Improving Citizen Science As a Tool for Biodiversity Monitoring

UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS

Improving citizen science as a tool for biodiversity monitoring

Doutoramento em Biologia Biodiversidade

Patrícia Maria Nunes Tiago

Tese orientada por: Professor Doutor Henrique Miguel Pereira Professora Doutora Margarida Santos-Reis Doutor César Capinha

Documento especialmente elaborado para a obtenção do grau de doutor

UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS

Improving citizen science as a tool for biodiversity monitoring

Doutoramento em Biologia Biodiversidade Patrícia Maria Nunes Tiago

Tese orientada por: Doutor Henrique Miguel Pereira Doutora Margarida Santos-Reis Doutor César Capinha

Júri: Presidente: ● Doutor Henrique Manuel Roque Nogueira Cabral, Professor Catedrático da Faculdade de Ciências da Universidade de Lisboa Vogais: ● Doutor Pedro Rui Correia de Oliveira Beja, Investigador Coordenador de Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO-InBIO Laboratório associado) da Universidade do Porto ● Doutor Henrique Miguel Leite de Freitas Pereira, Investigador Coordenador de Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO-InBIO Laboratório associado) da Universidade do Porto (orientador) ● Doutor Francisco Manuel Ribeiro Ferraria Moreira, Investigador Coordenador de Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO-InBIO Laboratório associado) da Universidade do Porto ● Doutora Ana Isabel Oliveira Delicado, Investigadora Auxiliar do Instituto de Ciências Sociais da Universidade de Lisboa ● Doutora Cristina Maria dos Santos Luís, Investigadora de Pós-Doutoramento do Museu Nacional de História Natural e da Ciência da Universidade de Lisboa ● Doutor José Pedro Oliveira Neves Granadeiro, Professor Auxiliar da Faculdade de Ciências da Universidade de Lisboa

Documento especialmente elaborado para a obtenção do grau de doutor

Fundação para a Ciência e Tecnologia (SFRH/BD/89543/2012)

Resumo

A ciência cidadã, ou seja, o envolvimento de não cientistas na investigação científica, teve um grande crescimento e desenvolvimento nos últimos anos. Algumas questões cientificas só podem ser respondidas com a recolha e tratamento de uma grande quantidade de dados e, a ajuda de cidadãos nestas tarefas, deu um grande contributo para a realização de alguns projetos que não poderiam ser desenvolvidos apenas com o trabalho de investigadores, devido a uma escassez de recursos, quer humanos, quer financeiros.

Tratados internacionais tais como a Convenção da Diversidade Biológica, a Convenção sobre o Comércio Internacional de Espécies da Fauna e da Flora Selvagem Ameaçadas de Extinção e a Convenção sobre a Conservação de Espécies Migradoras da Fauna Selvagem identificaram a necessidade de avaliar as alterações globais da biodiversidade. O Painel Intergovernamental da Biodiversidade e dos Serviços dos Ecossistemas também tem como uma das suas principais funções realizar avaliações regulares do conhecimento que temos da biodiversidade. A ciência cidadã pode ser uma ferramenta importante para a monitorização da biodiversidade a nível regional e a nível global.

Com esta tese pretende-se abordar cinco questões científicas principais na área da ciência cidadã, com o objetivo de melhorar estes projetos, para que possam, cada vez mais, complementar a informação científica e contribuir para uma mais eficaz monitorização da biodiversidade. Estas cinco questões são: Qual o contexto social de um projeto de ciência cidadã e que pontos devem ser tidos em consideração quando se desenha um projeto de ciência cidadã?; Quais são os benefícios e as limitações, existentes e potenciais, para diferentes grupos de cidadãos, para participar num projeto de ciência cidadã de registo de biodiversidade, utilizando como caso de estudo o projeto BioDiversity4All —um projeto português de ciência cidadã na área da biodiversidade?; Quais são as principais motivações intrínsecas para participar em projetos de ciência cidadã?; Quais as variáveis que influenciam as localizações onde os participantes estão a realizar observações?; Será que os dados de ciência cidadã poderão ser usados para estimar os nichos climáticos e a distribuição das espécies?

Resumo

A primeira parte desta tese pretende dar a conhecer o contexto social dos projetos de ciência cidadã, explorando, de uma forma sucinta, a sua história e identificando os principais atores envolvidos nestes projetos. Este capítulo analisa os principais pontos a considerar, da perspetiva dos diferentes grupos de participantes, quando se desenha um projeto desta natureza. O desenho do projeto deve ter em conta diferentes compromissos que se têm que assumir, tais como: decidir mantê-lo pequeno, com controlo local dos dados, perto das questões que interessam os voluntários e as comunidades locais, ou associá-lo a uma iniciativa global, com o benefício de ampliar as possibilidades de utilização dos dados; manter o foco principal na qualidade dos dados, com uma recolha de dados rigorosa, de uma forma sistemática, ou na facilidade de produção de dados, com benefícios para o volume de dados recolhidos e para educação e o envolvimento ambiental dos cidadãos. Quando se esperam do projeto resultados científicos e decisões de gestão, a recolha de dados verificáveis é essencial. Este requisito é também importante para atrair mais cientistas para estes projetos. Ser explícito sobre os objetivos do projeto é fundamental para evitar mal-entendidos nas expectativas dos diferentes grupos envolvidos. Desenhar cuidadosamente projetos de ciência cidadã poderá contribuir para um aumento do sucesso destas iniciativas.

A este trabalho seguiu-se uma análise dos benefícios e das limitações existentes e potenciais no registo da biodiversidade, para os diferentes grupos da sociedade (desde cidadãos até grandes empresas). Esta análise é baseada na experiência do projeto BioDiversity4All. Em Portugal, há uma grande ausência de tradição de observação da biodiversidade. Neste contexto, identificar distintos grupos de cidadãos para quem um projeto como o BioDIversity4All pudesse trazer benefícios, revelou-se uma tarefa importante, uma vez que o recrutamento pode ser mais difícil de atingir do que noutros países, onde os hábitos estabelecidos de ciência cidadã facilitam o recrutamento e a participação. Uma abordagem bottom-up, com uma comunicação e estratégias de envolvimento específicas para cada grupo é essencial para recrutar e reter grupos de cidadãos que possam ter interesse na iniciativa. Um benefício geral, relevante para a maioria dos grupos analisados, é a contribuição para um aumento do conhecimento dos valores naturais, uma melhoria da educação para a biodiversidade e maior consciencialização ambiental para a conservação.

O capítulo seguinte desta tese estuda as motivações dos cidadãos para participar em projetos de ciência cidadã, em Portugal, e analisa o padrão de motivações entre diferentes grupos de

Resumo utilizadores. A maioria das pessoas que se inscreveram no BioDiversity4All, e que responderam ao inquérito realizado, tem uma educação superior e não apresenta um nível de participação elevado. Verifica-se que, para o conjunto total dos participantes, é bastante valorizada a relação que têm com o projeto, e que o grau de participação influencia o valor atribuído a motivações que se prendem com o sentido de utilidade, ou o relacionamento com o grupo. Um trabalho criterioso no envolvimento dos participantes, tendo em conta as suas motivações intrínsecas, é fundamental para aumentar e manter a participação de cidadãos em projetos de ciência cidadã.

No capítulo seguinte é comparado o efeito das variáveis geográficas nas observações registadas na plataforma BioDiversity4All, para os diferentes grupos taxonómicos. Este estudo mostrou, como esperado, o enviesamento que bases de dados de ciência cidadã, oportunistas, podem ter. Algumas áreas do país são muito cobertas quando comparadas com outras, um número limitado de cidadãos é responsável por uma grande parte das observações, alguns períodos do ano têm muito mais observações. Considerando as variáveis selecionadas, a maioria acaba por refletir a acessibilidade aos locais como por exemplo a altitude, a densidade de estradas ou a densidade de caminhos pedonais. Apesar da variação existente entre grupos foi possível identificar alguns padrões. A densidade de caminhos pedonais foi uma variável com uma importância significativa para sete dos oito grupos taxonómicos. Ao contrário de outros estudos, a densidade de caminhos explica mais variação na distribuição de observações do que a densidade de estradas.

A última parte deste estudo avalia como bases de dados de ciência cidadã oportunistas, apesar das limitações já identificadas, são fontes viáveis de dados que podem ser usados na modelação da distribuição de espécies. Testou-se também se os atributos das espécies de répteis e anfíbios podem indicar a fiabilidade e a integridade de dados oportunistas de distribuição. Foi realizada uma análise dos nichos climáticos das espécies com dados de ciência cidadã, do BioDiversity4All, e comparados com dados científicos. Os resultados obtidos com espécies de répteis e anfíbios variam muito entre as espécies analisadas neste estudo, o que não é inesperado uma vez que, como na maioria dos grupos biológicos, as espécies de répteis e anfíbios diferem bastante, sendo alguns, por exemplo, mais esquivos, o que leva a uma variação na detetabilidade e facilidade de identificação nos ambientes naturais. Para algumas das espécies, os modelos de distribuição apresentam performances preditivas boas e semelhantes para dados de ciência cidadã e para dados científicos, apesar de os primeiros apresentarem, em média, valores de

Resumo

performance mais baixos. Os resultados sugerem que este tipo de bases de dados pode apresentar alternativas viáveis a dados científicos quando estes não estão disponíveis. O desafio será combinar as diferentes fontes de dados para atingir melhores resultados.

Como uma última contribuição desta tese apresentam-se linhas de investigação futuras nesta área. O futuro dos estudos em ciência cidadã parece estar grandemente ligado aos avanços na tecnologia digital tais como a capacidade de recolha e armazenamento de grandes volumes de dados a baixo custo, a possibilidade de análises complexas destes dados e a personalização de aplicações e projetos que servem os interesses e que se adequam às motivações dos participantes. A “gamificação”, ou seja, a tendência de incorporar elementos de jogos no desenho do projeto para promover a participação, motivação e envolvimento de participantes, também se está a tornar uma área popular de investigação estando a ser usada em diferentes âmbitos nos quais se inclui a ciência cidadã, sendo de esperar que, a sua utilização aumente nos próximos anos. Para que haja um, ainda maior, envolvimento da comunidade científica, outra área que deve ser analisada mais em detalhe no futuro diz respeito às motivações dos cientistas para participarem em projetos de ciência cidadã.

A ciência cidadã pode ser uma ferramenta interessante para o desenvolvimento de programas de monitorização da biodiversidade. A adoção de protocolos comuns e normalizados poderão ajudar na utilização destes dados. Estudos sobre a robustez da qualidade de dados e sobre as análises estatísticas mais adaptadas às características específicas destes projetos são importantes para dar suporte à utilização dos dados recolhidos, já que podem reduzir os erros de amostragem, permitindo um melhor equilíbrio entre a quantidade e qualidade dos dados recolhidos.

Palavras-chave: ciência cidadã, monitorização da biodiversidade, motivação dos participantes, planificação de projeto, bases de dados oportunistas.

Abstract

Citizen science, i.e. the engagement of non-scientists in research, had an impressive development in the last few years. Some scientific questions can only be addressed with the involvement of a huge number of data collectors and analysers. The effort of such work based on professionals hampers the feasibility of some experiments. Consequently, involving volunteer citizens in monitoring and research programs (so called citizen science projects) is a growing activity in many countries and is expanding to new scientific areas.

This thesis addresses five main research questions: What is the social context of a citizen science project and what points should be taken into consideration when designing a citizen science project?; What are the current and the potential benefits and limitations, for different citizen groups, in participating in a project of citizen science for registering biodiversity, using as a case- study BioDiversity4All —a Portuguese biodiversity citizen science project?; What are the main intrinsic motivations to participate in citizen science projects?; What are the variables that drive the locations where users are making observations?; Can we use citizen science data to estimate climatic niches and species distributions?

The first part of the research provides the social context of citizen science projects, exploring briefly the history of citizen science and identifying the main stakeholders involved in these projects. This chapter analyses the main points to take into consideration, from the perspectives of these different stakeholders, when designing a citizen science project. It is fundamental that project design acknowledges the existence of social trade-offs like: deciding the scope and scale

Abstract

of the project; deciding to keep small, with local data control, closer to the volunteers and community issues, or connecting with larger initiatives to benefit data usage; focusing more on guaranteeing data quality with the collection of rigorous, reliable data gathered in a systematized way, or on the easiness of producing data, with higher benefits to data volume, environmental education and engagement. When management decisions and scientific research outcomes are expected to arise from the project, verifiable and reliable data is essential. This requirement is also important to attract more scientists to citizen science projects. Being explicit about the goals of the project is fundamental to avoid misunderstanding of expectations and outcomes of stakeholders. Planning carefully the design of a citizen science contributes to the increased success of such initiatives.

This work is proceeded by an analysis of the current and potential benefits of biodiversity registering, and as well as its limitations, for different societal groups (from individual citizens to large companies). One overall benefit is that this ultimately contributes to an increased societal knowledge about natural values, an improved biodiversity-related education and higher environment and conservation awareness. This analysis is based on the experience of the BioDiversity4All project. In Portugal, there is a significant lack of tradition on biodiversity observations and citizen science. In this context, identifying distinct citizen groups to whom a project like BioDiversity4All could be beneficial, proved to be an important task, since recruiting can be more difficult to achieve than in other countries, where established citizen science habits facilitate recruiting and participation. A bottom-up approach, with customized communication and engagement strategies, seems essential to recruit and retain citizen groups’ interest in the initiative.

The next part of this research is to study the main motivations for the participation of citizens in a citizen science project, in Portugal, and assess the pattern of motivations across different groups of users. Analysing survey respondents registered in BioDiversity4All, the majority have higher education and low level of participation. Relatedness with the project is the motivational category most valued. Other categories, like Value/Usefulness and Group Relatedness are more important for users that participate more. Working carefully on people’s involvement is fundamental to increase and maintain their participation on citizen science projects.

Abstract

In the following chapter, the effect of geographic variables on the observations registered in BioDiversity4All, among different taxonomical groups, is compared. This study, showed as expected, the bias that opportunistic citizen science databases may have. Some areas of the country are highly covered by observations, compared to others, a limited number of participants is responsible for most of the observations, and there are differences in the number of observations throughout the year. Considering the variables selected, most of them reflect accessibility such as altitude, density of roads, or density of paths. Despite the variation between groups we could identify some patterns. Path density was the variable that showed significant importance for seven of the eight taxonomic groups. In contrast with other studies, density of paths explained more variation than the density of roads in taxa distribution records.

The last part of the research assesses whether opportunistic citizen science databases are viable data sources to use in the modelling of species distributions and test if species attributes can indicate the reliability and completeness of the opportunistic distribution data. The analysis of sampling of species’ climatic niches based on citizen science records from BioDiversity4All was performed and compared with scientific records. The results obtained varied greatly among different herptile species, the ones used in this analysis, which is not unexpected because, as in most biological groups, herptile species differ greatly in terms of elusiveness and secludedness, which leads to a variation in detectability and ease of identification in the natural environment. For some species, distribution models presented good predictive performances, highly similar between models using citizen science data and those using data from a scientific database, despite a lower average performance of the former. The results suggest that opportunistic citizen science databases of species observations can represent a viable alternative to scientific records when these are not available, and the challenge might be to combine different data sources to achieve better results.

A final contribution of this research is the presentation of future research avenues in this area. The future of citizen science studies seems to be highly related with digital technology improvements with low-cost collection and storage of big data, complex analysis of this data and personalization of applications and projects to suit each person’s interests and motivations to participate. “Gamification”, or the trend of incorporating game-like elements in project design to foster participation, motivation and engagement, is also becoming a popular research area being used in several different scopes including citizen science, being expected an increased use

Abstract

in the following years. Another area to be develop in the future concerns scientists’ motivations to participate in citizen science projects that should also be better understood.

Citizen science programs and the development of coordinated capacity building initiatives can be good approaches to develop biodiversity monitoring programs. The adoption of common and standardized protocols in citizen science projects could help to use these data in monitoring programs. Studies on the robustness of data quality and on the evaluation of the statistical analysis better adapted to the specific characteristics of citizen science projects are important to give support to the use of the data collected, once they can reduce the sampling error, allowing a better balance between quantity and quality of data collected.

Keywords: citizen-science, biodiversity monitoring, participants’ motivations, project design, opportunistic databases.

Acknowledgements

There are many persons to thank for helping me through these four years of my PhD. I have been lucky enough to work with great working groups and have the support of incredible friends and an extraordinary family.

I would like to thank my advisor Henrique Miguel Pereira. I feel really lucky to have worked and learned with him. He provided great support and believed in this project from the start. He is a brilliant person with a great passion for science and biodiversity. It was great to be able to share his perspectives and enthusiasm.

I would also like to thank Margarida Santos-Reis for accepting being my advisor despite all the work and responsibilities she has upon her shoulders. Having the chance to work with her was fantastic. She was a great support at the University and in my work. Her comments and feedback were always inspiring and of invaluable help.

Although César Capinha has become my advisor only in the last year of my PhD we worked together since the beginning. I learnt a lot from him: from R, geographic information systems or modelling procedures. He was quite patient in teaching me areas that were completely new for me. He offered me a continued support and friendship during these four years and I would like to extend a big thank.

When I started my PhD, back in 2013, the members of TheoEco group gave me a great support in my return to FCUL. I would like to thank Ana Ceia Hasse, Silvia Ceausu, Murilo Miranda, Laetitia Navarro, Inês Martins, Alexandra Marques, for the time, although short, we shared in Lisboa and for receiving me so well in my travels to Leipzig. I was inspired by the great work of Vânia Proença. I would like to thank all the support she gave me at different phases of this PhD (even when it was just a thought growing in my mind).

When part of the group moved to Leipzig I received an enormous support from the ones that stayed: Mia (Margarida Ferreira), Luís Borda de Água, César Capinha and Ainara Avizanda. I

Acknowledgements would like to thank each of them for specific reasons. Mia was a wonderful friend. She was permanently available to help in everything I needed and was always interested in knowing how my work was going. Luís was my anchor at FCUL. He helped me a lot when I started, introduced me to Bayesian statistics and turned our university lunches into friendly and pleasant times. César I have already mentioned and Ainara was a great friend. It was always nice to share our mid-morning coffees and catch up on conversation. When she left to Spain I missed her a lot.

I would like to thank José Pedro Granadeiro and their group for letting me stay in their office for a long time, when my group left. They made me feel quite well there and were a great company. A special thanks to Pedro Lourenço and Letizia Campioni.

At the Science Faculty, CE3C and MARE I cannot forget to thank Inês Teixeira do Rosário, Sofia Seabra, Ana Leal, Nuno Pedroso, Patrícia Garcia-Pereira, Casparus Crous, Marta Santos, Filomena Magalhães, Susana Varela, Manuel Sapage, Daniel Alves, Inês Órfão, Antonieta Charrua, Francisco Dionísio, Ricardo Rocha, Paula Gonçalves, Sérgio Chosas, Helena Serrano, Teresa Dias, Teresa Mexia, Zulema Rio, Otávio Paulo, Paula Simões, Sietze Norder, Mafalda Basto, Luísa Chaves, Paula Chainho for all their support, lunches and nice and fruitful conversations. Cláudia Oliveira was always a big support. I would like to thank her for being always so nice, helpful and available. Leonor Rodrigues gave me also a big help and support at the final stage. Cris Liotti was great making my days at FCUL more relax and healthy.

In ISA I met a fantastic group of scientists from CIBIO-INBIO that received me greatly and gave me a big encouragement in my last period of the PhD. A big thank to Luís Borda de Água, Pedro Beja, Francisco Moreira, Rui Figueira, Margarida Ferreira, Fernando Ascenção, Rafael Barrientos, Saeid Alirezazadeh and Fahimeh Alibabaei, Mário Ferreira, Lorenzo Quaglietta, Marcello D’Amico, Ricardo Martins, Andreia Penado, Sílvia Pena, Sasha Vasconcelos, Miguel Monteiro, António Ferreira, Francisco Amorim, Luis Reino, Joana Santana, Virginia Pimenta and Miguel Porto. A special thanks to Filipa Filipe and Hugo Rebelo for such a long and strong friendship.

I would like to thank José Carlos Brito, from CIBIO, for a very useful conversation that made me improve much a chapter of this thesis. To Maria João Gouveia I would like to thank the support I received to get into intrinsic motivation theories. To Ana Ceia Hasse I would like to thank the

Acknowledgements support she gave me in such a crucial period of my PhD and at a very hard time for her. I need to thank Tiago Marques for friendship, work advices, laughs and good mood. He is an amazing scientist, it was wonderful to learn with him and his comments were really helpful.

I would like to thank the members of the BioDiversity4All team. I am really proud of developing this project with them. I would like to thank Marcel Dix for being always there when we need him and Filipe Ribeiro and Luís Tiago Ferreira for our wonderful brainstormings and for the project development. We worked quite well together. I would like to thank very much Filipe for his huge support to my work and for an endless friendship. I like him a lot. It was great to feel that he always believed in this thesis. Marta Gromicho, Cláudia Baeta, Inês Teixeira do Rosário and Rita Baptista appeared a bit later but with lots of enthusiasm and commitments to BioDiversity4Al. Thanks for all the help, support and fellowship.

During all these seven years of BioDiversity4All we received a great support from Patrícia Garcia Pereira, Rui Figueira, Henk Feith, João Carlos Farinha, Francisco Barros and Henrique Pereira dos Santos to whom I would like to express my acknowledgements. I would also like to thank all volunteers who participate in BioDiversity4All project.

I would like to thank Cristina Luís for her support and for all the projects we share: European Research Night, Lisbon BioBlitz, ECSA and all future projects. She is really enthusiastic, hard- working and full of innovative ideas.

I would like to thank also the European Citizen Science Association community. It is a pleasure to go to the meetings, discuss ideas and to contribute to the development of this area in which we all believe. It was a pleasure to meet Linda Davies in that first meeting in Copenhagen, back in 2011. She is an enthusiastic and amazing woman that made us believe that we were going in the right direction. It is also a great pleasure to work with Martin Brocklehurst, David Slawson and Connor Smith, in the Policy Working Group. I have been learning a lot. I would also like to thank Andrea Sforzi, Malene Brunn, Claudia Göebel, Muki Haklay, Marisa Ponti, Lucy Robinson, Poppy Lakeman Fraser, Fermin Serrano, Luigi Cecaronni and Jaume Pierre. To Alletta Bonn, Susanne Hecker and Anett Richter I would like to thank for their support in Leipzig and for a

Acknowledgements wonderful workshop they organized. It was great to discuss with them about the scientific role of citizen science.

I would like to thank Pedro Ferreira, Ana José, Afonso, André, Leonor Brandão, Luís Filipe Ferreira, Miguel Pinto, Catarina Marques, Luís Costa, Susana Reis, Cláudia Mieiro, Filipe Martinho, Ana Lu, Marta Gromicho, Tiago Brito, Jaime, Ana Veríssimo, Filipe Ribeiro, Xavier, Tiago Marques, Ana Azevedo, Filipe, Sarah, Maria, Ana Filipa Filipe, Hugo Rebelo, Francisco, Rita and Rui Gonçalves Pereira, Inês, Rita, Mariana Oliveira, Pedro Borralho, Mia, Ana Luísa Rego, Carina Cunha, Patrícia Filipe, Patrícia Agostinho, Madalena Patacho, Sara Cândido, Fernando Jorge, Sara Fragoso, João Perdigão, Maria José Teixeira, António Velez, Anália Torres, Nélia Soares, Vanda Castanheira for being such wonderful and attending friends and family. Their patience and encouragement throughout this stage of my life has been invaluable, and I could not have done it without their help. I would like to thank Frederico Lyra for a great friendship and for the best PhD ending gift I could ever received.

My Mum and Dad were always my greatest support. Without them I would definitely not be able to do this thesis. Their huge support with my sons Guilherme and Francisco was priceless. They always supported my decisions and were there for me in all good and bad times. I love them a lot.

Almost finishing I would like to thank Tiago. There are not enough adjectives to describe how wonderful he has been in all these years. I am very lucky to have him in my life and I love him a lot. I need to thank for his support, endless help and patience. Thanks for all our shared projects and ideas that make my life always lively and full of emotion.

Guilherme and Francisco are for sure the stars of my life. Without them my life does not make sense anymore. I love them both with all my strength and I hope I can help, somehow, to make this world a bit shinier for them.

This PhD work was supported by a grant from the Fundação para a Ciência e Tecnologia (SFRH/BD/89543/2012) to whom I extend my acknowledgments.

Contents

1. Introduction ...... 1

1.1. Citizen Science in Context ...... 1

1.2. Benefits and Limitations of Citizen Science ...... 5

1.3. Performance Evaluation of Citizen Science ...... 7

1.4. Citizen Science and Biodiversity Observations ...... 9

1.5. Aims and Structure of the Thesis ...... 10

2. Social Context of Citizen Science Projects ...... 13

2.1. Introduction ...... 13

2.2. The history of citizen science in our societies...... 15

2.3. Who are the different stakeholders involved in citizen science projects? ...... 17

2.4. Points to take into consideration in project design ...... 18

2.5. Recommendations and future research directions ...... 37

2.6. Conclusion ...... 38

3. Involving different groups of citizens in biodiversity registering: the importance of a customized bottom-up approach to engage citizen groups ...... 41

3.1. Introduction ...... 42

3.2. Methods ...... 46

3.3. Results ...... 51

3.4. Discussion ...... 58

3.5. Conclusion ...... 61

4. The influence of motivational factors on the frequency of participation in citizen science activities ...... 63

4.1. Introduction ...... 64

4.2. Materials and methods ...... 67

4.3. Results ...... 71

4.4. Discussion ...... 77

4.5. Supporting information – survey ...... 80

Contents

5. Spatial distribution of citizen science casuistic observations for different taxonomic groups...... 85

5.1. Introduction ...... 86

5.2. Materials and methods ...... 88

5.3. Results ...... 89

5.4. Discussion ...... 99

6. Using citizen science data to estimate climatic niches and species distributions ...... 103

6.1. Introduction ...... 104

6.2. Materials and methods ...... 106

6.3. Results ...... 110

6.4. Discussion ...... 115

6.5. List of supplementary materials ...... 118

7. Synthesis of the main findings and future research avenues ...... 129

7.1. Synthesis of the main findings ...... 129

7.2. Future research avenues ...... 132

Chapter 1

1. Introduction

“Change will not come if we wait for some other person, or if we wait for some other time. We are the ones we’ve been waiting for. We are the change that we seek.” Barack Obama, 2008

1.1. Citizen Science in Context

Anthropogenic activities have become the major driver of changes and impacts on Earth causing e.g., deforestation, pollution, climate change and species loss (Gibson & Venkateswar, 2015). Recognising this, several scientists proposed a new geological age – Anthropocene – suggesting that the Earth has now left its natural geological epoch, the interglacial state, called the Holocene (Waters et al., 2016). Unequivocally the growth of human population, with a consequent increase in natural resources exploitation, is pushing up emergent challenges to humankind. In parallel, the digital revolution is changing completely our lifestyle and our society concerns, but also increasing our capabilities for citizenship and science progress.

Resources exploitation increased the need for a sustainable development. Sustainable development can be defined as a socio-economic development that meets the needs of present users without compromising the ability of future generations to meet their own needs, particularly regarding the use of natural resources and associated waste production (Maida, 2007). Sustainability demands interdisciplinary actions and a closer integration between science and education (Bruffee, 1999), driving citizens to recognise their roles in knowledge-production. However, to develop this sustainability literacy and citizen’s engagement it is necessary to take into consideration: different cultural and generational perspectives, the relation between local and global problems and a trustful relationship between scientists and citizens (Bonney et al., 2009).

Not surprisingly the involvement of non-professionals in contemporary scientific research and environmental monitoring, termed Citizen Science, has become a mainstream approach for

Chapter 1 scientific research (Miller-Rushing et al., 2012). Citizen science gained higher relevance in the last decade and three associations with a broad geographic base emerged to standardize concepts and objectives of the field, to unite and improve expertise, and to power citizen science encompassing educators, scientists, data managers, non-profit organizations and others. The first Citizen Science Association arose in the United States of America in 2010, and in 2013 the European Citizen Science Association stood up in Europe, based in Germany, followed in 2016 by the Australian Citizen Science Association (Figure 1.1).

Figure 1.1. – Logotypes of the three citizen science associations: Ciitizen Science Association, European Citizen Science Association, Australian Citizen Science Association.

Despite citizen science not being a new approach for collecting scientific information (Figure 1.2.), the term appeared recently. For many years, giving a definition for citizen science was not easy as no one had really settled on a name for the concept. Terms used to refer to the subject included participatory science, participatory action research, participatory monitoring, civic science, and even crowdsourced science (Haklay, 2015). Back in the 1990s the concept of citizen science appeared simultaneously in the United States of America and in the United Kingdom, meaning volunteer data collection to support ornithological research, in the United States of America, and integration of science and citizenship to advance policy goals, in the United Kingdom (Haklay, 2015). The term entered the Oxford English Dictionary, in June 2014, as “scientific work undertaken by members of the general public, often in collaboration with or under the direction of professional scientists and scientific institutions.” However, Wikipedia had already an entrance to the concept in 2005, defining citizen science as “a project (or ongoing program of work) which aims to make scientific discoveries, verify scientific hypotheses, or gather data which can be used for scientific purposes, and which involves large numbers of people, many of whom have no specific scientific training.” Citizens who were called, for years, as birdwatchers or birders, amateur astronomers, volunteer weather observers, or amateur archaeologists are now under the same umbrella as citizen scientists.

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Figure 1.2 - An advertisement in a Minnesota newspaper, in 1975, asking for volunteers to help tag butterflies. Student Norah Urquhart (pictured), participated. Credits: Willow Becker.

The emergence of this new approach in citizen science projects occurs due to a combination of factors, from technological to social. In terms of technology it can be highlighted the popularization of personal computers, mobile phones and spatially enabled portable devices, such as global positioning system (GPS), the growth of the Web and mobile communication (in particular Web 2.0 characterized by greater user interactivity and collaboration, more pervasive network connectivity and enhanced communication channels), and the development and spread of cheap sensors that can collect data from the environment (Figure 1.3.). Moreover, the rapid development of geospatial technologies allowed citizens to contribute with geospatial data (Goodchild 2007a, Elwood 2008) like georeferenced observations of the natural world (e.g., wildlife sightings) via interactive geovisualization interfaces (e.g., Google Maps, Google Earth, and Microsoft Virtual Earth), social media (e.g., Facebook, Twitter, Flickr, Instagram), or citizen science digital projects (e.g., eBird, iNaturalist, iSpot, Atlas of Living Australia, Observado) (Silvertown, 2009; Sullivan et al., 2009; Dickinson et al., 2012; Guan et al., 2012). In social terms, especially in developed countries, the last decades have seen a rapid growth in education

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(especially higher education), an increase in citizens’ leisure time and an increase in educated and able retirees (Haklay, 2015).

Figure 1.3. - Women from Komo (Republic of the Congo) learning to map in the forest, as part of the Extreme Citizen Science (ExCiteS) Intelligent Maps project. Credits: Gill Conquest, EXCITES, University College London.

In Portugal, citizen science has no significant tradition and related projects are still scarce. Even the Portuguese Society for the Study of Birds (SPEA), a nonprofit scientific association that involves many amateur naturalists in the study and conservation of birds in Portugal, was only founded in 1993. It is therefore a quite new organization compared to equivalent organizations from Northern Europe or North America. Most citizen science projects have appeared since 2010, mainly involving research data related to biodiversity and environment (e.g., BioDiversity4All, Invasoras.pt, Charcos com Vida, Portuguese monitoring network of Lucanus cervus, GelAvista), astronomy (e.g., Sun4All, Alunos Caçadores de Asteróides) (Figure 1.4.) and public health (e.g., mosquitoWEB, GripeNet).

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Figure 1.4. - Students from Aposenior (senior academy of Coimbra, Portugal) in a Sun4all workshop, an initiative of Socientize. Credits: Paulo Gama Mota.

1.2. Benefits and Limitations of Citizen Science

Citizen science projects offer many benefits for the different groups involved, from scientists, managers and participants to decision-makers. Technology creates opportunities for scientists to conveniently solicit useful information from citizens on many different features or phenomena of interest (Seeger, 2008; Anadón et al., 2009; Haklay, 2013). Projects that use citizens as “sensors” contain rich local information (Goodchild, 2007a, 2007b), others have the potential to provide information over large areas or to be timely updated (Goodchild, 2007b). Citizen science can also release some studies from funding constrain which, in many cases, limit the amount and scope of professional monitoring (Darwall & Dulvy, 1996; Danielsen et al., 2005).

