UNIVERSIDADE ESTADUAL PAULISTA “JÚLIO DE MESQUITA FILHO” INSTITUTO DE BIOCIÊNCIAS – RIO CLARO unesp PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA E BIODIVERSIDADE FENOLOGIA REMOTA E OS PADRÕES DE TROCAS FOLIARES AO LONGO DE UM GRADIENTE DE SAZONALIDADE BRUNA DE COSTA ALBERTON Tese apresentada ao Instituto de Biociências do Campus de Rio Claro, Universidade Estadual Paulista, como parte dos requisitos para obtenção do título de Doutor em Ecologia e Biodiversidade. Agosto – 2018 BRUNA DE COSTA ALBERTON FENOLOGIA REMOTA E OS PADRÕES DE TROCAS FOLIARES AO LONGO DE UM GRADIENTE DE SAZONALIDADE Tese de Doutorado apresentada ao Instituto de Biociências da Universidade Estadual Paulista “Júlio de Mesquita Filho”, Campus de Rio Claro, para obtenção do título de Doutora em Ecologia e Biodiversidade. Orientadora: Profa. Dra. LEONOR PATRICIA CERDEIRA MORELLATO Co-orientador: Prof. Dr. RICARDO DA SILVA TORRES Rio Claro – SP 2018 Alberton, Bruna de Costa Fenologia remota e os padrões de trocas foliares ao longo de um gradiente de sazonalidade / Bruna de Costa Alberton. - Rio Claro, 2018 252 f. : il., figs., gráfs. Tese (doutorado) - Universidade Estadual Paulista, Instituto de Biociências de Rio Claro Orientadora: Leonor Patricia Cerdeira Morellato Coorientador: Ricardo da Silva Torres Agência de fomento e n. de processo: FAPESP: 2014/00215-0, FAPESP: 2016/01413-5, FAPESP: 2013/50155-0 1. Ecologia vegetal. 2. Fenocâmeras. 3. Sazonalidade. 4. Fenologia vegetativa. 5. Produtividade do ecossistema. 6. Imagens digitais. I. Título. 581.5 A334f Ficha Catalográfica elaborada pela STATI - Biblioteca da UNESP Campus de Rio Claro/SP - Adriana Ap. Puerta Buzzá / CRB 8/7987 Powered by TCPDF (www.tcpdf.org) http://www.tcpdf.org À minha família, Pai, Mãe e Lucas AGRADECIMENTOS À minha família, que é o meu alicerce e o meu refúgio, onde sei que sempre poderei voltar e ser bem acolhida. Meu pai Antônio, minha mãe Janete e meu irmão Lucas, agradeço ao suporte de cada um e por tudo que representam em minha vida. Ao meu companheiro de vida, amigo, namorado, colega de profissão, Renan, que nunca mediu esforços em me ajudar e me apoiar, sobretudo nos últimos meses de finalização desta tese. É um prazer dividir a vida contigo. Eu amo vocês. Obrigada por tudo. Agradeço aos meus orientadores, Patrícia e Ricardo, aos quais sou eternamente em débito, por todo o aprendizado, conselhos, lições e network que me foi passado ao longo destes anos de mestrado e doutorado. Gratidão. Patrícia tem papel fundamental na construção de minha carreira científica ao longo da pós- graduação. Foi ela quem me apresentou as fenocâmeras e me incentivou a desenvolver meus estudos sobre a fenologia remota próxima. Graças à sua visão de carreira, pude aproveitar grandes oportunidades, interagir com excelentes pesquisadores e entrar no universo multidisciplinar da pesquisa acadêmica, que muito me inspira. São inúmeros os motivos para lhe agradecer, não somente como orientadora, mas como uma grande amiga que tive nestes últimos anos. Em especial neste final de tese, minha gratidão pelo esforço em me ajudar nos tópicos de maior dificuldade e pela paciência com todas as correções. Meu co-orientador, Ricardo Torres, com quem tive a oportunidade de aprender muito e poder contar sempre com sua disposição em me ajudar. Foram muitas reuniões, presenciais ou por Skype, com orientações muito bem direcionadas, esclarecedoras e sempre incentivadoras. Um agradecimento mais do que especial aos meus amigos. Àqueles de longa data e que ainda permanecem próximos, mesmo que distantes (fisicamente), e àqueles que se tornaram minha família ao longo dos lugares onde vivi e passei nestes últimos anos. Por sorte, tenho muitos, especialmente nesta cidade (Rio Claro), a qual tem sido minha casa na maior parte destes últimos sete anos. A minha amizade inabalável do sul do país, minha amiga e irmã da vida Mainara Cascaes, e minhas amigas “bioloukas”: Thereza Garbelotto, Patricia Correa, Gabriela Thomaz e Beatriz Wessler. Aos meus colegas de laboratório que se tornaram grandes amigos e por muito tempo minha família rio-clarense: Gabriela Camargo, Vanessa Staggemeier, Nathália Miranda, Daniel Carstensen, Irene Mendoza e Natália Costa, com quem não somente dividi o mesmo laboratório, mas também foi minha grande companheira ao dividirmos o mesmo teto por quase cinco anos. A todos os meus queridos amigos que o forró me trouxe, em especial a Thais Helena Condez e sua família, pela amizade sincera, leve e tão cheia de amor. Um agradecimento especial aos amigos incríveis que fiz durante minha vivência em Boston, os quais foram essenciais para tornar minha vida norte-americana mais leve, mais divertida e um pouco menos fria, em especial à Laura Zoffoli, por sua amizade e companheirismo. Agradeço ao Laboratório de Fenologia, a todos os membros que já passaram por ele e aos atuais, por sempre proporcionarem um ambiente leve e inspirador para o trabalho, pelas trocas de conhecimentos e colaborações ao longo de todos os encontros e workshops realizados. É realmente um prazer fazer parte deste grupo e um orgulho tê-lo dentro de minha carreira profissional. Agradeço especialmente aos meus queridos amigos e colegas, Bruno Defane e Leonardo Cancian, por todo o suporte técnico ao longo dos trabalhos de campo, processamento das imagens digitais e auxilio em SIG. Agradeço também aos alunos de graduação, os quais tive a oportunidade de auxiliar na orientação de seus trabalhos, e que se interessaram pela fenologia remota próxima me auxiliando no processamento das imagens: Marina Muller, Carolina Crivelin e Rodrigo Lacerda. Agradeço aos colaboradores na pesquisa desta tese. Ao Professor Dr. Andrew Richardson, por aceitar ser meu supervisor e me receber em seu laboratório durante os 12 meses de estágio na Universidade de Harvard, entre maio de 2016 e maio de 2017. Foi sob a supervisão de Andrew, que aprendi a manipular e processar os dados de alta frequência de fluxo de carbono, bem como associá- los aos dados da fenologia remota próxima das câmeras digitais e estabelecer as primeiras questões ecológicas dentro do trabalho que viria a se tornar o capítulo 3 desta tese. Agradeço ao Professor Dr. Humberto Rocha, da Universidade de São Paulo (USP), por colaborar disponibilizando os dados das estações meteorológicas e os dados de fluxo de carbono das áreas do cerrado Pé-de-Gigante e de Santa Virgínia, bem como pela disponibilidade presencial e remota com a qual vem auxiliando no desenvolvimento desta tese, através de sua experiência nas trocas de fluxos de energia em ecossistemas tropicais. Também agradeço à sua equipe, Emília Brasilio pela compilação e pré- processamento dos dados meteorológicos e de fluxo de carbono, e ao Eduardo Gomes Lopes e Helber Freitas pelo suporte técnico indispensável nos trabalhos realizados de instalação e manutenção das câmeras digitais nas torres de fluxo. Agradeço por toda colaboração da EMPRABA Semi-árido, em especial à Magna Soelma Bezerra de Moura, por todo o suporte técnico na instalação, manutenção e coleta dos dados da câmera digital na torre de fluxo da área de caatinga, por disponibilizar os dados meteorológicos e de fluxo de carbono, e por colaborar no desenvolvimento dos trabalhos desta tese, contando com sua experiencia em ecossistemas áridos. Agradeço aos pesquisadores que participaram e aos que ainda participam do projeto e- Phenology, com os quais tive a oportunidade de trabalhar, aprender e colaborar. Um agradecimento especial aos professores Jurandy Almeida, Jefersson dos Santos, Fabio Faria, João Comba e Lucas Schnorr, e aos colegas que também desenvolveram suas teses e dissertações dentro do projeto: Greice Mariano, Roger Leite e Alexandre Almeida. Agradeço ao Departamento de Botânica e a todos os técnicos que auxiliaram nos trabalhos de campo ao longo do doutorado, em especial ao Rafael Consolmagno, que participou ativamente das instalações, manutenções e reparos das câmeras digitais, pelos muitos quilômetros rodados por todas as áreas monitoradas e pela parceria leve e pró ativa sempre presente. Agradeço ao Instituto Florestal (IF) do Estado de São Paulo, bem como aos dirigentes das unidades, pela liberação concedida de desenvolver as pesquisas desta tese nas áreas da Estação Ecológica de Itirapina, do Parque Estadual do Vassununga (área do cerrado Pé-de-gigante), e no Parque Estadual da Serra do Mar Núcleo Santa Virgínia (área da Mata Atlântica). Também agradeço aos donos da Fazenda São José e do Instituto Arruda Botelho (IAB) por permitir o trabalho de campo realizado em sua fazenda (área de cerrado) em Itirapina. Agradeço à Seção de Pós-Graduação do Instituto de Biociências da UNESP, pelo suporte ao longo de todos os anos de doutorado, e ao conselho do Programa de Pós-Graduação em Ecologia e Biodiversidade, pelo suporte, boa relação e abertura estabelecida aos alunos matriculados, no intuito de sempre melhorar as normativas do curso. Agradeço à Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) pelas bolsas de doutorado e de estágio de pesquisa no exterior concedidas (#2014/00215-0, #2016/01413-5); pelo financiamento dos projetos e-Phenology (#2010/52113-5, #2013/50155-0, FAPESP-Microsoft Research Virtual Institute); e pelo financiamento de projetos paralelos que contribuíram nesta tese (#2007/59779-6, e FAPESP-VALE-FAPEMIG # #2010/51307-0). Ao CNPq pelo apoio dado por meio das bolsas de produtividade de meus orientadores (#306243/2010-5 #306587/2009-2). Ao Programa de Apoio à Pós-Graduação (PROAP) pelo auxílio concedido para participação em eventos científicos durante o período de doutorado. A maior riqueza do homem é sua incompletude. Nesse ponto sou abastado. Palavras que me aceitam como sou — eu não aceito. Não aguento ser apenas um sujeito que abre portas, que puxa válvulas, que olha o relógio, que compra pão às 6 da tarde, que vai lá fora, que aponta lápis, que vê a uva etc. etc. Perdoai. Mas eu preciso ser Outros. Eu penso renovar o homem usando borboletas. (Manuel de Barros) RESUMO O estudo da fenologia vegetal busca monitorar, compreender e prever os ciclos de vida recorrentes das plantas, principalmente influenciados pelo clima. O monitoramento