de ter fazer bicos extras para conseguir me sustentar enquanto a bolsa CAPES não vinha. Todo o processo foi bem estressante e em muitos momentos houve uma quase desistência da minha parte, pois está quase impossível ser cientista no Brasil. Em nosso país somos tratados como estudantes-vagabundos, não temos direitos trabalhistas e vivemos numa insegurança financeira absurda. Está cada vez mais difícil se dedicar a ciência com tantas dificuldades. Não foram poucas as vezes que trabalhei mais de 14 horas por dia na tese, ou que usei finais de semana e noites para realizar o trabalho. Mesmo assim, nosso trabalho não é considerado como tal e somos pouco vistos pela sociedade brasileira. A bolsa está completamente desajustada e os preços dos alimentos e alugueis estão altíssi mos. Fica muito difícil a vida desse modo, uma vez que a bolsa é o salário do cientista e deveria ser visto como tal pela sociedade brasileira. Não dá mais para o valor das bolsas ficar sem reajuste de inflação. Não dá mais para a bolsa atrasar ou a bolsa ser vista como um auxílio. Bolsa é salário de cientista e espero que as próximas gerações reestruturem isso no Brasil pois isso é fundamental para a continuação da ciência nacional. De todo modo, esse processo foi de grande aprendizado e nada seria possível sem o apoio de tantas pessoas: professores, familiares e amigos, que estiveram presentes comigo de diferentes formas. Por isso, primeiramente gostaria de agradecer ao meu orientador, Dr. Milton Cezar Ribeiro, que de forma humilde e simples se faz um grande mestre e professor da vida. Agradeço por todo carinho, pela paciência, pela compreensão, pelas conversas e pelo apoio incondicional às minhas ideias; muito obrigada por estimular a criatividade da mente e das ideias. Agradeço também ao meu coorientador, mestre e amigo, João Carlos Pena que sempre me esteve do meu lado o tempo todo e que sempre se fez presente de inúmeras formas. Obrigada por ser tão presente, por me compreender tão bem e por me acolher de um jeito lindo e carinhoso em Xalapa, durante o estágio BEPE. Você é como um grande irmão pra mim, que sempre me estimula a encontrar e mostrar o meu melhor. Muito obrigada por tudo e por sempre apoiar minhas ideias. Agradeço aos professores: Dr. Fábio Henrique Soares Angeoletto e Dra. Simone Rodrigues de Freitas, pelas ótimas contribuições durante a banca de qualificação, permitindo que esse trabalho pudesse chegar a um resultado mais satisfatório. Agradeço aos professores e pesquisadores, titulares e suplentes, Dra. Karlla Vanessa de Camargo Barbosa, Dra. Érica Hasui, Dr. Marco Aurelio Pizo Ferreira, Dr. Felipe Martello Ribeiro e Dra. Marina Correa Corte, por aceitarem fazer parte dessa banca e por contribuírem com o enriquecimento do presente trabalho. Agradeço aos professores e pesquisadores: Dr. Roberto Shigueru Nobuyasu Junior, Dr. Thiago Vernaschi Costa e Dra. Karlla Vanessa de Camargo Barbosa por me receberem de modo tão acolhedor na UNIFEi e em Itajubá e por acreditarem no potencial do trabalho a todo momento. Mesmo com a impossibilidade de continuação da pesquisa, a experiência foi muito enriquecedora, bem como todo o aprendizado que tive durante esse período. Agradeço aos meus estagiários que me ajudaram no levantamento de aves: Isabella Fracin, Henrique Heidi, Gabriela, Giovana e João Vitor Ra mos. Muito obrigada por serem meus companheiros de observação de aves. Foi um prazer trabalhar com vocês e me sinto orgulhosa de ver vários de vocês trilhando caminhos parecidos, como a Isabella que continua com as observações e campos, integrando esse conhecimento com a psicologia e o Henrique que criou uma página no Instagram para falar de aves. É bom saber como desse simples contato, muita coisa bonita surgiu. Agradeço a meu professor de desenho Ailton César Ribeiro, que esteve acompanhando a construção da Tese comigo e muito me inspirou no estudo das aves, me fazendo enxergar o lado artístiuco da vida e também das cores. Os desenhos das capas foram elaborados durante algumas de suas aulas e sou extremamente grata por ele ter tornado meus dias mais mágicos e mais inspiradores. Agradeço pelas aulas, bolos, cafés e todas as conversas. Também agradeço a Carol, sua companheira, uma mulher super sensível e inteligente, que muito me inspira. Obrigada Casa Amarela, por ser meu refúgio! Por último, agradeço a todos os meus pais, tios e avós por todo amor e carinho. Agradeço aos meus amigos, espalhados pelo Brasil e mundo afora, por serem meus canais de escuta e acolhimento; por serem meu abrigo nesse mundo. E agradeço a todos os seres visíveis e invisíveis, entidades, guias e anjos, por zelarem para que essa jornada se fizesse presente e pudesse ser concluída de forma concreta e eficiente. ABSTRACT Due to increasing of urbanization, cities have become the main human settlements, so it is not possible to think about sustainability without considering urban environments. Thus, Urban Ecology has emerged and substantially contributed to strengthen and to improve human wellbeing and biodiversity conservation in urban landscapes across the globe. Considering this context, this thesis is divided into five chapters that address different topics related to Urban Ecology. In the first chapter we aimed to assess how the different elements (tree cover, impervious built-cover, park area, noise and functional connectivity) of urban landscape influence taxonomic (species richness) and functional aspects (functional richness – FRic – and Rao's Quadratic Entropy - RaoQ) of the bird communities that inhabit parks and streets in Neotropical cities. We found that, if well planned, cities can have an important role on bird biodiversity conservation. Connecting fragments of urban vegetation through wooded streets contribute positively to taxonomic and functional richness and can reduce the negative effects of noise in the cities. In Chapter 2, I tested whether the findings of Leveau (2019a) are generalizable to two Neotropical cities (i.e., Bauru, Brazil; Xalapa, México); the autor stated that urbanization leads to the homogenization of bird coloration. Thus, we developed the ‛color percentage’ method, an approach that considers the birds’ whole body, producing continuous descriptions of bird plumage colors based on pixel intervals of scientific illustrations. We quantified the influence of habitat diversity and impervious cover on the richness and diversity of bird color. Our results indicate that it is impossible to generalize that urbanization intensity leads to bird color homogenization. In chapter 3, we suggested a future research regarding towards the influences of urbanization on the diversity of bird color, considering UV spectral characteristics, once birds see more colors than humans and are enable to discriminate a broad range of colors, allowing the perception of the UV wavelength. In chapter 4, we investigated how humans perceive birds in two Neotropical cities (Bauru and Belo Horizonte, Brazil). Our study showed that most people can recognize the most frequent bird species, are aware of the ecological importance of birds for the maintenance of ecosystems and have different feelings according to species: while the majority of bird species were associated with positive sensations, species such as the domestic pigeon were considered pests and generate negative sensations. Finally, in chapter 5, we reported the experience of participating on Challenge Campus 2030 and discussed how science can be used to propose practical solutions to overcome environmental challenges and problems. Nature- Based Solutions (NBS) appear as an efficient alternatives to overcome the urban challenges. Engaging the academic community in the pursuit of improvement in quality of life and conservation of natural resources is of utmost importance for a more sustainable urban future. Key-words: urban planning, sustainability, Agenda2030, Neotropical región, connectivity RESUMO Quando se pensa em sustentabilidade, é fundamental considerar os ambientes urbanos, uma vez que a maioria dos seres humanos vivem nas cidades. Assim, a Ecologia Urbana vem contribuindo substancialmente para fortalecer e melhorar o bem-estar humano e a conservação da biodiversidade em paisagens urbanas em todo o mundo. Partindo-se disso, esta tese está dividida em cinco capítulos que abordam diferentes temas relacionados a Ecologia Urbana. No primeiro, analisamos como os diferentes elementos (cobertura arbórea, cobertura impermeável, área de parque, ruído e conectividade funcional) da paisagem urbana influenciam os aspectos taxonômicos (riqueza de espécies) e funcionais (riqueza funcional – FRic – e Rao's Quadrática Entropy - RaoQ) das comunidades de aves que habitam parques e ruas de cidades neotropicais. Se bem planejadas, as cidades podem ter um papel importante na conservação da biodiversidade de aves. Conectar fragmentos de vegetação urbana por meio de ruas arborizadas contribui positivamente para a taxonomia e riqueza funcional e pode reduzir os efeitos negativos do ruído nas cidades. No capítulo 2, testamos se a urbanização leva à homogeneização da coloração das aves em duas cidades neotropicais (ou seja, Bauru, Brasil; Xalapa, México); para isso, desenvolvemos o método de ‛porcentagem de cor’, uma abordagem que considera todo o corpo das aves, produzindo descrições contínuas das cores com base em intervalos de pixels de ilustrações científicas. Quantificamos a influência da diversidade de habitat e cobertura impermeável na riqueza e diversidade da cor das aves. Nossos resultados indicam que não é possível generalizar que a intensidade da urbanização leva à homogeneização da coloração das aves. No capítulo 3, sugerimos uma pesquisa futura sobre as influências da urbanização na diversidade de cores das aves, considerando as características espectrais de UV, uma vez que as aves veem mais que os humanos e podem discriminar uma ampla gama de cores, permitindo a percepção do comprimento de onda UV. No capítulo 4, investigamos como a comunidade acadêmica percebe as aves em duas cidades neotropicais (Bauru e Belo Horizonte, Brasil). Nosso estudo mostrou que a maioria das pessoas consegue reconhecer as espécies mais frequentes, tem consciência da importância ecológica das aves para a manutenção dos ecossistemas e tem sentimentos diferentes conforme a espécie: enquanto a maioria das espécies de aves foram associadas a sensações positivas, espécies como o pombo doméstico gerava sensações negativas. Por fim, no capítulo 5, relatamos a experiência de participação no Challenge Campus 2030 e discutimos como a ciência pode ser utilizada para propor soluções práticas para superar desafios e problemas ambientais. As Soluções Baseadas na Natureza (NBS) surgem como uma alternativa eficiente para superar os desafios urbanos. Engajar a comunidade acadêmica na busca pela melhoria da qualidade de vida e conservação dos recursos naturais é de extrema importância para um futuro urbano mais sustentável. Palavras-chave: planejamento urbano, sustentabilidade, Agenda 2030, região Neotropical, conectividade TABLE OF CONTENTS INTRODUCTION ................................................................................................................... 