Being able to incorporate data collected by citizen scientists with scientific studies can allow scientists to fill knowledge gaps on species distribution (Pereira & Cooper, 2006; Danielsen et al., 2009; Danielsen et al., 2010; Szabo et al., 2010; Dickinson et al., 2012; Pereira et al., 2013).

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Citizen science projects are also important to increase public awareness on conservation issues (Darwall & Dulvy, 1996) and to improve science and technology literacy among participants (Jenkins, 1999; Trumbull et al., 2000; Danielsen et al., 2005). Although citizen science is important at a local scale, where people can get involved into the problems of their community like water supply, local sources of pollution, deforestation or public health issues, it is also quite relevant at a global scale, where it can support research on climate change, biodiversity loss, ocean acidification, overfishing, or natural resource depletion.

Despite the recent progress and widespread nature of the phenomena, there are scientists that are sceptical on what citizen science can allow them to do. The limited training, knowledge and expertise of contributors and their relative anonymity can lead to poor quality, misleading or even malicious data being submitted in the frame of those projects (Foster-Smith & Evans, 2003; Alabri & Hunter, 2010). Some ecologists argue that this type of data cannot be used to reliably detect and adequately characterize ecological change (Penrose & Call, 1995; Brandon et al., 2003; Rodriguez, 2003; Bhattacharjee, 2005), grounding their opinion on research studies that show an increase in variability in data collected by citizen scientists compared to data collected by experts (Ericsson & Wallin, 1999; Barrett et al., 2002; Genet & Sargent, 2003). Another argument is that those studies use simplistic protocols that do not produce reliable data (Ericsson & Wallin, 1999; Engel & Voshell, 2002), for instance by under- or over- estimating species abundance (McLaren & Cadman, 1999; Bray & Schramm, 2001) or leading to an inadequate identification of species (Mumby et al., 1995; Brandon el al., 2003; Genet and Sargent, 2003).

While citizen science limitations are largely acknowledged by the scientific community, several scientists argue that the benefits of these projects compensate their limitations once they are taken into consideration, evaluated and, when possible, mitigated by proper solutions. Ideally, one can develop integrated hierarchical models which will account for the particularities of the specific data collection process, adequately accounting for the potential biases or increased variability hence allowing robust inferences to be made with sensible measure of precision associated with them.

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Many approaches have recently been proposed to assure citizen science data quality (Goodchild & Li, 2012; Elwood et al., 2013; Ali & Schmid, 2014) and citizen data is now driving many successful applications. Among them, OpenStreetMap (Haklay & Weber, 2008) is producing geographic data (e.g., road networks) and this user-generated geographic data can present high location accuracy, comparable to survey products of government mapping agencies (e.g., Haklay, 2010). The eBird citizen science project (Sullivan et al., 2009) is documenting bird species (e.g., presence, abundance) with observations contributed by worldwide birders. For emergency management, citizen data are providing timely information for wildfires or earthquakes (Goodchild & Glennon, 2010; Zook et al., 2010). In the world’s remote areas, wildlife sightings can be solicited from farmers, herdsmen, and hunters whose livelihoods provide information on ecosystem services. For conservation programs with limited budgets, local citizens could serve as a cost-effective data source on wildlife distribution (e.g., Anadón et al., 2009). Several variables can be analysed with citizen scientists’ help like forest composition (Franklin, 1995), soil class and soil properties (Zhu et al., 2001; Scull et al., 2003; Zhu, 2008), species richness (Pittman et al., 2007), and habitat suitability (Franklin & Miller, 2009). Some authors consider that the error and bias in citizen science data are similar to those found in other large-scale datasets so they argue that there are statistical approaches used in ecological contexts that can be used in this field presenting good results (Bird et al., 2013). Most importantly, what is key is to carefully think about the specific objectives of each study and evaluate whether the potential biases induced from the specific nature of the data collection process can be incorporated, rather than condemning altoghether of blindly use citizen science data.

1.3. Performance Evaluation of Citizen Science

The emergence of citizen science projects, with meaningful contributions to science, policy, management and society, lead to the need for creating measures to evaluate these contributions in terms of their number, their effectiveness and the quality of the outputs (Bonney et al., 2009; Jordan et al., 2012; Riesch & Potter, 2014; Chandler et al., 2016).

Some measures concerning scientific outputs may be the easier ones to identify, for example:

• Number of papers using citizen science data published in peer-reviewed journals;

• Number of citations of results;

• Number of researchers publishing citizen science research papers;

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• Number and budgets of grants received for citizen science research;

• Size and quality of citizen science databases;

• Number of graduate theses completed using citizen science data.

Measures concerning management and policy outputs from citizen science might be adapted from other areas like, for instance, sustainability research. Some examples could be:

• Number and budgets of citizen science projects and initiatives (e.g., BioBlitz) funded by companies, public institutions or municipalities;

• Number of political and management decisions supported by citizen science initiatives;

• Improved attitude towards science.

Measures of the outcomes to society are more difficult to identify (Trumbull et al., 2000; Brossard et al., 2005; Bonney et al., 2009, Jordan et al., 2012). Some possible examples could be:

• Frequency of media exposure of results from scientific literacy outcomes;

• Number of participants and visits to citizen science projects Web sites;

• Duration of involvement by project participants;

• Improved participant understanding of science content;

• Enhanced participant understanding of science processes;

• Better participant attitudes toward science;

• Improved participant skills for conducting science;

• Increased participant interest in science as a career.

There must be demonstrated evidence that citizen science projects are important to address global and local environmental challenges. The need of investing in projects’ evaluation is fundamental to give further support to the field although a systematic evaluation is still a gap that should be filled (Chandler et al., 2016).

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1.4. Citizen Science and Biodiversity Observations

Biodiversity data has been gathered in global biodiversity monitoring databases such as the Global Biodiversity Information Facility (GBIF), which shares freely accessible biodiversity data, including digitalized data in museum collections. Many natural history museums around the world have contributed to the GBIF, and there are currently more than 700 million entries of biodiversity (occurrence) data. Existing data has however an uneven distribution. Several countries and regions (e.g., the Asia-Pacific region), have much less data than do the European Union or North America (GBIF, 2012; Yahara et al., 2012). By understanding the potential of citizen science, GBIF started to receive data from citizen science projects and nowadays these projects became important data providers However, the problems of inaccurate information (e.g., misidentification) and copyright issues still persist and need to be taken in consideration (e.g., impossible-to-share media files; Miyazaki et al., 2014).

International treaties such as the Convention on Biological Diversity, the Convention on International Trade in Endangered Species of Wild Fauna and Flora, and the Convention on the Conservation of Migratory Species identify the necessity to evaluate change in the status and trends of global biodiversity. The Intergovernmental Panel on Biodiversity and Ecosystem Services also has as one of its four main functions to “perform regular and timely assessments of knowledge on biodiversity” (IPBES, 2013). The Group on Earth Observations Biodiversity Observation Network (GEO BON) proposed a set of Essential Biodiversity Variables (EBVs; Pereira et al., 2013) to track global biodiversity change. In all these actions citizen science is being taken into consideration to evaluate regional and global changes in the trends and status of biodiversity (Pereira & Cooper, 2006; Pereira et al., 2010; Schmeller et al., 2015; Chandler et al., 2016; Proença et al., 2016; Chandler et al., 2017; Pereira et al., 2017) (Figure 1.5).

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Figure 1.5. – Citizen scientists collecting biodiversity and environmental data. Credits OPAL, GLOBE via UCAR, © Yifei Zhang, © Earthwatch

Citizen science is nowadays an emergent topic, critical for biodiversity observations and essential for public engagement. This thesis arose in this context, aiming to approach some of these issues with the perspective of helping to fill existing knowledge gaps.

1.5. Aims and Structure of the Thesis

This thesis aims to analyse different viewpoints of citizen science from local to global, from project design to practical uses for science, from the motivational perspective of users up to a geographical perspective. An interdisciplinary approach is followed applying tools from different scientific domains.

In particular, this thesis aims at addressing the following research questions:

• What is the social context of a citizen science project and what should be taken into consideration when designing a citizen science project?

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• What are the current and the potential benefits and limitations, for different citizen groups, in participating in a project of citizen science for registering biodiversity, using BioDiversity4All as a case-study?

• What are the main intrinsic motivations to participate in citizen science projects?

• What are the variables that drive the locations where users are making observations?

• Can we use citizen science data to estimate climatic niches and species distributions?

To address these questions, I organised the thesis in five chapters (Chapter 2 to Chapter 6), each corresponding to a submitted or published contribution (paper or book chapter), contextualised by a general introduction (Chapter 1) and a synthesis chapter further addressing future perspectives for citizen science projects (Chapter 7).

In Chapter 2, I provide the social context of citizen science projects, exploring briefly the history of citizen science and identifying the main stakeholders involved in these projects. In this chapter I also analyse the main points to take into consideration, from the perspectives of these different stakeholders, when designing a citizen science project. The work presented in this chapter was published as: Tiago, P. 2016. Social Context of Citizen Science Projects. In: Analyzing the Role of Citizen Science in Modern Research, Luigi Ceccaroni & Jaume Piera (eds). IGI Global, 168-191.

In Chapter 3, I identify and analyse the current and potential benefits of biodiversity registering, and as well as its limitations, for different societal groups (from individual citizens to large companies), that ultimately contribute to an increased societal knowledge about natural values and an improved biodiversity-related education and higher environment and conservation awareness. This analysis was based on the experience of BioDiversity4All project, a Portuguese nationwide project with a bottom-up approach based on locally organized groups and their initiatives. The work presented in this chapter was submitted to Citizen Science Theory and Practice as: Tiago, P., Ribeiro, F., Gromicho, M., Ferreira, L.T., & Santos-Reis, M. Involving citizens and stakeholders in biodiversity registering: the importance of a customized bottom-up approach to engage citizen groups.

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In Chapter 4, I study the main motivations for the participation of citizens in a Portuguese citizen science project, and assess the pattern of motivations across different groups of users. The work presented in this chapter was submitted to Nature Conservation as: Tiago, P., Gouveia, M.J., Capinha, C., Santos-Reis, M. & Pereira, H.M. Frequency of participation predicts participation motivations in citizen science activities.

In Chapter 5, I compare the effect of geographic variables on the number of observations registered among different taxonomic groups in BioDiversity4All project. The work presented in this chapter was submitted to Plos One as Tiago, P., Ceia-Hasse, A., Marques, T.A., Capinha, C. & Pereira, H.M. Spatial distribution of citizen science casuistic observations for different taxonomic groups.

In Chapter 6, I assess whether opportunistic citizen science databases are viable data sources to use in the modelling of species distributions and test if species attributes can indicate the reliability and completeness of the opportunistic distribution data. The work presented in this chapter was published in Basic and Applied Ecology as: Tiago, P., Pereira, H.M. & Capinha, C. 2017. Using citizen science data to estimate climatic niches and species distributions. DOI: 10.1016/j.baae.2017.04.001.

Finally, the last chapter synthesises the main findings of the previous chapters and addresses future perspectives for citizen science projects.

In Appendix A and B I include two publications that are complementary to the work presented in this thesis. In Appendix A: Proença, V., Martin, L.J., Pereira, H.M., Fernandez, M., McRae, L., Belnap, J., Böhm, M., Brummitt, N., García-Moreno, J., Gregory, R.D., Honrado, J.P., Jürgens, N., Opige, M., Schmeller, D.S., Tiago, P., & van Swaay, C.A.M. 2016. Global biodiversity monitoring: From data sources to essential biodiversity variables. Biological Conservation. DOI: 10.1016/j.biocon.2016.07.014. In Appendix B: Chandler, M., See, L., Buesching, C.D., Cousins, J.A., Gillies, C., Kays, R.W., Newman, C., Pereira, H.M. & Tiago, P. 2017. Involving citizen scientists in biodiversity observation. In The GEO Handbook on Biodiversity Observation Networks (eds M. Walters & R.J. Scholes), 211–237. Springer International Publishing, Cham. DOI: 10.1007/978-3- 319-27288-7_9.

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2. Social Context of Citizen Science Projects

“The greatest danger to our future is apathy” Jane Goodall

Tiago, P. 2016. Social Context of Citizen Science Projects. In: Analyzing the Role of Citizen Science in Modern Research, Luigi Ceccaroni & Jaume Piera (eds). IGI Global, 168-191.

Abstract

This chapter provides a brief history of citizen science in our societies, identifies the main stakeholders involved in projects of this topic, and analyses the main points to take into consideration, from a social perspective, when designing a citizen-science project: communicating; recruiting and motivating participants; fostering innovation, interdisciplinarity and group dynamics; promoting cultural changes, healthy habits, inclusion, awareness and education; and guiding policy goals and decisions. Different governance structures, and a coexistence of different approaches, are analysed together with how they suit different communities and scientific studies.

Keywords: history, project design, stakeholders.

2.1. Introduction

Citizen science engages the general public with scientific research activities, and while not new, is becoming a mainstream approach to collect data on a variety of scientific disciplines (Miller- Rushing et al., 2012). The consolidation of citizen science can be perceived from the adoption of a formal name, increased research about the field and formalization of international associations. Citizen science maturity has advanced with technology innovations of recent years.

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Societies are facing rapid changes in values, interests and expectations. The growth of social networks and collaborative web projects has implications for the relations between scientists, decision makers and different societal groups. Citizen science is growing to be a mechanism that allows citizens to have an active role in science development and in dealing with important environmental and scientific questions.

Scientists who support the rise in citizen science recognize the benefit of volunteer contribution to science in terms of increased scale, data collection and analysis, outreach capacity, while dealing with budget constraints. Consequently, an increasing number of studies have started to work with volunteer citizens, helped by easily accessible technological tools. Awareness among scientists for these social changes has increased, generally in a gradual way, but faster in countries with a higher tradition of public participation, especially scientific participation (Hess, 2010).

Citizen science can also have a positive impact on society and support sustainable development, by fostering connections between environment, society and economy and overcoming barriers between disciplines (Giddings et al., 2002).

Given its collaborative nature, citizen science is characterized by a wide range of stakeholders, whose motivations and interactions can be determinant for the success of a citizen science project and thus should be carefully taken into account on project design.

This chapter provides a brief history of citizen science and identifies the main stakeholders involved in these projects. The chapter then analyses the main points to take into consideration, from the perspectives of these different stakeholders, when designing a citizen science project.

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2.2. The history of citizen science in our societies

For centuries, scientific research was conducted by amateurs (people that were not paid to do science) (Vetter, 2011). Professionalization of science, in the late 19th century, drew those amateurs away from the scientific world and created a big gap between “real scientists” (people that are paid to do science) and citizens interested in those subjects (Vetter, 2011).

John Ray, Alfred Russell Wallace, Gregor Mendel are prime examples of amateurs who produced incredible scientific advances. John Ray published important works on botany, zoology, and natural theology and his classification of plants in Historia Plantarum, was an important step towards modern taxonomy (Raven, 1942). Alfred Russel Wallace was a British naturalist, explorer, geographer, anthropologist, and biologist. His best known work was on the theory of evolution through natural selection and his paper on the subject was jointly published with some of Charles Darwin's writings in 1858 (Raby, 2001). Gregor Mendel was a friar who gained posthumous fame as the founder of the modern science of genetics. His pea plant experiments established many of the rules of heredity, now referred to as the laws of Mendelian inheritance (Weiling, 1991). These individuals were largely pursuing research because of an innate interest in particular topics or questions (Vetter, 2011) and were recognized experts in their field, conducting research indistinguishable from today’s professional scientists.

On a different level of participation, though not yet called citizen scientists, general people have also been involved in scientific activities on a volunteer basis for centuries, documenting observations of nature. Farmers, hunters and amateur naturalists were some of the activities involved in collecting natural world data (Miller-Rushing et al., 2012). In the 18th century, Carl Linnaeus, collected, with the help of many volunteers, animal, plant, rock and fossils specimens and artifacts from around the world. For 1200 years court diarists in Kyoto, have been recording dates of the traditional cherry blossom festival (Primack et al., 2009) and in China citizens and officials have been tracking outbreaks of locust for at least 350 years (Tian et al., 2011).

In some specific science issues, such as weather, astronomy and bird surveys, there is a long history of citizen science, particularly in Anglo-Saxon countries and center and northern European countries such as England, United States of America, Australia, Netherlands or Finland.

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The project National Weather Service - Cooperative Observer Program (NWS-COOP) has been collecting basic weather data across United States since 1890 with results supporting much of what we know about variability and directional changes in climate (Miller-Rushing et al., 2012). With a two-fold mission of providing observational meteorological data and helping to measure long-term climate changes, the project has more than 8,700 volunteers taking observations in farms, in urban and suburban areas, National Parks, seashores, and mountaintops (NOAA, 2014).

In the astronomy area, the British government funded, in 1874, the Transit of Venus project to measure the Earth’s distance to the Sun. This project engaged the admiralty to support data collection all over the globe and recruited the services of amateur astronomers (Ratcliff, 2008).

Ornithology has a long linking history with citizen science. Bird monitoring in Europe goes back to 1749, when amateurs, in Finland, collected data on timing of migration (Greenwood, 2007). Wells Cooke, a member of the American Ornithologists’ Union, developed one of the earliest known formal citizen science programs in the United States, in the late 18th century. This project, overtime, transformed into today’s North American Bird Phenology Program. Citizens involved collect, on cards, information about migratory bird patterns and population figures. Those cards are being scanned and recorded into a public database for historical analysis (Dickinson et al., 2010). Another example of one of the oldest citizen science programs in the United States, which is still active, is the Christmas Bird Count, sponsored by the National Audubon Society. Since 1900, the organization has sponsored a bird count that runs from December 14 through January 5 each year. An experienced birder leads a group of volunteers as they collect information about local populations of birds. More than 2,000 groups operate across the United States and Canada (Dickinson et al., 2010).

Nowadays the focus of citizen science is changing from the traditional “scientists using citizens as data collectors” to citizens as scientists (Lakshminarayanan, 2007). In this new era of citizen science projects, citizens can participate at the diverse stages of the scientific process from co- creating a project with a scientist, following up all the steps of the project, raising new questions, collecting or analysing data, producing reports and disseminating findings (Tweddle et al., 2012). Depending on the desired level of engagement in science, different models of action can be

Chapter 2 adopted, such as pooling of resources, collective intelligence, grassroots activities, data collection, analysis of tasks, serious games or participatory experiments (Socientize, 2014).

Citizen science has already a long history and has recently begun to evolve into a broad research methodology with new applications and different stakeholders’ approaches. Several historical case studies and personalities, involved with this subject, may help us analyse what can be the future direction of citizen science.

2.3. Who are the different stakeholders involved in citizen science projects?

Citizen science, although in its basic form, was viewed as a partnership between volunteers and scientists to answer real world questions (Cohn, 2008), was expanded to a multiplicity of stakeholders, ranging from research scientists, teachers, students, managers, environmental organizations, and politicians (Bonney et al., 2009), due to its potential for educational purposes, raising awareness and driving policy changes, among other reasons. These stakeholders have many different interests in citizen science, and face particular constraints in their involvement.

Despite the considerable amount of stakeholders involved, clustering them into four groups: citizen scientists, scientists, other societal groups and policy makers, allows us to analyse the project design from these four different perspectives. Citizen scientists and scientists are directly involved in the scientific process, while other societal groups and policy makers are more indirectly involved, e.g. using data, promoting education, guiding policy goals and decisions or giving answers to social concerns.

Assuring a good and stable relationship between the interests of these groups is important for the project’s success (Figure 2.1).

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Figure 2.1 – Groups of stakeholders involved in citizen science projects.

Thinking specifically in the citizen scientists, depending on the project aims and activities, target people are diverse and may include hobby and professional groups such as schools, students, scouts, naturalists, tourists, sports enthusiasts, farmers, fishermen, or a multiplicity of actors. Engaging these different stakeholders into a shared framework with some common and some specific means of communication are good ways to achieve results. Projects like eBird, iSpot and iNaturalis have in their objectives and strategies specific ways of involving and engaging different groups (Sullivan et al., 2009; Clow & Makriyannis, 2011; Bowser et al., 2014).

2.4. Points to take into consideration in project design

Project design is a crucial step in ensuring the effectiveness of the project and the capacity to achieve its goals (Raddick et al., 2009). When designing a project, this will inevitably involve trade-offs, e.g. gathering comprehensive, high quality data according to rigorous scientific protocols, and the ease of data collection (Hochachka et al., 2012). If the data collection is too complex or too time consuming, volunteers may lose their desire to participate and thus, understanding and adapting the program to the skills, expectations and interests of the volunteers is critical (Shirk et al., 2012).

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When designing a citizen science project, it is thus important to take into account a social perspective meaning the interactions generated between the different stakeholders, their collective co-existence, regardless of whether they are aware of it or not, and of whether the interaction is voluntary or involuntary.

Although citizen science is nowadays a broad methodology used in many different scientific areas, there are several cross-cutting issues, common to all of them. Highlighting the importance of taking a stakeholder view when designing a citizen science project, table 2.1 summarizes issues to take into consideration, which will be analysed in detail below. Some, such as motivation or awareness, are important for several stakeholders, but in very different ways and assuming varying degrees of importance.

Table 2.1 – Points to take into consideration for different stakeholder groups, in a citizen science project design.

Project design

Citizen Scientists Scientists Other Societal Policy Makers Groups

• Communicating • Enabling outputs • Giving answers • Guiding policy and recruiting for scientific to social goals and participants studies concerns decisions

• Motivating • Assuring data • Promoting • Giving answers participants quality healthy habits to social concerns • Promoting • Sharing open • Promoting education source results inclusion • Promoting awareness • Giving feedback • Fostering • Promoting innovation, awareness and • Overcoming • Enabling personal interdisciplinarity education reluctance recognition and and group reward • Taking into dynamics consideration • Taking into • Motivating cultural account work participants differences scale preference • Overcoming • Overcoming reluctance reluctance

• Sharing open source results

Project Evaluation and Governance

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Communicating and recruiting participants

When designing the project, and after deciding that citizen science is the best methodology to achieve the project’s goals (Tweddle et al., 2012), one must identify the groups that might want to be involved, understand the reasons and motivations they have to participate, recruit them and maintain their participation over time. In recent years, much has been written on communication and recruiting (Van Den Berg et al., 2009; Dickinson et al., 2012; Pandya, 2012; Roy et al., 2012; Tweddle et al., 2012; Silvertown et al., 2013).

The starting point for recruitment is to determine who the target audience is, which can be from a specific group like students, amateur astronomers, bird watchers, divers or from a broad group like inhabitants of a certain area (Bonney et al., 2009). Knowing who will be the projects’ participants is important to decide how to reach them, what will be said, how it will be said, when it will be said, where it will be said and who will say it (Kotler et al., 2005). Successful citizen science projects like eBird, Galaxy Zoo, OPAL, attached great importance to the communication approach with the targeted volunteers (Sullivan et al., 2009; Raddick et al., 2010; Tweddle et al., 2012).

Then it is necessary that people who might want to participate get to know that the project exists, to whom it is directed and what are its main objectives (Cohn, 2008).

These recruitment efforts may vary, depending on the previous existence – or not - of a community (Robson et al., 2013). Nowadays there are websites dedicated to host citizen science projects where people can obtain information and enlist as volunteers (Dickinson et al., 2012; Newman et al., 2012). Platforms like Zooniverse, one of the world’s largest and most popular platform for people-powered research, covers many disciplines and topics across the sciences and humanities (Reed et al., 2013). With specific high motivated groups, or in countries with a higher tradition on citizen science, e-mails and newsletters can be sufficient ways of promoting the project (Dickinson et al., 2012). Social networks like Facebook or Twitter provide, nowadays, good opportunities to reach a high range of participants (Pickard et al., 2011; Robson et al., 2013).

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In countries facing barriers to public participation, due to lack of tradition in these areas, the actions to promote the project and then to engage people to participate, need to be much more active.

For a more effective recruitment process the use of different types of media is important. Sometimes it is the scale of the project that determines the capacity of the project manager to reach several media channels: print media (newspapers, magazines, direct mail, and specialist publications), broadcast media (radio, television), display media (signs, posters, billboards spread in a country, city, school – depending on the scale of the project), and online and electronic media (websites, social networks) (Kotler et al., 2005).

Organizing a launch event or an event at an existing festival or fair that allows face-to-face contact, can be an important social measure to promote the project (Wiggins & Crowston, 2011), allow citizens to interact directly with the scientists involved and establish a relationship (Tweddle et al., 2012).

Also, allowing time for participants to socialize during activities is important for recruitment and retention for longer-time (Silvertown et al., 2013). Word-of-mouth recruitment between peers is one of the most powerful ways of growing a network of contacts. Identifying “influencers” can bring other persons along, increase visibility, credibility and create bandwagon effects (Kotler et al., 2005).

Motivating participants

Motivations of volunteers and scientists to participate or conduct a citizen science project, have already been the subject of several studies from different authors (Bruyere & Rappe, 2007; Van den Berg et al., 2009; Bramston et al., 2011; Jordan et al., 2011; Silvertown et al., 2013).

Understanding citizen scientists’ motivations to contribute may improve the results obtained. These motivations may be different from country to country and for different societal groups or

Chapter 2 age groups (Dierkes & von Grote,2000; Forte & Lampe, 2013). Cultural differences also influence the reasons to collaborate (Rotman et al., 2014). Some of the main participant motivations highlighted in many of the existing studies include:

• The desire expressed by participants to learn new skills and about the scientific issues behind the project (Bell et al., 2008; Van den Berg et al., 2009; Raddick et al., 2010);

• The desire to see the impact of their work (being able to see and share the efforts undertaken and its further use within a scientific or policy community) (Van den Berg et al., 2009);

• The sense of making a discovery, e.g. finding a new galaxy in Galaxy Zoo project (Raddick et al., 2010);

• The desire to feel as active participants and co-owners of the project (Dickinson et al., 2012; Rotman et al., 2014);

• Gaining recognition for their input, e.g. through feedback and interaction with scientists and peers, and through gaining achievements (Rotman et al., 2014);

• The desire to feel competent in doing a task (Rotman et al., 2014), e.g. progression to expert status or from simple to more complex tasks requiring additional responsibility (Nov et al., 2014);

• The sense of participating in a project that might be relevant to their community (Van den Berg et al., 2009);

• The feeling that they are helping the environment and taking an active conservation action (Van den Berg et al., 2009);

• The enjoyment of developing activities in nature (Bell, et al., 2008; Van den Berg et al., 2009);

• Getting to know other people with similar interests and making new friends (Van den Berg et al., 2009);

• Allowing to explore different career options (Van den Berg et al., 2009);

• The enjoyment of developing team activities that put scientists and citizen scientists working together with a sense of camaraderie, making scientific exploration and discoveries enjoyable (Nov et al., 2011; Newman et al., 2012);

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• Competing with other participants. Some projects appeal to the competitiveness of the participants by providing tools for determining the relative status of volunteers (e.g. numbers of species seen) and geographical regions (e.g. checklists per area) (Hochachka et al., 2012).

While most people committed with citizen science projects are likely to belong to an already environmentally aware subsector of the population (Coghlan, 2005), a surprisingly large number of people are motivated by curiosity, tourism motives or because they want to make a new start in life (e.g. after divorce, job redundancy, etc.) (Silvertown et al., 2013), although these motivations may hardly sustain, by themselves, long-term participation.

Studies on motivations, from the perspective of scientists, to participate in citizen science projects are scarce. Some identified motivations for scientists include: professional reasons like further their own professional career, promoting their scientific work in society, the outreach obtained with those projects, advance science, and become more aware of local knowledge and expertise (Carolan, 2006; Rotman et al., 2012).

Providing education

Some studies show that many skills needed to do research can be obtained by non-experts when they are properly trained (Janzen, 2004; Cohn, 2008). Citizen science projects can benefit greatly from the educational materials provided by scientists, despite some scientists still framing this training and supervising as time wasted away from professional research, rather than a beneficial investment of time (Silvertown et al., 2013).

Most citizen science projects provide volunteers with educational material like training workshops, field lessons (e.g. on species identification, field guides, volunteer manuals or web- based educational tools; Crall et al., 2010). In some cases, participants also need to learn how to use maps, technological devices and applications, such as GPS units (Crall et al., 2010).

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Protocols used for citizen science should be easy to perform, explainable in a clear and straightforward manner, and engaging for volunteer participants (Bonney et al., 2009). Pilot- testing protocols with naive audiences is important when directed at a wide swath of potential participants. For example, Cornell Lab of Ornithology project designers have tested draft protocols with local bird clubs, school groups, and youth leaders by accompanying participants in the field and observing them as they collect and submit data and tested protocols at distant locations by collecting online feedback. When protocols prove to be confusing or overly complicated, they can be simplified, clarified, or otherwise modified until the participants can follow them with ease (Bonney et al., 2009).

The higher the interaction between scientists and citizen scientists, the higher is the engagement of participants (Dickinson et al., 2012). People tend to participate more when they feel supported by the appropriate expertise while doing the activity. Participants should have all the information about the project and should feel helped by the project team.

Some projects develop learning elements that align with relevant school curriculum standards (Zoellick et al., 2012). Partnerships between schools and these citizen science projects are likely to become much more important in the future with substantial gains for the projects’ multiple goals.

Giving feedback

Giving participants a rapid feedback and providing regular communication about their contribution and the outcomes of the project, is a powerful way of motivating them and maintaining their participation, since people like to know to what they are collaborating and how is being used the information they are collecting (Devictor et al., 2010). Giving feedback is also an important way for people to increase perceived competence and usefulness of their participation.

This feedback can be included by design of the project (e.g. real-time publication and/or validation of the information collected in the project website), or can also be accomplished in many different ways, such as through field events, email, phone, newsletters, blogs, discussion

Chapter 2 forums and various forms of social media. Organizing a closing event can also be a good way to share results and thank the participants (Tweddle et al, 2012).

Enabling personal recognition and reward

Rewarding citizen scientists, in a number of ways, provides a sense of achievement (Tweddle et al, 2012) and is thus an effective way to encourage and support participation. Volunteers like the idea of knowing that their work is important and that their contributions can help scientists make better and more comprehensive analyses (Musick & Wilson, 2007).

A reward system can be implemented in several different ways such as highlighting the identity of contributors with observations to acknowledge their contributions explicitly (e.g. in Observado, iSpot and iNaturalist; (Clow & Makriyannis, 2011; Bowser et al., 2014), providing participants with certificates of recognition, thanking participants and acknowledging their role (e.g. through organization of a closing event, which can also be used to solicit further inputs and give feedback of project’s results) (Tweddle et al, 2012), providing open access to all records in the database, or at least the non-sensitive (Newman et al., 2012), holding a competition (Newman et al., 2012), recognizing the degree of volunteer expertise (e.g. progressing from amateur to expert levels in iSpot; Clow & Makriyannis, 2011), fellowships and sponsorship, symbolic rewards such as badges (Clery, 2010; Cooper et al., 2010).

The eBird website was modified to provide direct rewards to participants and with these modifications participation rapidly increased and eBird has gathered more information in 1 month (almost 3 million observations) than it did during the entire first 2 years of the project (2 million observations) (Hochachka et al., 2012).

Project managers should make an effort to provide easy access to scientific, institutional, managerial and/or legislative information packages produced from project data, in ways of interest to stakeholders. Apart from their immediate value to the target stakeholders, this helps participants understand the value of their contribution. For example, it may not be readily apparent that a few species observations might contribute collectively to e.g. reveal the arrival of invasive / pest species, and eventually promote a policy measure.

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Taking into account work scale preference

Different citizen science projects have different goals and, depending on those, work at different scales such as local, regional, national, continental, global or virtual. Not all citizen scientists like to work at the same scale, preferring to be engage with local questions, helping to give answers to a community problem, while others like broad scale issues like climate change questions or species invasions problems (Cooper et al., 2007; Dickinson et al., 2010; Wiggins et al., 2011; Hochachka et al., 2012).

Enabling outputs for scientific studies

For research scientists, citizen science projects can offer many benefits for their work but usually require a balance between data quality and quantity. The amount of data that can be collected and the geographic scale of these data can give a completely different dimension to a scientific study (Bonney et al., 2009). Funding constrains often limit the amount and scope of these studies and citizen science projects allow them to become a reality (Darwall & Dulvy, 1996; Danielsen, et al., 2005). The incorporation of these new sources of data with scientific projects enable them to fill existing gaps e.g. on species distributions (Danielsen et al., 2005).