das fenofases vegetativas de brotamento e senescência foliar são essências para a compreensão de processos ecossistêmicos importantes como, a produtividade primária, as trocas de energia entre atmosfera e superfície terrestre, o fluxo de carbono, e a ciclagem de nutrientes. Logo, entender a resposta das plantas ao clima e ser capaz de prever alterações em sua fenologia, é essencial para o entendimento das dinâmicas de comunidades vegetais e da funcionalidade dos ecossistemas em frente a cenários de mudanças climáticas. Nos últimos anos, a busca por novas tecnologias dentro de estudos científicos vem crescendo com o intuito de tornar mais viável e amplo o monitoramento fenológico. Neste trabalho, buscamos incorporar novas tecnologias de observações fenológicas com o uso de câmeras digitais (fenocâmeras) instaladas em campo para o monitoramento de dados fenológicos de brotamento e senescência foliar em ambientes tropicais. Os objetivos gerais deste trabalho de tese, dividida em quatro seções, foram de : (i) compilar informações sobre conceitos e propriedades da técnica de fotografias repetidas (repeated photograph technic), criando um protocolo para o uso de câmeras digitais nos trópicos e levantar suas principais contribuições no âmbito na biologia da conservação; (ii) monitorar e descrever os padrões da fenologia vegetativa em diferentes comunidades vegetais sazonais, através dos dados obtidos de imagens digitais e investigar os principais gatilhos ambientais atuando nas diferentes vegetações; (iii) associar dados de fenologia foliar juntamente com dados de produtividade primária bruta dentro de ecossistemas sob diferentes pressões de sazonalidade para um melhor entendimento das respostas entre vegetação-atmosfera mediadas pela fenologia foliar; e (iv) apresentar resultados das principais iniciativas em análises de imagens digitais para o monitoramento da fenologia foliar desenvolvidas dentro da rede de fenocâmeras do projeto e- phenology, no âmbito da colaboração e-science. As vegetações estudadas neste trabalho estão distribuídas ao longo de um gradiente de sazonalidade de distribuição de chuvas e incluem: uma vegetação de caatinga; três diferentes fisionomias de cerrado que correspondem a um cerrado campo sujo, um cerrado sensu stricto, e um cerrado denso; e uma vegetação de Mata Atlântica. Séries temporais relacionadas às fenofases vegetativas foram extraídas do conjunto de imagens digitais coletadas em cada uma das áreas e analisadas dentro do contexto de cada um dos objetivos deste trabalho. Demonstramos que o estabelecimento de uma rede de fenocâmeras é uma poderosa ferramenta para a biologia da conservação, através da capacidade de obtermos dados de alta frequência temporal associados a uma ampla gama de dados ambientais monitorados. Através da aplicação de fenocâmeras pode-se obter novas informações sobre prática de manejo e restauração de ambientes, além de sua potencial contribuição nas esferas de educação ambiental e ciência cidadã. Observamos que a disponibilidade de água e luz no ambiente são os fatores mais importantes para o desenvolvimento foliar ao longo de diferentes comunidades sazonais. Relações hídricas em plantas foram mais importantes para vegetações mais áridas, como a caatinga, enquanto que a disponibilidade de luz, quantificada através da sazonalidade do comprimento do dia, teve maior influência no gatilho do desenvolvimento foliar nas comunidades de cerrado. No âmbito dos ecossistemas, demonstrou-se uma nova abordagem ao se relacionar a fenologia foliar derivada de câmeras com a produtividade de ecossistemas tropicais. A dinâmica fenológica analisada através da variabilidade de sinais fenológicos encontrados dentro das diferentes vegetações, foram importantes para explicar os padrões temporais de produtividade dos ecossistemas. A compilação de trabalhos realizados através da colaboração e- science, apresentados na seção 4, foram de grande importância para o desenvolvimento dos métodos e análises, bem como para o alcance dos resultados obtidos dentro desta tese. Este trabalho oferece uma nova ferramenta para o monitoramento fenológico em vegetações tropicais, e sugere novos desafios a serem desenvolvidos bem como incentiva uma maior abrangência no uso de fenocâmeras a fim de cobrir um maior número de áreas e diferentes tipos de vegetação, além de associar estes estudos com abordagens desenvolvidas em diferentes escalas como as observações diretas, a aplicação de imagens de drones, bem como com as imagens de satélite. A comparação de diferentes escalas espaciais e temporais irá nos ajudar a entender melhor a própria escala de informações provenientes das fenocâmeras em termos de abrangência e detalhamento dos ecossistemas. Palavras-chave: fenocâmeras, sazonalidade, fenologia vegetativa, produtividade do ecossistema, imagens digitais ABSTRACT Plant phenology is a traditional science focused on monitoring, understanding, and predicting recurrent life cycles events, which are mainly related to climate Leaf development stages are essential plant phenophases for the better understanding of ecosystems processes such as carbon and water fluxes, regulation of productivity, and nutrient cycling. Through the investigation of plant responses to climate and phenological shifts prediction, we can better forecast climatic change effects on vegetation dynamics and prevent loss of ecosystems functionalities. Aiming to become the phenological collection wider and more feasible worldwide, the seek for new technologies has stimulated several research centers of plant phenology monitoring. Here, we incorporated a new technology of field phenological observations using digital cameras for the monitoring of leaf exchanges in tropical vegetations. On this work, divided into four sections, we aimed to: (i) compile information about concepts and properties of the repeated photograph technic, create guidelines for the phenocams setup in tropical vegetation sites, and to provide key contributions of daily imagery monitoring on biological conservation; (ii) to stablish a monitoring of different seasonal vegetations, describing the phenological trends, and identify the environmental cues which are triggering the leaf flushing and senescence for each vegetation type; (iii) analyze the canopy greenness obtained from digital cameras in relation to gross primary productivity measurements, to better understand the role of leaf phenology controlling ecosystem productivity in the tropics; and (iv) present some results of phenocam image analysis research initiatives and tools devised in the context of e-Science collaborations and built in the framework of the e-phenology project and the e-phenology network of phenocams. The selected study sites belong to different seasonal biomes, which comprehends areas from caatinga, savanna grasslands, savanna woodlands and Atlantic rainforest. Temporal series representing foliar phenology were extracted from the data imagery of each vegetation site and were analyzed into the context of each section of this work. We demonstrated that the establishment of phenocam networking is a powerful tool for biological conservation through its capability of fine temporal resolution data associated with wide spatial monitoring coverage. Besides, phenocams applications can bring new information for management and restoration practices at several sites and environments and contribute for the education for conservation and citizen science initiatives. We observed that water and light were the most important predictors for the leaf phenological patterns across seasonal vegetation communities. Water-plant relationships were more important for the caatinga community, and light, through day-length seasonality, had more influence in the leafing patterns of the cerrado communities. Regarding the ecosystem, we demonstrated a novel approach to relate leaf phenology to seasonality of tropical ecosystems productivity. The phenological dynamics regarding the variability of species phenological signals, and how they are built in into each contrasting vegetation communities explains drivers of leaf phenology and productivity. A compilation of articles developed through the e-science collaboration, presented in Section 4, were of great importance for the generation of methods and analytical work, as well as for the results achievements in this thesis. This work will offer a new tool for the phenological monitoring in the tropics, and suggests next challenges to be addressed and the continuity of the e-phenology network and the spread of new cameras covering new vegetation types; the development of bottom-up studies, integrating on-the-ground observations, cameras, drones, and satellites, inter-comparing them and placing camera-derived phenology in its own scale, by understanding how much and what kind of information can be retrieved from ecosystems. Key words: phenocameras, seasonality, leaf phenology, ecosystem productivity, digital images SUMÁRIO INTRODUCTION.................................................................................................................................12 SECTION 1: Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation…………………………………………………………………………………………...17 SECTION 2: Leafing patterns and drivers across seasonally dry tropical communities……………...50 SECTION 3: Leaf phenology correlates to gross primary productivity: an inter-comparison across tropical biomes.………………………………...……………………………………………………...94 SECTION 4: e-Science and the multidisciplinary research built on the integrations of big data ecological research and computational science……………………...………………………………142 CONCLUSIONS……………………………………………………………………………………..146 REFERENCES……………………………………………………………………………………….148 APPENDIX A: Leafing patterns and leaf exchange strategies of a cerrado woody community…………….……………………………………………………………………………..150 APPENDIX B: Phenological visual rhythms: Compact representations for fine-grained plant species identification…………...………………………………………………….…………………………189 APPENDIX C: Time series-based classifier fusion for fine-grained plant species recognition…...