10 CHAPTER I: Urban landscape characteristics and their effects on taxonomic and functional diversities of birds in Neotropical cities.. ......................................................... 15 1.1 INTRODUCTION......................................................................................................... 16 1.2 QUESTIONS, HYPOTHESES AND PREDICTIONS ............................................ 19 1.3 MATERIAL AND METHODS..................................................................................... 20 1.4 DATA ANALYSIS ........................................................................................................ 31 1.5 RESULTS..................................................................................................................... 33 1.6 DISCUSSION .............................................................................................................. 52 1.7 CONCLUSION............................................................................................................. 55 REFERENCES................................................................................................................... 56 CHAPTER II: Colorful cities: urban bird assemblages can be as colorful as those from nonurban systems in the Neotropics......................................................................... 66 2.1 INTRODUCTION......................................................................................................... 67 2.2 MATERIAL AND METHODS..................................................................................... 69 2.3 RESULTS..................................................................................................................... 84 2.4 DISCUSSION .............................................................................................................. 92 2.5 CONCLUSION............................................................................................................. 96 REFERENCES................................................................................................................... 97 CHAPTER III: Using UV wavelengths: a suggestion for future research on bird color in cities ................................................................................................................................. 102 3.1. INTRODUCTION ..................................................................................................... 103 3.2. QUESTIONS, HYPOTHESES AND PREDICTIONS ......................................... 105 3.3 MATERIAL AND METHODS................................................................................... 106 3.4 PRELIMINARY RESULTS ...................................................................................... 111 3.5 NEXT STAGES AND DATA ANALYSIS: WHAT WE WERE PLANNING TO DO ............................................................................................................................................ 113 3.6 FINAL CONSIDERATIONS..................................................................................... 114 REFERENCES................................................................................................................. 115 CHAPTER IV: Academic community perception of birds in Neotropical cities ......... 118 4.1. INTRODUCTION ..................................................................................................... 119 4.2. MATERIAL AND METHODS ................................................................................. 121 4.3. RESULTS ................................................................................................................. 125 4.4. DISCUSSION ........................................................................................................... 133 4.5. CONCLUSION ......................................................................................................... 136 REFERENCES ................................................................................................................ 137 CHAPTER V: Araucaria project: Sustainable campus 2030 throughout nature-based- solutions................................................................................................................................ 141 5.1. INTRODUCTION ..................................................................................................... 142 5.2. THE CHALLENGE CAMPUS 2030 ...................................................................... 146 5.3. PROBLEMS TO BE SOLVED ............................................................................... 147 5.4. CREATING A TEAM ............................................................................................... 148 5.5. CONTEXTUALIZING OUR CAMPUS LOCATION ............................................ 150 5.6. OUR PROJECT IDEA............................................................................................. 153 5.7. THE VICTORY ......................................................................................................... 156 5.8. PULSE TRAINING .................................................................................................. 158 5.9. PROJECT IMPLEMENTATION ............................................................................ 161 5.10. FINAL CONSIDERATIONS ................................................................................. 165 GENERAL THESIS CONCLUSION ................................................................................. 166 REFERENCES .................................................................................................................... 168 77 INTRODUCTION Cities have become increasingly recognized as one of the main drivers of biodiversity loss, once they act as an environmental filter (LA SORTE et al., 2018; SOL et al., 2020). The urban sprawl favored the emergence of Urban Ecology as a science between the 1970s and 1990s (FORMAN, 2016), when ecological aspects of cities have been intensively researched (ANGEOLETTO et al. 2019; SUKOPP, 1998). According to Forman (2016), the Urban Ecology aim to study the “interactions of organisms, built structures and the physical environment where people are concentrated”. This is a very important field of knowledge, since the urbanization process has been responsible for increasing habitat loss and fragmentation (FISCHER; LINDENMAYER, 2007), for contributing to climate change (IPCC 2021) and for reducing taxonomic and phylogenetic diversities, as well as reducing the diversity of functional characteristics of several organisms in cities (SOL et al., 2020; LA SORTE et al., 2018). However, cities are also highly heterogeneous, dynamic (RAMALHO; HOBBS 2012) and function as a socio-ecological systems of living and nonliving things (MADDOX et al. 2017). If urban landscapes area well-planned, they can play a role in for maintaining biodiversity conservation and ecosystem functioning, which has been increasingly recognized and studied worldwide (GRAVIOLA et al. 2021; SPOTSWOOD et al. 2021; GRIMM et al. 2008). In 1981, in the Man and Biosphere (MAB) program emerged the concept of Green Infrastructure (UNESCO, 2017), which consists of planning and designing urban forests so that they can work as a multifunctional network of ecological corridors that interconnect patches of vegetation and permeable areas, such as parks and other green spaces (HERZOG, 2016). Ecological corridors provide connectivity, defined as "the degree to which the landscape facilitates or impedes movement among habitat patches" (TAYLOR et al., 1993). Connectivity is an essential attribute of landscapes because it helps to restore the flow of plants and animals and facilitate dispersal across the landscape (BENNETT, 2003). Small forest patches can act as ecological corridors, thus increasing the overall connectivity of the entire community along the urban matrix (BHAKTI et a. 2018; GRAFIUS, et al. 2017). Ecological corridors also promote the variety natural flows and resistance for negative effects of human activity (PENG 11 et.al. 2017) and facilitate birds’ movement (FERNANDEZ-JURICIC, 2000). Green spaces, natural habitat patches (forest or non-forest) and street trees appears as a promising strategy for ensuring the maintenance of biodiversity and promoting healthy environments for human populations (ARONSON et al. 2017). These factors have been explored independently for urban contexts and incorporated into the design of ecological corridors that provide better environments for humans, plants and animals (ALMENAR et al. 2019). Urbanization results