Some citizen science projects do not produce scientific peer reviewed publications (Theobald et al., 2015) but more awareness publications, in many cases due to a lack of data quality assurance.

To optimize the data quality and quantity provided by participants, researchers must understand which factors affect most their performance (Bueshing & Newman, 2005; Bueshing & Slade, 2012) and then find ways to optimize and mitigate these factors, for instance allocating tasks to the best suited individuals (Mackney & Spring, 2000), which can represent an increase on the quality of data collected.

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Another issue to take into account is that some projects, that have specific goals, can have outputs that might be useful for different purposes, not always foreseeable. Flexibility and data open access can increase the projects scientific value.

Assuring data quality

Assuring data quality is important in attracting more scientists to use and engage with citizen science projects and become this methodology widely accepted (Dickinson et al., 2010, Bonter & Cooper, 2012). In order to achieve the scientific goals of the projects data collected by citizens should be validated, e.g. checked for errors and entered reliably into databases suitable for further analysis and sharing (Crall et al., 2010).

Statistical approaches should be robust and adapted in order to achieve better data quality (Bird et al., 2014). Protocols need to be developed and later adjusted to any limitation identified (Bonney et al., 2009). Networking among scientists and citizen scientists is an important tool to improve protocols. In this regard a lot can be learnt with computer programmers open source systems (Bonney et al., 2009).

In citizen science projects concerning biology and ecology, the main problems identified in data quality include under or over estimation in species abundance measures (Bray & Schramm, 2001), species misidentification (Brandon et al., 2003; Genet & Sargent, 2003; Fitzpatrick et al., 2009), protocols too simple that did not produce useful data (Engel & Voshell, 2002). Even though taxonomic identification is a skill that requires years of training the rates of misidentification depend on species rarity or conspicuity (Genet & Sargent, 2003). Many citizen science projects ask people to add information on presence of species (ignoring absences), introducing in those projects problems of bias relating to presence-only data. However, the growing number of participants in citizen science projects can help to reduce uncertainty. A place where large numbers of volunteers submitted presence data for some species, but no data on presence of other species, confidence can be increased that the lack of data on the absent species is due to the true absence of the species, rather than from a lack of sampling effort (Stafford et al, 2010).

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Sharing open source results

Nowadays, in citizen science projects the most accepted culture is openness and free data access which is shifting some science paradigms (Newman et al., 2012; Socientize, 2014). However it should still take in consideration the intellectual property rights, fundamental personal data protection rights, ethical standards, legal requirement and scientific data quality (Newman et al., 2012; Socientize, 2014). Information and communication technologies foster open, efficient and agile systems, turning ideas into the actions required to mobilize citizens individual and collective.

There are still some concerns over sharing data due to data sensitivity like species cultural or biological significance (Jarnevich et al., 2007), or private property. To avoid this, many citizen science websites use features that protect those species like year filters (data can be hidden for a certain period) or data will be added with low resolution (Jarnevich et al, 2007).

Fostering innovation, interdisciplinarity and group dynamics

When different people with different backgrounds are working together there is all an unpredictable group dynamics that achieves interesting results. Research on collective intelligence indicates that diversity matters and that new leaps of logic, innovation, and invention are more likely to arise when people of different backgrounds and abilities work together toward a common goal (Wooley et al., 2010).

Comparing to more traditional scientific projects, a citizen science project aims to be more interdisciplinary involving both scientists and citizens with different backgrounds. Apart from the scientific thematic the study is about, a good project design should include people with different skills dealing with technologies available, social and communication aspects (Wiggins et al., 2011; Sullivan et al., 2014).

Creating opportunities for interaction between participants and scientists may also foster innovation and reach useful results (Newman et al., 2012).

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Overcoming reluctance

There is still high reluctance in people from different societal groups (scientists, politicians, decision makers, teachers, students), concerning citizen science projects. Some of them put in question data quality, some do not understand the aims of citizen science projects, the reasons to participate and to use data from these projects (Bonney et al., 2009; Catlin-Groves, 2012).

Project team should be as available as possible to answer questions, to provide any clarification required and to change communication strategies when they are not being effective (Bonney et al., 2009) in order to increase the perceived credibility of the project.

Giving answer to social concerns

Several societal groups take an interest in citizen science due to its ability to give citizens the opportunity to address social concerns and priorities.

In the citizen science process, concerned citizens, government agencies, industry, academia, community groups, and local institutions collaborate to monitor, track and respond to issues of common community concern (Whitelaw et al., 2003). Areas like pollution, public health or species monitoring invasions are sensitive to society in general and thus frequently are subjects for citizen science projects (Cohn, 2008; Bonney et al., 2009; Crall et al., 2011).

Public support for conservation can be increased by building social capital (Schwartz, 2006) and this has been measured by increased levels of trust, harmony, and cooperation in communities with scientific engagement (Sultana & Abeyasekera, 2008). This can lead to a more educated community (Pollock & Whitelaw, 2005; Cooper et al., 2007) and the creation of a stewardship ethic (Whitelaw et al., 2003; Cooper et al., 2007).

At the same time, there is evidence that long-term economic and environmental success arrives when people’s ideas and knowledge are valued, and power is given to them to make decisions

Chapter 2 independently of external agencies (Pretty et al., 1995). Citizen science projects also seemed to promote more sustainable communities (Whitelaw et al., 2003).

Communities where citizen science is prevalent tend to be more engaged in local issues, participate more in community development, and have more influence on policy-makers (Whitelaw et al., 2003; Pollock and Whitelaw, 2005; Lynam et al., 2007).

Highlighting the social concerns addressed by citizen science may thus be a strong argument when communicating with different society groups, in particular policy makers.

Promoting healthy habits

Many citizen science projects promote nature observations in the field. Getting more people into nature is, by itself, an excellent value that may be drawn from citizen science projects (Cohn, 2008). Projects like OPAL have a strong connection with young children and schools promoting a proper childhood development and physical and emotional health once it stimulates interactions with nature which can be quite important (Louv, 2005). The increasing prevalence of childhood obesity has lead policy makers to rank it as a critical public health threat for the 21st century (Koplan et al., 2005). When attending to many citizen science activities, child, young people and adults avoid sedentariness and engage in physical activity. For elder people, participating in citizen science projects is also a way to maintain brain and/or physical activity.

Promoting inclusion

Citizen science projects aim to be inclusive (Pandaya, 2012). Studies have shown that diversity benefits all participants (Gurin, 1999) and the project itself. Different groups of citizens from different age groups, race, educational level, background, are called directly to take part in some projects, so, it is possible to find projects with specific actions for universities of the third age, minority groups Bushway et al., 2011) or prisoners (Ulrich & Nadkarni, 2009), with interesting results.

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Some communities are frequently excluded from citizen science projects and identifying the barriers to participation is important for finding solutions to widening the community e.g. new technologies may inadvertently create barriers that widen the digital divide between those adopting/having the technology and those avoiding/lacking it (Newman et al., 2012).

Promoting awareness and education

Concerns about climate change, species extinctions, land use change, are recurrently discussed in the media but, nevertheless, there is still a widespread scientific illiteracy in society (Miller, 2004). This illiteracy depends on the cultural background of the societies involved (Dierkes & von Grote,2000) and citizen science projects can help promoting citizens’ education.

Participating in citizen science projects increases people’s awareness in many different areas. Due to its participatory nature, these projects appear well suited to elevating public understanding and support for science, environment and earth stewardship (Shirk, et al., 2012).

The increment of science literacy is a huge benefit of citizen science, giving a personal empowerment to the people involved (Brossard et al., 2005). Participants that improved their science and technology literacy are better informed to make decisions and can contribute more effectively to society as citizens, workers or consumers (National Science Board, 2008).

Some countries identify the need to put students in an educational environment that instigates them to ask questions, plan and conduct an investigation, use appropriate tools and techniques, think critically and logically about the relationships between evidence and explanations, construct and analyse alternative explanations, and communicate scientific arguments (Natural Research Council, 1996). Citizen science projects can play a role to achieve this objective.

Ideally, citizen scientists will be endowed with knowledge and skills to collect and disseminate this awareness and expertise.

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Scientists face nowadays citizen science as a way to connect scientific research to public outreach and education (Lepczyk et al, 2009). It is also true that a public educated on these issues is more likely to fund and support scientific research that seeks to address them (National Science Board, 2008).

Taking into consideration cultural differences

Multiple social and cultural drivers can affect the amount of information available and the efforts required from citizen science projects. To encourage participation in citizen science, project managers should recognize differences across countries, regions, and societal groups.

For example, the number of species observation records per square kilometer is high in countries with high per capita gross domestic product, high proportion of English speakers and high security levels, although those are not necessary countries with higher biodiversity (Amano & Sutherland, 2013). In some countries the value of public participation remains largely unknown to the society so, in these countries, the effort required should be much higher.

Sensitivity to cultural factors will be important to the success of projects that cross boundaries and involve local/traditional ecological knowledge (Dierkes & von Grote,2000; Ballard et al., 2008). Even inside a specific country, significant differences in cultural attitudes towards citizen science may be found between different regions (Rotman, et al., 2014).

The project’s strategy should match the target culture, so the two need to be in alignment. Sometimes, focusing on a few critical shifts in behavior may provide best results with the least effort. Measuring and monitoring cultural evolution are also best practices to take into account throughout the project, in order to identify backsliding or correct course (Katzenbach et al., 2012).

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Guiding policy goals and decisions

Citizen science projects and their results can guide and influence policy goals and decisions. Community’s awareness and literacy attained with these projects may lobby politicians concerning environmental issues.

The data collected by these projects can be used in policy area for management, like natural resource management (Brown et al., 2001) or for environmental regulation (Penrose & Call, 1995). Citizen science projects that focus on biodiversity monitoring, in general, are beneficial to government agencies for several reasons: they offer a cost-effective alternative to government employee monitoring (Whitelaw et al. 2003; Conrad & Daoust 2008), fieldwork can be undertaken over larger areas and during non-office hours (Whitelaw et al. 2003) and they respond to governments’ desire to have more stakeholders included in the process (Lawrence & Deagan 2001; Whitelaw et al. 2003). The specific projects of early warning, from pollution to invasive species, enable rapid responses. The inability to avoid invasions and control the existing ones resulted in enormous environmental and economic losses worldwide (Pimentel, 2011) and the costs associated to a false positive identification are much less than the cost of false negatives (Westbrooks, 2004). The need, in this area, of large amounts of data across multiple spatial and temporal scales requires strong collaborations among multiple stakeholders (Lodge et al., 2006). These particular subjects have a good media coverage and projects on these areas attract more participants and have a strong influence in policy decisions.

Conservation of biodiversity has become a major political issue, just like climate change. States are obliged, by international agreements, to implement the Convention on Biological Diversity and several indicators are being developed to achieve the convention’s objectives. In France, for instance, the implementation of the indicator “Trends in the abundance and distribution of selected species” is completely dependent on data collected by volunteers, which allows governments to save a significant amount of money (Levrel et al., 2010).

Another example of citizen science projects influencing policy measures comes from the USA. Pond associations from Martha’s Vineyard, an island located south of Cape Cod, Massachusetts, had big concerns about water quality, mainly because of local shellfish industry. Numerous dedicated water monitoring initiatives, led by nonprofit organizations, and the partnerships

Chapter 2 forged with environmental managers in the area, have resulted in policy measures being taken (e.g., pressuring the Board of Health to inspect and replace failed septic systems, address boat related pollution, distributing pamphlets and educating boaters, etc.) and, consequently, in improvements to water quality (Conrad & Hilchey, 2011).

Evaluation

Evaluation of the success of citizen science initiatives should be taken into consideration to achieve better results in the future (Newman et al., 2012) and contribute to socioecological system resilience (Jordan et al., 2012).

It is quite important to establish evaluation metrics regarding monitoring protocols, to ensure data quality (Engel & Voshell, 2002), to assess the effectiveness of the projects in meeting educational goals (Cohn, 2008; Bonney et al., 2009), to value the scientific outputs that come from the project e.g. scientific publications, and to identify policy goals and decisions influenced by the results.

Evaluating the impacts of citizen science projects on learning can be achieve by selecting appropriate indicators or measures of success, to ensure that the desired outcomes are achieved. Such indicators need to be targeted, feasible, valid, and reliable (Jordan et al., 2012). Though, in some cases there is still lack of effective evaluation mechanisms which can be filled, in educational area, by mechanisms from informal science education (Friedman, 2008).

Governance

The key principles of societal good governance have been categorized by: long-term vision, quality, openness, accountability, effectiveness, and coherence (Socientize, 2014). These principles can be achieved through a strategic commitment of society on citizen science. An urgent need for bottom-up initiatives that address community demands, is important if scientists want a more responsible, proactive and demanding society that uses its rights knowingly. New societies request from the governance area to establish new policies that prioritize science-society-policy interactions, fostering knowledge-based, intelligent and

Chapter 2 responsible selection choices. So, promoting a democratic governance of science, via public engagement and debate between policy makers, researchers, innovators and the general public in a structured channel for feedback and open criticism is fundamental. All different societal actors should play an important role, adding value to the scientific and social areas of society. Different background knowledge applied to different areas might allow creativity and joint solutions for solving problems.

The way citizens establish their participation commitments was traditionally categorized into top–down and bottom–up governance structures (Conrad & Hilchey, 2011). Lawrence (2006), suggested organizing participation into four forms: consultative (participants contribute with information); functional (participants contribute with information and are also engaged in implementing decisions); collaborative (participants work with governments to decide what is needed and contribute with knowledge) and transformative (participants make and implement decisions with support from experts where needed).

Consultative/functional governance

This form of participation is frequently referred as top-down. This case implies that citizen science promoters are asking for help in collecting information or making decisions. The purpose might be to provide early detection of issues of environmental concern, which can then be further investigated by scientific experts. (Whitelaw et al., 2003; Conrad & Daoust, 2008).

An example of the consultative/functional model is the Cornell Lab of Ornithology bird monitoring projects where teams of scientists determine the questions to be answered and decide what segment of the public will be targeted as participants (Ely, 2008a). Most large-scale ecosystem monitoring programs (e.g., bird monitoring programs) tend to be consultative.

Collaborative governance

In this kind of governance participants might be involved in co-management or adaptive management, if management is part of the goal of the organization (Cooper et al., 2007). In

Chapter 2 these cases, projects are often governed by a board or group representing as many facets of the community as possible.

An example of a collaborative governance project is the Global Systems Science, which combines advanced information and communications technology with citizen dialogues to understand and shape global systems. This produces evidence, concepts and doubts needed for effective and responsible policies dealing with global systems, engaging citizens into policy processes and process to acquire data. The vision guiding Global Systems Science is to make full use of the progress in information communication technologies to improve the way scientific knowledge can simulate, guide, be used by, and help evaluate policy and societal responses to global challenges like climate change, financial crisis, pandemics, and global growth of cities (http://global-systems-science.eu/).

Transformative governance

In this case of governance participants make and implement decisions with support from experts where needed. Participants are governed from the “bottom-up”, a model often arising out of crisis situations (this may also be called community based, grassroot, or advocacy groups). The group focuses on an issue hoping to initiate government action (Conrad & Daoust, 2008). These types of groups often focus on specific local issues and sometimes have no private sector or government support (Whitelaw et al., 2003). Initiation, organization, leadership, and funding of these groups are provided by the local community (Mullen & Allison, 1999). Emerging alternative funding mechanisms, such as crowdfunding, allow projects to be funded in more direct and democratic way by the public.

The transformative or community-based model has the advantage of involving participants in every stage of the project from defining the problem through communicating the results and taking action. In this case, the role of the scientist is to advise and guide community groups rather than to set their agendas (Ely, 2008b).

Some researchers believe that by transferring authority over decision-making to those most affected by it (the public), better, more sustainable management decisions will be made— thus,

Chapter 2 making the bottom–up model a desirable type of governance. However, many failures of bottom–up approaches have also been mentioned. These include lack of success due to little organization credibility and capacity (Bradshaw, 2003).

The Global Community Monitor serves as an example of how transformative governance structures can best serve the concerns of a community, although it has evolved into a collaborative framework. It was created to provide community-based tools for citizens to monitor the health of their neighborhoods, with a focus on air quality. One of the organizations in India is the SIPCOT Area Community Environmental Monitors. Villagers have been trained in the science of pollution and have been engaged in environmental monitoring, which over time has led to published scientific reports. This work formed the basis for a Supreme Court order calling for the establishment of national standards for toxic gases in ambient air in State Industries Promotion Corporation of Tamil Nadu (SIPCOT) (Global Community Monitor, 2006).

It is important to take into account that certain governance structures suit different communities and monitoring situations, with collaborative and transformative participation being associated with local scales of participation and consultative and functional participation being more feasible across broader geographic scales (Conrad & Hilchey). Regardless of the fact that different approaches are being held in an exclusive way, some cases of coexistence between different approaches can result in interesting outputs (Lawrence, 2006). Some longer term projects have already changed their typology of governance to better adjust to different scenarios.

2.5. Recommendations and future research directions

Citizen science projects have plenty of social trade-offs that need to be taken in consideration and evaluated when designing. Giving straight information about the goals of the project to stakeholders is fundamental for them not to feel disappointed with the expectations raised. For instance people should know whether the project intends to be rigorous, with a straight scientific approach, or to provide participation from a wide range of volunteers. The outcomes of the project also depend on those trade-offs.

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Some of the trade-offs include: deciding the scope and scale of the project, deciding to keep small with local data control, or connect with larger initiatives to benefit data usage, focusing more on guaranteeing data quality and reliability or on the easiness of producing data, with benefits to environmental education and engagement.

Future research should focus on scientists motivations for participating; transdisciplinary relations between stakeholders, from which added benefits may still be further exploited; robustness of data quality and statistical analysis of data; measures of project success, taking into account scientific, policy and social outputs.

2.6. Conclusion

Citizen science engages the general public with scientific research activities, and while not new, since for centuries scientific research was conducted by amateurs, is becoming a mainstream approach to collect data on a variety of scientific disciplines, much supported by technology advances. Nowadays the focus of citizen science is changing from the traditional “scientists using citizens as data collectors” to citizens as scientists.

Given its collaborative nature, citizen science is characterized by a wide range of stakeholders, ranging from research scientists, teachers, students, managers, environmental organizations, and politicians, whose motivations and interactions can be determinant for the success of a citizen science project and thus should be carefully taken into account on project design.

Despite the considerable amount of stakeholders involved, clustering them into four groups: citizen scientists, scientists, other societal groups and policy makers, allows us to analyse the project design from these four different perspectives, taking a stakeholder view, and identify issues for each group which are common to projects from many different scientific areas.

It is important that the design of a citizen science project takes into consideration issues such as communicating, recruiting and motivating participants, fostering innovation, interdisciplinarity and group dynamics, promoting cultural changes, healthy habits, inclusion, awareness and

Chapter 2 education, and guiding policy goals, among several others. Analysing these factors may contribute to the increased success of citizen science initiatives. Some issues, such as motivation or awareness, are important for several stakeholders, but in very different ways and assuming varying degrees of importance.

Around the globe, every day, new citizen science programs are being launched offering (i) new opportunities for citizen scientists to get involved and increase their scientific literacy (ii) new working challenges and opportunities for scientists (iii) chances for rethinking societies and (iv) new ways to influence policy makers.

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3. Involving different groups of citizens in biodiversity registering: the importance of a customized bottom-up approach to engage citizen groups

“Never doubt that a small group of thoughtful, committed people can change the world. Indeed, it is the only thing that ever has.” Margaret Mead

Tiago, P., Ribeiro, F., Gromicho, M., Ferreira, L.T., & Santos-Reis, M. Involving citizens and stakeholders in biodiversity registering: the importance of a customized bottom-up approach to engage citizen groups. Submitted to Citizen Science Theory and Practice.

Abstract

Public participation in environmental issues varies considerably across European countries. While in the United Kingdom citizens’ participation in birdwatching and butterfly registering is high, in most Southern European countries citizen science projects have trouble fostering community’s support and the regular engagement of participants. The BioDiversity4All is a Portuguese initiative which aims to foster cooperation from citizens, to increase societal knowledge about biodiversity in Portugal and awareness towards conservation issues, through the creation of a user-friendly-user online database, accessible to everyone, where species occurrences at the national level can be registered. In this paper we will use the BioDiversity4All platform as a case-study and will analyse current and potential benefits, as well as limitations, for the participation of different societal groups. We find that each group, from individual citizens to large companies, can provide valuable contributions for collecting and registering biodiversity. We highlight that each group´s benefits are closely related with their goals and activity sector and that acknowledging these differences may improve communication and engagement strategies at group-level. In a country with limited public participation, a bottom- up approach with customized communication and engagement strategies is essential to recruit

Chapter 3 and retain citizen groups’ interest, ultimately contributing to improve biodiversity-related education and higher environmental and conservation awareness.

Keywords: biodiversity databases, public participation, volunteer monitoring.

3.1. Introduction

Citizen science, i.e. the engagement of non-scientists in research, had an impressive development in the last few years (Miller-Rushing et al., 2012). Some scientific questions can only be addressed with the involvement of a huge number of data collectors. The cost of such data collection based on professionals hampers the feasibility of some experiments due to research budget constraints. Consequently, involving volunteer citizens in monitoring and research programs (so called citizen science projects) is a growing activity in many countries (Sullivan et al., 2009; Clow & Makriyannis, 2011; Bowser et al., 2014;) and is expanding to new scientific areas. It includes generalized biodiversity monitoring schemes (Danielsen et al., 2005; Couvet et al., 2008; Schmeller et al., 2009; Gervasi et al., 2014) or specific research questions in areas like conservation biology (Losey et al., 2007; Barnes et al., 2014; Sequeira et al., 2014; Yarnell et al., 2014), population ecology (Lepczyk, 2005; Pagel et al., 2014; Yoshikawa & Isagi, 2014; Péron & Altwegg, 2015), biogeography (Corser et al., 2014), animal diseases (Dhondt et al., 1998), migration and phenology (Cohn, 2008; Bartel et al., 2011; Fox et al., 2014; Strebel et al., 2014) or biological invasions (Delaney et al., 2008; Preuss et al., 2014; Scyphers et al., 2014; Purse et al., 2015; Gago et al., 2016). In a more applied context, it is also playing an important role in areas like the effects of climate and land-use change (La Sorte et al., 2014; Princé & Zunckerberg, 2015), noise and light pollution, water and air quality (D’Hondt et al., 2013; Wilderman et al., 2004), or street mapping and traffic congestion (Goodchild, 2007; Haklay & Weber, 2008; Kyba et al., 2013).

Outputs and outcomes of citizen science projects are reflecting participants’ greater knowledge in scientific fields and a deeper scientific literacy (Lowman et al., 2008; Bonney et al., 2009; Silvertown, 2009). Therefore, scientists analysing citizen science projects are trying to understand if it may be used not only as a methodological tool for a given research experiment, but also as an education and outreach tool.

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Citizen science connected with biodiversity observation somehow represents the revival of the amateur scientists, such as naturalists, who, in the past, largely contributed for increased knowledge and scientific development but, in the last 150 years, were marginalized as science become increasingly professionalized. On the other hand, emerging technologies are changing the landscape of participants that get involved with scientific projects (Miller-Rushing et al., 2012). There is a higher variety of participants and many of them are not traditionally involved with science knowledge (Newman et al., 2012). With those new technologies, such as the web 2.0, citizen scientists may cross social, cultural, economic, and political boundaries, and integrate local/traditional knowledge on their research (Ballard et al., 2008).

An important outcome of citizen science programs (e.g. World Water Monitoring Day, Christmas Bird Count, Breeding Bird Survey, or Project FeederWatch) is the mobilization capacity of volunteers to collect specific types of data at diverse locations and time frames, providing snapshots and time series of changing environmental conditions (Lepczyk, 2005; Dickinson et al., 2010; Dickinson et al., 2012; Princé & Zunckerberg, 2015). These programs have been very successful in North America and countries from Central/Northern Europe (e.g. United Kingdom), where volunteering and nature watching are relatively common activities among the general public and where there are numerous scientists and organizations promoting citizen science projects. However, in Southern Europe biodiversity-related citizen science is still emerging and there is a generalized lack of knowledge about local biodiversity (Hanski, 2005), which is somehow a paradox given that it is largely considered one of the world’s biodiversity hotspots (Myers et al., 2000). In Portugal, cultural traditions were deeply connected with rural practices like harvesting, fishing or hunting, but these practitioners generally prefer exotic species instead of the unique native values that the country holds (Marta et al., 2000). Contrasting this with different realities, one may refer to the tradition of feeding wildlife in the United States or Canada which involves more citizens than fishing and hunting all together (Horn et al., 2014).

For scientific research to fully profit from the new opportunities provided by the availability of this new tool, it is essential to understand which citizen groups are more prone to be engaged and their main motivations for participation, although recognizing that these may reflect spatial and social differences.

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The BioDiversity4All Project

The BioDiversity4All is a nationwide citizen science project that aims to increase citizens’ Portuguese biodiversity knowledge. This project, initiated in 2010, is an online platform — i.e. a platform-based organization that serves as a core function, facilitating transactions between external producers and consumers (Parker et al., 2016) — with its main database connected to the international project based in the Netherlands Waarneming international — www.observado.org. BioDiversity4All is a customized version of the Dutch website adapted to the Portuguese reality to suit the motivations of national participants — i.e. citizens with different roles in the project from data producers to data consumers. Users — i.e. citizens that are registered in BioDiversity4All project and add to the database either species observations or polygon areas — and that belong to different groups of citizens — i.e. citizens that might have a reason to participate in BioDiversity4All project. Species observations are validated by taxonomic specialists (invited scientists or non-academic experts) that are responsible for online sightings validation through commenting boxes, in a constructive and educational way. Through this interaction, users progressively learn to identify and recognize local and national biodiversity. By bringing scientists, local interest groups, policy makers and the public closer together, the project aims to increase and promote the interest and knowledge on biodiversity. In the Portuguese website, participants can have a personalized area (like social network profile pages), where they can add information about their objectives and projects. These profile pages may encompass a wide variety of participants, ranging from local non-profit organizations to eco-tourism companies, dive centers, scout groups, schools, municipalities, research centers, among others. Any website visitor can consult online basic statistics about user sightings and validation, observations per species or group of species or monthly observations, as well as have access to a photo gallery.

Currently the BioDiversity4All project has over 2 600 users in Portugal, a country of around 10 million inhabitants, a network of 50 partners representing different citizen groups and other stakeholders and a validation panel already encompassing 49 taxonomic experts. Up to today the project has accumulated over 450 000 observations, of 7 046 species, and includes over 103 000 pictures associated to observations. The social network page on Facebook has over 6 000 followers, and the official BioDiversity4All posts averages 3 000 people per week.

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Currently, “Birds” is the taxa with more registered sightings (71% of the total), followed by “Plants” (14%). All other taxonomic groups resulted in less than 5% of the observations (Figure 3.1). However, when we consider the number of species, “Plants” appear in the first place (41%), followed by “Birds” (10%), “Moths” (9%) and “Other Arthropods” (5%) (Figure 3.2).

Other groups

Plants 14%

Birds 71%

Figure 3.1 – Relative percentage of the number of sightings done in the BioDiversity4All platform, per taxonomic group considering a total of 450 000 observations.

Other groups

Plants 41%

Moths Birds 9% 10%

Other Arthropods 5%

Figure 3.2 – Relative percentage of the number of species done in the BioDiversity4All platform, per taxonomic group considering a total of 7 046 registered species.

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To foster interaction with different groups of citizens a communication strategy was implemented from the start of the project and more than 60 public talks were given by the BioDiversity4All team to publicize the project. These public talks aimed to involve different citizen groups and were specifically adapted to the different audiences that could go from students from different levels of education to school teachers, municipal officials, companies’ employees or environmental NGO workers. Moreover, identification guides and documents have been produced to inform and help participants in species identification. A national biodiversity blitz campaign (“Pé n’A Terra”) is organized yearly by the BioDiversity4All team with partners’ support, to celebrate the Biodiversity International Day (22nd of May). Workshops were also organized, targeting specific citizen groups, to discuss issues related to biodiversity and possible conflicting interests.

This paper reflects the experience of the BioDiversity4All project, a Portuguese nationwide project. Here we present current and potential benefits of biodiversity registering, and as well as its limitations, as identified for different citizen groups. The aim of this study is to recognize the main groups of citizens that are, or manifest their intention to, participating in such a citizen science project, and to understand in what ways the project suits their objectives. Identifying these reasons can help citizen science promoters to develop a community of participants engaged as they see their interests considered.

3.2. Methods

Our first goal was to identify citizen groups that could be considered to have common interests, benefits and limitations for their participation in citizen-science biodiversity registering, as well as allow for developing similar communication strategies, hence citizen-groups that receive biodiversity-related information from similar communication channels.

These citizens have in common the fact that they frequently interact with nature and biodiversity and, thus, may share biodiversity observations through the platform or use that information.

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We sought to identify citizen-groups that would have similar characteristics regarding their nature-related activities (Table 3.1):

1) biodiversity focus, identifying if biodiversity-watching plays a central role on the group’s nature-related activity or only a secondary subject, which can give us a measure of the general knowledge on biodiversity and frequency of interaction with nature that can be expected from these citizens;

2) professional group, regarding if there is a professional activity directly linked with biodiversity (e.g. scientists) or a leisure activity, which can help differentiate the type of interest and goals these citizens may have when interacting with nature;

3) degree of organization, describing the type of organizations of this group; which can mostly help to identify different ways of communicating with these citizens;

4) usage and value of biodiversity to their products / commodities, which can help in identifying common interests and benefits these citizens may get from their participation in citizen-science biodiversity registering.

This was a choice of grouping according to characteristics useful to our research and project objectives, and that was supported by our project experience and some previous studies (Goodchild, 2007; Bonney et al. 2009; Newman et al., 2012, Tiago, 2016).

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Table 3.1 – Societal and Citizen groups considered in the analysis, that use the BioDiversity4All – a citizen-science biodiversity platform - following the criteria of activity with a biodiversity focus, professional group directly linked with biodiversity, degree of organization, and biodiversity as added value for groups’ activity.

Value to their Biodiversity Professional Group Degree of organization products/ Focus Group commodities Universities, Scientific Community Central Yes Research Scientific centers Local groups, National Learning, Schools and Scouts Secondary Mixed organizations Leisure Naturalists,

Birdwatchers, Divers, Dispersed, Local groups, Sports Fishermen, Central No Leisure National organizations Hunters, Hikers, Geocachers Dispersed, Local Certification, Farmers, Professional Secondary Yes Fishermen associations Sustainability

Awareness, Local and National Environmental NGO’s Central Mixed Conservation, organizations Policy Municipalities and National Land use, Social Secondary Yes State organizations Environmental purposes, Policy Agencies Nature, Rural and Adventure Tourism Secondary Yes Local companies Client attraction companies Environmental Corporations Secondary Yes National companies goals, Certification

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This characterization enabled us to identify eight groups of citizens that exhibit some common features and characteristics within each group, namely:

1. “Scientific Community” - composed by citizens that are associated to universities or research centers, as professors, researchers, fellows or graduate students (e.g. MSc), and whose research activity is related to biodiversity;

2. “Schools and Scouts” - composed by teachers, students and young people in school environments or engaged in post-school activities dedicated to learning and/or leisure nature- related activities (e.g. scouts);

3. “Naturalists, Birdwatchers, Divers, Sports Fishermen, Hunters, Hikers and Geocachers” - composed by citizens that develop leisure activities in nature and contact with biodiversity values;

4. “Farmers and Professional Fishermen” - encompass citizens involved in agricultural practices or professional fishery, developing working activities directly connected with biodiversity, specifically in product certification or resource sustainability;

5. “Environmental NGOs” - comprises organized groups of citizens involved in environmental conservation organizations, to whom biodiversity is their main value to increase awareness, promote conservation and improve policy;

6. “Municipalities and National Environmental Agencies” - group directly related to governmental institutions (local, regional and national) with the main objective of territory management;

7. “Nature, Rural and Adventure Tourism Companies” - group of tourism professionals whose activity is related with nature and biodiversity values even if this may not be the central goal of their activity;

8. “Corporations” - large companies and their workers, at national scale whose activities involve or impact biodiversity in some way, and seek product certification.

These citizen groups have in common the fact that they frequently interact with nature and biodiversity and, thus, may share biodiversity observations through the platform or use that information. Based on our 5-year experience we described benefits and limitations relatively to each societal group.