………………………………………………………………………………………201 APPENDIX D: Fusion of Time Series Representations for Plant Recognition in Phenology Studies.………………………………………………………………………………………………211 APPENDIX E: PhenoVis – Visual Phenological Analysis of Forest Ecosystems.....……………….222 APPENDIX F: Unsupervised distance learning for plant species identification.......………...……...238 12 1 INTRODUCTION Plant phenology is a traditional science focused on monitoring, understanding, and predicting recurrent life cycles events, which are mainly related to climate (Morellato et al., 2016). Leaf development stages are plant phenophases responsible for indicating the growth season and for controlling crucial ecosystems processes such as carbon and water fluxes, regulation of productivity, and nutrient cycling (Reich, 1995; Baldocchi et al., 2005). Tropical ecosystems have significant importance in the global carbon budget (Field et al., 1998; Ometto et al., 2005). By understanding phenological patterns of tropical vegetations and what drives leaf production seasonality within tropical communities, we can better forecast climatic change effects on vegetation dynamics and prevent loss of ecosystems functionalities (Polgar and Primack, 2011). Phenological studies in the tropics preclude the observation of many species across several sites with intense human labor and costs (Alberton et al., 2014; Morellato et al., 2016). The scarcity of long- term monitoring in tropical regions, necessary to understand the effects of global warming on organisms (Abernethy et al., 2018) has stimulated several research centers to seek for new tools of plant phenology monitoring. The near-surface remote phenology consists in the application of sensors in the ground for the monitoring of plant to ecosystem-scale vegetation changes. The use of digital cameras to track leaf exchanges came to fill the gap between the traditional on-the-ground monitoring by human direct observation and remote sensing derived land surface phenology (Richardson et al., 2007; Morisette et al., 2009; Morellato et al., 2016). The technique of repeated photographs using digital cameras for phenology monitoring, or phenocams, has increased in the last 10 years due its advantages of low cost investment, reduction in size, easy set-up installation, the reduced human labor, increased temporal resolution, and the opportunity of simultaneously monitoring several sites improving the spatial resolution of ground-based phenology monitoring and offering the possibility of handling high resolution data (Crimmins and Crimmins, 2008, Morisette et al., 2009; Graham et al., 2010; Alberton et al., 2017), leading phenocams to a widely range of ecological applications worldwide. The ongoing addition of new devices, high-resolution data survey, and sensor networks has been improving the quality of data collected in biological studies, but at the same time increasing the 13 magnitude of scientific data collected. Near-surface monitoring systems in the tropics, for instance, are necessarily complex since the environmental conditions are harsh and the diversity of species is usually high. This leads to a next generation of scientific problems, which will require the establishment of multidisciplinary teams (Hey and Hey 2006). To the process of data retrieval, management, and analysis it will be required the collaboration between ecologists and computer scientists. e-Science is about the collaboration of key areas of science, not considered a discipline, but a network of research initiative focused on the specification and implementation of a set of tools and technologies capable of supporting, improving, and speeding up data analysis, knowledge discovery, and decision making (Hey and Hey 2006). In Brazil, we have the e-phenology Network (http://www.recod.ic.unicamp.br/ephenology - As of June 2018), introduced in this thesis, that comprehends the application of digital cameras as tools to detect leaf flushing and senescence across different vegetation types from drylands, grasslands, and cerrado savannas to rainforests. This project is innovative and puts Brazil in the state of the art of near- remote phenology monitoring, already established in areas of temperate forests in northern hemisphere. The e-phenology project also integrates the development of computational tools for the methodological applications of algorithms for data mining and time series analysis (e.g., Almeida et al., 2014; Almeida et al., 2016). In this context, the present thesis is divided into four sections that all together, aim to present the development of phenocams tools in tropical systems, their application in ecological studies and the integration of e-science insights on peripherical approaches that contributed for this work. Sections are described below. Section 1 - Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation Digital-camera-based monitoring phenology is growing in the tropics (e.g., Alberton et al., 2014; Nagai et al., 2016; Moore et al., 2017; Lopes et al., 2017), but it is still sparse when compared with temperate regions (Brown et al., 2017). Protocols and methodological approaches developed for camera systems were launched from networks researchers of these regions. We aimed to spread our http://www.recod.ic.unicamp.br/ephenology 14 experience of using digital cameras to monitor tropical vegetations across multiple sites to encourage, among the scientific community of Brazil and tropics worldwide, their potential usability in a broader ecological context. This section brings a published article with compiled information about concepts and properties of the technic, guidelines for the phenocams setup in tropical vegetation sites, and to provide key contributions of daily imagery monitoring on biological conservation (Alberton et al., 2017). Section 2 - Leafing patterns and drivers across seasonally dry tropical communities Investigating the main drivers of plant phenology is of paramount importance for a better understanding of plant shifts in response to a changing climate and for effective biodiversity conservation from species to ecosystems (Polgar and Primack, 2011; Morellato et al., 2016). As already mentioned, plant phenology remains poorly understood for the tropics, and so the triggers of phenological transitions (Morellato et al., 2013; Chambers et al., 2013; Abernethy et al., 2018). The traditional phenology monitoring, made by direct observations, provides confident data from individuals species to vegetation communities that contribute with pattern validation for remote- monitoring methods such as phenocameras and satellite images. Despite the coarse time-scale of observations (monthly), long-term on-the-ground phenology may be used for the investigation of leaf development drivers and further analysis of climatic changes impacts. A study using a 7-year on-the- ground phenological time series, compiled with leaf flushing and senescence monthly data, aimed to investigate the main environmental drivers of a cerrado sensu stricto community and their leaf exchange strategies species. This work, which I co-authored, was conducted in one of the sites, where an e- phenology camera was installed and was carried out in parallel along this thesis development (Appendix A). By tracking daily images combined with environmental measurements, we can fine-tune plant color changes to leafing exchange patterns and unravel the influence of climatic variables. A wide range of questions might be investigated. In this section, we conducted, for the first time, a phenological research across multiple-sites monitoring leafing exchange patterns using phenocams. We aimed to 15 describe the leaf phenological patterns across seasonally dry tropical communities monitored by digital cameras, investigating the main environmental factors influencing the timing and length of growing seasons, and intercompare vegetations under distinct severity of dry season and across key life forms (grassy – woody), regarding their phenological dynamics and drivers of leaf phenology. Section 3 - Leaf phenology correlates to gross primary productivity: an inter-comparison across tropical Biomes. Tropical leaf phenology is regarded as a first order mechanism regulating seasonality of carbon assimilation in tropical evergreen forests (Restrepo-Coupe et al., 2013; Restrepo-Coupe et al., 2017; Wu et al., 2016; Wu et al., 2017). Studies in temperate ecosystems have demonstrated that the camera- derived Gcc index, a measure of vegetation greenness, is well related to the gross primary productivity (GPP) curves (Richardson et al., 2010; Migliavacca et al., 2011; Keenan et al., 2014; Toomey et al., 2015). Phenocams have played an important role in those ecosystem-scale studies, contributing to link ground observed changes to ecosystem scale accessments derived from flux towers and remote sense indices (Ahrends et al., 2009; Migliavacca et al., 2011; Tomey et al., 2016). Leaf phenology should also be considered on Dynamic Global Vegetation Models (DGVMs). These models are based on coupled information between plant biogeography and biogeochemical processes to simulate ecosystem fluxes and climate shifts in a climatic change scenario (Foley et al., 1998; Restreppo-Coupe et al., 2017). In this context, understanding vegetative phenological transitions is essential to better estimate measurements of gross primary productivity (GPP), because there is still a gap of knowledge about the drivers of productivity in the tropics (Restreppo-Coupe et al., 2017). In this context, camera-derived phenological time series might provide key information to understand the photosynthetic seasonality of tropical forests and atmospheric-vegetation feedbacks to a changing climate (Richardson et al., 2013; Restreppo-Coupe et al., 2017). In this section, we propose to analyze the relationship between the leaf phenology extracted from phenocameras and the ecosystem photosynthetic activity from GPP measurements of eddy- covariance towers across three distinct Neotropical biomes: Caatinga, Cerrado, and Rainforest. We 16 aim to inter-compare the phenology-GPP dynamics across sites belonging to each vegetation domain and contrasting seasonality. Section 4 – e-Science and the multidisciplinary research built on the integrations of big data ecological research and computational science Throughout this section, we present some results of phenocam image analysis research initiatives and tools devised in the context of e-Science collaborations and built in the framework of the e-phenology project and the e-phenology network of phenocams. The published papers are presented in Section 4 and all are directly connected to the development of this thesis and opened up the possibility to explore the results presented in Sections 1 to 3. 