in the loss of biodiversity (SOL et al., 2014), thus leading to a decline in bird richness and an increase in avifauna biomass (SACCO et al., 2015; ARONSON et al., 2014; CHACE; WALSH, 2006). Concepción et al. (2016) found that urban expansion has favored common generalist bird species in detriment of habitat specialist species, contributing for the homogenization of the avifauna. Menon and Mohanraj (2016) found that resource availability in urban areas and successful adaption to human-related resources have made invasive birds the most successful urban dwellers, leading to gradual elimination of native, migratory and rare bird species. Nevertheless, studies about how nature-based solutions address environmental hazards and about the effects of urban landscapes on tropical biodiversity are still scarce (REGA-BRODSKY et al, 2022); both with regard to avifauna, a group widely studied in temperate cities (ORTEGA-ÁLVAREZ; MACGREGOR-FORS, 2009), and with regard to landscape connectivity (LAPOINT et al., 2015). In order to fill this gap, in chapter 1, we evaluated how the environmental characteristics — tree cover (%), impervious bulti-cover (%), vegetation patch size (ha), noise (dB) and functional connectivity — of urban landscapes influence the taxonomic and functional diversities of bird communities. Since urbanization can act as an environmental fi lter (LA SORTE et al., 2018; SOL et al., 2020), some scientists hypothesized that this process could also influence the diversity of plumage colors of birds that inhabit urban spaces (LEVEAU, 2019). Bird color variation was analyzed mainly at large geographic scales (DALRYMPLE et al., 2018; DELHEY, 2018). However, only few studies have addressed the influences of urbanization on bird color and they was conducted in subtropical urban landscapes (LEVEAU, 20221, 2019). The author observed that, in these cities, the intensity of urbanization is important to determine the color patterns of the species: highly 12 urbanized areas were dominated by gray birds, while places with high habitat diversity were inhabited by yellow and green birds. Due to the reduction in color diversity in these areas, Leveau (2019) concluded that urbanization leads to homogenization in the colors of bird plumage. However, Neotropical cities are home to highly diverse and colorful bird species, even in highly urbanized contexts, such as streets and avenues (GUIMARÃES et al., 2020; PENA et al., 2017), thus demonstrating the difficulty of generalize the relationships between urbanization and color diversity in urban bird communities. Considering this issue, in Chapter 2, we tested whether the findings of Leveau (2019a) are generalizable to two Neotropical cities (i.e., Bauru, Brazil; Xalapa, México) with similar latitudes in both the Southern and Northern Hemispheres. We also developed the ‛color percentage’ method, an approach that considers the birds’ whole body, producing continuous descriptions of bird plumage colors based on pixel intervals of scientific illustrations. Related to colors, we consider that studies need to take into account another important aspect: we must consider that birds see differently than humans: while humans have three types of color receptors or cones – which are sensitive to the red, blue and green spectral wavelengths – birds have a different fourth receptor, allowing the perception of the UV wavelength (TEDORE; NILSSON, 2019; CRONIN; BOK, 2016; LIND et al., 2013). Thus, in chapter 3, we suggest a new approach to analyze the influences of urban landscapes on bird colors, considering the ultraviolet spectrum. The idea was to describe bird coloration based on spectral characteristics (from visible to UV), using spectrophotometric analyzes of taxidermized specimens in museums. In chapter 4, we dealt with human perception of birds. According to Merleau-Ponty (1996, p.23), human perception is the way we interpret and apprehend the information transmitted by our senses and sensations in the world. When it comes to the urban landscape, our perception is a multisensory perspective and involves several actors (PALLASMAA, 2011), which also includes the animals that inhabit cities. According to Lynch (1960), a highly 'imaginable' city would invite our eyes and ears to have an active participation in the city, so that the sensory domain would expand and deepen. In this sense, studying birds and perceiving them as part of the urban landscape can be useful to stimulate the imaginability of cities 13 and, at the same time, contribute to the conservation of a group of animals that is particularly important for ecological balance, seed dispersal and pollination (SICK, 1997). In addition, the avifauna represents a group that is very present in urban areas, especially in Brazil, which is home to the second largest diversity of birds in the world (CBRO, 2017). Finally, the chapter 5 provides a discussion about the importance of Urban Ecology, science and its interdisciplinarity for planning sustainable cities. In this final chapter, we report the process of creation of the Araucaria Project, whose main goal is to bring sustainability by offering new views of planning the landscape, based on Nature Based Solutions (NbS). “Nature-based Solutions are defined as actions to protect, sustainably manage, and restore natural or modified ecosystems, that address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits” (IUCN, 2016, p.4). Understanding how nature-based solutions can drive the mechanisms of biodiversity in cities is still a substantial knowledge gap that need more research (REGA-BRODSKY et al, 2022) On June 11, 2021, the Araucária team project – representing UNESP/Rio Claro – won the Challenge Campus 2030, Edition 2. Overall, 680 teams and about 2000 people from 79 countries around the world participated in this challenge, organized by Agorize, in partnership with the United Nations (UNRIC) and the Agence Universitaire de la Francophonie (AUF). The aim of this challenge was to encourage students and professors from all over the world to create sustainable solutions for their university campuses that achieve at least 3 out of the 17 UN Sustainable Development Goals (SDGs). Our winning project has the following objectives: (a) creating a network of cycling paths to promote connectivity between the main areas of the campus; b) expanding three thousand square meters of the agroforest area to increase organic food production; and c) restoring 10 ha of the riparian forest of Ribeirão Claro River. Thereby, thinking about strategies to create and manage green infrastructure is important to mitigate and reduce the effects of urban sprawl. Urban vegetation is important for protection against geological problems and floods, and contributes to the balance of functions related to climate, humidity, air and water quality and acoustic management (FÁLCON, 2008; FONSECA et al. 2015). Green spaces also have a significant impact on urban microclimate (VAILSHERY. et al. 2013) and 14 provides water protection, reduction of heat island effect, floods and landslides prevention, increase of air quality and reduction of noise pollution (BOLUND; HUNHAMMAR 1999). The increase of air quality consequently prevents cardio respiratory problems such as bronchitis, allergic rhinitis and asthma in chi ldhood (LOUV, 2005; LOVASI et al., 2008). Proper planning and management of urban forests is indispensable to reduce the impacts of urbanization and meet social and environmental demands; they can also help to enhance the resilience of cities in the face of climate change (GRAVIOLA et al 2021). With the results of this thesis, I expect to produce important scientific information not only on how urbanization influence tropical biodiversity, but also on how people perceive the organisms that live around them in cities. Furthermore, from my experience with the Araucaria project, I expect to turn knowledge into action, assisting in the application of Nature Based Solution for a more sustainable urban future. 15 CHAPTER I Urban landscape characteristics and their effects on taxonomic and functional diversities of birds in Neotropical cities 16 1.1 INTRODUCTION Urban green spaces have important ecological roles in cities, providing resources and refuges for urban biodiversity (GRAVIOLA et al. 2021; ROSA, 2021; SCHÜTZ; SCHULZE 2015). They form a gradient of management intensity, economic input, and species diversity, leading a continuum of similarity with natural areas: from green roofs to native vegetation patches (ARONSON et al 2017). Green spaces vary in their local and surrounding characteristics, what have important influences on their ability to harbor native biodiversity (ARONSON et al., 2014; FERNÁNDEZ-JURICIC, 2000). Furthermore, green spaces must be planned and managed holistically considering their different socioecological functions across urban landscapes (GRAVIOLA et al. 2021). For example, native vegetation patches are fundamental for the conservation of several animal and plant species in cities (SCHÜTZ; SCHULZE, 2015; IKIN et al. 2013), while wooded streets can connect populations of different organisms between habitat patches (PENG et al. 2017; YOUNG et al. 2007). Therefore, green spaces can be considered as an ecological network that mimics natural processes, being fundamental for the maintenance of the integrity of urban ecosystems (GRAVIOLA et al. 2021; HERZOG et al 2016; PENA et al. 2016). Birds comprise the most studied group of organisms in cities (REGA‑BRODSKY et al 2022). They can be found occupying from native vegetation patches to streetscapes and community composition respond to different degrees of human disturbances (PENA et al. 2017). Thus, studies have been using birds as a model to assess how local and landscape characteristics influence urban biodiversity; for example, factors such as trees density, car traffic, pedestrian density and