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During this time, and considering the specificities of each group, we used different methodological approaches. At the beginning of the project, we had several meetings with NGOs, scientific community, corporations, naturalists, nature tourism companies, municipalities, national environmental agencies and corporations. We conducted semi- structured interviews — an open interview that allows new ideas to be brought up during the interview as a result of what the interviewee says (Cohen & Crabtree, 2006). People were questioned about their views of the potential benefits of the project, also promoting discussion about its limitations. There was an interview guide with several questions prepared such as: What do you think about the project? Would you use it? What do you think would be the main benefits of the project? What are the limitations and constrains you see in such a project? Other questions arose during the discussion. The main ideas were annotated during the interview and some comments were added at a later stage.

Afterwards we conducted two surveys. The first at a birdwatching fair targeting the group of naturalists and birdwatchers, nature, rural and adventure tourism companies. We collected 30 complete questionnaires from people with ages between 13 and 59 years old. The second survey was conducted to target school students and teachers. We obtained 114 complete questionnaires from students and teachers with ages between 13 and 42 years old. In these surveys people were asked about their relation with biodiversity, with citizen science projects in general and with the BioDiversity4All project, in particular.

We also organized a workshop, in partnership with a national corporation, to promote discussion between different biodiversity-related stakeholders. Societal groups represented in this workshop included: NGOs, municipalities and state agencies, scientific community and corporations in a total of 35 participants. After a block of four presentations of projects that foster the participation of different societal groups with biodiversity conservation, a debate moderated by a meaningful and independent person with relevant curricula in biodiversity management allowed a discussion between the participants. We wrote the minutes of the debate.

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Along the project lifetime, we further held an extensive range of activities such as: national and local bioblitzes, games and quizzes at the European Research Night and at corporations, field activities involving citizens and the scientific community, publications on scouts’ national magazine and presentations at different forums. These allowed us to gain a better understanding on the benefits and limitations of each of the defined groups in their involvement with the BioDiversity4All project.

3.3. Results

Scientific Community

The main benefit of BioDiversity4All, as stated by the scientific community is attaining large datasets on biodiversity, collected in a broad geographic area, which can support their research projects. This is supported by practical evidence, since information from BioDiversity4All has already been used in projects on species phenology, occupancy patterns and even in the discovery of a new species for science, resulting in scientific papers and master thesis (Castro, 2011; Lourenço 2011; Castro et al., 2012; Barat, 2013).

Another important benefit identified is the direct engagement of citizens, enabling scientists to either do outreach about their research, increasing data literacy and interest in conservation issues, or be recognized and advertised as an expert of a specific taxonomic group can also be a motive for a member of the scientific community to collaborate with the project. An example is the project “RIPAR - register, identify and share species in the network of Biodiversity Stations”, that was launched by the consortium of partners of the “European Researchers Night” (www.noitedosinvestigadores.org), aiming at, through a contest, putting people registering biodiversity.

Yet, the main obstacles stated by researchers during the interviews were the lack of time to dedicate to sightings validation, issues concerning data reliability and communication difficulties with users to whom these projects have different goals.

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Schools and Scouts

Teachers have used the project as a tool to teach conservation awareness and knowledge on biodiversity in an interactive dynamic way. Teachers suggest BioDiversity4All can be used to teach about the scientific process, to develop inquiry skills and to engage students with science and nature. It can also be a way to raise environmental stewardship behaviors.

Children and young people are quite familiar with the web environments, and social networks. While they share their BioDiversity4All achievements with friends, engagement and motivation is created towards nature related activities. BioDiversity4All has received numerous contacts from teachers and parents evidencing the existence of field activities with students and children related to the project. Teachers consider projects promoting healthy habits particularly important nowadays when sedentary habits are increasing among young people.

Collective engagement is another outcome of BioDiversity4All since schools establish areas (school areas) for long term monitoring of species occurrence, and create baseline information about the biodiversity to the school area. In the conducted survey 54% of the students say they believe they can contribute to increase the knowledge on Portuguese biodiversity.

A partnership between BioDiversity4All and the Eco-Schools Programme promoted activities where students were asked to create a short identification guide for a group of species (e.g. Portugal Endemic Trees or Mushrooms) and a contest to register biodiversity in the school area. In this competition 87 schools registered but just 22 finished their participation with all requirements fulfilled. The main limitations for this group are related with the lack of time for extracurricular activities, low familiarity with new technologies by some teachers and lack of knowledge about the educational potential of the site. Despite, the collaboration between Eco- Schools program and Biodiversity4All project, there is still a limited knowledge about this initiative among teachers.

Scouts are invaluable partners in projects of citizen science connected with the environment. BioDiversity4All, in a partnership with this group, contributed with one informative biodiversity news for the national Scouts monthly magazine. Expectation is that increasing their knowledge

Chapter 3 on the species occurring in Portugal will result in an increase in the number of observations registered in BioDiversity4All. Some scout groups use regularly the same camping sites and their involvement in the project can result in data recurrently collected for those areas almost as a monitoring procedure.

Scouts generally aim to be in direct contact with nature, detaching from most of the digital world, therefore it is difficult to implement this initiative inside the Scouts activities context.

Naturalists, Birdwatchers, Divers, Sports Fishermen, Hunters, Hikers and Geocachers

Through participation in BioDiversity4All, members of this group get social recognition for their biodiversity knowledge sometimes resulting in their participation in scientific studies, which has happened a few times in BioDiversity4All. They can also interact with people with shared interests and find new interesting places to develop their activities. The main benefits frequently stated by this large group, in the interviews and survey, are the gained knowledge about existing biodiversity values, obtained through the exploration of the website, the sightings registering and interactions with the experts (scientific community group).

The main limitations identified by this group were lack of time to collaborate and the existence of many different projects and blogs, some of them specific for a certain group, and their inability to collaborate with all. This is a very heterogeneous group and some of the members give the argument of lack of knowledge on taxonomy to not participate.

Farmers and Professional Fishermen

People involved in agricultural practices or professional fishery develop working activities directly connected with biodiversity and their potential for data collection represent unique opportunities aligned with BioDiversity4All citizen science goals.

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This professional context represents a bigger challenge to BioDiversity4All but their participation can be encouraged if they can recognize the usefulness of the information retrieved from the platform. For instance, becoming aware of biotic threats affecting their activity (e.g., plant diseases, presence of exotic species) might be a good reason to collaborate.

The main limitation to this group is related with lack of knowledge to use and apply web-based technologies and being unaware about the existence of the project itself. Despite being recognized as an important and specific group, no special actions have been created yet to attract this group to BioDiversity4All.

Environmental NGOs

NGOs accumulate specific knowledge and data with high potential for BioDiversity4All users and, therefore, represent ideal partners for the development of common initiatives in citizen science. The Biodiversity4All platform can also be a good outreach tool for NGOs helping to broaden the information and to reach to a specific public (general users) that may be potentially interested in getting involved with the NGOs mission. By helping to increase their science literacy, BioDiversity4All further creates common ground with this group, since this is part of their mission.

BioDiversity4All has an interface that allows mapping specific geographic areas (defined by any user), where information statistics about local biodiversity observations can be displayed, namely species richness and composition, number of observations per species, seasonality of observations and even data on users that registered observations in that region. Knowing users’ observations in an area, which a NGO is responsible to manage, can be a way to engage potential volunteers to develop activities and work on questions affecting local communities, such as changes in species phenology or migrations or track spread of alien species.

Additionally, the NGOs that work in a specific taxonomic group, such as the Portuguese Botanical Society, Carnivora (carnivore mammals), the Portuguese Bird Society or the Sea School (sea mammals), have, as associated members, experts with their own large databases that may be a valuable source of data, information and knowledge to society. The BioDiversity4All platform

Chapter 3 could be a mechanism to publicize their thematic NGO, broadening the contact and volunteer network.

Concerning NGOs, the goal of engaging citizens with science and data literacy, is an important reason to collaborate, recognized by members in the interviews Since BioDiversity4All has users with a wide range of interests, it can also create opportunities for user’s interest to cross borders from a species group to another, which has been evidenced by users who were initially specialized in a species group and started learning about other groups of species from seeing photos posted by other users in BioDiversity4All or from participating in promoted activities.

An activity of BioDiversity4All, developed in collaboration with the NGO Portuguese Botanical Society, was “12 months, 12 plants”; in which, each month, citizens were invited to register observations of a well-known plant species (supported by an identification sheet) and thus, contribute to a better knowledge of those plant species distribution and abundance patterns. This activity was recognized, by the NGO that promoted it, as being a very interesting way to get more information about common species that sometimes were not being scientifically studied and to increase citizens’ awareness on plants. The development of identification guides in collaboration with thematic NGOs or research groups gives them the opportunity to disseminate their work, and to help people with the identification of species while assembling observations to their own databases. A good example of such collaboration is the data contribution of BioDiversity4All to the Wintering and Migratory Birds Atlas, produced by the Portuguese Bird Society (SPEA).

Environmental NGOs identify as a limitation to participate the insufficient financial funds to all the projects they are developing and thus not having the economic capacity to have someone dedicated to add information on databases. Additionally, there is some resistance to share data, to be put publicly available due to 1) environmental sensitiveness of the data, 2) copyright- ownership of previous data, 3) lack of a long-term reliability on this initiative.

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Municipalities and national environmental agencies

This group, connected with land management at the national and regional levels, sometimes manages rich biodiversity areas and conservation projects, further promoting nature interaction programs to citizens. Nature and biodiversity are also increasingly becoming an important part of national and local policy actions. Contact of BioDiversity4All with this group has occurred mainly through participation in nature fairs and collaboration in joint projects.

By collaborating with BioDiversity4All, Municipalities and Agencies get information on the biodiversity existing in their territory, increase the potential of social-economic activities of the municipality, considering the sustainable use of natural resources and contributing to the understanding of the impact of these social-economic activities. Getting information about people who register observations in their territories is also valuable information to understand the community engagement in the valorization of natural resources.

As part of their outreach strategies, many municipalities have environmental and educational centers that can be advertised through the BioDiversity4All website to an interested and broad audience, namely its activities and initiatives. As mentioned, for local NGOs, Biodiversity4All allows the possibility to create specific geographic areas for these centers, displaying useful and informative statistics.

BioDiversity4All is also seen as an important way to inform about the presence of invasive and rare species. Despite the tentative contacts with national authorities, this is still an unexplored potential in Portugal. The main difficulty might be related with the bureaucratic procedures that prevent to work in a timely and practical manner. As well sharing data with the platform is relatively limited given the issue with data ownership and species sensitivity.

Nature, Rural and Adventure Tourism Companies

These companies can provide a useful channel to reach a wide audience of potential BioDiversity4All users, but also includes people with valuable specific local knowledge and a source of partnerships for citizen science projects.

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For nature, rural and adventure tourism companies BioDiversity4All can be a way to advertise their activities to potential customers (customer targeting), but also to inform them about species present in area(s) where their activities are being implemented (product improvement). Several users of BioDiversity4All, who registered using the institution name, are regular organizers of activities in the annual national biodiversity blitz campaign “Pé n’A Terra”, or promoted partnerships in contests and workshops (publicity).

In the same way, as for local NGOs or municipalities, BioDiversity4All tool for mapping regions, allows the definition of geographic areas associated with that company, providing specific information and useful statistics. The main limitation for this group is related with the lack of knowledge about the project, and, the tools associated that help to promote their business, specifically publicizing their company throughout the regular registering of observations.

Corporations

This group can be a valuable source of data and specific knowledge and can also provide funding for citizen science activities.

For corporations, the collaboration with BioDiversity4All is a way to contribute to its social and environmental mission, which frequently includes policy and initiatives to help in biodiversity conservation, namely for environmental certification purposes. They can also benefit from BioDiversity4All information to minimize / mitigate / compensate the impact of their activity.

Participating in citizen science projects can also be a way to create leisure activities for employees, increasing team-building, motivation, knowledge and wellbeing. BioDiversity4All has engaged in several outdoor activities with corporation employees, where interaction with scientists was much appreciated. A few workshops and other initiatives have also been developed in partnership with corporations with specific interests in biodiversity issues like a workshop with several stakeholders to discuss biodiversity problems developed with a forest

Chapter 3 company (Altri Florestal) or a yearly biodiversity workshop with the Portuguese postal company (CTT).

The main limitation recognized by this group is the specificity of the project. Some companies have several requests and prefer to be related with projects that address a wider range of their environmental concerns (e.g. residues. energy, water).

3.4. Discussion

In this study, we identified different citizen groups that could participate in a citizen science initiative for biodiversity in Portugal (BioDiversity4All) and outlined the main current and potential benefits for these groups and for the initiative. Despite the several limitations and constrains identified for each group, in terms of outreach efficiency, involvement and participation, this citizen science initiative represents the largest online and open database of biodiversity in Portugal. We recognized that each societal group can have specific benefits, which are closely related with their goals and activities, and these benefits are obtained through public participation and biodiversity data availability. Nevertheless, each group can provide valuable contributions for collecting and registering biodiversity and the acknowledgement of these differences may guarantee successful communication and engagement strategies for each group.

We considered the customized bottom-up approach to be essential to keep users and groups engaged and involved in the global citizen science initiative. A good way to allow a balance between global and local approaches is the use of open–data standards and open-source software, which should be encouraged, to maintain the open-access spirit of this kind of projects (Newman et al., 2012), with a good worldwide cooperation so that data can be shared with international scientists.

Recruitment and engagement plans should be translated in an effective participant attraction and retention strategies and must be aligned with their established activities and ways of interacting with nature and biodiversity. Valuable tools include direct motivational mechanisms, such as incentives, certificates of recognition and challenges, which can stimulate people’s

Chapter 3 interest in the project (Dickinson et al., 2012). Nevertheless, these mechanisms should recognize the different potential motivations of participants and specific efforts, which are important arguments to maintain projects decentralized and local like Carta Piscícola (Ribeiro et al., 2007), despite the need to embrace centralized and international-coordinated programs.

The continued interest and retention of participants is particularly important for the citizen science initiative long term sustainability. Participants need other values and emotional responses to support and maintain their involvement in citizen science participation after initial participation (Cialdini, 2008). These values may include a sense of purpose, learning, and interaction with a similar community or getting emotional feedback from activities. Tools to foster this feedback may include real-time publication of data and validation of the information through the interaction with experts, organization of events like field events, sending newsletters, enabling discussion forums (Tweedle et al., 2012, Tiago, 2016). Enabling personal recognition and reward are also fundamental in a way that provides a sense of achievement. Tools used for this may include highlighting the identity of contributors with observations to acknowledge their contributions explicitly, providing participants with certificates of recognition, thanking participants and acknowledging their role, providing open access to all records in the database, or at least the non-sensitive (Newman et al., 2012), holding a competition (Newman et al., 2012), recognizing the degree of volunteer expertise (e.g. progressing from amateur to expert levels in iSpot; Clow & Makriyannis, 2011), or giving symbolic rewards such as badges (Clery, 2010; Cooper et al, 2010).

The CoralWatch.org is a good example on how people can behave in an unexpected way if they feel engaged with a project (Marshall et al., 2012). In this case of reef monitoring vacationers become aware of reef degradation and destruction, which could discourage participation in reef-monitoring efforts. However, by providing a sense of community and opportunities to participate and learn about cutting-edge science, the project helps to facilitate and maintain engagement with society, including tourists. Some guests return to reef resorts specifically to follow monitoring surveys, which is viewed as a positive incentive for resort managers who organize specific events where marine biologists and reef tourists mingle and exchange ideas. Information updates, newsletters, website announcements, interactions with resort staff, and being “on call” for guest questions also help to foster participation among tourists (Marshall et al., 2012).

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BioDiversity4All project highlights the identity of users and provides real-time publication of data and validation of the information through the interaction with experts. To give feedback to the users about the project periodic newsletters are sent and the connection between participants through the website or Facebook page is facilitated. Events requiring physical presence are also organize and the promotion of connections with corporations and municipalities aims to facilitate the incorporation of biodiversity data knowledge in political and planning decisions, by having easy access to the data generated by citizens. Open-access biodiversity data is essential for territory planning and management and translates into transparent policies by municipalities and well informed citizens. Yet, this approach presents some limitation for partners to share their data, due to copyright or ownership issues or to rare species sensitiveness.

Despite the identification of the several societal groups and respective motivations, not all groups have been addressed with the same level of effort since the start of BioDiversity4All. Due to human constraints, some groups have received a low level of effort, namely farmers, hunters, fishermen. A project is currently under way to address the fishermen community participation. This group actively shares their catches on online sites, such as recreational fishermen forums, Facebook pages and Youtube channels, being time consuming to aggregate this information (but see Gago et al., 2016). Nevertheless, this type of information provided by recreational fishermen is very useful to track follow invasive species (Gago et al., 2016).

On groups that have received specific activities, the level of participation is much higher than in the remaining groups. However even with organized activities, the level of feedback can be crucial to foster participation – activities with a low level of feedback and interaction can receive very low participation rates. Some groups have presented unexpectedly low levels of participation, such as recreational divers. Despite presentations at dive centers, mailed information and personal contacts, favorable conditions, such as existence of a high technological level and internet usage, the level of participation among this group has remained very low. In the future, a deeper analysis and usage of different strategies should allow understanding and improving participation in this group.

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3.5. Conclusion

The present study identified distinct citizen groups to whom this project could be beneficial. These citizen’s groups have been an important asset regarding biodiversity registering and, along these five years of experience, they contributed to generate the largest Portuguese biodiversity database available to any person, agency or organization. In a country with limited public participation, a bottom-up approach with customized communication and engagement strategies is essential to recruit and retain citizen groups’ interest in the Biodiversity4all initiative, ultimately contributing for improved biodiversity education, environmental awareness and conservation efforts. However, considering its five years of activity, in the future, is important to direct efforts for the engagement of public participation aiming to increase general diversity knowledge and awareness for next generations to come.

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4. The influence of motivational factors on the frequency of participation in citizen science activities

“The ones who are crazy enough to think that they can change the world, are the ones who do.” Steve Jobs

Tiago, P., Gouveia, M.J., Capinha, C., Santos-Reis, M., & Pereira, H.. Frequency of participation predicts participation motivations in citizen science activities Submitted to Nature Conservation.

Abstract

Citizen science has become a mainstream approach to collect information and data on many different scientific subjects. In this study, we assess the effectiveness of engagement and meaningful experience of participants in citizen science projects. We use motivational measures calculated from a web survey where respondents answered questions regarding to their motivation to participate in BioDiversity4All, a Portuguese citizen science project. We adapted the intrinsic motivation inventory (IMI) and considered seven categories of measurement: Interest/Enjoyment, Perceived Competence, Effort/Importance, Perceived Choice, Value/Usefulness, Project Relatedness, and Group Relatedness each of them with statements rated on a seven-point Likert scale. We received 149 survey responses, corresponding to 10.3 % of BioDiversity4All Newsletter’s receivers. We analyzed for possible differences among the categories pertaining to gender, age, level of education and level of participation in the project. Finally, we assessed the different patterns of motivation existing among the users. No statistical differences were found between genders, age classes and levels of education for the averages in any category of analysis. However, IMI categories presented different results for respondents with different levels of participation. The highest value of Interest/Enjoyment and Perceived Competence was obtained by the group of respondents that participate a lot and the lowest by the ones that never participated. Project Relatedness had the highest value for all groups except

Chapter 4 for the group that never participated. This group had completely different motivations from the other groups, showing the lowest levels in categories such as Perceived Competence, Value/Usefulness, Project Relatedness and Group Relatedness. In conclusion, the results from our work show that working deeply on people’s involvement is fundamental to increase and maintain their participation on citizen science projects. If, for initial recruitment and in countries with low participation culture, mechanisms of external motivation may be necessary, to guarantee higher levels of long term participation, citizen science projects should foster intrinsic motivations which can be done by incorporating in project design experiences of relatedness, capacity building, positive feedback and adapted participation modes.

Keywords: Citizen Science, Self Determination Theory, Intrinsic Motivation.

4.1. Introduction

Citizen Science can be defined as the general public involvement in scientific research activities and has recently become a mainstream approach to collect information and data on many different scientific subjects (Miller-Rushing et al., 2012). The huge number of data collectors engaged in citizen science allows scientists to tackle questions that were previously out of their reach (Silvertown et al., 2011). With traditional scientific methods, the cost of such data collection would become a limitation due to budget and time constraints. Therefore, an increasing number of researchers have started to work with citizens, realizing that those directly involved in research activities exhibit a rapid increase in scientific literacy (Bonney et al., 2009; Lowman et al., 2008; Silvertown, 2009). As such, citizen science has been recognized not only as an instrument for a given research experiment, but also as an education and outreach tool for researchers.

The level of participation in citizen science studies is however remarkably different between regions and countries (Dierkes and von Grote,2000; Forte and Lampe, 2013). For citizen science projects to become successful, it is therefore essential to understand the motivations behind the different levels of participation of citizens. These motivations may be different, depending on the local historical and cultural background and among different societal groups.

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Some studies aimed to identify the main motivations for people to participate in citizen science projects and have identified several reasons. The desire to learn more about scientific issues behind the project, the feeling that they are helping the environment and the enjoyment of developing activities in nature were recognize as important motivations to participate (Bell et al., 2008; Van den Berg et al., 2009; Raddick et al., 2010, Rotman et al., 2012). It was also described that getting to know other people with similar interests, making new friends, having the feeling that they are an active participant and co-owner of the project and gain recognition for their input and achievements were also reasons that encourage people to participate in citizen science projects (Bell et al., 2008; Van den Berg et al., 2009; Raddick et al., 2010, Rotman et al., 2012).

In this study, we aim to analyze differences in motivations concerning gender, age, level of education and level of participation in one of the largest and longest running citizen science project in Portugal, the biodiversity web portal Biodiversity4all (www.biodiversity4all.org). The BioDiversity4All is a nationwide project that aims to increase citizens’ biodiversity knowledge. Currently BioDiversity4All has nearly 2500 registered users, a network of 50 partners representing different citizen groups and other stakeholders and a validation panel already encompassing 49 taxonomic experts. The project has currently over 400000 observations of 7000 species, and includes nearly 98000 pictures associated to sightings. Users can add to the database either point species observations (sightings) or polygon areas for species occurrence which are later validated by taxonomic specialists (invited scientists or non-academic experts) and through this validation process, users progressively learn to identify and recognize local and national biodiversity.

In order to understand the level of intrinsic motivation of Portuguese participants in this citizen science project, we tested the self-determination theory (SDT). SDT is grounded in the assumption that people have basic psychological needs to feel competent, autonomous and have a sense of belonging or relatedness to others (Ryan and Deci, 2008). Autonomy involves feelings of willingness and choice in regards to activities undertaken; relatedness refers to feelings of closeness to other people; and competence involves feeling able to master challenges and having effective interactions with the environment (Katz and Assor, 2007) (Figure 4.1). SDT predicts that, as a result of developmental experiences that engender competence, autonomy, and relatedness, individuals will advance towards more autonomous motivational orientations

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(in other words, the amount of self-determined motivation increases) (Katartzi and Vlachopoulos, 2011). The most self-determined form of motivation is intrinsic motivation, representing the motivation to engage in an activity purely for the sake of the activity itself and because it is inherently pleasurable (Deci and Ryan 1985; Lepper et al. 1973). Intrinsic Motivation Inventory (IMI) is a multidimensional measurement instrument intended to assess participants’ intrinsic motivations related to a target activity’s subjective experience. It has been used in several experiments related to intrinsic motivation and self-regulation (e.g., Ryan, 1982; Ryan et al., 1983; Plant and Ryan, 1985; Ryan et al., 1990; Ryan et al., 1991; Deci et al., 1994). It assesses participants’ Interest/Enjoyment, Perceived Choice, Perceived Competence, Pressure/Tension, Effort, Value/Usefulness and Relatedness. The category Interest/Enjoyment is the most direct measure (self-report) of intrinsic motivation. This category assesses the interest and inherent pleasure when doing a specific activity. Perceived Choice and Perceived Competence are theorized as positive predictors of intrinsic motivation and are related to the SDT innate psychological needs of autonomy and competence. Perceived Choice evaluates how individuals feel they engage in one activity because they choose to do it, and Perceived Competence measures how effective individuals feel when they are performing a task. Pressure/Tension, conceived as a negative predictor of intrinsic motivation, evaluates if participants feel pressure to succeed in an activity. Effort is a separate variable, which is important when taking into account motivation in specific issues and contexts. It assesses the person's investment of his/her capacities in what he/she is doing. The Value/Usefulness category embodies the idea that people internalize and develop more self-regulatory activities when experience is considered as valuable and useful for them. Finally, Relatedness refers to the degree of a person's feelings connected to others and is used in studies where interpersonal interactions are relevant (Monteiro et al., 2015). The IMI statements are often slightly modified to fit specific activities. Thus, for example, a statement such as “I tried very hard to do well at this activity” can be changed to “I tried very hard to do well on these puzzles” or “…in learning this material” without effecting its reliability or validity. Concerning redundancy there are statements within the categories that can overlap. Making a randomization of the presentation of the statements makes these categories less evident to the respondents.

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Need for autonomy Need for competence Need for relatedness

Basic psychological needs

Type of motivation Intrinsic motivation Extrinsic motivation Amotivation

Activity performed out of Activity performed with Lack of intentionality and interest, enjoyment, the intention of personal causation. and/or satisfaction. supporting personally Defining The purpose of the held values, avoiding features and activity is the activity guilt, obtaining approval, reward itself. or a reward or avoiding contingencies No influence of punishment. consequences or threats of external or internal origin Position on relative autonomy Autonomous motives Controlling motives continuum (High autonomy) (Low autonomy)

Figure 4.1. – Self-determination theory, illustrating basic psychological needs, defining features of the types of motivation and position in the relative autonomy continuum (adapted from Ryan and Deci, 2007).

4.2. Materials and methods

Survey Instrument

We prepared a web survey that was sent to citizens registered in the BioDiversity4All project through the project’s Newsletter’s (Supplementary Material).

The survey was composed of three sections. The first introduced the research and addressed survey ethics and data security. The second section asked about respondents’ demographic and professional characteristics like gender, age, self-reporting level of participation in the project (from never participated to participate a lot), nationality, profession, and level of education. The third section (see Table 1 for all the questions in this section of the survey) was an adaptation of Fonseca and Brito’s (2001) version of the IMI (McAuley et al., 1989).

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The seven categories employed, (Table 4.1) although derived from the Intrinsic Motivation Inventory (IMI), were generated by reviewing the theoretical literature and relevant published instruments. In the present case, these were modified to refer to citizen science activities connected with biodiversity assessments. In the analysis seven categories were considered: Interest/Enjoyment (eight statements), Perceived Competence (nine statements), Effort/Importance (five statements), Perceived Choice (seven statements), Value/Usefulness (seven statements), Project Relatedness (six statements), and Group Relatedness (five statements). All motivational statements were rated on a seven-point Likert scale ranging from one (strongly disagree) to seven (strongly agree), with an intermediate score of four (moderately agree) (Munshi, 2014). From the original IMI we excluded the category Pressure/Tension once is not expected to be felt by participants that do this activity in a volunteer basis and included the category Group Relatedness created according to the features of the project. Ryan and Deci (2000) describe relatedness as a sense of belonging and connectedness to the persons, group or culture disseminating a goal. Although the IMI analysis is designed to tap into individual motivation for doing a certain activity, the statements on the Group Relatedness category lend themselves readily to the assessment of the degree to which a person feels connected to other persons that do the same activities.

Because BioDiversity4All is a project developed in Portuguese language, the survey was only available in Portuguese even if the participants were from other nationalities. It was assumed that, if they had registered in the Portuguese platform, they could read in Portuguese.

The link to the web survey was sent in May 2015 to all the Newsletter’s receivers of BioDiversity4All Project (N=1450), independently of their participation or not in the project. Five answering reminders were sent till October 2015.

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Table 4.1. – IMI categories used in the survey with corresponding statements. The (R) after a statement is just a reminder that the score attributed is the reverse of the participant’s response on that particular statement.

Categories Statements

Interest/ I enjoyed doing this activity very much. Enjoyment I thought this was a boring activity. (R) This activity did not hold my attention at all. (R) I would describe this activity as very interesting. I thought this activity was quite enjoyable. While I was doing this activity, I was thinking about how much I enjoyed it. This is one of my favorite leisure activities. Perceived I think I am pretty good at this activity. Competence Is important to me to feel that I did this activity as well as or better than other participants. After working at this activity for a while, I felt pretty competent. I am satisfied with my performance at this task. I was pretty skilled at this activity. This was an activity that I couldn’t do very well. (R) This activity allows me to increase my competences. To feel that I performed well on this activity made me want to participate again. To feel that I performed worse than the others on this activity made me not want to participate again. (R) Effort/ I put a lot of effort into this. Importance I didn’t try very hard to do well at this activity. (R) I tried very hard on this activity. It was important to me to do well at this task. I didn’t put much energy into this. (R) Perceived I believe I had some choice about doing this activity. Choice I felt like it was not my own choice to do this task. (R) I didn’t really have a choice about doing this task. (R) I felt like I had to do this. (R) I did this activity because I had no choice. (R) I did this activity because I wanted to. I did this activity because I had to. (R)

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Categories Statements Value/ I believe this activity could be of some value to me. Usefulness I think that doing this activity is useful for helping in the scientific knowledge of national biodiversity. I think this is important to do because it allow us to know better national biodiversity. I would be willing to do this again because it has some value to me. I think doing this activity could help me to be closer to nature and biodiversity. I believe doing this activity could be beneficial to me. I think this is an important activity. Relatedness I felt really distant to this project. (R) I felt like I could really trust this project. I’d like to have the chance to collaborate more often with this project. I’d really prefer not to collaborate more with this project. (R) I don’t feel like I could really on this project. (R) I feel close to this project. Group Doing this activity, I feel I can learn with other participants. Relatedness Doing this activity, I can help other participants to get to know what I already know. With this activity, I feel I can relate with other participants.

With this activity, I get to know people with the same interests than me. Participating in this activity is important to make me feel that I belong to a community.

Data Analysis

The results from the survey were ranked and analyzed considering the questions referring to the participants’ socio-demography and the IMI-related statements. All results describing the characteristics of participants and their motivation to participate were reported as a percentage of total responses.

To analyze differences between gender, of the average scores of the statements ranked on Likert-scales, we did a Mann-Whitney-Wilcoxon test. After calculating the medians with an

Chapter 4 interquartile interval (Q3-Q1) for age classes, levels of education and levels of participation, a multiple comparisons analysis was performed with the Kruskal-Wallis test (multiple comparisons and unbalanced sample sizes). Significant differences between average scores were determined for α≤0.05. All the statistical analysis was performed using R 3.1 (R Development Core Team 2014).

Finally, we performed a cluster analysis to group participants according to similarities in the answers they provided. We used hierarchical agglomerative clustering with Ward method (Murtagh and Legendre, 2014) and performed the cluster analysis using the package Cluster of R 3.1.

4.3. Results

We received 149 survey responses corresponding to 10.3 % of the Newsletter’s receivers. Most of the responses were given by Portuguese citizens 92.6% with the remaining representing six other nationalities: Brazilian, Spanish, British, French, Dutch, and Swiss.

From the total amount of responses 77 were given by males (51.7%) and 72 by females (48.3%) and participants’ ages varied between 19 and 71 years old with an average of 43.5 ± 11.4 (Figure 2). Concerning the level of education 83.1% had higher education (44.3% bachelor degree, 25.5% MSc, 12.8% PhD) and 16.9% high school (Figure 4.2).

Respondents that had registered in the project and only occasionally participate were responsible for largest (55.7%) fraction of the survey’s responses, followed by those that had registered in the project but never participated (30.2%). Of the remaining a small fraction (12.1%) regularly participate and very few (2.0%) showed a high degree of participation (Figure 4.2). Concerning their professional activity, 28.9% of the respondents to the survey have education related jobs and 25.5% have environmental related jobs.

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100% 100%

75% 75% 67.8%

51.7% 48.3% 50% 50%

21.5% 25% 25% 5.4% 5.4% 0% 0% Female Male <25 [25,50[ [50,65[ ≥65

100% 100%

75% 75% 55.7%

50% 44.3% 50% 30.2% 25.5% 25% 16.8% 25% 12.8% 12.1% 2.0% 0% 0% High School Bachelor Degree MSc PhD Never Occasionally Regularly Greatly

Figure 4.2. – Percentage of responses per sex gender, age, level of education, and level of participation.