17 Section 1 Article published in the Journal of Perspectives in Ecology and Conservation: ALBERTON, B.C. et al. Introducing digital cameras to monitor plant phenology in the tropics: Applications for conservation. Perspectives in Ecology and Conservation, v. 15, p. 82-90, 2017. 18 Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation Authors: Bruna Alberton1*, Ricardo da S. Torres2, Leonardo F. Cancian1, Bruno D. Borges1, Jurandy Almeida3, Greice Mariano2, Jefersson dos Santos4, Leonor Patricia C. Morellato1 1 UNESP Universidade Estadual Paulista, Instituto de Biociências, Departamento de Botânica, Laboratório de Fenologia, Rio Claro, São Paulo, Brazil. *Corresponding author Email address: brualberton@gmail.com 2 RECOD Lab, Institute of Computing, University of Campinas – UNICAMP 13083-852, Campinas, SP – Brazil 3 Institute of Science and Technology, Federal University of São Paulo – UNIFESP, 12247-014, São José dos Campos, SP, Brazil 4 Department of Computer Science, Universidade Federal de Minas Gerais – UFMG, 31270- 010, Belo Horizonte, MG, Brazil Short title: Introducing phenocams and their applications on conservation biology. 19 Keywords: leaf phenology; repeated photography; RGB color channels; conservation biology; e-science Abstract The application of digital cameras to monitor the environment is becoming global and changing the way of phenological data collection. The technique of repeated digital photographs to monitor plant phenology (phenocams) has increased due to its low-cost investment, reduced size, easy set up installation, and the possibility of handling high-resolution near-remote data. Considering the widespread use of phenocams worldwide, our main goals here are: (i) to provide a step-by-step guide for phenocam set up in the tropics, reinforce its appliance as an efficient tool for monitoring tropical phenology and foster networking, (ii) to discuss phenocam applications for biological conservation, management, and ecological restoration. We provide the concepts and properties for image analysis which allow representing the phenological status of the vegetation. The association of a long-term imagery data with local sensors (e.g., meteorological stations and surface-atmosphere flux towers) allows a wide range of studies, especially linking phenological patterns to climatic drivers; and the impact of climate changes on plant responses. We show phenocams applications for conservation as to document disturbances and changes on vegetation structure, such as deforestation, fire events, and flooding and the vegetation recovery. Networks of phenocams are growing globally and represent an important tool for conservation and restoration, as it provides hourly to daily information of monitored systems spread over several sites, ecosystems, and climatic zones. Moreover, websites enriched by vegetation dynamic imagery data can promote science knowledge by engaging citizen science participation. 20 1 INTRODUCTION The use of digital cameras to document plant changes is not novel. Photographs have been used to monitor landscape since 1965 by Hastings and Turner to verify changes in the ecosystem dynamics and structure of the arid southwest region of the US. Thompson et al. (2002) used photographic registers for the long-term study of glacial retreat in the Antarctic ice sheet. Repeated digital images have been used to document changes in cultural landscapes (Peñuelas & Boada 2003, Webb et al. 2007); to measure vegetation growth and biomass (Crimmins & Crimmins 2008, Graham et al. 2009); to detect plant stress and nitrogen status (Wang et al. 2004) and to monitor crops (Slaughter et al. 2008). More recently, the application for monitoring leaf exchanges patterns or leafing phenology (Richardson et al. 2007, 2009, Nagai et al. 2011) has brought the technique to the agenda of global change research and conservation (Richardson et al 2013, Morellato et al. 2016). Phenology is an integrative environmental science focused on monitoring, understanding, and predicting recurrent life cycles events of organisms, which are mainly related to climate (Morellato et al. 2016). Leafing is the plant phenological event that defines the growth season and controls crucial ecosystems processes such as, nutrient cycling, water storage, regulates productivity in terrestrial ecosystems, and the dynamics of carbon sequestration (Reich 1995, Baldocchi et al. 2005). Phenological studies have been efficiently applied to track effects of environmental changes on plants and animals in temperate regions, answering questions about the current scenario of global climate change and stimulating the search for innovative tools of plant monitoring (Polgar & Primack 2011). Detect plant responses to environmental changes across tropical systems, especially in the Southern Hemisphere, is an important question on the global agenda since few studies have addressed trends related to global warming (Rosenzweig et al. 2008, Morellato et al. 2013, 2016, Chambers et al. 2013). However, the tropical high diversity of species precludes the observation of many species across several sites due to the intense human labor and costs (Alberton et al. 2014, Morellato et al. 2016). 21 The technique of repeated photographs to monitor plant phenology may overcome those difficulties. The application has increased due to its low-cost, reduced size, easy set up, and the possibility of handling high-resolution data, making digital cameras reliable tools for a wide range of ecological applications (Crimmins & Crimmins 2008, Morisette et al. 2009, Graham et al. 2010, Nasahara et al. 2015, Brown et al. 2016). Digital cameras for plant phenology observation, also called phenocams, have allowed the detection of leaf phenological events through the analysis of color changes along time. By quantifying the red, green, and blue (RGB) color channels, it is possible to estimate, for instance, leaf flushing and senescence, using the green and red channels, respectively (Ahrends et al. 2009, Morisette et al. 2009, Richardson et al. 2009). The term “Near-surface remote phenology” consists in the use of sensors installed on the ground, as the phenocams, with the objective of monitoring ecosystem-scale vegetation changes. Digital cameras monitoring canopy vegetation has an important role by filling the “gap of observations” between satellite monitoring and the traditional on-the-ground phenology (Alberton et al. 2014, Brown et al. 2016, Morellato et al. 2016, Morisette et al. 2009). The use of imagery data over the traditional phenological observations allows simultaneous multi-sites monitoring, long-term monitoring collecting high-frequency data (daily, hourly), and reduced human labor fieldwork for data acquisition. Phenocams networks are already covering a wide range of ecosystems in the world (Richardson et al. 2013, Brown et al. 2016). The main networks websites are the Phenocam Network in the United States (http://phenocam.sr.unh.edu - as of Jan. 2017), the EuroPhen in Europe (http://european-webcam-network.net - as of Jan. 2017) and the Phenological Eyes Network (PEN) in Japan (http://pen.agbi.tsukuba.ac.jp - as of Jan. 2017). Together these initiatives combine more than 250 outdoor cameras (Brown et al. 2016, Nasahara & Nagai et al. 2015). For the tropics, we have the Tropidry project as a successful example of ecological project with intense multidisciplinary data collection, including the use of phenology towers with phenocams, covering dry tropical sites (http://tropi-dry.eas.ualberta.ca/ - as of May 2017). In Brazil, the e-phenology Network (http://www.recod.ic.unicamp.br/ephenology - as of Jan. 2017) introduced in this paper, target the challenge of monitoring different vegetation types from dry forest, grasslands, and cerrado savannas to rainforests. http://tropi-dry.eas.ualberta.ca/ 22 Therefore, considering the worldwide applications of phenocams in ecological studies, our main goals here are: (i) to provide a step-by-step guide for phenocam set up in the tropics, reinforce its appliance as an efficient tool for monitoring tropical phenology, (ii) to show how phenocams can provide key contributions to biological conservation, and (iii) to encourage this promising research field in Brazil and tropical areas based on the e-phenology project experience, and foster networking and e-science collaborative research. 2 PHENOCAMS AS TOOLS FOR THE MONITORING OF PLANT PHENOLOGY Digital images are typically based on the RGB color model (red, green, and blue color channels). These channels encode the brightness values of the scene and can be combined in more than 16 million of colors, representing basically all the colors perceived by humans (Cheng 2001). Through the quantification of the RGB color channels, it is possible to calculate vegetation indices, which are related to leaf color changes representing the phenological status of the vegetation (Richardson et al. 2007, Sonnentag et al. 2012) (Fig. 1). By capturing daily digital images of a given site, we derivate time series encoding RGB color changes over time. Thus, the leaf patterns can be described based, for instance, on the proportion of the green fraction in the images (Richardson et al. 2007). The association of digital imagery data with local sensors (e.g., meteorological stations and surface-atmosphere fluxes) uncovers a wide range of research opportunities, especially linking phenological patterns to climatic drivers, and analyzing long-term data to detect phenological shifts due to the impact of anthropogenic changes (Polgar & Primack 2011, Brown et al. 2016, Morellato et al. 2016). The collection of daily vegetation color changes has been motivated also by the need to understand ecosystem-scale energy fluxes (Baldocchi et al. 2005, Richardson et al. 2007). Studies from temperate vegetation have found the start of the vegetation greenness controls the gross primary productivity (GPP) curves (Richardson et al. 2010, Migliavacca et al. 2011, Keenan et al. 2014). Therefore, temporal changes in the vegetation drive carbon exchange processes via influencing the photosynthesis process, respiration, and litter production (Peichl et al. 2014). 