the surrounding matrix influence patterns of distribution of bird species within cities (FERNÁNDEZ-JURICIC, 2000). The streetscape is usually inhabited by a community dominated by a few species harboring most generalist traits, such as omnivorous birds (PENA et al 2017). Related to streets and highways, the traffic, as well as the noise generated by it, exert negative effects on the avifauna (VAN DER REE et al., 2011). Noise intensity is a proxy for negative effects within streets, once the higher the exposure to noise, the greater the impacts associated with 17 urbanization, and therefore, the smaller the richness of birds species in the streets (PENA et al., 2017). Vegetation patches can be inhabited by highly diverse bird communities, even by endangered species (PERILLO et al., 2017; BHAKTI et al 2014). Local characteristics, such as the presence of dead and old trees, vegetation structure, plant diversity and the proximity to parks and squares modulate bird species composition (CAMPOS-SILVA; PIRATELLI 2021; CONCEPCIÓN et al., 2016). The vegetation structure is an important component for the conservation of bird species in cities (ARONSON et al., 2014). Schütz and Schulze (2015) found that the increase in the amount of arboreal vegetation led to an increase in the community’s functional aspects (IKIN et al., 2013), and larger amount of bird feeding guilds (WHITE et al., 2005). Further, urban areas with a predominance of native trees have greater bird species richness (IKIN et al., 2013), while the introduction of exotic plants into the urban environment are processes that negatively impact bird communities (CHACE; WALSH, 2006). Birds can use wooded streets as ecological corridors to move between urban vegetation patches (GRAVIOLA et al. 2021), increasing bird richness and diversity (YOUNG et al. 2007), the community’s functional aspects (SCHÜTZ; SCHULZE, 2015) and the number of bird feeding guilds (WHITE et al. 2005). The presence of urban parks and wooded streets can increase urban landscape connectivity (GRAVIOLA et al. 2021), which is essential to link vegetation patches across the landscape (BENNETT, 2003) and to restore the flow of different species (GRAFIUS et al.2017; PENG et al. 2017). Up to date, most urban ecological studies have been conducted in native vegetation patches of cities in temperate climate (REGA‑BRODSKY et al 2022). It was verified that only 5 out of 174 articles about landscape connectivity in urban areas were carried out in South America (LAPOINT et al., 2015). Only few studies assessed how local and landscape characteristics influence biodiversity in tropical cities, especially the functional composition of communities inhabiting streetscapes (REGA‑BRODSKY et al 2022, PENA; MARTELLO in press). Furthermore, event that in Brazil birds represent an important fauna group, ranking the second largest diversity of bird population in the world (CBRO, 2017), in South American cities, the 18 connectivity and urban bird communities are understudied (ORTEGA-ÁLVAREZ; MACGREGOR-FORS, 2009). This further emphasizes the importance of studying the effects of urbanization on landscape connectivity in the Neotropics The knowledge of how urban landscape influence bird community can be useful to direct conservation plans in cities. In this way, this study aimed to assess how different aspects of urban landscape (proportion of tree cover and impermeable surfaces) and local (vegetation patch size and noise exposure) aspects, as well as the landscape functional connectivity, influence functional and taxonomic aspects of birds inhabiting vegetation patches and the streetscape of a Neotropical city. We expected to find higher species richness and number of individuals , as well as higher values of FRic (i.e.: high functional richness) and RaoQ (i.e.:less dominance of functional traits) within parks with greater area, density of trees, connectivity and percentage of native forest and less noise. In streets, we also expected to find higher species richness and number of individuals, as well as higher values of FRic and RaoQ in streets with higher tree density, higher percentage of native trees and less background noise. Since cities are social-ecological ecosystems of living and nonliving things (MADDOX et al. 2017), the understanding of how humans, biodiversity and ecological processes can coexist in human-dominated systems can help societies to become more sustainable (MARZLUFF et al., 2008). 19 1.2 QUESTIONS, HYPOTHESES AND PREDICTIONS Question 1: What is the relative contribution of landscape, vegetation structure and urban noise to explain the taxonomic and functional characteristics of bird communities? Hypothesis 1: Urban parks act as refuges for bird species. Larger fragments of vegetation, located in places with higher tree cover, higher percentage of native forest, less noise and more connected positively influence taxonomic (higher species richness and greater number of individuals) and functional aspects (higher values of functional richness – FRic – and Rao's Quadratic Entropy - RaoQ) of urban bird communities. Hypothesis 2: Streets with higher tree cover, higher percentage of native trees and less background noise positively influence taxonomic (higher species richness and greater number of individuals) and functional (higher values of FRic and RaoQ) aspects of urban bird communities. They also can increase the connectivity between the urban parks. Figure 1.1: Expected influences of connectivity, vegetation patch size, percentage of native forest and tree cover and noise on the functional and taxonomic aspects of the bird community in a Neotropical city. The illustration to the left of the image represents the richness of bird species in the form of an inverted pyramid, showing what is expected to happen for each variable: in the top, there is the higher functional and taxonomic richness and below, there is lower functional and taxonomic richness. 20 1.3 MATERIAL AND METHODS 1.3.1 Study area Bauru is the most populous municipality in the central-west region of São Paulo’s State, Brazil. The city has more than 371 thousand inhabitants and has an important economy, with the 18th largest GDP (Gross Domestic Product) in the state of São Paulo and the 68th in the whole country (IBGE, 2017). The city is located in the transition zone between Atlantic Rainforest and Brazilian Savanna (Cerrado), two biodiversity hotspots. Due to the presence of streams and rivers with different levels of conservation of their riparian forests (including canalized stretches), fragments of vegetation of different sizes distributed across the city and its periphery, and a streetscape with a heterogeneous distribution of woody cover (SEMMA, 2015). Bauru landscape presents an excellent scenario to test how urban local and landscape characteristics influence bird communities. 1.3.2 Production of land use/land cover map Firstly, we produced a land use/land cover map using ArcGIS software. Because our focus is urban landscape, for creating the map, we only selected the territory that is within the urban perimeter of Bauru. The satellite imagery was obtained from the PlanetScope satellite with 3 meters of resolution captured in October 2018, from Planet Team1. Then, we carried out a supervised classification, using the ArcGIS Maximum Likelihood Classifier too. This classification uses training sites selected manually according to land/uses classes of interest so that the software is trained and it performs the classification from which all the pixels in the image are classified based on the similarity of their spectral characteristics with those of the training areas (MAGNANI, 2015). In the second stage, we carried out the classification assessment, comparing the classification results with the satellite images. The landscape was classified into four classes: urban, water, woody vegetation and herbaceous vegetation (Figure 1.2). 1 https://www.planet.com/pulse/planet-offers -broader-data-access-to-the-academic-community/ https://www.planet.com/pulse/planet-offers-broader-data-access-to-the-academic-community/ 21 Figure 1.2: land cover map of Bauru’s territory, within the urban perimeter (São Paulo, Brazil) 1.3.3 Parks sampling sites We selected 36 sampling sites distributed through different contexts of Bauru’s landscape: 12 in vegetation patches (main parks, squares and small green spaces of the city) and 24 across the streetscape. To select the sites in the streets, we adopt an approach to estimate their ability to connect habitat patches across the urban landscape (i.e. their degree of functional connectivity). By simulating Multiple Least- Cost Path (MLCP), using the software LSCorridors (RIBEIRO et al., 2017). Considering the vegetation patches, the first idea was to choose two or more samples points in each site, considering the area size of each one; however, they have really different sizes and characteristics, and it was difficult to establish a method for choosing the number of sampling points for each one. In discussion with other ornithologists, we concluded that it was not necessary more than one sampling point per park. Thus, for the vegetation patches, we defined as sampling sites a central point created a 25m radius buffer surrounding each central point (Figure 1.3), 22 an approach used by Barbosa et al., (2017). Once most bird species fly long distances (SICK, 1997), choosing a central point avoids overestimating the occurrences and creating pseudoreplicates (HURLBERT et al., 1984). Figure 1.3: 25m radius buffers from 12 sampling sites in Bauru (São Paulo, Brazil) 1.3.4 Simulating Multiple Least-Cost Paths (MLCP) to define sampling points in the streets To select the 24 streets with different levels of potential connectivity (from very low to high), we simulated Multiple Path Corridors (MLCP), using the software LSCorridors (RIBEIRO et al., 2017), considering