Considering all survey participants, the highest IMI scale-score was obtained by the category Project Relatedness, with an average of 5.8 out of 7, followed by Perceived choice and Value/Usefulness with an average of 5.7. Interest/Enjoyment had an average of 5.3, Group Relatedness an average of 4.7 and Perceived competence an average of 4.5. The lowest average obtained referred to Effort/Importance with 3.8. In the correlation analysis of the different IMI scores, Interest/Enjoyment and Value/Usefulness, Interest/Enjoyment and Project Relatedness, and Value/Usefulness and Project Relatedness were strongly correlated (Table 4.2).

Table 4.2. – Correlation between the different IMI categories.

IMI Categories Interest/ Perceived Effort/ Perceived Value/ Relatedness Group Enjoyment Competence Importance Choice Usefulness Relatedness Interest/Enjoyment

Perceived Competence 0.69

Effort/Importance 0.46 0.53

Perceived Choice 0.35 0.18 0.02

Value/Usefulness 0.72 0.49 0.33 0.49

Relatedness 0.77 0.58 0.29 0.48 0.79

Group Relatedness 0.67 0.64 0.36 0.21 0.66 0.64

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No statistical differences were found between genders, age classes and levels of education for the averages in any category of analysis. However, levels of participation were significantly different for all categories except Interest/Enjoyment (Table 4.3 and Figure 4.3).

Table 4.3. – Kruskal-Wallis summary table for analysis of the median of the scores of each IMI categories by age, level of education and level of participation.

Age Level of Education Level of Participation IMI Scales Chi squared P value Chi squared P value Chi squared P value

Interest/Enjoyment 3.58 0.31 3.53 0.32 2.66 0.45

Perceived 6.20 0.10 1.19 0.76 40.71 7.54e-09

Competence

Effort/Importance 2.83 0.42 1.34 0.72 15.83 0.00

Perceived Choice 1.70 0.64 1.30 0.73 14.30 0.00

Value/Usefulness 4.54 0.21 2.83 0.42 14.00 0.00

Relatedness 2.45 0.48 1.65 0.65 26.52 7.43e-06

Group Relatedness 3.91 0.27 2.50 0.48 11.73 0.01

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Figure 4.3. – Percentage of answers of the IMI categories, rated from 1 (strongly disagree) to 7 (strongly agree), with an intermediate score of 4 (moderately agree), for the four groups of people with different levels of participation in the project (never participate, participate occasionally, participate regularly, participate a lot).

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The cluster analysis of the answers given by the participants supports the differences of motivations of the respondents with different levels of participation (Figure 4.4). The first cluster group was composed of people that never participated or that participate only occasionally. The third group included people that participate a lot and most of the people that participate regularly.

The highest value of Interest/Enjoyment and Perceived Competence was obtained by the group of respondents that participate a lot and the lowest by the ones that never participated. For Effort/Importance, the lowest value was obtained by the group that participates occasionally and the highest by those who never participated. For Value/Usefulness, Project Relatedness and Group Relatedness, the highest value was obtained by the ones who show high participation levels and the lowest by the ones that never participated. For Perceived Choice the highest value was obtained by the ones that participate regularly and the lowest by those that never participated.

Concerning the group of people that never participated, the lowest IMI was Perceived Competence and the highest was Perceived Choice. For all the other groups, the lowest IMI was Effort/Importance and the highest was Project Relatedness.

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Figure 4.4. – Hierarchical agglomerative clustering of the answers given by the participants, with Ward method. The symbol represents the level of participation in the project

(never participate - , participate occasionally - , participate regularly - , participate a lot - )

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4.4. Discussion

In this study, we wanted to assess citizens’ engagement and meaningful experience in citizen science projects, using motivational measures. This study revealed lessons of interest for citizen science projects when participants’ motivations is concerned, in a country with limited culture of public participation. Assessment of intrinsic motivations in countries with higher levels of engagement with biodiversity and participation in citizen science, could present different results and a comparative analysis would be an interesting approach.

Analyzing survey respondents, the majority of participants have higher education, a fact which is not representative of the Portuguese reality (only 16.5% of Portuguese people have or are undertaking higher education, PORDATA, 2015). Moreover, the age groups <25 and ≥65 were the ones with less answers to the survey (5% each); one reason might be that these are the groups with less participants in the project, or that these are the groups showing less willingness to answer to web surveys. For a general characterization of respondents, we also included questions about nationality and professional activity. The survey was developed for Portuguese speakers and this may have hampered people from other nationalities to participate. Several participants from other nationalities collaborate with the project either through the Portuguese project or through the international platform. Some of these participants are residents in Portugal and presumably speak Portuguese however, less than 8% of survey respondents were from other nationalities. Although the professional activities of respondents are diverse, 54.4% of respondents have education or environmental related jobs. The demographic factors of nationality and profession were just used to characterize respondents and not to test the motivational differences. Nationality because the project has an inherently national scope and answers to profession because they were too generic to allow any conclusions.

A high percentage of respondents had registered in the project BioDiversity4All but never participated. When we analyzed the responses to IMI categories given by groups with different levels of participation, we found that people who never participated were the ones responding more differently compared to other groups. This group shows the lowest levels in all categories except Effort/Importance. This might indicate that those people do not have intrinsic motivations for participating in such a project. Of these people, some registered after a project

Chapter 4 presentation, a media news or a launch of a contest but did not pursue with their participation. A possible lesson to draw from these results is that extrinsic motivations may be needed to foster participation in these cases, while creating mechanisms to increase competence, autonomy and relatedness on participants, to drive more autonomous (self-determined) motivations.

Frequently, citizen science projects use extrinsic motivation instruments to induce citizens’ participation, such as incentives, certificates of recognition and challenges, which stimulate people’s interest in the project (Dickinson et al., 2012). Nevertheless, it is important to include mechanisms to foster intrinsic motivations in order to create continued support and involvement in citizen science initiatives after these initial extrinsic motivations erode (Cialdini, 2008). For example, one could use contests and prizes that include educational material, feedback on the effort already invested, group activities, interacting with a similar community and different ways of participating, increasing perceived choice.

In contrast with the respondents that never participated a small percentage (2%) participate a lot. This is not unexpected regarding results from other citizen science projects. In the Wikipedia project, with one million registered users, about 10% contribute with ten or more entries and about 0.5% contribute to a large number of tasks to keep Wikipedia running (Tapscott & Williams, 2008). The group of respondents that participate a lot had the highest levels of intrinsic motivation, scoring highest in all categories except Effort/Importance and Perceived Choice.

These findings are aligned with past research on intrinsic motivation which has focused on identifying and examining the activity-level psychological factors that promote or inhibit the development of intrinsic motivation. This approach has yielded important insights, some of which that (1) enjoyment is positively related to competence valuation (i.e. the degree to which one cares about performing well at a given activity; Elliot et al. 2000; Goudas et al., 1995; Harackiewicz and Manderlink 1984; Reeve and Deci 1996; Sansone 1989; Tauer and Harackiewicz 1999), and (2) enjoyment is positively related to the degree to which activities are perceived to be ‘‘optimally challenging’’— not too easy and not too difficult (e.g. Harter 1978; Keller and Bless 2008; Moneta and Csikszentmihalyi 1996). Stated more generally, the degree to

Chapter 4 which the potential rewards of ongoing activity engagement are realized would seem to be dependent on the degree to which attentional resources are devoted towards these potential rewards.

Early experiments showed that positive feedback enhanced intrinsic motivation relative to no feedback (Boggiano and Ruble, 1979; Deci, 1971) and that negative feedback decreased intrinsic motivation (Deci and Cascio, 1972). Deci and Ryan (1985) linked these results to the need for competence (White, 1959), suggesting that events such as positive feedback provide satisfaction of the feeling of competence, thus enhancing intrinsic motivation, whereas events such as negative feedback tend to thwart the feeling of competence and thus undermine intrinsic motivation. That is why it is understandable that people who participate a lot in the Biodiversity4All project had the highest levels of Perceived Competence. The feeling of competence leads them wanting to participate more.

These results indicate that citizen science projects should nurture participants with positive feedback and adapted participation modes to their level of competence. This may yield higher levels of motivation to participate and foster intrinsic motivation.

Project Relatedness and Value/Usefulness were the highest scoring IMI categories for all groups except those who never participated. People tend to value the feeling of relationship and trust in the project, moreover since they feel that the project has an important mission to accomplish.

A note should be given about the category of Perceived Choice. Most respondents feel they had a high level of Perceived Choice which is in line with the voluntary nature of the project. However, we have students participating in the project and some contests with schools and scouts which may explain why some respondents may feel that they had no choice in their participation.

With the cluster analysis we wanted to confirm similarities in the answers given by different respondents to find, if people with the same level of participation, have comparable intrinsic motivations and in fact, we detected the expected result.

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In conclusion, in recent years much has been written on communication and recruiting participants for citizen science projects (Dickinson et al., 2012; Roy et al., 2012; Silvertown et al., 2013). However the results from our work show that working deeply on people’s involvement is fundamental to increase and maintain their participation on citizen science projects. If, for initial recruitment and in countries with low participation culture, mechanisms of external motivation may be necessary, to guarantee higher levels of long term participation, citizen science projects should foster intrinsic motivations which can be done by incorporating in project design experiences of relatedness, capacity building, positive feedback and adapted participation modes.

4.5. Supporting information – survey

Motivation Sourvey

We are inviting you to participate in a study, under a PhD thesis, taking place at the Faculty of Sciences - University of Lisbon. The study aims to investigate the motivations that different users of the BioDiversity4All project have to participate in citizen science projects, particularly those that seek to establish a link between people and biodiversity. Participation in this study is anonymous, entirely voluntary, and may be discontinued at any time without any loss or damage. The data collected here will be considered for statistical purposes and only analyzed collectively. All data provided will be strictly within the scope of this investigation and will not be used for other purposes. There are no right or wrong answers - all answers are valid. We therefore ask you to respond with the utmost sincerity. Be spontaneous and do not reflect too much on the answers. This study is not only aimed at users who use the platform but also to those who registered but later found that they had no interest or time to participate. Your participation is fundamental to the success of this study.

You arrived at this questionnaire through: BioDiversity4All/Facebook page

Personal data:

Gender (Female/Male)

Age:

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Number of people in the household:

Number of children in the household:

Level of education (None/Elementary/High School/Bachelor Degree/MSc/PhD)

Professional activity:

Nationality:

Area of residence:

Project Participation (Never Participated/Participate Occasionally/Participate Regularly/Participate a Lot)

For each of the following sentences, indicate how true it is for you by ticking the space that corresponds to the number that best describes your opinion. Use the following scale:

1- Strongly disagree; 4 – Moderately agree; 7 – Strongly agree

I enjoyed doing this activity very much.

I think I am pretty good at this activity.

I tried very hard on this activity.

I believe I had some choice about doing this activity.

I believe doing this activity could be beneficial to me.

I felt really distant to this project. (R)

Doing this activity, I feel I can learn with other participants.

I thought this activity was quite enjoyable.

It is important to me to feel that I did this activity as well as or better than other participants.

I didn’t try very hard to do well at this activity. (R)

I felt like it was not my own choice to do this task. (R)

I think that doing this activity is useful for helping in the scientific knowledge of national biodiversity.

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I thought this was a boring activity.

After working at this activity for a while, I felt pretty competent.

I put a lot of effort into this.

I didn’t really have a choice about doing this task. (R)

I’d like to have the chance to collaborate more often with this project.

With this activity I feel I can relate with other participants.

This activity did not hold my attention at all.

I am satisfied with my performance at this task.

It was important to me to do well at this task.

I felt like I had to do this. (R)

I would be willing to do this again because it has some value to me.

I’d really prefer not to collaborate more with this project. (R)

With this activity I get to know people with the same interests than me.

I would describe this activity as very interesting.

I was pretty skilled at this activity.

I didn’t put much energy into this.

I did this activity because I had to. (R)

I think doing this activity could help me to be closer to nature and biodiversity.

I don’t feel like I could rely on this project. (R)

Participating in this activity is important to make me feel that I belong to a community.

I thought this activity was quite enjoyable.

This was an activity that I couldn’t do very well. (R)

I did this activity because I wanted to.

I think this is an important activity.

I feel close to this project.

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While I was doing this activity, I was thinking about how much I enjoyed it.

This activity allows me to increase my competences.

To feel that I performed well on this activity made me want to participate again.

This is one of my favorite leisure activities.

To feel that I performed worse than the others on this activity made me not want to participate again. (R)

I feel close to this project.

I believe this activity could be of some value to me.

I think this is important to do because it allow us to know better national biodiversity.

I felt like I could really trust this project.

Doing this activity, I can help other participants to get to know what I already know.

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5. Spatial distribution of citizen science casuistic observations for different taxonomic groups

“It is the tension between creativity and scepticism that has produced the stunning and unexpected findings of science” Carl Sagan

Tiago, P., Ceia-Hasse, A., Marques, T.A., Capinha, C. & Pereira, H.M. 5. Spatial distribution of citizen science casuistic observations for different taxonomic groups. Submitted to Plos One.

Abstract

Opportunistic citizen science databases are becoming an important way of gathering information on species distributions. These data are temporally and spatially dispersed and could have limitations regarding biases in the distribution of the observations in space and/or time. In this work, we test the influence of landscape variables in the distribution of citizen science observations for eight taxonomic groups. We use data collected through a Portuguese citizen science database (biodiversity4all.org). We use a zero-inflated negative binomial regression to model the distribution of observations as a function of a set of variables representing the landscape features plausibly influencing the spatial distribution of the records. Results suggest that the density of paths is the most important variable, having a statistically significant positive relationship with number of observations for seven of the eight taxa considered. Wetland coverage was also identified as having a significant, positive relationship, for birds, amphibians and reptiles, and mammals. Our results highlight that the distribution of species observations, in citizen science projects, is spatially biased. Higher frequency of observations is driven largely by accessibility and by the presence of water bodies. We conclude that efforts are required to increase the spatial evenness of sampling effort from volunteers.

Keywords: citizen science, species observations, spatial sampling bias, wildlife monitoring, Portugal

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5.1. Introduction

Citizen science has become a relevant tool for collecting species data (Dickinson et al., 2012). Observations gathered by a large number of volunteers, over broad spatial extents and temporal periods often provide a large number of records (Chandler et al., 2012), allowing studies that would otherwise be unfeasible. The increment of species from citizen science initiatives in recent years, seems to be particularly important for taxonomic groups that were less usually targeted in traditional citizen science projects, which were directed to species groups more conspicuous and easier to identify. Groups such as invertebrates or aquatic organisms were traditionally less targeted, and have benefitted in recent years.

Emerging technologies are also changing the type of volunteers that get involved with scientific projects (Miller-Rushing et al., 2012). Web 2.0, characterized by greater user interactivity and collaboration, more pervasive network connectivity and enhanced communication channels, permits easy overcrossing of social, cultural, economic, and political boundaries, and, also the integration of local/traditional knowledge in these projects (Ballard et al., 2008). The possibility of collecting, through mobile applications with internet connections, georeferenced observations of the natural world (e.g., wildlife sightings) via interactive geovisualization interfaces (e.g., Google Maps, Google Earth, and Microsoft Virtual Earth) or the use of sensors in the mobile devices allowing to collect data from the environment like air quality or noise.

The data collected can have different applications, such as creating species distribution maps (e.g. Tiago et al., 2017) or identifying a biological invasion (e.g. Crall et al., 2010; Preuss et al., 2014). The identification of spatial biases in the sampling provided by citizen science projects is fundamental to interpret the outcomes obtained. Only taking these biases into consideration, such as the existence of under-sampled regions, we can turn the results found useful for supporting the adoption of conservation measures by decision makers [8,9]. (Tulloch et al., 2013; Tiago, 2016).

Understanding where volunteers of biodiversity recording are collecting their observations is fundamental for a sensible use of the data collected. These volunteers do not select their survey

Chapter 5 locations randomly, but most likely as a combined influence of a number of factors (Mair & Ruete, 2016) such as accessibility (Romo et al., 2006), proximity to urban centres, topographic variation, time of the year, species richness (Kéry et al., 2009; Tulloch et al., 2013) or other geographical or physical characteristic. Therefore, these databases may incorporate an important spatial bias, with some areas almost not being surveyed, while others corresponding to “hotspots” of observations (Beck et al., 2013; Higa et al., 2014).

Another potential source of bias is the taxonomic group being recorded. Observations tend to focus on certain groups, generally those that are more easily detected and identified, such as birds or butterflies, or even certain species within a group. Moreover, volunteers may not record all the species they observe either because they are not able to identify them, due to lack of taxonomic expertise (Dickinson et al., 2010), or because they aim to register only those that are rare, without an interest in recording species that are common (Tulloch & Szabo, 2012; van Strien et al., 2013).

These data also have the limitation of being presence-only. In such cases, the non-recording of a species in a certain location by volunteers may correspond to the true absence of the species, to the inability of the volunteer to observe it or, to the overall absence of recording efforts (Mair & Ruete, 2016).

In this work, we explore the relationship between physical and geographical variables such as land cover, road or path density, human population and altitude, and the distribution of species observations of different taxonomic groups, as recorded by volunteers. We use records from the BioDiversity4All database (www.biodiversity4all.org), a country-wide citizen science project in Portugal. We aim to understand how observations are distributed across the country, which factors drive their distribution, and what type of relationship (e.g. negative or positive) the different variables form with the distribution of observations for the different taxonomic groups.

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5.2. Materials and methods

Species and volunteer data

We used opportunistic species observations data retrieved from the BioDiversity4All web portal (http://www.biodiversity4all.org/), a Portuguese citizen science project connected to an international project based in the Netherlands, Waarneming international (http://www.observado.org/), and which is similar to citizen science biodiversity databases elsewhere such as iNaturalist (http://www.inaturalist.org/) or iSpot (http://www.ispot.org/). BioDiversity4All started in 2010 but volunteers could add historical data so there is information referring to previous years. We only used species occurrences that provided GPS derived geographical coordinates, - ranging from 1982 until August 2016. We gathered the species observation records by their taxonomic group. In total, we considered data for 8 taxonomic groups: (1) plants, (2) mushrooms, (3) birds, (4) amphibians and reptiles, (5) mammals, (6) butterflies, (7) moths, and (8) other insects. For each of these groups we summed the number of species observations made in each 5 x 5 km grid cell. We only considered records for mainland Portugal, due to the inability of obtaining data for some of the predictive variables (below) for insular regions. We also collected the number of volunteers and the number of observations that each registered in the website.

Geographic data

We identified a total of eight spatially explicit variables that had a potential to explain variation in the distribution of species observations: percentage of cover by artificial areas, percentage of cover by agriculture and agro-foresty areas, percentage of cover by forest and natural and semi- natural areas, percentage of cover by wetland areas (all sourced by: IGP, 2010), road density (paved roads; km/km2), paths and footpaths density (i.e., paths open to non -motorized vehicles, and paths used mainly or exclusively by pedestrians; km/km2) (sourced by Geofabrik, 2017), human population density (individuals/km2; log-transformed) (CIESIN, 2016), and altitude (m) (Jarvis et al., 2008). We selected these geographical variables because they are presumably relevant in driving the spatial behavior of species observers (Dickinson et al., 2010; Tulloch & Szabo, 2012; Beck et al., 2013; van Strien et al., 2013; Higa et al., 2014). All variables covered the extent of mainland Portugal, at a 5 km resolution and were processed in QGis (QGIS Development Team, 2014). We tested for redundancy among data in the variables by calculating pairwise Pearson correlation.

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Statistical analyses

Given the large number of grid cells without species observations, we used a zero-inflated negative binomial regression (ZINB) to identify the variables that were related to the spatial distribution of the observations. ZINB are a default choice to deal with overdispersed counts, and in particular under situations where there are more zeros than the ‘simple’ negative binomial model might reasonably cope with (e.g. Zuur et al., 2009). The ZINB models were implemented in R (R Core Team, 2016) using the package pscl (Zeileis et al., 2008; Jackman, 2015). We tested for the significance and type of relationship of the explanatory factors and the counts of species observations in each grid cell for each taxonomic group, and also for all groups combined. We have not accounted explicitly for spatial autocorrelation in our models (Dormann et al., 2007).

5.3. Results

We adopted a spatial grid system where mainland Portugal comprises a total of 3 816 grid cells. The data compiled from Biodiversity4All included a total of 368 030 species observation records, from 1982 to 2016. Birds were the taxonomic group having the highest number of records, with a total of 180 911 records, followed by plants with 159 128 records. Mushrooms were the least recorded group having only 1 175 records (Figure 5.1.). The mean number of records per grid cell is 88, and 1 030 cells have no observations. The classes of explanatory variables for Portugal used in the analysis after being tested for redundancy are presented in figure 5.2. The mean number of records per grid cell is 88, and 1 030 cells have no observations (about 28% of the total area of mainland Portugal). The distribution of the number of records per grid cell for the different taxonomic groups, and for all groups combined, is shown in figure 5.3. and 5.4.

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Figure 5.1. – Number of citizen science observations registered in BioDiversity4All from May 1982 until August 2016 (y-axis) for each of the eight taxonomic groups analyzed(x-axis).

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Figure 5.2. – Explanatory variables tested for spatial association with the distribution of citizen science observations in mainland Portugal (FOR – percentage of cover of forest and natural and semi-natural territories, WET – percentage of cover of wetland territories, ROADS – density of roads, PATH – density of paths and footpaths, POP_LOG – logarithm of human population density, ALT – altitude). Figure created with QGis. 2014. Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project. URL http://www.qgis.org/en/site/.

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Figure 5.3. – Location of the study area within Europe and total number of observations in mainland Portugal per grid cell. Figure created with QGis. 2014. Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project. URL http://www.qgis.org/en/site/.

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Figure 5.4. – Number of citizen science species observations in mainland Portugal per grid cell, for each of the eight taxonomic groups analyzed. Figure created with QGis. 2014. Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project. URL http://www.qgis.org/en/site/.

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A temporal analysis of the data, for complete years (from 2010 to 2015), shows that April has the highest number of observations (34 497), followed by May (30 981) and by March (23 001) (Figure 5.5).

Figure 5.5. – Total number of citizen science species observations (y-axis) made in each month from 2010 to 2015 (x-axis).

The total number of volunteers in BioDiversity4All, for the period considered, is 1 398. The number of volunteers with highest and lowest number of observations registered is shown in figure 5.6. The group of volunteers with 1 to 10 observations is the largest one with 639 people and only five volunteers recorded >10 000 observations. The number of volunteers responsible for 50% of the observations is 4 while 175 volunteers are responsible for 90% of the total amount of observations (Figure 5.7.).

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Figure 5.6. – Number of volunteers (y-axis) grouped by level of species observations provided (x-axis).

Figure 5.7. – Cumulative number of species observations (y-axis) and the number of volunteers providing these observations (x-axis).

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We tested the correlation between the selected explanatory variables and excluded those that were highly correlated. In all cases, we kept the variables that we considered to provide a clearer link with causal mechanisms driving the behavior of observers. Hence, we excluded the percentage of cover by artificial areas, which was highly correlated with road density (Pearson correlation coefficient = 0.80, P < 0.05) and with logarithm of human population density (Pearson correlation coefficient = 0.71, P < 0.05). We also excluded the percentage of cover by agriculture or agro-foresty territories, which was highly negatively correlated with percentage of cover of forest and natural and semi-natural territories (Pearson correlation coefficient = - 0.89, P < 0.05) (Table 5.1.).

Based on ZINB models we found that different explanatory variables relate to the distribution patterns of the observations for the different taxonomic groups (Table 5.2.). Path density was the variable that most consistently explained the variation in the distribution of observations, being deemed as having a significant positive association in the models of 7 out of the 8 taxonomic groups considered (plants, birds, amphibians and reptiles, mammals, butterflies, moths, and other insects), as well as in the model for all the observations combined. The percentage of cover by forest and natural and semi-natural areas had a statistically significant positive relationship for plants, mushrooms, amphibians and reptiles, butterflies and other insects, as well as for the total number of observations. This was the second most important variable in the analysis. The logarithm of population density also showed a positive, statistically significant, relationship for plants, mushrooms, birds, other insects and the total observations. The percentage cover of wetland territories had a significant, positive relationship, for birds, and reptiles and amphibians. Finally, altitude had a statistically significant, negative relationship, with number of bird observations.

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Table 5.1. - Pearson correlation coefficients between the different explanatory variables: ART - percentage of cover of artificial areas, FOR – percentage of cover of forest and natural and semi-natural territories, AGR - percentage of cover of agriculture and agro-foresty areas, WET – percentage of cover of wetland territories, ROADS – density of roads, PATH – density of paths and footpaths, POP_LOG – logarithm of human population density, ALT – altitude.

Explanatory ART FOR AGR WET ROADS PATH POP_LOG ALT

variables

ART 1.00

FOR -0.20 1.00

AGR -0.15 -0.89 1.00

WET 0.07 -0.13 -0.07 1.00

ROADS 0.80 -0.10 -0.15 -0.00 1.00

PATH 0.46 0.02 -0.20 0.03 0.40 1.00

POP_LOG 0.71 -0.09 -0.17 0.14 0.68 0.27 1.00

ALT -0.26 0.37 -0.23 -0.14 -0.13 -0.11 -0.16 1.00

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Table 5.2. – Table 2 – Zero Inflated Negative Binomial Model (ZINB) relating the number of observations in each 5 x 5 km grid cells of Portugal (for the total amount of observations and for amount of observations of the different taxonomic groups: plants, mushrooms, birds, amphibians and reptiles, mammals, butterflies, moths and other insects) and a set of variables (FOR – percentage of cover of forest and natural and semi-natural territories, WET – percentage of cover of wetland territories, ROADS – density of roads, PATH – density of paths and footpaths, POP_LOG – logarithm of human population density, ALT – altitude) (Level of significance * P < 0.05, ** P < 0.01, *** P < 0.001).

Variables

Taxonomic Model FOR HUM ROADS PATH POP_LOG ALT Intercept Group Summary

Model 0.01 0.13 0.07 0.86 0.51 -4.05e-4 2.99 Coeficient Total (all groups) Std Error 1.41e-3 0.02 0.08 0.12 0.08 1.80e-4 0.12

Pr (>|z|) 2.62e-05*** 1.71e-10*** 0.41 4.70e-12*** 3.42e-10*** 0.02* <2e-16***

Model 1.16e-2 4.50e-02 1.07e-01 5.49e-01 4.84e-01 -5.87e-05 2.00e+00 Coeficient

Plants Std Error 2.22e-03 2.76e-02 1.23e-01 1.72e-01 1.25e-01 2.76e-04 1.85e-01

Pr (>|z|) 1.76e-07*** 0.10 0.38 1.41e-03** 1.11e-04*** 0.83 <2e-16***

Model 0.03 -0.12 -0.17 0.17 1.73 8.53e-04 -5.94 Coeficient

Mushrooms Std Error 0.01 0.07 0.24 0.42 0.26 5.64e-04 0.40

Pr (>|z|) 8.55e-09*** 0.12 0.47 0.69 5.30e-11*** 0.13 <2e-16***

Model 6.9e-04 0.17 0.13 1.30 0.34 -1.76e-03 2.69 Coeficient

Birds Std Error 1.61e-03 0.02 0.10 0.17 0.09 2.05e-04 0.14

Pr (>|z|) 0.67 1.84e-11*** 0.16 3.49e-15*** 2.56e-04*** <2e-16*** <2e-16***

Model 0.02 0.13 0.25 0.79 0.22 5.08e-04 -2.21 Coeficient Amphibians and Reptiles Std Error 2.66e-03 0.03 0.14 0.21 0.14 3.46e-04 0.23

Pr (>|z|) 5.00e-14*** 1.14e-4*** 0.06 0.40e-4*** 0.11 0.14 <2e-16***

Model -2.12e-03 0.03 0.09 1.04 -0.13 9.86e-04 -0.40 Coeficient

Mammals Std Error 2.23e-03 0.02 0.13 0.22 0.14 3.40e-04 0.12

Pr (>|z|) 0.34 0.20 0.50 2.64e-06*** 0.37 3.77e-03** 0.05*

Model 0.01 0.08 0.43 1.31 0.06 1.71e-3 1.30 Coeficient

Butterflies Std Error 2.99e-03 0.03 0.17 0.29 0.16 3.77e-4 0.26

Pr (>|z|) 6.02e-04*** 0.02* 0.01** 4.97e-06*** 0.70 3.77e-04 4.80e-07***

Model 0.01 0.06 -0.20 2.84 0.02 1.50e-03 -1.97 Coeficient

Moths Std Error 0.01 0.06 0.32 0.66 0.32 6.79 0.40

Pr (>|z|) 0.16 0.33 0.53 1.75e-05*** 0.95 0.02* 6.61e-07***

Model 0.02 0.01 -0.06 1.44 0.75 1.53e-04 1.40 Coeficient Other Insects Std Error 2.81e-03 0.02 0.16 0.29 0.15 3.39e-04 0.20

Pr (>|z|) 1.99e-08*** 0.55 0.69 9.15e-07*** 4.63e-07*** 0.65 8.18e-13***

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5.4. Discussion

We quantified spatial recording of species observations, for 8 individual taxonomic groups and pooled across these, across mainland Portugal, and related these quantities to eight geographic variables likely to explain spatial variation in the number of observations. The interpretation of the results assumes that patterns found are mostly driven by changes in observer effort, either in space or across taxa, not by real differences in abundance/occurrence patterns for the taxa considered. This is a reasonable assumption provided the probability of detecting a given taxa in a given sampling unit is independent of the taxa abundance on that sampling unit. In other words, that all taxa considered and present in any given place would be detected by an observer. This seems reasonable at the coarse taxonomic level that the observations are made, which means that patterns found are either due to taxonomic differences (e.g. some observers prefer some taxa) or sampling differences (some areas are preferred by observers).

While we have not modelled explicitly spatial auto-correlation, we do not expect results presented to be sensitive to that choice. We therefore decided for this simple approach for the sake of pragmatism, avoiding the perhaps more elegant but necessarily more complex modelling approach, running the risk of obscuring the paper main messages.

A general characterization of our data shows that the distribution of records has a strong spatial bias, with areas of the country being highly covered while others having no observations, and that a limited number of volunteers are responsible for the majority of observations. The results also show strong seasonal patterns. This is not unexpected, since opportunistic citizen science databases are described as spatially and temporally biased (Beck et al., 2013; Higa et al., 2014). The scarce number of volunteers responsible for a large proportion of the observations may be the main reason for this. In the case of this study, the reduced number of volunteers is also due to the lack of citizen science tradition in Portugal, leading to greater spatial data bias. It is also important to note that, for some specific taxonomic groups with different life histories, there are periods of the year when the groups/species can be observed and others when they cannot, or are more difficult to, such as hibernating reptiles, migratory species, and plants with different flowering periods.

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Considering the variables that were identified to better explain the number of observations made, most of them indicate a positive effect of the accessibility of the survey area, such as altitude, density of roads (accessibility to a site - only found to be important for butterflies), or density of paths (accessibility within a site). Accessibility was already found to be important in determining where volunteers record observations (Dennis & Thomas, 2000; Fernández & Nakamura, 2015). Previous studies examining the spatial patterns of observations found strong roadside biases within woody plant records (Kadmon et al., 2004), and have also showed that patterns differ between different taxonomic groups, such as between butterflies and mammals (Fernández & Nakamura, 2015).

Despite the variation between groups identified in the literature, we could identify some patterns across taxa. Path density showed a significant association with seven out of the eight taxonomic groups considered. In contrast with other studies (Mair & Ruete, 2016), density of paths explained more variation than the density of roads in taxa distribution records. Possibly these places also represent locations that people know will provide good outdoor walks and where it is easier to observe and identify species. While walking, volunteers have a higher availability to identify species and that is particularly important, for instance, for insects or plants that require a more detailed level of observation.

When considering the total number of observations, the group of birds and the group of amphibians and reptiles, the percentage of wetland areas also drives the frequency of observations. This can be explained by one or several different factors such as a higher attractiveness of these areas for the observers of a specific group (e.g., several birdwatchers go to wetland areas to observe birds, as these are ornithological-rich areas (Signorello, 1998)), or by physiological characteristics of these groups, highly dependent of this type of habitat (Semlitsch & Bodie, 2003).