23 Most of the studies using phenocams have been developed in the Northern hemisphere, covering mainly deciduous forests (Richardson et al. 2007, 2009, Nagai et al. 2011). However, the application of repeated digital photographs is also efficient for the phenology monitoring of temperate grasslands (Inoue et al. 2015, Julitta et al. 2014), peatland (Peich et al. 2015), and evergreen forest (Toomey et al. 2015). Its reliability for tropical vegetation was recently validated for woody cerrado savanna (Alberton et al. 2014) and applied for tropical forest (Nagai et al. 2016, Lopes et al. 2016). The use of camera-derived vegetation indices in association with leaf demography-ontogeny models has been recently applied in the Amazon forest to investigate ecosystem-scale photosynthetic seasonality (Wu et al. 2016). However, there is still little focus on the species level analysis and on grasslands, mountains and other tropical vegetation. 3 PROCEDURES FOR PHENOLOGICAL MONITORING USING DIGITAL CAMERAS Digital cameras are reliable tools for the monitoring of vegetation because they have low price and easy setup, while providing high frequency and resolution data. Here, we introduce the main steps for phenology camera set up and basic information about image processing for data analysis (Fig. 1). A detailed protocol is available in the Supplementary Material. 3.1 Camera set up and image settings In general, the camera is placed in a tower built in the middle of vegetation (Fig. 2a and b). The choice of the site and the field of view must maximize the vegetation to be monitored. Hemispherical lens cameras are reliable for capturing images of the canopy, reducing crown cover among individual species (Fig. 2c). Cameras should be positioned facing North - Northeast to maximize the light over the canopy and to minimize lens flare. Cameras can be set up on small towers, close to the ground, to capture landscape images (Fig. 2d and e) when the focus are shrublands, grasslands, or other vegetations with short canopies and across heterogeneous landscapes as rupestrian grasslands (Fig. 2f). Different digital cameras have been used in repeated photography monitoring (see Sonnentag et al. 2012, Steenweg et al. 2017). Internet protocol (IP) cameras are ideal because they can be connected to a network and the image download performed remotely. For instance, 24 Stardot IP cameras record landscape images, have been successfully applied in temperate ecosystem monitoring, and were chosen as the standard camera for two of the major networks in North Hemisphere (Brown et al. 2016). Hemispherical lens (also called fish-eye) have been chosen for monitoring tropical vegetation sites (Alberton et al. 2014, Nagai et al. 2016) and by PEN (Nasahara & Nagai et al. 2015). The fish-eyes lens (360o) improve the selection of crowns with more precision and less covered areas (see Alberton et al. 2014). We recommend capturing a high frequency of images (a set of 3-5 images per hour, from 6 a.m. to 6 p.m.), which provides fine-tuned information about phenology, a confident quality data collection, and also a high volume of data for light calibration, smoothing and the development of computational tools (Alberton et al. 2014, Almeida et al. 2014, 2015, 2016). When it is not possible due to storage constraints, we recommend taking at least one image per hour during the midday hours (10 a.m. to 2 p.m., for more details see the SM). A complete meteorological station or at least some minimum set of sensors (rain gauge, thermometers, and Photosynthetically Active Radiation (PAR) sensors) is an important additional component to phenology towers. If not possible, it is important to search for the closest meteorological station to the study site. 3.2 Color information analysis The image analysis usually depends on the definition of regions of interest (ROI). The ROI is a region within the input images defined for analysis (Fig. S1 and see Alberton et al. (2014). After defining a ROI, we can remove irrelevant areas, such as those lacking vegetation or depicting the tower structure. Therefore, we define the sample size as ROIs from crowns of several species, a population, a portion of the canopy, a community profile, or a habitat or vegetation type in a heterogeneous landscape (Fig. S1). Several indexes have been applied to detect leaf color changes in time series of digital images exploring the RGB color channels (Richardson et al. 2007, Nagai et al. 2011, Sonnentag et al. 2012, Zhao et al. 2012, Zhou et al. 2013). Woebbecke (1995) was one of the first to calculate several indexes using RGB channels of digital images to evaluate which are better to detect weeds considering different types of soil, residue, and light conditions. A normalized index called RGB 25 chromatic coordinates (RGBcc) was developed by Gilespie et al. (1987) and it is considered up to now the most efficient to detect the color of plants in relation to their background (Sonnentag et al. 2012). The RGB chromatic coordinates (Rcc, Gcc, and Bcc) is a normalized index, defined by dividing each component (R, G, or B) by the sum of all components (R + G + B): (1) 𝑅𝑐𝑐 = 𝑅 𝑅 + 𝐺 + 𝐵 𝐺𝑐𝑐 = 𝐺 𝑅 + 𝐺 + 𝐵 𝐵𝑐𝑐 = 𝐵 𝑅 + 𝐺 + 𝐵 The Excess Green (ExG) index is also applied in color time series analysis (Sonnentag et al. 2012). This metric has proved to be a consistent color index, able to distinguish between green plants and their background (soil, residue), as well as to minimize variations in illumination, enhancing the green signal of the plants (Woebbecke 1995). (2) 𝐸𝑥𝐺 = 2𝐺 − (𝑅 + 𝐵) After performing the RGB color extraction and the vegetation index computation, it is necessary a data filtering to minimize noise in the time-series information (RGBcc) caused by illumination effects of seasonal changes and time of day (Sonnentag et al. 2012). To that end, the 90th percentile value is calculated from all daily values in a 3-day window (Sonnentag et al. 2012). 4. PHENOCAMS CONTRIBUTIONS FOR BIOLOGICAL CONSERVATION The importance of phenology for biodiversity conservation and ecological restoration has been recently explored by Morellato et al. (2016) and Buisson et al. (2017) respectively, with a special focus on conservation of tropical systems (Morellato et al. 2016). Phenology is recognized as an essential biodiversity variable required for study, report, and manage biodiversity (Pereira et al., 2013), pointing out the potential of remote sensing phenology and phenocam networks. 26 Therefore, near-surface phenology with cameras can play a key role for biodiversity conservation at several scales. On the other hand, phenology has not yet been included in the formal guidelines or recommendations for ecological restoration by SER (Society for Ecological Restoration), but Buisson et al. (2017) bring a fresh perspective on why and how phenology should be incorporated to ecological restoration guidelines. Systematic, long-term phenological monitoring programs are needed at local to large spatial scales to ensure conservation and effective management and for the success of ecological restoration programs. Plant responses to climate The search for the main factors triggering plant phenology is of paramount importance for better understanding plant responses facing climate changes and the conservation of species to ecosystems (Polgar & Primack 2011, Morellato et al. 2016). Plant phenology triggers remain poorly understood across the tropics. Therefore, systematic and long-term phenological observations are needed at large spatial scales for tropical ecosystems (Morellato et al. 2013, Chambers et al. 2013). However, high diversity of species precludes the observation of many species across several sites, due to the intense human labor and high costs (Alberton et al. 2014, Morellato et al. 2016). The e-phenology network was built based first on a core cerrado area where we tested and validated all protocols considering the local long-term cerrado phenology project (Alberton et al. 2014). We expanded the network, integrating flux measurements towers and larger research projects, choosing sites across a seasonality gradient. We are reaching out several key tropical vegetations from Amazon forest, Atlantic rainforest, Cerrado, to Caatinga, tracking changes and investigating drivers for phenology. Also, within the Amazon-FACE project, we will be able to monitor vegetation phenological responses to CO2 enrichment on Amazon forest. Elevated CO2 (eCO2) would affect photosynthesis biochemistry leading to an increase of productivity for tropical ecosystems. (Norby et al. 2016) Through daily color changes information in association with daily measurements of climatic variables (Fig. 3a and b), a wide range of questions might be investigated. For instance, 27 modeling leaf phenology patterns of multi sites and time series to investigate drivers of leaf development and senescence. Another important approach is in the ecosystem scale studies. Vegetative phenology has a significant role in the Dynamic Global Vegetation Models (DGVM). These models are based on a coupled information between plant biogeography and biogeochemical process to simulate ecosystem fluxes and climate shifts in a climatic change scenario (Noormets, 2010). In the tropics, understand leaf phenological stages is essential to better estimate measurements of gross primary productivity (GPP), because there is a gap of knowledge about drivers of carbon fluxes (Restreppo-Coupe 2017). Camera derived color time series might provide