regional characteristics. MPC is a more suitable alternative for the design of ecological corridors when compared to the traditional Least-Cost Path (LCP) method (Pinto and Keith 2009). Whereas LCP generates a solution only for paired source-targets, the MLCP method generates multiple alternative routes connecting the source-target areas; it is a more robust 23 solution when seeking a better distance–cost relationship for a variety of species with different landscape perceptions (RIBEIRO et al., 2017). In this study, we aimed to identify the routes that present the best balance between distance and potential cost of movement by birds in the urban landscape. The software LSCorridors (LSCORR) simulates multiple routes of a focal organism between pairs of sources and targets (habitat patches or other features to be connected), obtaining a map as a proxy for functional connectivity. The output is a set of maps that contain the route selection frequency index (RSFI) of every pixel, which allows us to identify a variety of routes that would guarantee the connectivity within the landscape (RIBEIRO et al. 2017). To produce the resistance surface, different values were attributed to the four land cover classes: the highest to urban areas (12), intermediate to herbaceous vegetation/agriculture (4) and the lowest to woody cover (1). Then, 50 Multiple Least- Cost Paths simulations were carried out for each of the 78 pairs formed between the 12 vegetation patches in the municipality (i.e. source and target areas). The result from the simulations was a set of raster files with the values of the Route Selection Frequency Index (RSFI; Route Selection Frequency Index), which indicates the frequency in which the area was crossed (Figure 1.4). Figure 1.4: Multiple Least-Cost Path generated by LSCORR in the resistance land use map of Bauru, São Paulo, Brazil 24 We used the intersection of the MLCP that crossed the streets and avenues in urban landscape the city, by performing the Extract tool of ArcgGis 10.2, to select the streets with different levels of potential connectivity. Then, we classified the RSFI values of the streets and avenues into 5 classes using the Jenk's Natural Breaks method: class 0 [RSFI = 0]; class 1 [RSFI 900 1879]; class 2 [RSFI from 1880 to 2959]; class 3 [RSFI 21960 to 4159]; class 4 [RSFI 4160 to 6049]; class 5 [RSFI 6050 to 8070]. We distributed random points using the Create Random Points tool, that selected 4 points for each of those classes 1 to 5 (4 x 5 = 20 points). We also selected 4 sites that were not crossed by MLCP (class 0). At the end, we had 24 sample points in the streets and 5 classes (Figures 1.5). Figure 1.5: Sampling points: 12 parks and squares (pink polygons) e the 24 streets (colorful points) selected considering the 5 classes based on RSFI intervals of Multiple Least-Cost Path simulations, in Bauru, São Paulo, Brasil 25 1.3.5 Estimating local and landscape characteristics In order to characterize the landscape structure, we considered five variables: tree cover (%), impermeable built-cover (%), vegetation patch size (ha), noise exposure (dB) and functional connectivity. The first three variables were calculated using ArcGIS Map tools considering a 25m radius around each sampling site. The background noise levels were measured using the Tacklife Decibel Meter, donated by Idea Wild2 (Figure 1.6). Finally, functional connectivi ty was calculated using the Probability of Connectivity Index (dPC), with the help of Conefor software. This index calculates the importance of a given fragment (in our case, a vegetation patch) to maintain the overall functional connectivity of the landscape. The dPC is based on a model of probable connections between each pair of parks, considering the distances between them and the probability values of a species moving in these spaces (SAURA; PASCUAL-HORTAL, 2007). In this case, to calculate the distance between the parks and calculate the dPC, we used the average of the length of the MLPC generated by our simulations on LSCorridors (RIBEIRO et al. 2017). Figure 1.6: material donated by Idea Wild 2 The project had the institutional support of the Biodiversity Department, which provided some binoculars and GPSs for use in the field. In addition, the project was selected to be contemplated with the donation of field equipment by Idea Wild, thus we received: 1 Tacklife Decibel Meter, 1 Laser Distance Meter and 2 Vortex brand binoculars. 26 Table 1.1: Vegetation patches areas with the values of the variables analyzed, considering the buffer radius of 25 meters in Bauru (São Paulo, Brazil) Vegetation patch (parks and squares) Vegetation patch size (ha) Tree cover (%) Impermeable built-cover (%) Connectivty (dPC) x 1000 Noise (Decibeis) Bosque da Comunidade 1,75 100,0 0,0 1,37 59,6 Buffet Fiu-Fiu 13,34 74,6 0,0 20,98 64,2 Estádio Noroeste 8,38 22,6 26,3 7,71 55,7 Horto Florestal 43,62 100,0 0,0 210,21 50,8 Jardim Botânico - Lago 671.77 29,2 4,9 49.544,34 46,1 Jardim Botânico - Mata 671,77 100,0 0,0 49.538,27 48,7 Museu Ferroviário 1,03 5,6 94,4 0,12 48,6 Parque Vitória Régia 5,65 59,0 0,0 3,59 58,3 Praça Rui Barbosa 0,92 57,6 40,3 0,09 68,8 Residencial Jardins do Sul 36,32 82,9 0,0 144,82 44,4 SESC 2,37 61,8 28,5 0,62 64,0 UNESP 69,50 91,9 6,0 536,32 52,5 Table 1.2: Streets with the values of tree cover and impervious built -cover calculated considering the 25-meter radius buffer, as well as the noise values in Bauru (São Paulo, Brazil) Streets Tree cover (%) Impermeable built-cover (%) Noise (Decibeis) R. 15 de novembro 0,0 100,0 77,3 R. Afrânio Roberto 48,6 51,4 63,2 R. Castro Alves 47,3 52,7 58,7 Av. Comendador J.S. Marta 42,8 48,5 69,1 Condomínio Samambaia 43,6 56,4 64,5 Av. Cruzeiro do Sul 0,0 100,0 86,7 Av. Elias Miguel Maluf 0,0 100,0 78,3 R. Gerson França 48,2 40,6 64,4 R. Guatemala 71,2 13,5 64,6 R. João Abe Arrage 63,4 36,6 64,1 R. João Ramos Nascimento 19,8 62,8 49,8 Av. Laranjeiras 11,5 88,5 59,9 R. Maranhão 22,9 77,1 53,3 Av. Nações Unidas 36,9 54,8 75,8 Av. Nações Unidas - Leão 20,4 31,8 66,6 Av. Nuno de Assis 34,6 37,6 78,6 R. Olavo Bilac 0,0 100,0 79,0 R. Osvaldo Alvarenga 14,7 72,6 46,8 R. Vigíl io Malta 24,3 61,9 66,2 Rotatória Comendador 11,2 25,4 70,5 Rotatória Educação Física 0,0 17,8 61,7 Av. Eng. Luiz E. Carrijo Coube 30,2 23,1 57,1 R. Severino Dantas Souza 14,0 86,0 74,8 R. da Startemp 54,9 40,3 49,5 27 1.3.6 Bird survey data Pilot sampling fieldwork was carried out, during September 2018, to confirm if the number of sampling points were enough and also to verify if the sampling vegetation patches selected by satellite image were safe and accessible. When the site was considered unsuitable for carrying out the surveys, a new selection was made from the points extracted from the intersection between the streets and the RSFI. We also verified which of those areas would need authorization to access the site. For public squares and streets, no authorization was required. However, it was necessary to apply for authorization to conduct research in some areas as: SESC, Railway Museum and the private residential condominiums. For the bird survey, observations were made by point count method (LLOYD et al., 2000), used for bird sampling in urban and forest landscapes (PENA et al., 2017; TOLEDO et al., 2012; FONTANA et al., 2011; VOLPATO et al., 2009), in which individual points are selected and the observer stop at predefined spots, recording all the birds seen or heard for a predetermined time (GREGORY et al., 2004), that in our study was 10 min (TOLEDO et al., 2012). The point count was used instead of line transects because this is the preferred method for closed forest habitats with high canopies, particularly in rainforests because by standing in a location over a fixed period of time, the observer has a better chance of detecting birds (LLOYD et al., 2000). We considered birds perched in trees and those flying between trees. Birds flying at distances greater than 6 meters in height were not included. To carry out the fieldwork, I had the help of five undergraduate students in Biology (2 or 3 of them assisted me each day; figure 1.7 and 1.8) and the co-adviser and PhD researcher João Carlos Pena (Fapesp: 2018/00107-3). Figure 1.7: undergraduate students during bird surveys at SESC, in Bauru (São Paulo, Brazil) 28 Figure 1.8: Bird survey at Botanic Garden Park, Bauru (São Paulo, Brazil) Bird observations were conducted during the mornings (LLOYD et al., 2000; GREGORY et al., 2004), between December 2018 and March 2019 (summer season in Brazil) and from September to December 2019 (spring season). This period coincides with the breeding period of most species in southeastern Brazil, including migratory species (SICK, 1997). Sites were visited six times, three during the summer and three during the spring, totaling 108 field observations. The selection of one period aimed to avoid the temporal fluctuation caused by the presence of migratory birds (ABILHOA; AMORIN, 2017; PENA et al., 2017). Recent studies also have exclusively sampled during the spring season because it is considered the best time to evaluate the birds responses to the landscape (BANKS-LEITE et al., 2014; MARTENSEN et al., 2012; METZGER et al., 2009; MARTENSEN et al., 2008) . 