It seems clear that analyzing patterns in volunteers’ distribution of observations is fundamental for planning different surveys that could help increase the data quality of these databases, and a better scientific use of the available information. Developing methods that evaluate and account for bias derived from different observation efforts (e.g. Kéry et al., 2010) is a promising research topic and a good opportunity for collaboration between statisticians and conservation

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Chapter 5 scientists, promoting the development of novel statistical approaches and survey designs (Bird et al., 2014). In the absence of such approaches, at the very least the interpretation of such data must be made while considering the influence of the potential sources of bias. We note that the potential bias may be taxa specific, and its influence might change depending on the specific inferences being derived from the data. To conclude, with this work, we show that efforts are required to increase the spatial evenness of sampling effort in citizen science projects. That could be addressed with the use of additional incentive mechanisms or gamification baselines in order to increase sampling effort in some regions or for some taxonomic groups (Huatari & Hamari, 2017).

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6. Using citizen science data to estimate climatic niches and species distributions

“Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.” Marie Curie

Published as: Tiago, P., Pereira, H.M., & Capinha, C. 2017. Using citizen science data to estimate climatic niches and species distributions. Basic and Applied Ecology. DOI: 10.1016/j.baae.2017.04.001.

Abstract

Opportunistic citizen data documenting species observations — i.e. observations collected by citizens in a non-standardized way — is becoming increasingly available. In the absence of scientific observations, this data may be a viable alternative for a number of research questions. Here we test the ability of opportunistic species records to provide predictions of the realized distribution of species and if species attributes can act as indicators of the reliability and completeness of these data. We use data for 39 reptile and amphibian species across mainland Portugal as a case study. We collected distribution data from two independent sources: a national citizen-science project and a scientific. We measure and compare the climatic niche width of the species as represented by each of the two data sources. Generalized linear mixed models (GLMM) were used to relate a set of response variables describing the species’ morphology, life-history, communication, type of locomotion, habitat and geographic distribution, to observed differences in niche widths. We also performed species distribution models (SDMs) for each of the two types of data using generalized additive models. We found that 12 species had more than 50% of their climate niche covered by citizen science data. Results from GLMMs suggested that the number of grid cells in which a species occurs and its use of forest habitat were positively related to the comprehensiveness of the sampling of climatic

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Chapter 6 niches by citizen science data. Variation in the p of SDMs for both types of data (as measured by the true skill statistic; TSS) was highly similar but SDMs from citizen science data had an overall lower performance. Nevertheless a few species achieved good predictions (TSS > 0.6) using these data. We conclude that species observations in citizen science projects can provide accurate predictions of species realized distributions, however, efforts should be made to identify the conditions under which these data are more likely to provide reliable representations of the species niches.

Keywords: citizen science, opportunistic species observations, climatic niche, species distribution models.

6.1. Introduction

Currently there is a large interest in citizen science — i.e. the engagement of non-professionals in scientific research (Miller-Rushing et al., 2012) — marked by a strong increase on citizen science programs (Dickinson et al., 2012). The scope of these programs is wide, covering research areas such as conservation biology and biodiversity monitoring, which are using citizen science programs to collect large amounts of species distribution data allowing to fill existing gaps of information (e.g. Pereira & Cooper, 2006; Danielsen et al., 2009; Danielsen et al., 2010; Pereira et al., 2010; Szabo et al., 2010; Dickinson et al., 2012). These citizen science projects can collect data in a similar way to scientific surveys, i.e. following strict protocols. In these cases, the main difference between the two types of survey concerns the general lack of experience of the citizen scientists which can lead to taxonomic misidentifications, reducing data accuracy. Alternatively, citizen science surveys of species distributions can take place through the collection of opportunistic data, data collected by non-standardized methods, with no sampling design and no systematic protocol (Dickinson et al., 2010). These later programs can cover wide spatial extents and often provide a large number of records (Chandler et al., 2012; Chandler et al., 2017). These opportunistic records have the same problem of lack of taxonomic expertise of the participants and can, in many cases, be spatially and temporally biased (Beck et al., 2013; Higa et al., 2014). Bias in species observations provided by citizen-science programs may hinder the usefulness of these records in ecological research. Importantly, the sampling effort of opportunistic records is generally not known but it can vary widely over time (Dickinson et al.,

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2010; Snäll et al., 2011) and across space (Dickinson et al., 2010; van Strien et al, 2013)) and between and within taxonomic groups (Kéry et al., 2010; Snäll et al., 2011).

Species distribution models (SDMs) — also known as ecological niche models or habitat selection models — are now widely used in ecological and evolutionary research (Kozak et al., 2008; Elith & Leathwick, 2009). These models relate data on species distributions with spatial environmental data in order to estimate locations where the species could occur (Elith & Leathwick, 2009). The questions they allow to address are wide-ranging and include how climate change may modify biodiversity patterns (Thuiller et al., 2008), where invasive species may become established (Capinha & Anastácio, 2011), where the hotspots of endangered species are located (Godown & Peterson, 2000), which areas should be prioritized for conservation (Chen & Peterson, 2002) or which locations are suitable for species translocations or cultivation (Jovanovic et al., 2000; Cunningham et al., 2002). SDMs rely on two types of data, species distribution data and environmental data. While the latter is now widely available at high spatial resolution and for wide spatial extents (Kozak et al., 2008), mainly due to large-scale mapping and modelling projects (e.g. Levinsky et al., 2013; Higa et al., 2014), the geographic distribution of many species still remains poorly known (Scheffers et al., 2012). In this context, it is relevant to understand if species observation records coming from citizen science projects are useful for inferring species distributions, and if so, under which conditions these records are more or less reliable.

In this work we assess whether opportunistic citizen science databases are viable data sources to model species distributions and test if species attributes can indicate the reliability and completeness of the opportunistic distribution data. We use amphibians and reptiles records from the BioDiversity4All database (www.biodiversity4all.org), a country-wide citizen science project in Portugal. We use herptiles (i.e. reptiles and amphibians) because many of these species tend to be cryptic and pass unnoticed and also because the prejudice associated with this group can affect the observations recorded in a citizen science project, as several of these species are feared and despised by many people (Price & Dorcas, 2011). These characteristics contribute to a distributional data shortfall – as opposed, for instance, to a few other conspicuous and ‘attractive’ groups such as birds for which distributional data is more abundant. We use opportunistic citizen science records to measure the climatic niche width of 39 herptiles. We then compare these niche widths to the ones obtained using records from an, independent,

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Chapter 6 long-term, scientific atlas and test for species traits and characteristics of the species distributions as indicators of the differences found. Finally, we also build projections of species distribution models based on each of the two distinct sources of species records and compare their predictive performances.

6.2. Materials and methods

In this work, we perform three main analyses to assess the merits of opportunistic citizen science records of species observations. In the first analysis, we measure the climatic niche width of the species, as represented by citizen science observations and as represented by an atlas using scientific observations. In the second analysis, we statistically relate the differences of widths we found between these two data sources with a large set of variables describing properties of the species. This aims to test if species properties can serve as predictors of the sampling of climatic niches by citizen science records. Finally, in our third analysis, we perform species distribution models for each type of species observation data and compare the predictive performances of each. We describe each of these analyses in detail below.

Species data

We used citizen science data for reptiles and amphibians recorded in mainland Portugal. These data were retrieved from the BioDiversity4All web portal (www.biodiversity4all.org), a Portuguese project that is similar to citizen science biodiversity databases elsewhere such as iNaturalist (http://www.inaturalist.org/) or iSpot (http://www.ispot.org/). We used only species occurrences that provided GPS recordings of geographical coordinates, from 1990 until December 2013 and resampled these records to a unique record per 10 x 10 km grid cell. Species occurring in less than three distinct grid cells were not considered because of insufficient records to calculate niche breadths (see below). In total we used data for 39 species (15 amphibians and 24 reptiles) (see Supplementary materials of Chapter 6.: Table 1), representing 91% of all herptile species known for mainland Portugal. The data set had a mean number of grid cells per species of 23.95 ± 20.27 (SD).

Simultaneously, we also collected 10 x 10 km grid cell distribution data of these species from the atlas of Portuguese herptofauna (Loureiro et al., 2008; hereafter referred to as “atlas”). This is

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Chapter 6 the most updated inventory of the herptiles species distributions available for Portugal and, unlike opportunistic citizen science databases, this atlas provides distribution data collected from previous scientific publications, systematic surveying in sampling stations and ad hoc observations from experts (Crespo & Oliveira, 1989; Malkmus, 1995; Godinho et al., 1999; Malkmus, 2004; Loureiro et al., 2008).

Environmental data

We collected five spatial variables for representing climatic variation across mainland Portugal: annual mean temperature; temperature seasonality; maximum temperature of warmest month; annual precipitation and precipitation seasonality. These variables were collected from the Worldclim database (Hijmans et al., 2005) at a spatial resolution of 5 arc minutes (c. 9 km at the mid-latitude of Portugal). We selected these variables because temperature and humidity are known to be highly influential in determining the distribution of herptiles, affecting their physiology and key aspects of biology and phenology (Girardello et al., 2010). Moreover, mainland Portugal has a strong latitudinal climatic gradient, ranging from semi-aridity in regions of the south to moist Atlantic climate in the north, allowing for the filtering effect of climate to be highly influential in the distribution of the studied species (Márquez et al., 2011). We performed a principal component analysis (PCA) to remove multi-collinearity among the 5 climatic variables and retained the two first components, which together accounted for 89% of the total variance in the original data (see Supplementary materials of Chapter 6.: Table 2).

Measuring sampling of climatic niche from citizen science data

For each species we calculated two distinct climatic niches, one using the species occurrence records found in the citizen science project (opportunistic data), and the other using the occurrences found in the atlas: for each of the data sources, we projected the occurrences of each species into the climatic space defined by the two first principal components obtained from the PCA. Next, we used a convex hull to delimit the climatic space occupied by each species. The width of the climatic niche of each species corresponded to the area enclosed by each convex hull. Finally, for each species we calculated the percentage of the total climatic niche width measured using occurrences from the atlas that was captured by the climatic niche calculated for the citizen science data — i.e. the area of the intersected polygons divided by the area of the scientific atlas polygon, multiplied by 100. By considering only the intersected polygon to

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Chapter 6 represent the climatic niched sampled by citizen science, we exclude any portion falling outside the niche space that is sampled by the atlas, as these cases are likely to have its origin on observation errors (see Discussion). A value of 100% corresponds to a situation where the citizen science data fully captures the climatic niche as represented by the atlas.

Species traits and characteristics of species distributions

In order to potentially explain interspecific differences in the climatic niche sampling from citizen science data, we collected a set of explanatory variables describing species morphology, life- history, communication mode, type of locomotion, habitat and geographic distribution. Data for these variables were collected from Trochet et al. (2014), Encyclopedia of Life (http://www.eol.org), Reptile database (http://reptile-database.org/), Reptile Trait Database (http://scales.ckff.si/scaletool), HerpNet (http:www.herpnet.org), Naturdata (http://naturdata.org) and Loureiro et al. (2008). A total of 27 variables were considered (see Supplementary materials of Chapter 6.: Table 3),

Relating variation of climatic niche and species traits

We used generalized linear mixed models (GLMM) (Breslow & Clayton, 1993) to relate the variation in the percentage of climatic niche sampled by citizen science (response variable) with species traits (the explanatory variables). The advantage of using GLMMs instead of simpler generalized linear models is that the former allow to incorporate information on the clustering of the sampling units, hence providing estimates of standard errors of model coefficients corrected for non-independence among observations. Here, to account for the phylogenetic non-independence of species (i.e., species that are phylogenetically closer should provide more similar observations than those that are further away), we built GLMMs using family (the taxonomic clustering unit used here) as random effect, while the remaining variables entered the models as fixed effects (Cassey et al., 2004). The response variable, which varies between 0 and 100% of climate niche overlap, was arcsine transformed before analyses to meet assumptions of normality. The models assumed a Gaussian distribution of errors and used an identity link function (Breslow & Clayton, 1993). To avoid problems of multi-collinearity among the explanatory variables, we calculated variance inflation factors (VIFs) for each. We then sequentially dropped the explanatory variable with the largest VIF until all values were below 5

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— a threshold below which collinearity is not a problem (Bowerman & O’Connell, 1990). This procedure led to the removal of 5 variables (see Supplementary materials of Chapter 6.: Table 3). Using the remaining variables, we built GLMMs for all possible combinations of fixed effects. All models used restricted maximum likelihood estimation and Akaike’s information criterion for small samples size (AICc) was used for inter-model ranking. We obtained final model coefficients from averaging the set of ‘best’ models, instead of selecting a single best model. The best ranked models were considered to be those holding a cumulative AICc weight of at least 0.95 (Karanth, 2012). We used the lme4 package (Bates et al., 2009) for R 3.1 (R Development Core Team, 2014) to implement the GLMMs, while model selection and averaging were conducted using the MuMIn 1.10.0 package (Barton, 2014).

Species distribution models

We also built species distribution models (SDMs) for all species that had 20 or more unique grid cells in the citizen science data set. Species having a lower number of records were discarded because of insufficient variability to include in a model with two explanatory variables (i.e., the two first components of a PCA; see below) (Vittinghoff & McCulloch, 2007). This corresponded to a total of 20 species (9 amphibians and 11 reptiles). SDMs for these species were made independently for the citizen science data set and the atlas data set. All models were made using generalized additive models (GAM) (Guisan et al., 2002) which contrasted the climatic conditions where the species was observed (as represented by the two main axes of the PCA, see above) with a random sample of climatic conditions in the grid cells where it was not recorded (i.e. ‘pseudo-absences’) (Capinha et al., 2012). The GAM models were implemented using the raster2 package of R using default values (see Supplementary materials of Chapter 6.: Text 1). A total of 10 replicate predictions were made for each species, each using a new random set of pseudo- absences. Final prediction corresponded to the average of the 10 replicate predictions.

We evaluated the performance of SDMs using citizen science observations by comparing the predictions with the species presences and absences found in the atlas. The atlas-based SDMs were evaluated by means of 5-fold cross-validation. Model evaluation was assessed using the true skills statistics - TSS (Allouche et al., 2006). This metric accounts for both sensitivity (i.e. proportion of species presences correctly predicted) and specificity (i.e. proportion of pseudo- absences correctly predicted) of the predictions. This metric ranges from -1 to 1 and requires

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Chapter 6 the definition of a probability threshold value for continuous predictions (Freeman & Moisen, 2008), so we performed its calculation along all possible thresholds using sequential increments of 0.01. We retained the maximum value obtained across all thresholds. TSS values >0.6 indicate good predictive performance, 0.2–0.6 fair to moderate, and <0.2 poor (Capinha et al., 2014).

6.3. Results

The proportion of the climatic niche width that was captured by citizen science data differed markedly among species (Figure 6.1(A).; Figure 6.1(B).; Figure 6.2.). Of the 39 species analysed, 12 had more than 50% of their known climate space covered by citizen science data (Figure 6.1(A).; Figure 6.1(B).) (see Supplementary materials of Chapter 6.: Table 4). One amphibian (Hyla meridionalis) and three reptiles (Tarentola mauritanica, Timon lepidus and Psammodromus algirus) had niche coverages higher than 70%. The anurans Pelophylax perezi, Bufo bufo and Hyla arborea achieved niche coverages higher than 60% while one salamander (Salamandra salamandra) and four reptiles (Psammodromus hispanicus, Rinechis scalaris, Natrix maura and Podarcis hispanicus) had coverages higher than 50%. The species with lowest coverages were two snakes: Coronella austriaca (5.9%) and Vipera latastei (6.0%) and two lizards: Chalcides bedriagai (1.2%) and Hemydactylus turcicus (6.9%). Eight of the 39 species had portions of the climatic niche sampled by citizen science that were not represented by observations of the atlas data set. For six of these species this ‘outside region’ corresponded to about 5% or less of all niche breadth measured by citizen science (Alytes obstetricans, Blanus cinereus, Hyla arborea, Podarcis hispanicus, Psamodromus hispanicus and Tarentola mauritanica). For two species, this region, however, corresponded to about 35% of the total breadth of the niche (Hyla meridionalis and Podarcis bocagei).

Based on averaged model coefficients of GLMMs relating variation in the percentages of climatic niche sampled by citizen science with species traits, we found that the two variables that had higher relative importance were the number of grid cells occupied by the species and the use of forest habitat; both showing a positive association with niche coverage (Table 6.1.). The remaining explanatory variables either had negligible relevance in the models or were not selected by any model (Table 6.1.).

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(A)

(B)

Figure 6.1(A). – Width of the climatic niche of amphibians as measured from atlas records (light grey), citizen science records (dark grey) and the total climatic space existing in the study area (mainland Portugal) (x axis).

Figure 6.1(B). – Width of the climatic niche of reptiles as measured from atlas records (light grey), citizen science records (dark grey) and the total climatic space existing in the study area (mainland Portugal) (x axis).

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Coronella austriaca Epidalea calamita

Timon lepidus Pelophylax perezi

Tarentola mauritanica Rana ibérica

Figure 6.2.- Representative examples of species’ climatic niche widths based on opportunistic data (blue) and scientific atlas data (light grey). The total climatic space existing in the study area (mainland Portugal) is also shown (dark grey). Polygons refer to the convex envelope of the corresponding species observations projected into a two-dimensional climatic space. The climatic space is defined by the two main axes of a principal components analyses summarizing overall climatic variation in the study area.

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Table 6.1. – Model averages of Generalized Linear Mixed Models (GLMM) relating the variation in the percentage of climatic niche sampled by citizen science to species traits (GridAtlas – number of grid atlas cells presence, H_Forest – forest habitat, StD_Altitude – standard deviation of the altitude, Climber – mode of displacement climber, H_Shrubland – shrubland habitat, H_Sand – sand habitat, H_Grassland – grassland habitat, Latmax – maximum latitude) (Level of significance * P < 0.05, ** P < 0.01, *** P < 0.001). A total of 39 herptile species that occur in mainland Portugal is used in the models.

Variables Relative variable Model-averaged coefficients Std. Error Pr (>|z|)

importance

GridAtlas 1.00 6.217e-04 1.325e-04 < 0.001 ***

H_Forest 0.99 2.198e-01 7.266e-02 0.01 **

StD_Altitude 0.51 -5.264e-04 6.433e-04 0.42

Climber 0.36 4.957e-02 8.138e-02 0.55

H_Shrubland 0.31 3.926e-02 7.325e-02 0.60

H_Sand 0.16 -1.570e-02 4.992e-02 0.76

H_Grassland 0.12 -8.510e-03 3.392e-02 0.80

Latmax 0.10 -2.499e-03 1.790e-02 0.89

Intercept 5.823e-01 8.301e-01 0.49

We built SDMs for 20 species based on citizen science records and validated the predictions using the scientific atlas. The performance of the models varied strongly among species (Fig. 6.3). The performance was good (TSS≥0.6) for three species: Hyla meridionalis, Lacerta schreiberi and Rana iberica, fair (0.6 >TSS≥0.2) for nine species:Hemorrhois hippocrepis, Hyla arborea, Lissotriton boscai, Mauremys leprosa, Pelophylax perezi, Psammodromus algirus, Salamandra salamandra, Tarentola mauritanica and Triturus marmoratus, while models for eight species: Bufo bufo , Epidalea calamita, Malpolon monspessulanus, Natrix maura, Podarcis hispanicus, Psammodromus hispanicus, Rinechis scalarisand Timon lepidus performed poorly (TSS<0.2) (Figure 6.3.) (see Supplementary materials of Chapter 6.: Table 5). We also found that the percentage of niche covered by citizen science is not associated with the predictive performance of the SDMs (Pearson correlation coefficient = -0.38, P = 0.096).

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We also built SDMs using scientific species occurrences which were validated by means of of a 5-fold cross-validation. The variation of the predictive performance of these models and of those from citizen science was very similar (Pearson correlation coefficient = 0.9, P < 0.05) but the latter had, on average, a lower performance (mean difference in TSS = -0.151, SD: 0.101) (Figure 6.3.).

Figure 6.3. – Comparison of the performance of species distribution models based on opportunistic data (y-axis) and on scientific atlas data (x-axis). The models were performed using a total of 20 species of herptiles occurring in mainland in Portugal. Each dot represents one species. Species distribution models were made using generalized additive models (GAM) and the predictive performance of each model was measured by means of the true skills statistics (TSS). The predictive performances achieved by the two types of data are strongly correlated (Pearson correlation coefficient = 0.9, P < 0.05). The dotted line indicates the relationship expected if performances from both types of predictions matched perfectly. The straight line corresponds to the relationship that was indeed observed.

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6.4. Discussion

In this work, we assessed whether species records from opportunistic citizen science databases were able to provide useful estimates of 1) the climatic niches of the species and of 2) their potential distributions, as drawn from species distribution models. We further aimed to identify how the species’ characteristics may act as indicators of the reliability and completeness of opportunistic distribution data.

Our results showed that the sampling of the species climatic niches based on citizen science records can vary greatly. This is not unexpected because, as in most biological groups, herptile species differ greatly in terms of elusiveness and secludedness, inherently leading to variation in detectability and ease of identification in the natural environment (Mazerolle et al., 2007). However, we also found that some species attributes may serve as indicators of the usefulness of the opportunistic sampling. The number of grid cells where the species occurs was the variable with higher explanatory power, suggesting that climatic niches are better sampled for species that have wide ranges. This could be because these are, in general, common species, including for instance Pelophylax perezi, Timon lepidus, Bufo bufo which are also relatively well known by the citizens. The other indicator we found good support for was if a species uses forest habitats, with those using them having a significantly better sampling of their climatic niche. The reason for this relationship is not clear. One possible explanation might be that this type of habitat is preferred by citizen scientists for making observations, but this requires further investigation.

We also identified some species having portions of the climatic niche represented by citizen science data that were not found in the climatic niches using the atlas data set. This was found for a small number of species (eight) and only for two of them this ‘novel’ portion of the niche represented more than 6% of its total niche breadth. Two non-mutually exclusive reasons can account for the niche expansions observed. The first is that the additional niche breadth is an ‘artifact’ resulting from misidentifications of the species, leading citizen scientists to record the species in places where it is in fact absent. The second reason is that it could indeed represent a niche expansion, resulting from true observations of the species in areas not recorded in the atlas data set. While it is hard to disentangle the relative role of the two possible causes, we expect the former to certainly have some expression, given the uncertain reliability of citizen

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Regarding the performance of potential distribution models, we also found a high interspecific variability, but importantly, several species were well modeled using citizen science occurrence records (3 species with good predictive performance and 9 with fair predictive performance). Moreover, variation in predictive performances among species was highly similar between models using citizen science data and those using data from a scientific database, despite a lower average performance of the former. This strongly suggests that opportunistic citizen science databases of species observations can represent a viable alternative to scientific records when these are not available, and the challenge might be to combine different data sources to achieve better results.

Interestingly, we found that SDMs for species having their climatic niches well sampled by citizen science were not necessarily the ones having higher predictive performance. This finding highlights that having good citizen science data does not guarantee having good SDMs. This is because the performance of SDMs is contingent on a number of other factors besides the comprehensiveness of species distribution data. We found that the performance of SDMs (both opportunistic and atlas-based) had a strong negative relationship with the total climatic niche of the species, which suggests that the performance of models for the study area is strongly determined by the degree of climatic specialization of the species. In other words, generalist species are less well predicted, as found elsewhere (Evangelista et al., 2008).

Other factors can further contribute to reducing SDM performance when one uses opportunistic species occurrences. One of such factors is that the opportunistic databases do not provide records of species absences which are highly desirable to improve SDMs. Hence our models using pseudo-absences (i.e. a sampling of all climatic conditions available) may be biasing the species potential distributions (VanDerWall et al., 2009) and it would be of great interest to have citizen science records of species absences as well. This is possible when observers use species check-lists (Sullivan et al, 2009). For studies in countries such as the Netherlands, where opportunistic data is numerous and widespread, geographical bias presents limited relevance,

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Chapter 6 since for many species the data collection is already very good (van Strien et al, 2013). With fewer data or when particular areas or habitat types remain under-sampled, post-stratification and weighting of sites according to their share in the statistical population under study may be a useful option (van Strien et al, 2013).

Some studies demonstrated how widespread, inexpensive count data, collected by citizen scientists can be analysed to reveal important information about the habitat preference and population dynamics of broadly dispersed and difficult-to-observe species (Thorson et al., 2014). Even though studies emphasized problems associated with opportunistic data, like being generated with uneven sampling effort over time (Prendergast et al., 1993; Botts et al., 2012; Maes et al., 2012), others gave evidence that opportunistically-gathered data has a great potential to make meaningful contributions to biodiversity science and policy-making (Schmeller et al., 2009; Tulloch et al., 2013; Isaac et al., 2014). Knowledge of species’ distributions is of particularly importance for ecology, evolution and conservation science. Opportunistic citizen science databases can provide scientists with a vast sets of occurrence records yet, understanding the benefits and limitations of this information is fundamental to achieve better results in modelling species distributions.

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6.5. List of supplementary materials

Table 1. List of the species of amphibians and reptiles analysed, number of grid cells where the species was present (atlas and opportunistic data).

Table 2. PCA loadings - contributions of the five climatic variables to the variance explained by the two PCA axes.

Table 3. List of species traits and characteristics of species distributions considered as explanatory variables in Generalized Linear Mixed Models.

Table 4. Width of the climatic niche of the herptile species, as measured from the scientific atlas and the opportunistic data.

Table 5. Performance of species distribution models for herptiles in Portugal, based on opportunistic data and on scientific (atlas), using generalized additive models (GAM) and the performance was measured by means of the true skills statistics (TSS).

Text 1. Default values of Raster2 package of R used to implement GAM models.

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Table 1. – List of the species of amphibians and reptiles analysed, number of grid cells where the species was present (atlas and opportunistic data).

Species Amphibians (A) Number of grid Number of grid Reptiles (R) cells with the cells with the species present species present (atlas data) (opportunistic data)

Acanthodactylus erythrurus (Schinz R 127 9 [1834])

Alytes obstetricans (Laurenti, 1768) A 340 10

Anguis fragilis Linnaeus, 1758 R 220 11

Blanus cinereus (Vandelli, 1797) R 422 9

Bufo bufo (Linnaeus, 1758) A 830 55

Chalcides bedriagai (Boscá, 1880) R 208 4

Chalcides striatus (Cuvier, 1829) R 399 17

Chamaeleo chamaeleon (Linnaeus, 1758) R 23 5

Chioglossa lusitanica Barbosa do Bocage, A 217 11 1864

Coronella austríaca (Laurenti, 1768) R 53 4

Coronella girondica (Daudin, 1803) R 298 8

Discoglossus galganoi Capula, Nascetti, A 414 13 Lanza, Bullini & Crespo, 1985

Emys orbicularis (Linnaeus, 1758) R 114 11

Epidalea calamita Laurenti, 1768 A 583 25

Hemorrhois hippocrepis (Linnaeus, 1758) R 360 24

Hemydactylus turcicus (Linnaeus, 1758) R 74 6

Hyla arborea Linnaeus, 1758 A 402 21

Hyla meridionalis Boettger, 1874 A 348 22

Lacerta schreiberi Bedriaga, 1878 R 371 22

Lissotriton boscai (Lataste, 1879) A 660 23

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Species Amphibians (A) Number of grid Number of grid Reptiles (R) cells with the cells with the species present species present (atlas data) (opportunistic data)

Malpolon monspessulanus (Hermann, R 771 31 1804)

Mauremys leprosa (Schweigger, 1812) R 571 36

Natrix maura (Linnaeus, 1758) R 726 26

Natrix natrix (Linnaeus, 1758) R 383 12

Pelobates cultripes (Cuvier, 1829) A 450 8

Pelodytes puntactus. Bonaparte, 1838 A 242 13

Pelophylax perezi (Seoane, 1885) A 983 94

Pleurodeles waltl Michahelles, 1830 A 459 11

Podarcis bocagei (Seoane, 1884) R 117 13

Podarcis hispanicus (Steindachner, 1870) R 665 41

Psammodromus algirus (Linnaeus, 1758) R 866 85

Psammodromus hispanicus Fitzinger, R 244 41 1826

Rana iberica Boulenger, 1879 A 359 27

Rinechis scalaris (Schinz, 1822) R 593 31

Salamandra salamandra (Linnaeus, 1758) A 767 39

Tarentola mauritanica (Linnaeus, 1758) R 404 43

Timon lepidus (Daudin, 1802) R 736 43

Triturus marmoratus (Latreille, 1800) A 685 25

Vipera latastei Boscá, 1878 R 135 5

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Table 2. – PCA loadings - contributions of the five climatic variables to the variance explained by the two PCA axes.

Variables Component 1 Component 2

Annual mean temperature 0.568

Temperature seasonality -0.110 -0.730

Maximum temperature of 0.396 -0.555 warmest month

Annual precipitation -0.535 0.155

Precipitation seasonality 0.471 0.355

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Table 3. – List of species traits and characteristics of species distributions considered as explanatory variables in Generalized Linear Mixed Models identifying potential indicators of proportion of climatic niches sampled by opportunistic citizen science observations. After calculation of variance inflation factors five variables were not used in the models (marked with *) Type of variable continuous or binomial (yes or no) are identified and range of values is provided for the first. The data for the variables were collected from Trochet et al. (2014), Encyclopedia of Life (http://www.eol.org), Reptile database (http://reptile-database.org/), Reptile Trait Database (http://scales.ckff.si/scaletool), HerpNet (http:www.herpnet.org), Naturdata (http://naturdata.org) and Loureiro et al. (2008).

Group of variables Variables Type of variable

Morphology Body-mass (g) Continuous (min: 1.77; max: 1500)

Total length (mm)* Continuous (min: 39.2; max: 1850)

Conspicuous coloration Binomial (yes/no)

Life-history Diurnal Binomial (yes/no)

Nocturnal Binomial (yes/no)

Months of hibernation Continuous (min: 3; max: 8)

Communication Accoustic communication* Binomial (yes/no) Type of locomotion Runner Binomial (yes/no) Climber Binomial (yes/no) Walker Binomial (yes/no) Jumper Binomial (yes/no) Crawler Binomial (yes/no) Swimmer Binomial (yes/no) Habitat Forest Binomial (yes/no) Shrubland Binomial (yes/no) Grassland Binomial (yes/no) Sand Binomial (yes/no) Wetland* Binomial (yes/no) Rocky Binomial (yes/no) Human Binomial (yes/no) Number of habitats* Continuous (min: 1; max: 7)

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Group of variables Variables Type of variable

Geographic distribution Altitude (mean; m) Continuous (min.:206.41; max: 497,93) Altitude (standard deviation; m)* Continuous (min.:114; max:290) Maximum latitude Continuous (min.:40.32; max:42.13) Minimum latitude Continuous (min.:36.99; max: 39.06) Latitude difference Continuous (min.: 3.08; max:5.14) Number of grid atlas cells presence Continuous (min: 244; max: 983)

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Table 4. – Width of the climatic niche of the herptile species, as measured from the scientific atlas and the opportunistic data. The study area corresponds to mainland Portugal. Niche widths correspond to the total area of the convex-hull that comprises the species observations when projected into a two-dimensional climatic space. NWA - corresponds to the niche width based on atlas records, NWCS - corresponds to the niche width based on citizen science records, %NWA - covered by CS corresponds to the percentage of atlas-based niche width that is covered by citizen science-based niche-width, %NWT covered by A - corresponds to the percentage of the total climatic space in the study area that is covered by species records in the atlas.