high frequency and quality information to understand photosynthetic seasonality as the vegetation responses and feedbacks to a changing climate (Richardson et al 2013, Restreppro- Coupe et al 2017). Beyond phenology: repeated photography monitoring for conservation, management and restoration Phenocams can monitor one to several tropical vegetation types and species with a reduced manpower and high temporal scale (daily basis). Near remote monitoring systems using digital repeated photograph can be also one of the most powerful tools to observe and detect shifts on vegetation structure to land-use changes, disturbances, climate warming and pre- and post- restoration of natural and agroecosystem. Changes detected by cameras such as events of deforestation, fire, flooding, vegetation recovery after disturbances, and species invasion, likely help to take fast and appropriated conservation and management measures. For example, digital cameras from e-phenology project have been integrated into the Brazilian long-term ecological program (PELD) conducted in the fire-prone vegetation mosaic of campo rupestre of Serra do Cipó, Minas Gerais, Southeastern Brazil (PELD CRSC, Fernandes 2016, http://labs.icb.ufmg.br/leeb/index_peld.html). The resulting time series are the first description of the leafing patterns across four campo rupestre vegetations (Borges in prep.). The phenocam monitoring system has also allowed detecting the time of fire occurrence and vegetation recovery after fire in real time at Serra do Cipó (Fig. 4) (Alberton et al. in prep.). http://labs.icb.ufmg.br/leeb/index_peld.html 28 Through a set of daily photographs, it is possible to visualize the process of post fire vegetation recovery showing the regrowth response of this wet grassland vegetation (Fig. 5, see legend for more details), also tracked by the camera derived vegetation index (Fig. 5 a and b). Anthropogenic fire may threat even the campo rupestre fire-prone vegetation, since the time, intensity, and frequency of human-induced fires impose additional stress on plants (Alvarado et al. 2017). The time lapse cameras are therefore accessible tools to monitor, manage and prevent fire. Phenological information has a key role in restoration process, such as timing improvement for restoration implementation, provides suitable indicator to assess restoration success, and allows schedule restoration actions through continuum monitoring (Buisson et 2017). Phenocams can improve restoration by matching key steps raised by Buisson et al. (2017) for restoration projects, such as: identifying and monitoring fire regime; using phenological metrics as indicators of restoration success, optimizing fire management; and improving restoration monitoring with continuum vegetation record that might be used to evaluate predefined goals and future practices of the restoration process. Successful restoration ideally requires previous knowledge of the vegetation structure and species’ phenology, a critical information to define restoration practices, access the post-restoration success, plan management actions and improve new restoration procedures (e.g., Carter and Blair 2012). Biological conservation in a digital world Phenocams networks built in interactive websites enriched with dynamic vegetation imagery may engage volunteer participation of population to generate science knowledge, playing an important role in education for conservation and citizen science programs. One example is the project called Season Spotter (Kosmala et al. 2016). Through volunteer participation involving tasks as detection of flowers and new leaves in an image database, the project has gained useful science knowledge. The main results were related to: detection of reproductive phenophases; selection of tree individuals by the users, facilitating the scaling from organisms to ecosystems; and the validation of phenological observations by the images, which improves the development of new algorithms for automatic detection. Besides, these initiatives go beyond scientific knowledge valuing citizen participation and boosting population interest for nature conservation. 29 The growth of cameras sensors technology has the potential to build global networks able to monitor not only plants, but also all biodiversity. A worldwide system with standardized metadata, field protocols, and databases developed by scientific community and integrated with citizen science participation, is one of the actions needed to achieve the objectives of the Convention on Biological Diversity’s 2011-2020 plans. Current applications to collect ecological data using remote cameras have been used by eMammal and TEAM projects (Steenweg et al. 2017). Examples of focal species included were: grizzly bear (Ursus arctos), tragopan (Tragopan blythii), wolverine (Gulo gulo), mule deer (Odocoileus hemionus), coyote (Canis latrans), African bush elephant (Loxodonta Africana), and others. The studies involved not only biodiversity measurements, but also the underlying causes of biodiversity changes (e.g., impacts of climate change and trophic interactions in a cervid in Brodie et al. 2014; evaluating landscape connectivity in Barrueto et al. 2014; camera surveys including large carnivors and herbivores communities, and the effects in food webs respectively in Ripple et al. 2014, Hooper et al. 2012; and evaluate reproductive success in female grizzly bears in Fisher et al 2014). The ongoing addition of new devices, high-resolution data survey, and sensor networks has improving the quality of data collected in biological studies, but at the same time increasing the magnitude of scientific data collected. Big data is one important challenge for biodiversity conservation. This leads to the next generation of scientific problems, which will require the establishment of multidisciplinary teams (Hey & Hey 2006). e-Science is about the collaboration of key areas of science, as a network of research initiative focused on the specification and implementation of a set of tools and technologies capable of supporting, improving, and speeding up data analysis, knowledge discovery, and decision making (Hey & Hey 2006). The e-phenology was designed as an e-Science project, and we present some examples of our research on digital camera image analysis and the tools devised in the context of e-Science collaboration. We use machine learning algorithms to plant species identification concerning the identification of each tree crown of the vegetation in the image (Almeida et al. 2014). The tool helps important steps from plant identification in the field to the definition of new ROIs for the image analysis speeding up the process and allowing grouping similar species in an image even with no previous 30 identification, a useful tool for conservation remote monitoring systems. Through the years, several approaches have been proposed to support the identification of individuals of particular species (Almeida et al. 2015, Almeida el al. 2016, Faria et al. 2016a, Faria et al. 2016b). We developed a tool to map the greenness in the image time series, the chronological percent map by PhenoVis (Leite et al. 2016). A database specific for phenolgical data was also developed in the framework of e-phenology project do deal big-data issues and improve access to information (Mariano et al, 2016). 5 Conclusions We have presented a first-step protocol with the main information about repeated photography method and set up (Supplementary Material), to increase the potential of a new tropical phenology research program in this promising area, fostering network and collaboration (Box 1). Phenology has its well-defined role in conservation biology (Morellato et al. 2016) and, in this context, we demonstrate that near-surface remote phenology and phenocam networks are powerful tools for conservation. Besides the capability of a fine temporal resolution associated with wide spatial monitoring coverage, phenocams can bring new information for management and restoration practices at several sites and environments, and can also be applied in education for conservation and citizen science through websites with phenological databases enriched by imagery data. The creation of phenology networks, still lacking for tropical countries, will broader and fine-tune research on phenological drivers and long-term monitoring to investigate and model the impacts of climate changes in the tropics. The pioneer e-phenology is the venue to reach those goals in Brazil. Lastly, phenocams could be easily integrated as a monitoring tool at any conservation unity, aggregating invaluable information of wide use for researchers and managers, from phenology to ecosystem dynamics and changes over space and time. 6 Acknowledgements 31 Our research is supported by the São Paulo Research Foundation (FAPESP), the FAPESP - Microsoft Research Virtual Institute (grants #2010/52113-5, #2011/51523-8 and #2013/50155- 0). LPCM and RST receive a Research Productivity Fellowship from CNPq (grants #306243/2010-5 and #306587/2009-2). FAPESP also provided fellowships to BA (grants #2014/00215-0 PhD and #2016/01413-5 BEPE), LFC (grant #2014/13354-8), BDB (grant #2014/07700) and GM (grant #2011/51523-8). GM receives a PhD fellowship from CNPq (grant #162312/2015-6). We have also been benefited from funds of CNPq, CAPES, and FAPESP (grants #2007/52015-0, #2007/59779-6, #2009/18438-7, #2010/51307-0, and #2016/06441-7). 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Inform. 18, 69–78. 