1.3.7 Functional traits and taxonomic aspects We defined as response variables, bird species richness (SRic; the number of individuals), and the overall abundance (SAbund) observed at each sample point (vegetation patches and streets). Because the bird individuals were not identified, the overall abundance was calculated considering the visit with the highest number of individuals found. In all, 6 visits were made at the same point. We also calculated community’ functional diversity (FD), functional dispersion (FDis), functional richness (FRic), and Rao’s Quadratic Entropy (RaoQ). FD is based on the presence/absence of species data (PETCHEY; GASTON, 2002). FDis 29 considers the relative abundance of species (LALIBERTE; LEGENDRE, 2010). FRic represents the amount of functional space occupied by species in a community (MOUCHET et al., 2010). RaoQ incorporates relative abundances of species and pairwise functional differences between species (BOTTA-DUKÁT, 2005). This last index is an indirect measure of functional uniformity, since the higher the RaoQ value, the greater the difference between species (PENA et al, 2017). To calculate the functional diversity indices, we considered five bird traits: 1) body mass, 2) foraging substrate (soil, medium foliage extract, top foliage extract, air), 3) diet (flowers/nectar, invertebrates, fruits, seeds, carrion, vertebrates) and 4) clutch size and 5) nesting site (cavities, trees, ground) (PENA et al., 2017). We collected data on the functional traits from published literature e.g. Sick (1997), Del Hoyo et al. (2004), Gussoni (2009), Uejima et al. (2012), Wilman et al., (2014), Zima; Francisco (2016) e Pigot et al. (2020). After defining the indices, we constructed a matrix containing two continuous traits and three categorical (fuzzy) traits (PENA et al. 2017). Prior to calculating the functional diversity indices, we converted the fuzzy variables into proportional variables (PAVOINE et al., 2009). This was done using the prep.fuzzy function of the R package (R CORE TEAM, 2020) and ade4 (DRAY et al., 2020). Subsequently, the feature matrix was converted into a distance matrix using the dist.ktab function. This final matrix was used to calculate the functional diversity indices in R using the dbFD function of the FD package (LALIBERTE; LEGENDRE, 2010). Thus, the values of richness and functional diversity were obtained (Tables 1.3 and 1.4). Table 1.3: Functional diversity indices obtained for bird communities in 12 parks in Bauru (São Paulo, Brazil). FD: functional diversity; FRic: functional richness; FDis: functional dispersion; RaoQ: Rao's quadratic entropy. FRic values are multiplied by 100 only to facilitate numerical representation. Vegetation patches (parks and squares) FD FRic*100 FDis RaoQ Bosque da Comunidade 0,931 0,63 0,355 0,132 Buffet Fiu-Fiu 0,914 1,84 0,382 0,149 Estádio Noroeste 0,929 1,59 0,392 0,157 Horto Florestal 0,894 3,42 0,400 0,164 Residencial Jardins do Sul 0,918 0,42 0,366 0,138 Jardim Botânico - Lago 0,868 19,60 0,395 0,160 Jardim Botânico - Mata 0,902 0,95 0,378 0,147 30 Museu Ferroviário 0,903 0,05 0,306 0,109 Praça Rui Barbosa 0,963 0,32 0,286 0,101 SESC 0,873 1,34 0,383 0,150 UNESP 0,885 5,18 0,389 0,154 Parque Vitória Régia 0,936 1,02 0,396 0,159 Table 1.4: Functional diversity indices obtained for bird communities in 24 streets and avenues of Bauru (São Paulo, Brazil). FD: functional diversity; FRic: functional richness; FDis: functional dispersion; RaoQ: Rao's quadratic entropy. FRic values are multiplied by 100 only to facilitate numerical representation. Streets FD FRic*1000 FDis RaoQ R. 15 de novembro 0,808 0,00 0,319 0,111 R. Afrânio Roberto 0,952 0,07 0,351 0,129 R. Castro Alves 0,892 0,48 0,299 0,106 Av. Comendador J.S. Marta 0,880 0,87 0,357 0,134 R. Condomínio Samambaia 0,946 1,14 0,365 0,138 Av. Cruzeiro do Sul 0,886 0,04 0,375 0,149 Av. Elias Miguel Maluf 0,828 0,09 0,323 0,115 R. Gerson França 0,910 1,62 0,378 0,147 R. Guatemala 0,892 39,80 0,401 0,163 R. João Abe Arrage 0,950 2,19 0,355 0,132 R. João Ramos Nascimento 0,948 2,65 0,373 0,142 Av. Laranjeiras 0,939 1,30 0,373 0,146 R. Maranhão 0,931 1,34 0,366 0,139 Av. Nações Unidas 0,883 1,16 0,334 0,126 Av. Nações Unidas - Leão 0,930 1,92 0,392 0,157 Av. Nuno de Assis 0,918 3,94 0,376 0,148 R. Olavo Bilac 0,807 0,09 0,327 0,119 R. Osvaldo Tav 0,944 4,36 0,340 0,127 R Virgílio Malta 0,915 3,54 0,392 0,156 Rotatória Comendador 0,907 5,99 0,345 0,134 Rotatória Educação Física 0,925 6,69 0,396 0,162 Av. Eng. Luiz E. Carrijo Coube 0,923 7,29e 0,370 0,144 R. Severino Dantas Souza 0,911 0,60 0,355 0,134 R. da Startemp 0,909 5,24 0,389 0,153 ., 31 1.4 DATA ANALYSIS 1.4.1 Assessing the relationships between local and landscape characteristics and the bird community First, we calculated the average and medians of bird species richness per sampling site. Then, the richness found in the streets and parks were compared, through analysis of variance, modeled and calculated with the aid of the Rstudio program. To understand how the landscape influences the functional and taxonomic characteristics of the bird community, we built Generalized Linear Models (GLM) between each response variable (SRic; the number of individuals; SAbund: overall abundance; FD: functional diversity; FDis: functional dispersion; FRic: functional richness and RaoQ: Rao’s Quadratic Entropy) and each one of the five predictor variables (tree cover, vegetation patch size, exposure to noise, and functional connectivity). The linear regression was used to analyze continuous variables and describe the linear relationship between a predictor variable, represented graphically by the X axis, and a response variable, represented graphically by the Y axis (GOTELLI & ELLISON, 2013). Prior to the modelling approach, we performed a Person’s correlation analysis between each pair of predictor variables, using the cor function available in R. and only kept variables that presented Person’s R < 0.6. For each GLM, the Akaike information criterion for small samples (AICc) was calculated. Models with ΔAICc <2.0 are considered to have substantial support (BEIER; BURNHAM; ANDERSON, 2001). For this, we used the RStudio: psych packages to correlate the urban environmental variables and the MuMIn and AICcmodavg packages to assess which were the significant models of interaction between the independent variables and which of these interactions actually influence the functional and taxonomic richness observed in the parks and on the streets. We used P values smaller than 0.05 as a level of significance. 32 1.4.2 Verifying if the streets most selected by LSCorridors simulation present better taxonomic and functional aspects of the bird community It was verified if the streets crossed by a high number of modeled paths coincide with the streets where the higher taxonomic and functional diversities were observed. For this, new GLMs were built considering the street classes, separated according to RSFI values of the streets and avenues using the Jenk's Natural Breaks method: white class (RSFI=0); yellow class (RSFI of 900 1879); blue class (RSFI from 1880 to 2959); class orange (RSFI from 21960 to 4159); class green (RSFI from 4160 to 6049); red class (RSFI from 6050 to 8070). Next, it was verified whether the different classes of functional connectivity are related with the different degrees of bird community functional and taxonomic diversities observed in the selected streets. 1.4.3 Multiscale influence of the landscape on the avifauna Finally, we analyzed the multiscale influence of the landscape on the richness and number of species records in streets and parks. For this, we used the rgdal (BIVAND et al., 2020), raster (HIJMANS et al., 2020), tidyverse (WICKHAM, 2019) and landscapemetrics (MAXIMILLIAN et al., 2020) packages in RStudio to analyze the correlation between the results of the richness and number of records with the tree and herbaceous cover of the ground cover map, in a scale of 25m, 50m, 100m, 250m, 500m and 750m, in relation to the sample observation point. 33 1.5 RESULTS A total of 2342 bird records were counted across streets and vegetation patches of Bauru. They belong to 96 species, 35 families and 16 orders (Appendix A). The families with the highest number of species were Tyrannidae (18 species) and Thraupidae (10). On the WikiAves website, 270 bird species are registered in the municipality (including rural, forest and urban areas) of Bauru, of which we recorded 36% in the urban area. The most frequent species were all native from the Brazilian territory (with one exception: House Sparrow): Great Kiskadee (Pitangus sulphuratus, 149 records), Eared Dove (Zenaida auriculata, 141), White-eyed Parakeet (Psittacara leucophthalmus, 139), House Sparrow (Passer domesticus, 136), Blue-and-white Swallow (Pygochelidon cyanoleuca, 132), Picazuro Pigeon (Patagioenas picazuro, 123) and Yellow-chevroned Parakeet (Brotogeris chiriri, 89). 1.5.1.Comparison between richness and bird records between streets and parks Comparing the species richness found in the streets and green areas, we confirmed our hypothesis that green areas present higher richness compared to the streets. Analysis of variance showed that parks has significantly better richness (F=41.23; p<0.0001; df=35) when compared to streets. For the number of records, parks again presented significantly higher values (F=71.41; p=<0.0001; df=35), when compared to streets (Figure 1.9). 34 Figure 1.9: Number of records and species richness for urban avifauna of green areas and streets in Bauru (São Paulo, Brazil) We also compared the richness species and records between the different classes selected by the MLCC simulations at LSCorridors: white class (RSFI=0); yellow class (RSFI from 900 to 1879); blue class (RSFI from 1880 to 2959); class orange (RSFI from 21960 to 4159); class green (RSFI from 4160 to 6049); red class (RSFI from 6050 to 8070). Thus, the classes go from the least frequent to the most frequent. As a result of the statistical analysis, we concluded that the different connectivity classes also explain the number of records (F=58.29; p=0.001; df=23) and species richness (F=19.63; p=0.001; df =23). Furthermore, the streets not selected by any route (Class 0 = white) had considerably fewer records and species compared to the others selected (F=56.65; p<0.001; df=23. Figure 1.10). 