Species NWA NWCS % NWA covered %NWT covered by A by CS

Acanthodactylus erythrurus 24.3 6.3 25.9 68.7

Alytes obstetricans 22.3 10.2 43.4 63.0

Anguis fragilis 21.7 6.7 31.1 61.3

Blanus cinereus 27.1 11.4 42.2 76.6

Bufo bufo 33.6 21.3 63.4 95.2

Chalcides bedriagai 28.5 0.3 1.2 80.7

Chalcides striatus 32.7 13.2 40.4 92.5

Chamaeleo chamaeleon 0.9 0.3 31.3 2.5

Chioglossa lusitanica 12.8 2.0 15.2 36.4

Coronella austriaca 13.0 0.7 5.6 36.7

Coronella girondica 28.9 6.8 23.5 81.9

Discoglossus galganoi 33.3 7.3 21.9 94.2

Emys orbicularis 27.7 5.9 21.2 78.4

Epidalea calamita 35.0 15.6 44.5 98.9

Hemorrhois hippocrepis 29.5 13.2 44.6 83.4

Hemydactylus turcicus 4.2 0.3 6.9 11.8

Hyla arborea 29.6 18.5 62.0 83.8

Hyla meridionalis 16.5 19.1 75.5 46.6

Lacerta schereiberi 28.0 8.6 30.7 79.2

Lissotriton boscai 32.8 15.8 48.1 92.7

Malpolon monspessulanus 35.1 15.3 43.4 99.4

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Species NWA NWCS % NWA covered %NWT covered by A by CS

Mauremys leprosa 29.1 13.5 46.3 82.4

Natrix maura 34.7 16.1 53.6 98.1

Natrix natrix 32.0 11.8 36.9 90.4

Pelobates cultripes 33.7 14.1 41.7 95.3

Pelodytes puntactus 17.6 6.8 38.8 49.8

Pelophylax perezi 34.1 22.6 66.2 96.3

Pleurodeles waltl 27.3 8.5 31.0 77.3

Podarcis bocagei 9.5 4.4 30.3 26.8

Podarcis hispanicus 34.5 18.2 52.5 97.5

Psammodromus algirus 33.4 23.4 70.0 94.6

Psammodromus hispanicus 29.9 18.2 58.9 84.5

Rana iberica 21.9 5.9 27.2 61.9

Rinechis scalaris 32.3 19.0 58.7 91.5

Salamandra salamandra 32.9 19.6 59.5 93.0

Tarentola mauritanica 29.0 21.3 72.6 82.1

Timon lepidus 35.1 24.7 70.3 99.4

Triturus marmoratus 33.5 14.9 44.4 94.6

Vipera latastei 27.4 1.6 6.0 77.6

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Table 5. – Performance of species distribution models for herptiles in Portugal, based on opportunistic data and on scientific (atlas), using generalized additive models (GAM) and the performance was measured by means of the true skills statistics (TSS).

GAM Species/Models (TSS) GAM (atlas) (opportunistic)

Bufo bufo 0.08 0.25

Epidalea calamita 0.16 0.41

Hemorrhois hippocrepis 0.20 0.35

Hyla arborea 0.21 0.16

Hyla meridionalis 0.64 0.68

Lacerta schreiberi 0.68 0.75

Lissotriton boscai 0.41 0.48

Malpolon monspessulanus 0.06 0.23

Mauremys leprosa 0.48 0.63

Natrix maura 0.12 0.40

Pelophylax perezi 0.20 0.35

Podarcis hispanicus 0.19 0.43

Psammodromus algirus 0.36 0.41

Psammodromus hispanicus 0.01 0.35

Rana iberica 0.77 0.80

Rinechis scalaris 0.02 0.26

Salamandra salamandra 0.22 0.39

Tarentola mauritanica 0.27 0.43

Timon lepidus 0.11 0.39

Triturus marmoratus 0.40 0.46

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Text 1. – Default values of Raster2 package of R used to implement GAM models.

GAM = list( algo = 'GAM_mgcv',

type = 's_smoother',

k = -1,

interaction.level = 0,

myFormula = NULL,

family = binomial(link = 'logit'),

method = 'GCV.Cp',

optimizer = c('outer','newton'),

select = FALSE,

knots = NULL,

paraPen = NULL,

control = list(nthreads = 1, irls.reg = 0, epsilon = 1e-07

, maxit = 100, trace = FALSE, mgcv.tol = 1e-07, mgcv.half = 15

, rank.tol = 1.49011611938477e-08

, nlm = list(ndigit=7, gradtol=1e-06, stepmax=2, steptol=1e-04, iterlim=200, check.analyticals=0)

, optim = list(factr=1e+07)

, newton = list(conv.tol=1e-06, maxNstep=5, maxSstep=2, maxHalf=30, use.svd=0)

, outerPIsteps = 0, idLinksBases = TRUE, scalePenalty = TRUE, keepData = FALSE

) ),

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7. Synthesis of the main findings and future research avenues

“No one will protect what they don’t care about, and no one will care about what they have never experienced.” David Attenborough

7.1. Synthesis of the main findings

The aim of this thesis was to analyse different viewpoints of citizen science: from global to local, from project design to its practical uses, from the users’ psychological perspective up to a geographical and ecological perspective of the data collected. The following section synthesizes the main findings of the thesis and discusses further perspectives for research on citizen science.

Citizen science is becoming a mainstream approach to collect data on a variety of scientific disciplines, much supported by technology advances. In some sense, it is changing from the traditional “scientists using citizens as data collectors” to citizens as scientists. Every day, around the globe, new citizen science programs are being launched offering (i) new opportunities for citizen scientists to get involved and increase their scientific literacy (ii) new working challenges and opportunities for scientists (iii) chances for rethinking societies and (iv) new ways to influence policy makers (Tiago, 2016).

It is fundamental that a citizen science project design acknowledges the existence of social trade-offs like: deciding the scope and scale of the project; deciding to keep small, with local data control, closer to the volunteers and community issues, or connecting with larger initiatives to benefit data usage (Chandler et al., 2016); focusing more on guaranteeing data quality with the collection of rigorous, reliable data gathered in a systematized way, or on the easiness of producing data, with higher benefits to data volume, environmental education and engagement. When management decisions and

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Given its collaborative nature, citizen science involves a wide range of stakeholders, whose motivations and interactions can be determinant for the success of a citizen science project. Therefore, these motivations and interactions should be carefully considered when designing a project. It is important to plan carefully the design of a citizen science project taking into consideration issues such as project communication, recruiting and motivating participants, fostering innovation, interdisciplinary approach and group dynamics, promoting cultural changes, healthy habits and inclusion, awareness and education of participants, and guiding policy goals, among several others. Analysing these factors may contribute to the increased success of citizen science initiatives.

In Portugal, there is a significant lack of tradition on biodiversity observations and citizen science. In this context, identifying distinct citizen groups to whom a project like BioDiversity4All — the case-study analysed in this thesis — could be beneficial, proved to be an important task. In countries, such as Portugal, recruiting can be more difficult to achieve than in other countries, where established citizen science habits facilitate recruiting and participation. A bottom-up approach, with customized communication and engagement strategies, seems essential to recruit and retain citizen groups’ interest in the initiative. For example, school groups motivation and participation contrasts with those of specialists and naturalists. These citizen groups, with their different participation modes, have been an important asset, regarding biodiversity registering and, along the seven years of project’s experience, they contributed to generate a wide biodiversity database available online. Unfortunately, the lack of tradition, did not allow to get a higher number of answers, for instance, to the motivational questionnaire. This

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Motivational measures seemed to be appropriate to assess the effectiveness of citizens’ engagement and experience. In recent years, much has been written on communication and recruitment of participants for citizen science projects (Dickinson et al., 2012; Roy et al., 2012; Silvertown et al., 2013). Working carefully on people’s involvement is fundamental to increase and maintain their participation on citizen science projects. Other values and emotional responses regulate the participants support and involvement in citizen science initiatives after their initial experience (Cialdini, 2008). These other values may include a sense of purpose, such as learning, the interaction with a similar community or the gain of an emotional feedback. So, the affective response to participate in a citizen science project and the satisfaction of psychological needs will be independently and directly related to the intrinsic motivation for doing it.

Citizen science provides many opportunities to increase data collection and involve citizens in scientific research across many areas that are of relevance to monitor biodiversity globally, tracking species or environmental events. While this proliferation of projects offers great opportunity, there are also several challenges that will need to be taken into account.

One of such challenges is that, although some citizen science projects contribute to global biodiversity monitoring databases such as the Global Biodiversity Information Facility, many others do not. More taxa should be considered and extensively monitored and the geographic coverage should be expanded. To improve the number of taxa monitored and the geographic coverage, global biodiversity monitoring databases need to allow local citizen science projects to build on the interests and needs of participants, while at the same time provide them with tools and services to facilitate reporting.

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Another challenge relates to using citizen science data for further inferences. An analysis of sampling of species’ climatic niches based on citizen science records from BioDiversity4All was performed and compared with scientific records. The results obtained varied greatly between different species, which is not unexpected because, as in most biological groups, herptile species, the ones used in this analysis, differ greatly in terms of elusiveness and secludedness, leading to a variation in detectability and ease of identification in the natural environment (Mazerolle et al., 2007). We found that some species attributes may serve as indicators of the usefulness of the opportunistic sampling. Moreover, for some species, species distribution models presented good predictive performances highly similar between models using citizen science data and those using data from a scientific database, despite a lower average performance of the former. This was also true for species where the correspondin distribution models presented poor predictive performances. This strongly suggests that opportunistic citizen science databases of species observations can represent a viable alternative to scientific records when these are not available, and the challenge might be to combine different data sources to achieve reliable results. For studies in countries such as the Netherlands, where opportunistic data is abundant and widespread, geographical bias presents limited relevance, since for many species the data collection is already very good (van Strien et al, 2013). Although opportunistic citizen science databases provide scientists with vast sets of occurrence records, understanding the benefits and limitations of this information is fundamental to achieve better results in modelling species distributions.

7.2. Future research avenues

Citizen science is a scientific area growing and developing exponentially. New studies on citizen science, or using it as a tool, are being published on a daily basis. Based on this thesis experience, future research avenues for citizen science research are presented here.

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Citizen science and the new digital era

Digital technology is transforming most of human’s activities and will undoubtedly produce major changes in our societies, also having a strong impact on citizen science. This new digital era is changing people’s behaviour and the expectations they have while doing a certain activity (Schwab, 2015). Moreover, technological innovations are allowing not only the low-cost collection and storage of big data but also its complex analysis, and personalization of applications and projects to suit each person’s interests and motivations to participate. All these innovations will eventually be incorporated in citizen science projects and the results obtained must be analysed in future research in this area.

“Gamification”, or the trend of incorporating game-like elements in project design to foster participation, motivation and engagement (Huotari & Hamari, 2017), is also becoming a popular research area being used in several different areas including citizen science. It is expected that, in the following years, gamification will increase its strength in this area once it aims at growing users’ positive motivations towards a certain activity or a rising use of technology, and thereby, the quantity and quality of the output obtained (Hamari & Koivisto, 2015). Several studies, in various contexts, have shown gamification as an effective approach of increasing motivation and engagement (Hamari et al., 2014), although, it is not always easy to implement due to a poor understanding of how to successfully design it (Hamari & Koivisto, 2015). Studying how gamification can improve data quality and quantity and how it can increase the involvement of participants in a certain project is going to be a future avenue of research on citizen science.

Citizen science and the scientists’ motivations

Citizen scientists’ motivations for spending their spare time participating in scientific projects are much better studied than the motivation of scientists to participate in citizen science projects which is still not well understood (Raddick et al., 2010;

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Mankowski et al., 2011). The work published in this area tends to face a certain bias, as projects that, for some reason, did not work very well, tend to provide little motivation for scientists publishing about it (Riesch & Potter, 2014). However, as reported in Riesch & Potter (2014), scientists recognised that their participation in a citizen science program was a positive experience once it allowed them to publish peer-reviewed papers, defend successful PhD theses and occasionally receive- promotions. The two main concerns scientists identified in citizen science projects were: methodological, namely related to data quality issues, and ethical, involving issues related to volunteers handling, like data ownership, and implications for scientists, like volunteers doing scientific work for free. They had expectations about negative peer review reaction to these projects, although these did not prove to be real, once the reception was more positive than expected (Riesch & Potter, 2014). This positive reaction to citizen science can be further proven by the huge amount of papers published in this area, in the last years. In 2013, 2014 and 2015 there were 294, 353, 402 citizen science publications, respectively (Kullenberg & Kasperowski, 2016). Future research should focus on the scientists’ perspective and on further analysis of their concerns and motivations. It is also important to proceed with the analysis of the measures of success on citizen science initiatives, considering scientific, policy and social outputs.

Citizen science and biodiversity monitoring

Having access to biodiversity monitoring data is critical for a timely information of biodiversity state and change and should be encouraged (Costello et al., 2014). New biodiversity monitoring programs are particularly important in the case of gap regions and taxa for which data is scarce or virtually missing and should be prepared within the efforts to build a global biodiversity monitoring network (Pereira & Cooper, 2006). However, the establishment of these programs faces many challenges, with monitoring costs, training of human resources and political instability among the most important (Pereira & Cooper, 2006; Han et al., 2014). Citizen science programs and the development of coordinated capacity building initiatives can be good approaches to develop these monitoring programs. These associated with satellite remote sensing and

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Chapter 7 new in situ technologies, such as genetic barcoding, camera traps and drones, can also reduce monitoring costs, as new technologies become more accessible, enabling the expansion of current monitoring networks (Pimm et al., 2015). It would be useful, in the future, to develop further synergies between citizen science and these new technologies. The improvement of online toolkits, data repositories, and network portals, that coordinate and support citizen science projects could be beneficial to increase data interoperability. The adoption of common and standardized protocols in citizen science projects could help to use these data in monitoring programs. Even when these programs differ in their scale of implementation and aims, spatial integration can be fostered through integrated monitoring designs for a more efficient use of available data (Magnusson et al., 2013). Rewarding data providers, ensuring data quality standards and the sustainability of public databases are important measures to provide a prompt access to these data (Costello et al. 2013, 2014).

Citizen science and BioBlitz events

BioBlitz activities — i.e. an event where members of the public, professional scientists and voluntary naturalists work together to record as many species as possible within a delimited geographical area over a defined time period (from a combination of the words Bio = life and Blitz = something quick and intense) — are increasing throughout the world, in general, and in Europe and North America, in particular. These events are, simultaneously, contributing to public engagement with science and collecting information on biodiversity data. It is therefore important to analyse and evaluate the potential of these activities in all domains: from science outreach, up to conservation and managing measures. It is also important to foster cross-border research and collaboration in this area, to improve the outcomes taken from these events.

Citizen science and data quality

In-depth studies on the robustness of data quality and on the evaluation of statistical analysis approaches adapted to the specific characteristics of citizen science projects are

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Chapter 7 important to provide support to the use of the data collected. These should allow to reduce and/or to incorporate the sampling error, allowing a better balance between quantity and quality of data collected. Challenges associated with the analysis of citizen science data present good opportunities of collaboration between statisticians and conservation scientists promoting the development of novel statistical approaches and survey designs (Bird et al., 2013).

It was recognized in this thesis that, for a small number of herptiles, portions of the climatic niche represented by citizen science were not found in the climatic niches using the atlas data set (considered a baseline gold-standard). Two non-mutually exclusive reasons for this were identified. The first is that the additional niche breadth is an ‘artifact’ resulting from misidentifications of the species, leading citizen scientists to record the species in places where it is in fact absent. The second reason is that it could indeed represent a niche expansion, resulting from true observations of the species in areas not recorded in the atlas data set. To better distinguish between these two possibilities, which correspond to fundamentally different scenarios which have implications for how to use the data further, future work should consider the validation of such citizen science observations, by experts, in those new areas. A role for citizen science observations of species in new locations is perfectly possible and was already found in other studies (Dickinson et al., 2010).

Citizen science in Portugal

There is a long hard work to be developed regarding citizen science in Portugal, in general, and regarding biodiversity conservation, in particular. Studies to understand how to engage more people with scientific projects with the benefit of increasing biodiversity knowledge and awareness for generations to come, would be welcome. It would also be important to do future work with Portuguese scientists so that they can realize better the outcomes that can be reached through citizen science projects, identify good practices in other countries and understand the limitations and

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Appendix A

Proença, V., Martin, L.J., Pereira, H.M., Fernandez, M., McRae, L., Belnap, J., Böhm, M., Brummitt, N., García-Moreno, J., Gregory, R.D., Honrado, J.P., Jürgens, N., Opige, M., Schmeller, D.S., Tiago, P., & van Swaay, C.A.M. 2016. Global biodiversity monitoring: From data sources to essential biodiversity variables. Biological Conservation. DOI: 10.1016/j.biocon.2016.07.014.

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BIOC-06881; No of Pages 8 Biological Conservation xxx (2016) xxx–xxx

Contents lists available at ScienceDirect

Biological Conservation

journal homepage: www.elsevier.com/locate/bioc

Short Communication Global biodiversity monitoring: From data sources to Essential Biodiversity Variables

Vânia Proença a,⁎, Laura Jane Martin b, Henrique Miguel Pereira c,d,e,MiguelFernandezc,f, Louise McRae g, Jayne Belnap h, Monika Böhm g, Neil Brummitt i, Jaime García-Moreno j, Richard D. Gregory k, João Pradinho Honrado e,l, Norbert Jürgens m, Michael Opige n, Dirk S. Schmeller o,p, Patrícia Tiago e,q, Chris A.M. van Swaay r a MARETEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal b Harvard University, Center for the Environment, Harvard University, Cambridge, MA 02138, USA c German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany d Institute of Biology, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, 06108 Halle (Saale), Germany e CIBIO/InBIO - Rede de Investigação em Biodiversidade e Biologia Evolutiva, Universidade do Porto, Campus Agrário de Vairão, 4485-601 Vairão, Portugal f Instituto de Ecología, Universidad Mayor de San Andrés, Campus Universitario, Cota-cota, Calle 27, La Paz, Bolivia g Institute of Zoology, Zoological Society of London, Regent's Park, London NW1 4RY, UK h U. S. Geological Survey, Southwest Biological Science Center, Moab, UT 84532, USA i Department of Life Sciences, Natural History Museum, Cromwell Road, London SW7 5BD, UK j ESiLi Consulting. Het Haam 16, 6846 KW, Arnhem, The Netherlands k RSPB Centre for Conservation Science, RSPB, The Lodge, Sandy, Bedfordshire, SG19 2DL, UK l Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, FCUP-Ediﬁcio FC4 (Biologia), 4169-007 Porto, Portugal m Biodiversity, Evolution and Ecology (BEE), Biocenter Klein Flottbek, University of Hamburg, Ohnhorststrasse 18, 22609 Hamburg, Germany n Nature Uganda, The East Africa Natural History Society, P. O. Box 27034, Katalima Crescent, Naguru, Kampala, Uganda o Helmholtz Center for Environmental Research, UFZ, Department of Conservation Biology, Permoserstrasse 15, 04318 Leipzig, Germany p ECOLAB, Université de Toulouse, UPS, INPT, Toulouse, France q Centre for Ecology, Evolution and Environmental Changes (CE3C), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal r Dutch Butterﬂy Conservation, P.O. Box 506, 6700 AM Wageningen, Netherlands article info abstract

Article history: Essential Biodiversity Variables (EBVs) consolidate information from varied biodiversity observation sources. Received 26 February 2016 Here we demonstrate the links between data sources, EBVs and indicators and discuss how different sources of Received in revised form 15 June 2016 biodiversity observations can be harnessed to inform EBVs. We classify sources of primary observations into Accepted 11 July 2016 four types: extensive and intensive monitoring schemes, ecological field studies and satellite remote sensing. Available online xxxx We characterize their geographic, taxonomic and temporal coverage. Ecological field studies and intensive monitoring schemes inform a wide range of EBVs, but the former tend to deliver short-term data, while the Keywords: Primary biodiversity observations geographic coverage of the latter is limited. In contrast, extensive monitoring schemes mostly inform the popu- Biodiversity monitoring schemes lation abundance EBV, but deliver long-term data across an extensive network of sites. Satellite remote sensing is Essential Biodiversity Variables particularly suited to providing information on ecosystem function and structure EBVs. Biases behind data GEO BON sources may affect the representativeness of global biodiversity datasets. To improve them, researchers must Global biodiversity monitoring assess data sources and then develop strategies to compensate for identified gaps. We draw on the population Living Planet Index abundance dataset informing the Living Planet Index (LPI) to illustrate the effects of data sources on EBV representativeness. We find that long-term monitoring schemes informing the LPI are still scarce outside of Europe and North America and that ecological field studies play a key role in covering that gap. Achieving representative EBV datasets will depend both on the ability to integrate available data, through data harmonization and modeling efforts, and on the establishment of new monitoring programs to address critical data gaps. © 2016 Published by Elsevier Ltd.

⁎ Corresponding author. E-mail addresses: [email protected] (V. Proença), [email protected] (L.J. Martin), [email protected] (H.M. Pereira), [email protected] (M. Fernandez), [email protected] (L. McRae), [email protected] (J. Belnap), [email protected] (M. Böhm), [email protected] (N. Brummitt), [email protected] (J. García-Moreno), [email protected] (R.D. Gregory), [email protected] (J.P. Honrado), [email protected] (N. Jürgens), [email protected] (M. Opige), [email protected] (D.S. Schmeller), [email protected] (P. Tiago), [email protected] (C.A.M. van Swaay).

1. Introduction implementation of the Global Climate Observing System by Parties to the UN Framework Convention on Climate Change. EBVs are state In 2010, the parties of the United Nations Convention on Biological variables that stand between primary observations (i.e., raw data) and Diversity (CBD) adopted the Aichi Targets for 2020, which include high level indicators (e.g., the Living Planet Index (Collen et al., goals such as “reducing the direct pressures on biodiversity” and 2009)), and may represent essential aspects of biodiversity (from “improving the status of biodiversity by safeguarding ecosystems, genetic composition to ecosystem functioning) or may be integrated species and genetic diversity.” A mid-term assessment of the Aichi with other EBVs or with other types of data, such as data on drivers Targets (Tittensor et al. 2014) suggested that while actions to counter- and pressures, to deliver high-level indicators (Pereira et al., 2013; act the decline of biodiversity have increased, so too have pressures, GEO BON 2015a). The aim of the EBV framework is to identify a mini- and there has been a further deterioration in the state and trends of mum set of variables that can be used to inform scientists, managers biodiversity. In order to be effective, actions towards the Aichi targets and the public on global biodiversity change. will have to be supported by updated information on regional and In a first attempt to identify a minimum set of EBVs, GEO BON global patterns of biodiversity change, on drivers of biodiversity change, aggregated candidate variables into six classes: “genetic composition,” and on the effectiveness of conservation policies (Pereira and Cooper, “species populations,”“species traits,”“community composition,” 2006; Scholes et al., 2012; Tittensor et al., 2014). However, such data “ecosystem structure,” and “ecosystem function” (Pereira et al., 2013). are either missing or not readily accessible, as reflected by the lack Recently, Geijzendorffer et al. (2015) compared the EBV framework of quantitative data on biodiversity change in two-thirds of the 4th with indicators currently used for reporting biodiversity information national reports submitted by Parties to the CBD (Bubb et al., 2011), by seven biodiversity policy instruments. They found that the current and this affects the indicators too (Tittensor et al., 2014). suite of biodiversity indicators does not incorporate EBV classes equally. Researchers and conservation managers hoping to assess biodiversi- For instance, some EBV classes, like “species populations,” were well ty change at the regional or global level face a number of obstacles. First, represented in current indicators, while others, like “genetic composi- the geographic coverage of extant biodiversity monitoring programs tion,” were not. This asymmetry in EBV coverage is related to biases in is insufficient and uneven (Pereira et al., 2010, 2012). In particular, indicator selection, and ultimately to biases in extant biodiversity biodiversity monitoring efforts and ecological fieldwork are biased monitoring data, as indicator selection is often driven by data availabil- towards developed countries in temperate regions (McGeoch, et al. ity for reasons of feasibility (Geijzendorffer et al., 2015). Hence, the 2010; Martin et al., 2012; Hudson et al., 2014). Second, monitoring current set of indicators misses important biodiversity facets, due to schemes are typically not implemented at regional scales and few gaps in primary data. Instead, monitoring efforts should be driven by deliver long-term data, making it difficult to monitor biodiversity the information needs of selected indicators. The EBV framework change across space and time (Schmeller, 2008; Hudson et al. 2014; could become an important tool towards that end, by promoting cost- McGeoch et al., 2015;butseeJürgens et al., 2012). efficient approaches (Pereira et al. 2013, Fig.1). In an effort to optimize biodiversity monitoring initiatives, the Group This article aims to discuss how primary data sources affect the on Earth Observations Biodiversity Observation Network (GEO BON; representativeness of current EBV datasets. That is, if available, are Scholes et al., 2012) has developed the concept of Essential Biodiversity primary data well distributed across spatial and temporal scales of Variables (EBVs) that could form the basis of efficient and coordinated interest to provide meaningful measures on biodiversity change? Do monitoring programs worldwide (Pereira et al., 2013). The EBV data cover a diverse range of species groups? Previous studies have concept was inspired by the Essential Climate Variables that guide identified the existence of geographic and taxonomic biases in data

Fig. 1. Data ﬂow from different data sources of primary biodiversity observations into EBVs, followed by EBVs input to build biodiversity indicators used to monitor Aichi targets. The width of the arrows represents the relative input of each source into EBVs and of EBVs into indicators. Only a few EBVs are shown to illustrate the ﬂow of data from sources to biodiversity indicators, the relative contribution of each source to inform indicators will vary depending on the chosen indicators. LPI – Living Planet Index, RLI – Red List Index.

Please cite this article as: Proença, V., et al., Global biodiversity monitoring: From data sources to Essential Biodiversity Variables, BiologicalCon- servation (2016), http://dx.doi.org/10.1016/j.biocon.2016.07.014 V. Proença et al. / Biological Conservation xxx (2016) xxx–xxx 3 availability (e.g., Boakes et al., 2010, Pereira et al., 2010; Martin et al., monitoring schemes provide long-term data series and both can spread 2012; Hudson et al., 2014; Velasco et al., 2015). Data asymmetries will over a large spatial extent, but with different levels of coverage density. be a barrier to effective policy responses (Pereira et al., 2010; Moreover, although the above categorization is useful for data compari- Geijzendorffer et al., 2015). Hence, a first step towards improving global son purposes, it is important to note that because the geographic coverage EBV datasets is to assess the underlying data sources and to identify of monitoring schemes falls along a continuous gradient, the threshold existing biases. Only then will it be possible to develop strategies to between extensive and intensive monitoring schemes is not always cover data gaps and to optimize the use of available data. precise. For instance, while intensive schemes are applied in LTER sites, Here we demonstrate the links between data sources, EBVs and the ILTER network, which aggregates national LTER networks (i.e., a indicators. We classify the main sources of primary observations of ter- network of networks), has a widespread global coverage composed by a restrial biodiversity as: (1) extensive monitoring schemes, (2) intensive vast number of sites (Vanderbilt, et al. 2015; Table 2). monitoring schemes, (3) ecological field studies, and (4) satellite In recent years, data from ecological field studies and satellite remote sensing. We define each class and its scope by its geographic, remote sensing have been used increasingly as aggregated datasets taxonomic, and temporal coverage. We then analyze the dataset have become more accessible (Karl et al., 2013; Hudson et al., 2014; informing the Living Planet Index indicator (LPI; Collen et al., 2009)to Pimm et al., 2014). Ecological field studies, here defined as experimental illustrate the effects of primary data sources on EBV representativeness. or observational studies located outdoors (Martin et al., 2012), are The LPI is one of the most complete datasets of biodiversity observations numerous but often conducted independently of each other. Despite on population abundances. Therefore, the identified gaps should the large spatial coverage achieved when independent studies are provide an overview of the challenges in building a spatially explicit aggregated (e.g., Hudson et al., 2014), data scalability is constrained by and globally representative dataset for the population abundance EBV. the fact that ecological field studies do not share a common design or Finally, we discuss how biases in data sources affect the representative- data recording scheme. Also, compared with the other sources of ness of biodiversity monitoring datasets and we suggest methods to biodiversity data, ecological field studies tend to deliver short-term address data gaps. data series (Hudson et al., 2014). Yet, because ecological field studies explore different research questions and report many different types 2. Types of primary data sources of data, they also cover multiple EBV classes. Similarly, citizen science generates numerous opportunistic data on species observations. Sources of primary biodiversity observations can be characterized by Despite their large number and spatial coverage, the use of these data their geographic, taxonomic, and temporal coverage (Couvet et al., has been limited by quality issues, namely the lack of sampling 2011). In order to develop a typology we consider the following protocols. Recent developments in data correction methods promise features: (1) the coverage density (geographic coverage); (2) the to allow researchers to use opportunistic citizen science data to monitor observation effort per site and the sampling frequency (impacting on species distribution trends (van Strien et al., 2013; Isaac et al., 2014). taxonomic coverage, seasonal and day/night biases); and (3) the length Satellite remote sensing can deliver long-term data series with a high of time series (temporal coverage). Along these dimensions source sampling frequency and extensive geographic coverage. Satellite remote types fall into four categories: extensive monitoring schemes, intensive sensing can be distinguished from other types of remote sensing, such as monitoring schemes, ecological field studies and remote sensing aircraft or drones, by its global and continuous coverage. Moreover, and (Table 1). for sake of simplicity, the latter can be framed within the techniques used Extensive monitoring schemes maximize geographic coverage at the in long-term monitoring schemes and ecological field studies. Although expense of sampling effort per site, expressed as the number of ecosystem satellite remote sensing data are often vegetation-related, and are variables or functional groups monitored and/or sampling frequency typically used to monitor ecosystem function and ecosystem structure (Couvet et al., 2011). A widespread spatial coverage is often achieved EBVs (e.g., NPP, ecosystem extent and fragmentation), there is some through the simplification of the observation effort per site, namely by potential to monitor a broader range of EBVs (Turner, 2014; Skidmore focusing on a target species group. This trade-off not only reduces the et al., 2015; O'Connor et al., 2015; Pettorelli et al., 2016). Still, the ongoing costs per site but also enables volunteer engagement (Couvet et al., development of techniques for remote sensing data collection and 2011; Schmeller et al., 2009). Consequently, extensive monitoring processing creates challenges for the aggregation of time series, but schemes tend to focus on popular and conspicuous species groups, such also opportunities for the use of these data in biodiversity monitoring as birds and butterflies. Intensive monitoring schemes, meanwhile, invest (Pasher et al., 2013; Skidmore et al., 2015). Moreover, while the resolu- in the effort per site at the expense of geographic coverage. The goal of in- tion of satellite imagery is rapidly improving, enabling a more diverse tensive schemes is to capture ecological responses to environmental range of applications, the high costs and time needed for data processing change, by monitoring ecosystem functioning and species interactions could be a constraint (Pasher et al., 2013). This is reflected in the paucity (Couvet et al., 2011; Jürgens et al., 2012). Overall, extensive monitoring of map products, despite frequent data collection, and stresses the need schemes are best suited for monitoring trends in species distribution for international and multidisciplinary approaches that harness the use and abundance whereas intensive schemes can generate data for multiple of earth observation data (Pasher et al., 2013). In addition, the produc- EBVs. On the other hand, the larger and denser the network of sites in an tion of EBV datasets from satellite data will require coordinated action extensive monitoring scheme, the better the data scalability (i.e., the from data providers, biodiversity and remote sensing experts, and policy ability to aggregate data at multiple scales). Both extensive and intensive makers (Pettorelli et al., 2016).

Table 1 Qualitative assessment of the key attributes of primary sources of global biodiversity monitoring data and their coverage of EBV classes.