43 8 BOX Recommended Procedures • install phenocams in a wide variety of landscapes and ecosystems, taking advantage of low-cost cameras when necessary • use electricity in the towers whenever it is possible, when setting up powerful cameras • choose sensors with good seal capability, adopt careful procedures in the installation process considering problems as excess of humidity, sun exposition, and invasion by bugs • standardize images formats and settings for multi-site monitoring • integrate your phenocams to a network, facilitating wide-scale collaborative research, enabling combine information across biomes and climatic zones, and widening the applicability for biodiversity conservation • set up new cameras in association with sites where long-term studies are being developed, such as the Brazilian Long-term Ecological Research PELD (Projetos Ecológicos de Longa Duração) and flux towers • explore tools or even create your own software and scripts for images processing, to support and facilitate data analysis • establish a e-Science collaborative research with computer scientists, improving visual and image analysis techniques, big-data management, investing in a new generation of “hybrid” scientists with a multidisciplinary profile and larger spectrum of actuation. 44 9 FIGURES LEGENDS Figure 1 Workflow showing the steps of a protocol for implementation of a repeated photograph monitoring system. Figure 2 Brazilian sites from e-phenology network and their different phenology monitoring setups for woody and open vegetation: (a) sketch of the hemispherical lens camera mounting design for forest canopy; (b) camera set up in the field; (c) sample image captured by the hemispherical lens digital camera in the cerrado sensu stricto vegetation (Itirapina, SP); (d) sketch of the camera mounting design for a landscape perspective; (e) camera set up in the field, (f) sample image of the heterogeneous landscape in the Serra do Cipó mountain range (Santana do Riacho, Minas Gerais State, Brazil). Figure 3 Vegetation canopy greenness, as quantified by green chromatic coordinate (Gcc; green dots) using phenocam imagery, in relation to local seasonal patterns of daily precipitation (blue bars) and air temperature (black line). In both graphics, Gcc values represent a 3-day window filter time-series of the growing season length (from day of the year DOY 152 to 214, 2013-2014) of two cerrado physiognomies, a grassland savanna vegetation (a) and a woody savanna (b), both in the same location in Itirapina city, São Paulo State, Brazil. Figure 4 Sequence of photographs showing timing of burn and the post-fire vegetation recovery process in a heterogeneous landscape, Serra do Cipó, Minas Gerais, Southeasten Brazil. Figure 5 Visual analysis showing post-fire vegetation recovery scheme of a wet grassland habitat (red dots selection) in Serra do Cipó, southern part of the Espinhaço Mountain Range, Minas Gerais State, Brazil. The graphic represents a camera derived vegetation index (Excess Green) from a set of photographs showing the green curve after a fire event. (a) first day after fire event; (b) vegetation recovered after 34 days; Green dots represent Green daily value of the 90th percentile of the Excess green index (90th ExcG) from digital images taken every hour (6:00 h to 18 h) 45 Fig. 1 46 Fig. 2 47 Fig. 3 48 Fig. 4 49 Fig. 5 50 Section 2 51 LEAFING PATTERNS AND DRIVERS ACROSS SEASONALLY DRY TROPICAL COMMUNITIES Bruna Alberton1, L., Ricardo da S. Torres2, Thiago S. F. Silva3, Humberto Rocha4, Magna Soelma5, Patricia C. Morellato1 1 Laboratório de Fenologia, Universidade Estadual Paulista (Unesp), Instituto de Biociências, Rio Claro, São Paulo, Brazil 2 Institute of Computing, University of Campinas, Brazil 3 Ecosystem Dynamics Observatory, Universidade Estadual Paulista (Unesp), Instituto de Geociências e Ciências Exatas, Rio Claro, São Paulo, Brazil; 4 Instituto de Astronomia, Geofísica e Ciências Atmosféricas, USP, São Paulo (Brasil) 5 EMBRAPA Empresa Brasileira de Pesquisas Agropecuárias, Semi Árido, Petrolina, Pernambuco (Brasil) 1 ABSTRACT The study of phenology is a key component to track vegetation transitions that respond to climate changes, since plants are mainly constrained by the environment. Water and light availability are considered the main abiotic limitations for leaf production in the tropics. The search for answering questions about the environmental triggers of tropical phenology has stimulated the application of new tools for plant monitoring. Digital cameras have been applied for the monitoring of leaf temporal changes in seasonal tropical environments. We conducted, for the first time, a multi-site phenological monitoring across tropical vegetations using leaf phenology derived by digital cameras. An analytical procedure was used to unravel the main drivers influencing leaf phenology time series across seasonally dry vegetations using digital cameras to describe the leafing patterns of seasonally dry tropical vegetation communities. Our main questions are: (i) Can community growing seasons be detected by near-surface remote cameras in tropical vegetation sites? (ii) Do the growing seasons vary across different seasonality conditions? (iii) Do the environmental factors driving leaf phenology differ and how they vary across sites? Growing seasons from each vegetation type were delineated using derivatives. From our results, we demonstrated that water and light were the most important predictors 52 for the leaf phenological patterns across the sites. Water-plant relationships were more important for the Caatinga community, and light, through day-length seasonality, had more influence in the leafing patterns of the cerrado communities. An interesting outcome was the increasing variability of phenological signals (leafing behaviors) and predictor-response relationships (distinct smooth functions) across sites where seasonality was less pronounced and/or distinct species life-form were capable of overcoming drought-effects, such as deep root systems trees from woodland cerrado compared to grassy cerrado. 2 INTRODUCTION The patterns of temporal leaf replacement or vegetative phenology is of major importance to understand ecosystem processes, such as carbon, water, and energy exchanges controlling seasonal cycles of photosynthetic activity (REICH, 1995; RÖTZER et al., 2004; RICHARDSON et al., 2013). Leafing patterns of plant species define the growing season of a vegetation community, and are mainly constrained by environmental cues, from temperature in temperate regions to water in the tropical realm (REICH, 1995). Therefore, the study of leaf phenology is a key component to track vegetation transitions that respond to climate changes (POLGAR and PRIMACK, 2011; MORELLATO et al., 2016). There is high heterogeneity among leafing patterns across tropical forests, mostly related to the intensity and length of the dry season (REICH, 1995, CAMARGO et al., 2018). A wide range of studies are necessary to identify the main factors regulating leaf phenology across the tropics and, therefore, understand vegetation dynamics, and efficiently forecast climate change impacts (POLGAR and PRIMACK, 2011; CHAMBERS et al., 2013). The majority of phenology studies have accessed reproductive and leafing patters through the direct observation of individual trees (MORELLATO et al., 2013, 2016; ABERNETHY et al., 2018), and try to access the cues for leaf fall and leaf flushing. Water and light availability have been considered the main abiotic cues regulating the time and periodicity of leaf production in the tropics (VAN SCHAIK; TERBORGH; WRIGHT, 1993; WRIGHT and VAN SCHAIK, 1994; MORELLATO et al., 2000; RIVERA et al., 2002; BORCHERT et al., 2015; CAMARGO et al., 2018). A major factor regulating the length of growing season and species synchronicity is the seasonal availability of water. Seasonal tropical forests, with increasing dry season 53 severity, present a marked annual periodicity of the community leaf flushing and senescence, and as a consequence, a greater proportion of deciduous species (MURPHY and LUGO, 1986; REICH, 1995, WILLIAMS et al., 2008). In fact, in the seasonally-dry tropical forests or dry forests, nearly the entire assemblage of individuals of their species lose all leaves in a synchronic deciduous behavior during the dry season (REICH and BORCHERT, 1984; REICH, 1995; QUESADA et al., 2009; SINGH and SINGH, 1992). Under a reduced rainfall seasonality and less pronounced dry season, species and individuals may display different degrees of deciduity, forming communities with a widely range of leafing behaviors, that may change in proportion according to conditions of soil moisture, topography (BORCHERT 1994; RIVERA et al., 2002), and intensity of dry season (CAMARGO et al., 2018 and references therein). Regarding light, the consistent signal of day length seasonality in the tropics would have a major importance towards higher latitudes, triggering the input of new leaves (WRIGHT and VAN SCHAIK 1994; RIVERA et al., 2002). Day length seasonality has also been reported as a trigger for the onset of the community early leaf flushing in the dry season, anticipating the rains, in seasonal tropical vegetations, as semideciduous forests and savannas (RIVERA ET AL., 2002; MORELLATO ET AL., 1989, SINGH and KUSHWAHA, 2005, HIGGINGS et al., 2011; CAMARGO et al., 2018). Conversely, in tropical rain or moist forests, where moisture is not a constrain, elevated irradiance due reduced cloud cover during the dry season can promote a species synchronicity of leaf production (VAN SCHAIK; TERBORGH; WRIGHT, 1993; WRIGHT and VAN SCHAIK, 1994, RIVERA et al., 2002). Plant life forms have direct implications on water and light species adaptation and thereby on leafing phenology (ARCHIBALD and SCHOLES, 2007, HIGGINGS et al., 2011, WHITECROSS et al., 2017). The temporal niche separation hypothesis proposed by Scholes and Walker (1993) indicates that trees would deploy leaves earlier than grasses, even before the start of the growing season, given trees a competitive advantage over grasses. Trees can input new leaves still in the dry season due to stored carbon reserves and their better capacity to access and accumulate groundwater sources (rooting depths), allowing plant growth when radiation is maximum (ELLIOTT ET AL., 2006; EAMUS, 1999; ARCHIBALD and SCHOLES, 2007). On the contrary, grasses are much more dependent on the rainfall seasonality given their shallow root system, and can present multiple peaks of leaf production along the 54 growing season as a fast response to rainfall events in seasonally dry environments (BATALHA and MANTOVANI, 2000; ARCHIBALD and SCHOLES, 2007). A comprehensive survey of phenology data and trends over the Southern