35 Figure 1.10: Number of records and richness of bird species, considering the different levels of potential connectivity of streets. The RSFI values refer to the frequency in which the MLCP crossed the streets, simulated by LSCorridos. The value 0 represents streets that were not selected, and the values 1 to 5 represent a gradient of connectivity from low to high, considering the landscape context in Bauru (São Paulo, Brazil) 36 1.5.2. Taxonomic richness and urban environmental variables The results showed that the impermeable built-cover negatively influences the species richness in streets (r=-0.65; F = 16.05; p = 0.0006) and in parks (r=-0.62; F = 6, 15; p = 0.03; figure 1.11). Figure 1.11: Influence of impervious built-cover (%) and taxonomic richness on streets in parks Regarding the other variables, none - apart from the impermeable built-cover variable (%) - had a significant influence on the taxonomic richness in the parks. However, the same does not occur in the case of streets: both noise (r=-0.62; F = 37 14.10; p = 0.001) and tree cover (r=0.53; F = 8.52; p = 0.008) are correlated with the taxonomic richness of birds (Figure 1.12). Figure 1.12: Influence of noise and tree cover on taxonomic richness in the streets 1.5.3. Functional diversity indices (FD, FDis, FRic and RaoQ) and urban environmental variables The GLM results for the functional diversity (FD index) on streets were significant for all quantitative variables (impermeable cover, tree cover and noise; figure 1.13). On the streets, FD is negatively correlated with noise (r = -0.62; F = 14.11; p = 0.001), 38% correlated with tree cover (r = 0.38; F = 3.76; p = 0.05) and - 38 48% correlated with waterproof coverage (r = -0.48; F = 6.57; p = 0.01). However, considering the parks, these three variables, as well as the functional connectivity and size variables, do not have a significant influence on the FD index. 39 Figure 1.13: Influence of noise, tree cover and impervious built-cover on FD index, in the streets. Considering the functional dispersion (FDis index), only the impervious built- cover variable showed a significant correlation in the streets (r = -0.49; F = 6.83; p = 0.02) and in the parks (r = - 0.72; F = 10.99; p = 0.008; figure 1.14). Figure 1.14: Influence of impervious built-cover on FDis, in streets and in parks. In relation to functional richness (FRic index), it was found that noise has no significant relationship (F = 0.50; p = 0.48). However, in the streets, FRic is correlated 40 with impermeable cover (r = -0.51; F = 7.63; p = 0.01) and with tree cover (r = 0.42; F = 4, 8; p = 0.04; figure 1.15). Figure 1.15: Influence of impervious built-cover and tree cover on FRic, on the streets. In parks, vegetation patch size and connectivi ty are the variables that significantly influence the functional richness (FRic; Figure 1.16). The correlation values between these variables and the FRic index are similar: 64% correlation with the vegetation patch size (r = 0.64; F = 6.98; p = 0.02) and 63% with connectivity (r = 0.63; F = 6.46; p = 0.03). 41 Figure 1.16: Influence of vegetation patch size and connectivity variables on FRic, in parks. Regarding the Rao's Quadratic Entropy (RaoQ index), which incorporates relative abundances of species and functional differences in pairs between species, a significant relationship was found only with the impermeable cover variable (Figure 1.17). As a result, it was found that impervious built-cover influences RaoQ in a correlation of -52% in streets (r = -0.52; F = 8.32; p = 0.009) and -71% in parks (r = -0 .71; F = 9.98; p = 0.01). 42 Figure 1.17: Influence of the impervious built-cover variable on RaoQ, in streets and parks. 1.5.4 Selection of correlation models between urban environmental variables, using the Akaike information criterion (AICc) After analyzing the effect between each pair of environmental variable and response variable, we calculated the correlation analysis between the urban environmental variables. Thus, we verified which variables were highly correlated and which, therefore, overlapped their effects. Thus, we found that tree cover and impervious built-cover are -83% correlated in parks and -60% in streets, respectively, 43 (Figure 1.18 and 1.19). Therefore, we discard this relationship, since their effects overlap to explain the response variables. Figure 1.18: Result of correlation analysis between urban environmental variables, in parks 44 Figure 1.19: Result of the correlation analysis between urban environmental variables, in streets Then, we used the Akaike information criterion for small samples (AICc), with ΔAICc < 2.0 to assess which interaction models were significant. Considering the streets, the results validated the models confirming that: 1) The three variables: noise, tree cover and impermeable cover influence taxonomic richness and functional diversity (FD) and that 2) functional dispersion (FDis), functional richness (FRic) and Rao's Quadratic Entropy (RaoQ) have a negative relationship with impermeable cover. Analyzing the bivariate interaction models, we identified two models that were statistically significant: noise and impermeable cover, which have negative relationships with and richness (ΔAICc = 0) and FD (ΔAICc = 1.24; figure 1.20). 45 Figure 1.20: Bivariate model of the interaction between noise and impervious built- cover on richness and FD, in streets In the parks, the results validated the models confirming that: 1) richness has a negative correlation with impervious built-cover; 2) functional dispersion (FDis) has a negative correlation with impervious built-cover; 3) functional richness (FRic) has a positive correlation with connectivity and vegetation patch size and 4) Rao's Quadratic Entropy (RaoQ) has a negative correlation with impervious built-cover. Considering the bivariate models, the one that presented a significant result was 1) the interaction between connectivity (positive correlation) and tree cover (negative correlation) to explain FRic (ΔAICc = 0). 46 Figure 1.21: Bivariate model of the interaction between connectivity and tree cover on FRic in parks 1.5.5 LSCorridors simulations and taxonomic and functional aspects of the bird community The analysis considering the different RSFI classes selected by the simulation showed that these classes can be statistically significant to identify possible groups of streets with similar urban environmental characteristics. As it was possible to verify, the division of the RSFI classes managed to group streets with similar environmental variables in the same class, so the functional diversity (FD) could be explained both by the urban environmental variables and by the division of the classes. Therefore, FD is explained by both impervious-built cover (F = 13.14; p = 0.002), tree cover (F = 7.92; p = 0.01) and noise (F = 21.78; p = 0.0002), and by the RSFI classes (F = 5.39 and p = 0.004; F = 5.86 and p = 0.002; F = 3.39 and p = 0.02) respectively (Figure 1.22). While the pair FD and impervious-built cover has an r² = 0.23, when adding the class variable, the effect of the sum of the two variables rises to r² = 0.70. In the case of the pair FD~tree cover + class, the r² = 0.15 for r² = 0.69; and the pair FD~noise + class, the r² = 0.39 goes up from to r² = 0.69. 47 Figure 1.22: Influence of the interaction of the variables impervious-built cover, tree cover and noise and RSFI classes [class (RSFI=0); yellow class (RSFI of 900 1879); blue class (RSFI from 1880 to 2959); orange class (RSFI from 21960 to 4159); green class (RSFI from 4160 to 6049); red class (RSFI from 6050 to 8070)] in functional diversity (FD), in the streets. Similar to the interactions of variables on functional diversity (FD), we found that the sum of the variables: impervious-built cover+class (F = 14.48; p = 0.04) and noise+class (F = 13.42; p = 0 .04) have a significant effect on richness (Figure 1.23). 48 While the pair richness~impervious-built cover has an r² = 0.42, when adding the class variable, the effect of the sum of the two variables increases to r² = 0.50. In the case of the richness~noise pair, the r² = 0.39 rises to the r² = 0.50. Figure 1.23: Influence of the interaction between impervious-built cover and noise variables and RSFI classes [white class (RSFI=0); yellow class (RSFI of 900 1879); blue class (RSFI from 1880 to 2959); orange class (RSFI from 21960 to 4159); green class (RSFI from 4160 to 6049); red class (RSFI from 6050 to 8070)] on taxonomic richness, in the streets. Regarding the other indices, only the interaction between RSFI classes and tree cover proved to be significant to explain functional richness (FRic). This happened because tree cover defines the RSFI classes that in turn influences FRic 49 (Figure 1.24). Therefore, the interaction FRic~tree cover*class has F = 4.22; p = 0.04 p = 0.009 and r² = 0.76. Figure 1.24: Influence of the interaction of the variables tree cover and RSFI classes [white class (RSFI=0); yellow class (RSFI of 900 1879); blue class (RSFI from 1880 to 2959); orange class (RSFI from 21960 to 4159); green class (RSFI from 4160 to 6049); red class (RSFI from 6050 to 8070)] on functional richness (FRic), in the streets However, it is important to note that despite the division of streets into RSFI classes influences significantly functional diversity (FD), taxonomic richness and functional richness (FRic), when calculating the difference between the classes selected by LSCorridors, there was no significant difference (all classes have p > 0.05). The significant difference is between the effect of the white class (unselected routes - RSFI=0) in relation to the effect of colored classes [Selected routes: yellow class (RSFI of 900 1879); blue class (RSFI from 1880 to 2959); orange class (RSFI from 21960 to 4159); green class (RSFI from 4160 to 6049); red class (RSFI from 6050 to 8070)]. 1.5.6 Multiscale analysis of landscape influence The multiscale analysis of the influence of landscape on taxonomic richness and the number of bird records revealed that there is a significantly positive effect between richness and tree cover, in the streets, at scales of 50 (F = 7.02; p = 0.01; r² = 0.21), 100 (F = 24.96; p < 0.001; r² = 0.51), 250 (F = 30.47; p < 0.001; r² = 0.56), 500 (F = 6.88; p = 0.02; r² = 0.20) and 750 meters: (F = 6.38; p = 0.02; r² = 0.19; Figure 1.25). 50 Figure 1.25: influence of tree cover on the bird richness, at scales from 50 to 750 meters in relation to the sampling point of streets. The number of records and tree cover, in the streets, at scales of 50 (F = 9.14; p = 0.01; r² = 0.26), 100 (F = 18.78; p < 0.001; r² = 0.44) and 250 (F = 9.24; p < 0.001; r² = 0.26) also showed positively significant relationships (Figure 26). Figure 1.26: influence of tree cover on records of bird species, at scales of 50 to 250 meters in relation to the sampling point of streets. 51 A pattern similar to the number of records was registered between richness and herbaceous cover, in the streets, at the scales of 250 (F = 4.91; p = 0.04; r² = 0.15) and 500 (F = 5.10; p = 0.03; r² = 0.15) meters (Figure 1.27). Figure 1.27: influence of herbaceous cover on the bird species richness, at scales from 250 to 500 meters in relation to the sampling point of streets The relation between the number of records and herbaceous cover also was positively significant, in the parks, at scales of 25 (F = 5.89; p = 0.04; r² = 0.31), 50 (F = 6.07; p = 0 .03; r² = 0.32) and 100 (F = 6.20; p = 0.03; r² = 0.32) meters (Figure 1.28). Figure 1.28: Influence of herbaceous cover on the records of bird species, at scales from 25 to 100 meters in relation to the sampling point in parks. 52 1.6 DISCUSSION Our study provides evidence that impermeable-built cover is the main variable to explain the effects of landscape on taxonomic and functional richness. The proportion of impervious surface is negatively correlated with taxonomic richness and functional diversity (FD), functional dispersion (FDis), functional richness (FRic) and Rao's Quadratic Entropy (RaoQ) indices on the streets; and with the taxonomic richness, FDis and RaoQ in the parks. Thus, in all sampling points, whose buffer with a radius of 25 meters contained less the percentage of impermeable-built cover, the communities presented better taxonomic and functional characteristics. Although impermeable-built cover is inversely correlated with the percentage of tree cover, there are other land uses/covers that must be considered such as water and herbaceous vegetation. Multiscale analysis of herbaceous vegetation revealed that it has a positive effect on taxonomic richness of streets (at a scale of 250 to 500) and on records in parks (at a scale of 25 to 100 meters). The percentage of tree cover explains taxonomic richness (at the multiscale from 50 to 750 meters), functional richness (FRic) and functional diversity (FD) only in streets, but they do not explain these variables in parks. This reveals that there are other factors that influence the taxonomic and functional characteristics of birds in parks; more important than the afforestation itself, is the existence of a diversity of uses and land cover, such as water, herbaceous vegetation and tree cover. Therefore, in parks, the presence of these three environments provide a higher richness and diversity in the functional characteristics of the species. Herbaceous vegetation may be important in a city such as Bauru, in the transition zone between Atlantic Forest and Cerrado (SCHULZE 2015). Many species of birds need herbaceous vegetation to forage (eg Furnarius rufus and Sicalis flaveola) and to nest (eg Vanellus chilensis and Athene cunicularia) (SICK, 1997). So, having a highly wooded environment makes it difficult for species that need more open areas to live. Therefore, in cities located in Atlantic Forest and Cerrado transition environments, it's necessary to have urban green areas that mix both herbaceous and arboreal vegetation. These areas have important ecological roles in cities and provides resources and refuges for urban biodiversity (GRAVIOLA et al. 2021; ROSA, 2021; SCHÜTZ; ; SCHULZE 2015). 53 Also, in parks, the variables vegetation patch size and connectivity positively influence the functional richness of species (FRic), which means that such factors influence the richness of bird species from different food guilds, morphologies, substrate foraging and nesting sites. The presence of wooded streets can increase urban landscape connectivity, allowing the movement of different species between habitat patches (FERNÁNDEZ-JURICIC 2000; GRAFIUS et al. 2017; PENG et al. 2017). The interaction between tree cover and connectivity is significant to explain FRic, with connectivity having a positive correlation and tree cover a negative correlation. This may be related to the greater presence of forest species traits with the increase in tree cover, while in regions with less tree cover there are generalist and open area species, increasing the richness of traits. Interestingly, noise did not influence any index within the parks. This shows that parks and gardens may be functioning as buffers that minimize the effect of noise within them - a result that contrasts with other studies that have seen that influence (BARBOSA et al., 2019; PENA et al., 2017). Contrary to this result, in the streets, noise is a limiting factor and is the variable that most appears in the models with statistical support AICc. In the street, the impervious-built cover is also a limiting factor, but noise appears to modulate some aspects of the community, such as the functional diversity and taxonomic richness. In parks, the amount of habitat and connectivity influence the community, while in the streets, birds are more exposed to disturbances such as noise. Still considering the streets, our study revealed a very important aspect: if well planned, they can help to connect fragments of urban vegetation and serve as ecological corridors. Knowing that: noise negatively influences richness and functional diversity (FD); tree cover is positively related to taxonomic richness, functional richness (FRic) and FD; the Impervious-built cover has a negative influence on all indices; - it is possible to guide urban planning to create wooded streets, which also present gardens with different vegetation strata (from herbaceous to arboreal). Another point to consider is that, although noise has a negative effect on the streets, afforestation can reduce its effects. Furthermore, the MLCC simulations showed that the LSCorridors program can be a very useful tool to identify the streets with the greatest potential to increase connectivity between the vegetation fragments in cities. Using the program, it is also 54 possible to identify which roads have the potential to function as multifunctional ecological corridors, allowing the circulation of bird species in urban areas (GRAVIOLA et al., 2021). As connectivity is an important factor in parks, implementing wooded streets can increase connectivity in squares and parks. Wooded streets have a positive influence on avifauna: they increase the richness and diversity of birds (YOUNG et al., 2007), the functional aspects of the community (SCHÜTZ; SCHULZE, 2015) and the number of bird feeding associations (WHITE et al., 2005). These influences are stronger in urban areas where native trees predominate (IKIN et al. 2013). Wooded streets also improve urban mobility for birds (KENWORTHY, 2006), as they use them to move through urban landscapes (FERNÁNDEZ-JURICIC, 2000). Considering that Brazil is a tropical country, wooded streets also protect people from the sun and can improve microclimate conditions (VAILSHERY et al, 2013). Cities need to be planned in a manner that reduces negative impacts of urban landscape characteristics on the biosphere and that increases their ability to sustain biodiversity (ANGEOLETTO et al. 2019). If well planned, cities can provide good places to bird lives. 55 1.7 CONCLUSION This study showed that, if well planned, cities can have an important role on bird biodiversity conversation. By connecting fragments of urban vegetation through wooded streets, we can create functional ecological corridors that contribute positively to taxonomic and functional richness and can reduce the negative effects of noise in the cities. Considering the streets, impervious-built cover is a limiting factor, but noise appears to modulate some aspects of the community, such as the functional diversity and taxonomic richness. In parks, the amount of habitat and connectivity influence the community, while in the streets, birds are more exposed to disturbances such as noise. Furthermore, considering the parks, we observed that tree cover may not necessarily leads to higher functional diversity but the herbaceous vegetation may be important in a city such as Bauru, in the transition zone between Atlantic Forest and Cerrado. More important than the afforestation itself, is the existence of different land cover/land uses, such as water, herbaceous vegetation and tree cover. Therefore, the presence of these three environments provide a higher richness and diversity in the functional characteristics of the species. Multiscale analysis also revealed that herbaceous vegetation has a positive effect on taxonomic richness of streets and on records in parks and that tree cover (%) explains taxonomic richness at the multiscale from 50 to 750 meters. Finally, the MLCC simulations showed that LSCorridors program can be a very useful tool for urban planners if they want to simulate which roads have the potential to function as multifunctional ecological corridors. Thus, the set of such results reveal interesting guidelines to urban planning: If we want to reduce the negative impacts of urban landscape characteristics on birdlife, we must study the land use/cover, analyze the local fauna and understand how animals occupies the landscape. 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