Hemisphere (SH) highlights the gaps in the phenology knowledge of tropical species and ecosystems (CHAMBERS et al., 2013). The search for answering questions about the environmental triggers of tropical phenology and the potential changes in the current scenario of climatic changes has stimulated the application of new tools of plant monitoring. Imagery based on satellite and digital cameras imagery has been considered alternative methods for successfully monitoring plant greening continuously across the landscape (RICHARDSON et al., 2007; MORISETTE et al., 2009). In particular, repeated photographs taken by digital cameras (phenocams) have been applied for the monitoring of leafing temporal changes in seasonal tropical environments (ALBERTON et al., 2014; ALBERTON et al., 2017; MOORE et al., 2017). The application of phenocams to monitor leaf phenology reduces human labor, increases accuracy by eliminating possible discrepancies related to observer subjectivity, improves temporal resolution to hourly/daily basis and the special resolution, allowing simultaneous monitoring of different vegetations and sites (CRIMMINS and CRIMMINS, 2008, RICHARDSON et al., 2007; BROWN et al., 2016; ALBERTON et al., 2017). We conducted, for the first time, a phenological multi-site monitoring across Neotropical seasonal vegetations using digital cameras or phenocams to describe the leafing patterns of four seasonally dry tropical vegetation communities: three cerrado savanna vegetations and one xeric shrubland, the Brazilian Caatinga. We combined the camera-derived phenology with local environmental variables to evaluate the constraints imposed by temperature, water, and light on the patterns of leaf flushing and senescence across different seasonality condition and vegetation structure. Our main questions are: (i) Can we detect distinct community growing seasons across the four tropical vegetations? We expect leafing season at caatinga responding to immediate rainfall while cerrado may present a more variable response due to mild and shorter dry season; wood cerrado would have reserves or access to underground water and may be able to start leafing earlier in the dry season, before the first rains; (ii) Do the growing seasons vary according to the vegetation structure (woody-grassy) and degree of seasonality? We expect a cerrado vegetation response differs between woody and grassy dominated 55 vegetations in accordance with the temporal niche separation hypothesis (SCHOLES and WALKER, 1993), while Caatinga response is restricted to the first rains starting the wet season; and (iii) Do the main environmental drivers of start and length of growing season differ according to environmental seasonality and vegetations? We expect light and accumulated rainfall to be the main drivers for cerrado and immediate rainfall the cue for the start of caatinga growing season. The proportion of woody species and individuals at each leaf exchange strategy (deciduous, semideciduous, or evergreen) may also vary among the vegetations studied and influence the plant responses to environmental cues and were considered in our analyses (EAMUS, 1999; WILLIAMS et al., 2008, CAMARGO et al., 2018). 3 METHODS 3.1 Sites description Sites are geographically distributed across two main vegetational domains (VELOSO et al., 1991) or biomes (OLSON et al., 2001), the Caatinga or desert and xeric shrubland and, the Cerrado or grasslands, savannas and shrublands, respectively (see map in Fig. 1). We collected near-surface leaf phenology of four sites, one from Caatinga and three belonging to different vegetation types of Cerrado. Table 1 summarizes the study sites characteristics and phenocam monitoring period analyzed here. The four study sites are part of the e-phenology network (http://www.recod.ic.unicamp.br/ephenology/client/index.html#/home - As of June 2018). Caatinga - The first site is the exclusive Brazilian vegetation, the caatinga, a xeric, sclerophyllous vegetation located in the Semi-Arid region distributed mostly in Northeastern Brazil, denominated the xeric shrubland Biome according to Olsen et al., (2001). The area of phenocam monitoring has approximately 600 ha, 342 m a.s.l, and belongs to the Reserva Legal da Embrapa Semiárido (9°05’S; 40°19’ W), Petrolina municipality, Pernambuco State, Northeastern Brazil. (KILL, 2017). The climate is classified as semiarid (KÖPPEN, 1931) and, according to the normal climate from 1970 to 2014 (source: Experimental station of Bebedouro, 10km from the site), the total annual mean precipitation is 510 mm distributed mainly from January to April, and the mean annual temperature is 26.2°C. During our three-years of study, annual mean precipitation was around 260mm, and annual mean temperature 27.05°C (Fig. 2A), characterizing a period even drier than usual. Local vegetation is http://www.recod.ic.unicamp.br/ephenology/client/index.html#/home 56 composed of xerophilous trees and shrubs and a continuous herbaceous layer of species adapted to the xeric and harsh conditions. The plant species belong mostly to the Fabaceae, Euphorbiaceae, Poaceae, and Cactaceae families, and the discontinuous canopy reaches proximally 5 m high (KILL, 2017). Cerrado - The other three sites encompass different vegetation physiognomies of Cerrado, a neotropical savanna, from a wooded grassland or scrubland to a dense woodland, all part of the Cerrado domain or Cerrado sensu lato classification (OLIVEIRA-FILHO and RATTER, 2002) and included in the grasslands, savannas, and shrublands Biome according to Olsen et al., (2001) (Fig. 1). All three sites are in the São Paulo state, Southeastern Brazil: the second and third at Itirapina municipality and the last one at Santa Rita do Passa Quatro (Fig. 1). Climate classification is humid subtropical (KÖPPEN, 1931), with a cool dry winter (mean monthly temperature of 18°C) and a hot wet summer (mean monthly temperature of 32°C), and a total annual precipitation of 1524 mm. A cluster analysis using 30 years of climatic data (1982-2012) showed four seasons: a rainy season (November to March), a transitional rainy-to-dry season (April), a dry season (May to August), and a transitional dry-to-rainy season (September to October) (ESCOBAR et al., 2018). Historic climatic information was made available by the Centro de Recursos Hídricos e Estudos Ambientais (CRHEA-EESC/USP), about 15 km distant from the study sites. Local environmental variables at each site were daily collected from meteorological stations (Hobo U30 USB Weather Station Data Logger) set up in the phenological towers at each vegetation site. Sensors were connected to a central data logger constantly sending data information to a web platform (www.hobolink.com) via GSM. Although cerrado scrubland and cerrado woodland sites belong to the same original vegetation patch (see below) and are just about 10km distance, a meteorological station was placed within each physiognomy. Gap filling for second and third sites were obtained from a nearby meteorological station (CRHEA-EESC/USP). Our second monitoring site, the cerrado campo sujo (“dirty grassland”), is a wooded savanna or shrubland vegetation physiognomy dominated by an herbaceous layer with scattering shrubs and small trees (OLIVEIRA-FILHO and RATTER, 2002, BATALHA and MANTOVANI, 2001). The study site is part of the Itirapina Ecological Station (22°13'23"S;47°53'02.67"W), encompassing 2,300 ha, 700 m a.s.l. A local vegetation survey found a proportion of 79% of species from the herbaceous- shrubland layer, dominated by Asteraceae, Fabaceae, Poaceae, and Cyperaceae families, and 21% of http://www.hobolink.com/ 57 small trees species most from families, Fabaceae, Myrtaceae, and Melastomataceae (TANNUS et al., 2006). During the three years of monitoring, local climatic time series show a mean annual total precipitation of 1,272 mm and mean annual temperature of 23.5°C (Fig. 2B). The third study site is located in a nearby private area of 260 hectares and 700 m a.s.l. (22°10′52″ S, 47°52′25″ W), belonging to the same original “patch” of cerrado that once covered all that region. The associated vegetation is a cerrado sensu stricto, a woody cerrado savanna dominated by trees and shrubs from 3 to 8 m tall, sometimes reaching up to 12 m, with crowns arranged in a discontinuous canopy, and a presence of a fair amount of herbaceous vegetation (REYS et al., 2013). From the local 5-year meteorological data (2011 - 2015), mean annual total precipitation was 1,478 mm and mean annual temperature 22.9 °C (Fig. 2C). Plant species composition is distributed mostly in the Myrtaceae, Fabaceae, and Malpighiaceae families (REYS et al., 2013), and vegetation was classified as a semideciduous, according to the species long-term leaf exchange strategies (CAMARGO et al., 2018). The last site it is also a woody cerrado formation that belongs to the Reserva Ecológica Pé-de- Gigante (PEG), located within the Parque Estadual do Vassununga, at Santa Rita do Passa Quatro county. The PEG reserve comprehends a contiguous area of 1,060 ha and 649 m a.s.l. (47° 34’ – 47° 41’ W; 21° 36’ – 21° 44’ S), covered by a heterogeneous landscape of savanna vegetations, from open grasslands to woody dense cerrado. The seasonal humid subtropical climate (KÖPPEN, 1931) presents a dry season from May to September and the wet season from October to April, a total annual rainfall of 1,499 mm and mean temperature 21.5°C (BATALHA and MANTOVANI, 2000). Our local climatic data from 2013 to 2015 shows a total annual average precipitation of 1,150 mm and mean air temperature of 22.5°C (Fig. 2D). The study site, where the camera system is located, is a transition from woody cerrado to a cerradão (OLIVEIRA-FILHO and RATTER, 2002), that we classified as a dense cerrado, characterized by a discontinuous canopy, nearly without an herbaceous layer and high density of shrubs and trees (RIBEIRO and WALTER, 1998). The site has a predominant woody layer reaching 10m to 15m high, composed mainly of the species Ptedoron pubecens, Copaifera langsdofii, and Anadenanthera peregrina var. falcata from the Fabaceae family. The closed canopy results in a