Extensive schemes Intensive schemes Ecological ﬁeld studies Remote sensing

Spatial coverage density High Low High Very high Effort per site Low High Low to high Low Time series Long-term Long-term Short-term Medium to long-term Sampling frequency Moderate High Moderate to high Very high Main biases or limitations Often directed to common, Low density of network Short-term data series; Low resolution data; often vegetation-related conspicuous or charismatic taxa sites (i.e. few sites) diverse ﬁeld protocols variables measured at the ecosystem level EBV classes Species populations, Community Multiple EBVs Multiple EBVs Ecosystem structure, Ecosystem function composition

Biodiversity monitoring datasets may combine primary biodiversity (i.e., baseline monitoring, conservation or natural resource management, observations from a single source, from different sources of the same or population studies) to infer if data originate from long-term monitor- type, or from different sources of different types (Fig. 1). The global ing schemes (i.e., extensive and intensive schemes) or from ecological map of 21st century forest cover change by Hansen et al. (2013) is an field studies. Lindenmayer and Likens (2010) proposed a minimum example of the first case. It combines time series of Landsat data to time series length of 10 years to qualify a study as long-term, while monitor forest cover change at the global scale. An example of the emphasizing that this is an operational criterion and that the adequate second case is the global Wild Bird Index (WBI; Gregory et al., 2005; threshold depends on the taxa or ecosystem processes being monitored. Gregory & van Strien 2010) dataset, which currently combines the For the purpose of our analysis, we assigned long-term monitoring species abundance data delivered by the Pan-European Common Bird schemes to long-term time series, here defined by a minimum of 10 Monitoring Scheme and the North American Breeding Bird Survey data points if the series started after 1995, or a minimum of 15 data (Table 2). The Living Planet Index (LPI; Collen et al., 2009)dataset points if the series started in 1995 or before. We do not discriminate provides an example of the last case, as it combines data from extensive extensive from intensive schemes because the dataset does not provide schemes, intensive schemes, and ecological field studies (see next precise information on the number of sampling sites and spatial cover- section). Moreover, primary biodiversity observations may be compiled age of the primary source. For that reason, we do not discuss the relative and made available through secondary sources, such as databases contributionofthesesourcetypestotheLPI.Ecologicalfield studies were (e.g., the PREDICTS database (Hudson et al., 2014)), data repositories, assigned to short-term time series of non-baseline monitoring studies. or institutional reports. Moreover, recently started time series collected for baseline monitoring purposes could evolve in the future into long-term time series. 3. The data sources behind the Living Planet Index Results show that for both birds and mammals a large share of the available data stems from temperate regions, in particular Europe and The LPI is among the best established indicators of the state of global North America (Fig. 2b-c); in the case of mammals, the equatorial region biodiversity (Butchart et al., 2010; Tittensor et al., 2014). It monitors is also better represented than other world regions. In order to correct changes in population abundance relative to a 1970 baseline using for geographic biases, a method of proportional weighting is currently time series of vertebrate populations across the globe (Collen et al., applied to the data when calculating the global LPI (McLellan et al., 2009; Fig. 2a). The underlying dataset aggregates time series for 2014). Regarding the temporal coverage, long-term time series N16,000 populations of over 3600 species of vertebrates (http://www. comprise a large share of bird population data but are largely confined livingplanetindex.org/, accessed 14.06.2016) and is one of the most to temperate regions (Figs. 2b, 3a, Appendix A). On the other hand, complete datasets on the population abundance EBV (Collen et al., short-term time series dominate for mammals across all world regions 2009; Tittensor et al., 2014). New data are added to the dataset as (Figs. 2c, 3b, Appendix A). Relatedly, both species groups are weakly they become available. A candidate population is included in the dataset represented by long-term data in tropical regions. Finally, the breadth only if data on population size are available for at least two years of the taxonomic coverage also differs between the two species groups. and data were always collected using the same method on the same Most sources of bird time-series target multiple taxonomic orders, population throughout the time series (Collen et al., 2009). Data gener- while in the case of mammals, with the exception of the equatorial ated by different types of primary sources, namely, extensive schemes, region, most sources target a single order (Figs. 2b-c, 3), and of these intensive schemes, and ecological field studies, are collected from a 60% target a single species (Appendix B). variety of available sources, including published scientificliterature, The bias towards the northern hemisphere is particularly accentuated on-line databases and gray literature (Collen et al., 2009). Therefore, in the case of birds, where extensive monitoring schemes, such as the the LPI dataset emerges from ongoing data survey and collection. North American Breeding Bird Survey and the Pan-European Common Here we analyze two subsets of the LPI dataset, the subset of terres- Bird Monitoring Scheme (Table 2), deliver many of the long-term trial birds (4406 time series of 1025 species, time interval: 1900–2013) (multi order) data on bird populations (Fig. 3a, Appendix C). Efforts are and the subset of terrestrial mammals (2229 time series of 438 species, being pursued to implement similar schemes in gap regions, particularly time interval: 1900–2014). We use information on the length of the in African countries (BirdLife International, 2013). Once implemented, time series (i.e., the time interval between the start and end year), these schemes will contribute to reducing the data gap and consequently number of data points (i.e., number of measurements made during the geographic bias. Notwithstanding, the International Waterbird Cen- the time interval) and the purpose of primary data collection sus (Table 2) has global coverage, operating in 143 countries and building

Table 2 Examples of large scale (i.e., international or continental) extensive (E) and intensive (I) monitoring schemes.

Monitoring schemea Type Coverage Network Species groups Start year Sampling frequency

Pan-European Common Bird Monitoring Scheme (PECBMS) E Europe N12,000 sites Birds 1980 Annual Breeding Bird Survey (BBS) E North America N1000 sites Birds 1966 Annual International Waterbird Census (IWC) E Global N25,000 sites Birds 1967 Annual Great Backyard Bird Count (GBBC) E Global N137,000 checklistsb Birds 2013c Annual National Butterﬂy Monitoring Schemes (BMS) E Europe 2000 sites Butterﬂies 1990 Annual Important Bird and Biodiversity Areas (IBAs) E Global N3000 sites Birds 1994 Annual International Long-Term Ecological Research Network (ILTER) I Global N600 sites Unrestricted d1993 Depends on species group National Ecological Observation Network (NEON) I U.S. e60 terrestrial sites Unrestricted 2011 Depends on species group Biodiversity Monitoring Transect Analysis in Africa (BIOTA) I Africa 4 regional transects Unrestricted 1999 Depends on species group Tropical Ecology Assessment & Monitoring network (TEAM) I Africa, Asia, 16 sites Plants, Mammals, Birds 2009 Annual (dry season) Latin America

a Websites: PECBMS (http://www.ebcc.info/); BBS (https://www.pwrc.usgs.gov/bbs/); IWC (http://www.wetlands.org/); GBBC (http://gbbc.birdcount.org/); BMS (http:// www.bc-europe.eu/); IBA (http://www.birdlife.org/); NEON (http://www.neoninc.org/); ILTER (http://www.ilternet.edu/); BIOTA (http://www.biota-africa.org/); TEAM (http://www.teamnetwork.org). b GBBC is a citizen science project, participants reported 137,998 checklists in 2013. c Global scope since 2013, running in US since 1998. d Some national LTER networks started before ILTER (e.g., US LTER started in 1980). e NEON operates 20 core terrestrial sites +40 relocatable terrestrial sites.

Fig. 2. Global distribution of terrestrial Living Planet Index (LPI) time series over a map of forest change (a), the size of each dot is proportional to the number of populations monitored (adapted from Pereira et al. 2010 and Hansen et al. 2013). Forest change is shown 1 km-pixels and includes areas of forest loss, forest gain and areas of both loss and gain; Latitudinal distribution of LPI time series of population abundance of terrestrial birds (b) and mammals (c) classiﬁed by time series length and taxonomic coverage of the data source: STSO - short-term single order, STMO - short-term multiple order, LTSO - long-term single order, LTMO - long-term multiple order (see Section 3 for a deﬁnition of each class). The midpoints of the latitude classes are shown. Source: ZSL/WWF, Hansen et al. 2013.

on the contribution of thousands of volunteers (Wetlands International, 4. Improving the representativeness of EBV datasets 2016). Long-term time series on mammal population abundances also show a bias towards the northern hemisphere, with data, equally distrib- The goal of global biodiversity monitoring is to measure biodiversity uted across North America, Europe and Asia. For this species group, responses to environmental change. This goal implies the use of time se- ecological field studies seem to be providing more long-term data than ries, in particular long-term data capable of capturing on-going changes monitoring schemes (Fig. 3b, Appendix C). through time (Scholes et al., 2012; Han et al., 2014). Data must also be Concurrently, short-term ecological field studies constitute an scalable, so that biodiversity change can be assessed across scales and alternative, relatively abundant and globally distributed source of time compared between sites (Pereira and Cooper, 2006; Han et al., 2014; series of population abundance. Ecological field studies deliver a great Latombe et al., this issue), and taxonomically representative, so that a part of the available time series of mammal population abundance more complete understanding of biodiversity change, which includes and are essential to complement bird monitoring data (Fig. 3,Appendix community level changes, can be achieved. C). However, differences in sampling protocols affect data scalability Here, we have examined what is arguably the most representative across space and time, hence limiting the full use of primary data global dataset of population abundance, a dataset produced to inform (Henry et al., 2008). Furthermore, while the aggregation of data from the LPI indicator. The analysis revealed two types of data biases, different sources confers a broad taxonomic coverage (within the geographic (i.e., more data from temperate regions) and temporal vertebrates) to the LPI dataset, many of the sources, particularly for (i.e., a predominance of short-term time series). Moreover, the mammals, target a single species or order (Figs. 2c, 3b, Appendix B). taxonomic coverage is currently restricted to vertebrates, which, This represents a limitation if community-level responses are relevant despite being the best known taxonomic group, represent only a small for monitoring the impacts of environmental change, such as the fraction of life on earth (Pereira et al., 2012). Long-term monitoring assessment of trophic chain effects or the identification of potential schemes for non-vertebrate taxa are still scarce and should be targeted “loser” and “winner” species. by future monitoring efforts. New programs, such as the National But- Finally, the global distribution of post-2010 data (Appendix D) terfly Monitoring Schemes in Europe (Table 2), are already helping to suggests that the commitment to the Aichi Targets has yet to be followed address this gap. The selection of target taxa is challenging however. by the implementation of new monitoring programs in gap regions. First, it is not feasible to select a comprehensive set of taxa for global

and monitoring have been proposed previously (Pereira et al., 2010; Schmeller et al., 2015). These include the acknowledgement and support of citizen science programs and the development of coordinated capacity building initiatives. With the aim of strengthening local capacity and promoting the engagement of new actors, from local communities, to NGOs and governments, GEO BON has launched the “BON in a Box” toolkit to support the development of new monitoring programs, and more specifically to support the development of national and regional Biodiversity Observation Networks (BONs) (GEO BON, 2015a). These efforts are complemented by the dissemination of standardized field protocols, such as the manual for butterfly monitoring (Van Swaay et al., 2012), to guide monitoring activities. Satellite remote sensing and new in situ technologies, such as genetic barcoding, camera traps and drones, are also expected to reduce monitoring costs as new technologies become more accessible, enabling the expansion of current monitoring networks (Pimm et al., 2015). For instance, camera traps are already being used by the TEAM network (Table 2) in large scale standardized monitoring of mammal and bird populations in the tropics (Beaudrot et al., 2016). This method is not only helping to address data gaps in an expedite way, but it is also delivering more robust data that supports a more precise assessment of on-going changes. The adoption of common protocols by future monitoring programs would be the most straightforward way to promote the integration of collected data (e.g., the BIOTA Africa Observation System (Jürgens et al., 2012)). Even when schemes differ in their scale of implementation and aims, spatial integration can be fostered through integrated monitoring designs for a more efficient use of available data (Magnusson et al. 2013). Concurrently, the implementation of minimum standards for EBV measurement and metadata could foster the integration of data from both new and ongoing monitoring schemes and ecological field studies (Schmeller et al., 2015). It should be noted, however, that top-down solutions that standardize monitoring protocols or at least the minimum requirements for data collection are not applicable to past data, and it might not be feasible or advisable to change existing protocols, for reasons of time series consistency. In those cases, data integration will depend on post-collection data harmonization techniques (Henry et al., 2008; Schmeller et al., 2015), as happens with the LPI Fig. 3. Reasons for primary data collection of LPI time series of birds (a) and mammals (b). dataset. Time series are classified by their length and the taxonomic coverage of the data source. Both new monitoring schemes and better integration of data from Conserv/NRM studies include studies on conservation and natural resource different schemes are necessary to enhance global datasets. However, management; population studies include studies on population dynamics and studies tracking declining species. STSO - short-term single order, STMO - short-term multiple even with these efforts, the level of data completeness will inevitably order, LTSO - long-term single order, LTMO - long-term multiple order (see Section 3 for be low at the global scale, requiring complementary approaches. Models adefinition of each class). can be used to estimate missing values and cover data gaps. Intensive Source: ZSL/WWF. monitoring schemes, in particular, collect a comprehensive set of variables at each site that can be used to support the development of monitoring purposes, and second, taxon groups respond differently to process-based models of ecosystem and community functioning. pressures and differ in their distribution patterns. Therefore, taxon Remote sensing data need to be linked to local observations to generate selection should follow pragmatic criteria, such as the feasibility of models on ecosystem processes, which can then be used to upscale local monitoring at the global scale and their functional role in ecosystem measures and estimate values at the regional scale based on proxies processes (GEO BON, 2010). measured by remote sensing (Pereira et al., 2013). Remote sensing pro- Generating new data through the establishment of new monitoring vides the matrix to integrate local observations across space and time, as schemes is one of two main approaches to enhance the representative- it delivers virtually continuous observations on ecosystem distribution ness of EBV datasets. The second approach is through data integration, and structure and other vegetation-related variables, and local observa- that is, by making the best use of data generated by ecological field tions might allow downscaling remote sensing products (Nagendra et studies and monitoring schemes, from local to regional scales, and by al., 2013). For instance, estimates of species presence or population satellite remote sensing. abundance can be obtained from models using ecosystem variables The establishment of new monitoring programs will be particularly measured by remote sensing and previously calibrated using in-situ important in the case of gap regions and taxa for which data is scarce data (GEO BON, 2015b). or virtually missing. For a more effective use of resources and coordinated Note that in addition to aggregation barriers and data gaps there are action, new monitoring programs should be prepared within the efforts upstream issues of data accessibility. Limitations in access to primary to build a global biodiversity monitoring network (Pereira and Cooper, data and data-holders' reluctance to share information remain a critical 2006). However, the establishment of new monitoring programs, barrier to global and cross-scale monitoring (Han et al., 2014; particularly in gap regions, faces many challenges, with monitoring Geijzendorffer et al., 2015). Publication of biodiversity monitoring data costs, training of human resources and political instability among the is critical for a timely assessment of biodiversity state and change and most important (Pereira and Cooper, 2006; Han et al., 2014). While no should be encouraged (Costello et al., 2013). Required actions include easy solution exists for the latter, options to reduce the costs of training the implementation of publishing mechanisms that reward data

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Appendix B

Chandler, M., See, L., Buesching, C.D., Cousins, J.A., Gillies, C., Kays, R.W., Newman, C., Pereira, H.M. & Tiago, P. 2017. Involving citizen scientists in biodiversity observation. In The GEO Handbook on Biodiversity Observation Networks (eds M. Walters & R.J. Scholes), 211–237. Springer International Publishing, Cham. DOI: 10.1007/978-3-319-27288-7_9.

185

Michele Walters • Robert J. Scholes Editors

The GEO Handbook on Biodiversity Observation Networks Editors Michele Walters Robert J. Scholes Natural Resources and Environment Global Change and Sustainability Research Council for Scientiﬁc and Industrial Institute Research (CSIR) University of the Witwatersrand Pretoria Johannesburg South Africa South Africa and

Centre for Wildlife Management University of Pretoria Pretoria South Africa

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This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Chapter 9 Involving Citizen Scientists in Biodiversity Observation

Mark Chandler, Linda See, Christina D. Buesching, Jenny A. Cousins, Chris Gillies, Roland W. Kays, Chris Newman, Henrique M. Pereira and Patricia Tiago

Abstract The involvement of non-professionals in scientiﬁc research and environmental monitoring, termed Citizen Science (CS), has now become a mainstream approach for collecting data on earth processes, ecosystems and biodiversity. This chapter examines how CS might contribute to ongoing efforts in biodiversity monitoring, enhancing observation and recording of key species and systems in a standardised manner, thereby supporting data relevant to the Essential Biodiversity Variables (EBVs), as well as reaching key constituencies who would beneﬁt Biodiversity Observation Networks (BONs). The design of successful monitoring or observation networks that rely on citizen observers requires a careful balancing of the two primary user groups, namely data users and data contributors (i.e., citizen

M. Chandler (&) Earthwatch Institute, 114 Western Avenue, Boston, MA 02143, USA e-mail: [email protected] L. See International Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg, Austria e-mail: [email protected] C.D. Buesching Wildlife Conservation Research Unit, Department of Zoology, The Recanati Kaplan-Centre, University of Oxford, Tubney House, Abingdon Road, Tubney, Abingdon OX13 5QL, UK e-mail: [email protected] J.A. Cousins Earthwatch Institute, Mayﬁeld House, 256 Banbury Road, Oxford OX2 7DE, UK e-mail: [email protected] C. Gillies The Nature Conservancy Australia, 60 Leicester St., Carlton, Australia e-mail: [email protected] R.W. Kays North Carolina State University and NC Museum of Natural Sciences, Raleigh, NC, USA e-mail: [email protected] C. Newman WildCRU, Department of Zoology, The Recanati Kaplan-Centre, University of Oxford, Tubney House, Abingdon Road, Tubney, Abingdon OX13 5QL, UK e-mail: [email protected]

© The Author(s) 2017 211 M. Walters and R.J. Scholes (eds.), The GEO Handbook on Biodiversity Observation Networks, DOI 10.1007/978-3-319-27288-7_9 212 M. Chandler et al. scientists). To this end, this chapter identiﬁes examples of successful CS programs as well as considering practical issues such as the reliability of the data, participant recruitment and motivation, and the use of emerging technologies.

Keywords Citizen science Á Essential biodiversity variables Á Biodiversity monitoring Á Data reliability Á Data standards Á Emerging technologies

9.1 Citizen Science

The involvement of non-professionals in scientiﬁc research and environmental monitoring, termed Citizen Science (CS), has now become a mainstream approach for collecting data on earth processes, ecosystems and biodiversity. Although the term has appeared only more recently as a formal way of referring to these activities, CS actually has a very long history. In the past, amateur scientists have contributed a great deal to science, particularly with networks of weather collectors and ocean monitoring. Famous names such as Alfred Russell Wallace, Thomas Edison and Gregor Mendel are all prime historical examples of citizen scientists. With recent changes in technology and social media enabling outreach and interaction with a much wider audience than ever before, CS is becoming an increasingly integral part of contemporary scientiﬁc research, particularly in terms of data acquisition. With limited budgets to pay for professional scientists, or to support government-sponsored environmental monitoring, engaging citizens to help with ground-based monitoring efforts and the reporting of rare events, makes sense. By achieving hitherto unrealised levels of large-scale monitoring for features which remain invisible to remote sensing, CS is likely the most realistic way of covering much of the planet’s biosphere (Pereira and Cooper 2006; Pereira et al. 2010).

H.M. Pereira German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany e-mail: [email protected] H.M. Pereira Institute of Biology, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, 06108 Halle (Saale), Germany H.M. Pereira Cátedra Infraestruturas de Portugal Biodiversidade, Centro de Investigaçãoem Biodiversidade e Recursos Genéticos (CIBIO/InBIO), Universidade do Porto, Campus Agrário de Vairão, 4485-661 Vairão, Portugal P. Tiago Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisbon, Portugal e-mail: [email protected] 9 Involving Citizen Scientists in Biodiversity Observation 213

This chapter provides examples of how CS can contribute to ongoing efforts in biodiversity monitoring, enhancing observation and recording of key species and systems in a standardised manner, and supporting the collection of Essential Biodiversity Variables (EBVs), as well as reaching key constituencies who would benefit Biodiversity Observation Networks (BONs). Referred to as contributory CS, which is based on a typology developed by Bonney et al. (2009a, b) and Miller-Rushing et al. (2012), involving citizens primarily in data collection is the most common form and probably the simplest starting point for those interested in developing new CS projects. Other forms of CS are also possible, such as through the Earthwatch model (http://earthwatch.org/) where members of the public join research projects; these require more training, direction and supervision of participants to ensure systematic data collection for answering specific scientific research questions. The design of successful monitoring or observation networks that rely on citizen observers requires a careful balancing of the two primary user groups, namely data users and data contributors (i.e., citizen scientists; Pocock et al. 2015). To this end, this chapter also considers practical issues such as reliability of the data (Buesching et al. 2014), participant recruitment and motivation (Buesching et al. 2015; Silvertown et al. 2013), and the use of emerging technologies. All are important issues that determine whether useable data are collected and how a team of willing and capable participants is maintained.

9.2 Citizen Science and Biodiversity Observation Networks (BONs)

The aim of a BON is to help improve information available on the distribution and change of biodiversity in a given region or associated with a speciﬁc theme (e.g., an ecosystem domain or a particular type of monitoring) (GEO BON 2015a, b). BONs obtain baseline data, develop monitoring programs to detect change, publish biodiversity observations, and help identify the factors underlying the observed changes. This supports the modelling communities and the development of ecosystem assessments and future scenarios supporting conservation mitigation strategies (Akçkaya et al. 2016). CS can contribute to this aim in a number of different ways, as outlined below.

9.2.1 Monitoring Biodiversity Over Large Spatial and Temporal Scales

Using citizen scientists in biodiversity monitoring networks signiﬁcantly expands the spatial and temporal scale of what is possible, because the additional people 214 M. Chandler et al. allows considerably more data to be collected, both in terms of range and quantity. CS can be a practical way to achieve the geographic coverage required to document ecological patterns and address ecological questions at scales relevant to regional population trends, shifts in species range, patterns of migration, impacts of environmental processes like Human Induced Rapid Environmental Change (Sih 2013), spread of infectious disease and invasive species, and national environmental policy assessment. This is especially important for smaller, rarer or more fragmented habitats and species that may be hard to detect in coarse or infrequent surveys, but also for very common and widespread species where the sheer size of the species range may prove challenging to sample (Buesching et al. 2015). Large-scale CS projects are thus valuable when attempting to gather data on large geographical scales, such as engaging participants in national or even global surveys, with participants collecting data in many locations simultaneously. These projects can involve very substantial numbers of contributors, and can persist for a long time, making it possible to map trends. Moreover, CS can lead to the engagement and coordination of an active and long-lasting community around permanent monitoring sites such as those established by existing BONs (e.g., National Ecological Observatory Network (NEON) in the USA through its Citizen Science Academy, Terrestrial Ecosystem Research Network (TERN) sites in Australia who have partnered with Earthwatch) or long-term research and monitoring plots such as Hawkwatch Monitoring North America sites. The results can also be used to inform population management decisions and even international environmental and conservation policy.

9.2.2 Mapping Species Location and Abundance

Most biodiversity-oriented CS programs aim to record the location and abundance of species through time (Table 9.1). These observations are used to monitor population trends and geographic range dynamics (e.g., eBird http://ebird.org/content/ ebird, iNaturalist http://www.inaturalist.org, iSpot http://www.ispotnature.org). Indeed, close to 50 % of all species occurrence records in GBIF are published from sources that already publish data collected through CS projects (http://www.gbif. org). Most of these programs contribute largely to collaborative projects, rely on high participation rates to reduce data errors (e.g., by 2015 eBird had over 200 million observations contributed to GBIF; http://www.gbif.org), and in many cases there is little or no formal training required for participation. Some programs are designed to ensure a balance between providing regular scientific updates on species location and movements while engaging the public in enjoyable, hobby-like activities. Some of these programs have stemmed from rapid biodiversity surveys that involve both researchers and the public, e.g. a BioBlitz (Lundmark 2003). They are often run in association with local museums, naturalist clubs and schools on international days of environmental recognition. BioBlitzes are still immensely popular and continue to contribute to the discovery of new Table 9.1 Potential contribution of citizen science projects to the candidate Essential Biodiversity Variables 215 Observation Biodiversity in Scientists Citizen Involving 9 EBV Class CandidateEBV Description Project/Database Name Website Country Genetic Breed and Number of animals of each livestock breed and No known CS projects as yet, composition variety proportion of farmed area under each local crop but CS approaches could be diversity variety, at multiple locations used to study this EBV—see Bohmann et al. (2014) Species Species Presence surveys for groups of species easy to iNaturalist http://www.inaturalist.org/ World population distribution monitor, over an extensive network of sites iSpot http://www.ispot.org.uk/ World with geographic representativeness. Potential Observado http://www.observado.org/ World role for incidental data from any spatial locations eBird http://ebird.org/ World eMammal http://eMammal.org World The Great SunFlower Project http://www.greatsunflower.org/ US Breeding Bird Survey http://bto.org/bbs/index.htm/ UK French Common Bat http://mnhn.fr/vigie-nature/ France Monitoring Pan European Common Bird http://ebcc.info/pecbm.html/ Europe Monitoring FrogWatch USA http://www.nwf.org/ USA frogwatchUSA/ Ontario Turtle Tally http://torontozoo.com/ Canada adopttapond/ German Butterfly Monitoring http://science4you.org Germany Spiders Web Watch http://spiderwebwatch.org/ Canada, US Anglers Monitoring Initiative http://riverflies.org/ UK Great Lake Worm Watch http://nrri.umn.edu/worms/ US Plant Watch http://www.naturewatch.ca/ Canada BioDiversity4All http://www.biodiversity4all.org/ Portugal Coral Watch http://www.coralwatch.org/ World (continued) Table 9.1 (continued) al. et Chandler M. 216 EBV Class CandidateEBV Description Project/Database Name Website Country Species Population Population counts for groups of species easy to eBird http://ebird.org/ World population abundance monitor and/or important for ecosystem German Butterfly Monitoring http://science4you.org Germany services, over an extensive network of sites Garden Moths Count http://www.mothcount.brc.ac.uk/ UK with geographic representativeness REEF (Coral Reef Fish) http://www.reef.org/ World Custodians for Rare and http://www.sanbi.org/ South Endangered Wildflowers biodiversity-science/state- Africa biodiversity/biodiversity- monitoring-assessment/ custodians-rare-and-endan Species Phenology Record timing of periodic biological events for National Phenology Network https://www.usanpn.org/ US fi traits selected taxa/phenomena at de ned locations BudBurst http://windows.ucar.edu/citizen_ US science/budburst/ Seasons Observatory (plant http://www.obs-saison.fr France bloom and migratory birds) Species Natal Record median/frequency distribution of No projects known at present, traits Dispersal dispersal distances of a sample of selected taxa but advanced CS observers Distance could contribute to professional databases such as TRY Species Migratory Record eBird http://ebird.org/ World traits behaviour presence/absence/destinations/pathways of Migrant Watch in India http://www.migrantwatch.in/ India migrant selected taxa Migrant watch in UK http://butterfly-conservation.org/ UK 612/migrant-watch.html (continued) Table 9.1 (continued) 217 Observation Biodiversity in Scientists Citizen Involving 9 EBV Class CandidateEBV Description Project/Database Name Website Country Community Taxonomic Multi-taxa surveys (including by iNaturalist http://www.inaturalist.org/ World composition diversity morphospecies) and metagenomics at selected iSpot http://www.ispot.org.uk/ World in situ locations at consistent sampling scales Observado http://www.observado.org/ World over time BioDiversity4All http://www.biodiversity4all.org/ Portugal Watchers—Intertidal http://www.beachwatchers.wsu. US Monitoring edu/ Community Species Studies of important interactions or interaction Naturalist http://www.inaturalist.org/ World composition interactions networks in selected communities, such as iSpot http://www.ispot.org.uk/ World plant-bird seed dispersal systems Observado http://www.observado.org/ World 218 M. Chandler et al. species and range extensions. In some countries (e.g., Ireland), nationally organised Bioblitzes have become an important avenue to collecting biodiversity data as well as engaging citizens. Environmentally distributed ecological networks (EDENs) are growing increasingly important in ecology, coordinating research in more disciplines and over larger areas than ever before (Craine et al. 2007).

9.2.3 Timing of Nature’s Events

Recently, the potential for broad scale analyses of phenology and migration has increased considerably due to public interest in conservation and particularly the development of several online CS projects (Table 9.1). Ranging from national to international efforts, examples include Nature’s Notebook https://www.usanpn.org/ natures_notebook, which supports large-scale plant phenology observations to collect ecological data on the timing of leafing, flowering, and fruiting of plants across the USA, attracting thousands of participants, and Project Budburst in the USA http://www.budburst.org/, which also has a strong educational focus. In the UK, Nature’s Calendar http://www.naturescalendar.org.uk/ addresses the lack of long term phenological data available, as does BirdTrack http://www.bto.org/ volunteer-surveys/birdtrack/about. ClimateWatch http://www.climatewatch.org.au asks volunteers to record the seasonal behaviour and location of over 180 marine and terrestrial animals across Australia. Engaging educators in the program has increased the number of sightings recorded significantly, while raising awareness about the impacts of climate change. Other national systems include Observatory of Seasons http://www.obs-saisons.fr/ in France, NatureWatch https://www. naturewatch.ca/english/ in Canada, and MigrantWatch http://www.migrantwatch. in and SeasonWatch http://www.seasonwatch.in/ in India. Finally, Journey North http://www.journeynorth.org is global in scope, aiming to study wildlife migration and seasonal change via various projects, e.g. the Spring Monarch Butterfly Migration Monitoring project http://www.learner.org/jnorth/monarch/index.html, which allows participants to track monarch butterfly migrations each fall and spring. Collectively, these projects span a vast range of plant and animal species, using web platforms and mobile apps to record data from the field.

9.2.4 Early Detection and Mapping of Pests and Invasive Species

CS projects can contribute to finding and tracking invasive species, which is especially important in detecting early outbreaks of important pests and exotics. At a more local level, apps developed for Outsmart Invasive Species http://masswoods. net/outsmart, and IveGot1 http://www.eddmaps.org/florida/report/index.cfm allow 9 Involving Citizen Scientists in Biodiversity Observation 219 species observations to be submitted directly from the field in order to help detect and map the extent of invasive species in Massachusetts and Florida. CS programs are increasingly working at larger scale such as monitoring marine invasive species along the east coast of North America (Invasive Tracers, http://www. InvasiveTracers.com), with a focus on recently introduced non-native crabs, and tracking the spread of exotic lionfish in the Caribbean (e.g., http://www.reef.org/ programs/exotic/report and http://nas.er.usgs.gov/SightingReport.aspx). In the UK, the Big Seaweed Search http://www.nhm.ac.uk/nature-online/british-natural- history/seaweeds-survey/ is asking citizens to record sightings of 12 species of live seaweed in order to track and monitor the effects of climate change and invasive species such as wireweed (Polygonum aviculare L.) on the UK’s seaweeds. Larger and collaborative government sponsored initiatives have been developed that bundle together reporting of exotics by CS, verification by experts, automated notification of agencies to act on potential threats, as well as tools to manage exotics (http://www.imapinvasives.org).

9.2.5 Desk Assessment and Field Validation of Imagery

CS can help to process large amounts of digital footage created by the recent explosion of low cost-high resolution video, photographic and satellite imagery. Previously, such footage would have been too cumbersome to analyse in its entirety by any single researcher or institution. While automated software can assist in this process, online crowdsourcing is particularly useful in instances where the human eye performs better than image analysis algorithms. For example, Digital Fishers http://digitalﬁshers.net/ allows volunteers to analyse deep sea video footage and describe what they see through a web interface that resembles the control panel of a deep sea submersible. As volunteers become more experienced, they are asked to improve their descriptions and are rewarded with new facts about deep sea species. The same video is analysed by multiple volunteers to improve consistency of descriptions. The program provides the public with an opportunity to see under- water habitats and rarely sighted deep sea species. Moreover, it continually provides new ‘missions’ for volunteers to maintain interest while providing researchers with valuable biodiversity data. Another example is the crowdsourcing of species from photographs taken by a camera trap, e.g. the Zooniverse Wildcam Gorongosa project http://www.wildcamgorongosa.org/ and Snapshot Serengeti (Swanson et al. 2015) as well as the multitude of other Zooniverse projects http://www.zooniverse. org that involve citizens in analysing photographs and images. Crowdsourcing of digital imagery analysis has been shown to improve existing online data sets such as global land cover. Geo-Wiki http://www.geo-wiki.org/ involves volunteers in clarifying discrepancies between different land cover maps from their observations of Google Earth images. This removes areas of ambiguity for the development of integrated land cover maps and, as a more accurate baseline, to inform integrated assessment models. Other programs such as ForestWatchers 220 M. Chandler et al. http://forestwatchers.net/ ask volunteers to clean satellite images by selecting those with the least cloud cover, or identify areas of deforestation by marking suspect areas on a satellite image using online drawing tools. Moreover, other CS programs such as http://www.tela-botanica.org/page:herbonautes and http://herbariaunited. org/atHome/ are now engaging citizens to assist in interpreting and digitizing their museum collections making historic records accessible to wider audiences.

9.2.6 Linking Citizen Science and Large Scale Biodiversity Monitoring Databases

The global scale of anthropogenic change and the significant variance in its impact across regions has resulted in international environmental agreements, such as the Convention on Biological Diversity (Balmford et al. 2005). GEO BON aims to develop a global observation system that provides regular and timely information on biodiversity change to the CBD and other users. The examples above demonstrate the power of CS in data collection and science communication at both local and regional levels. We think there are three key initiatives that could be developed to scale up CS efforts to a global level: • Foster data compatibility, standards, quality, storage and sharing of CS data in nationally or internationally recognised databases and support CS programs in choosing which of these are most appropriate for their program. Wiggins et al. (2013) have produced a guide on data management for CS projects that covers the full data management cycle and provides best practice guidance on many CS data issues; • Identify data that can be collected by CS projects around the world (see Table 9.1) and carry out a gap analysis to determine where existing and future CS programs can best compliment or enhance other global data sets. For example, GEO BON has produced a candidate list of Essential Biodiversity Variables (Pereira et al. 2013) that may be appropriate to be collected by CS; and • Build capacity globally within organisations to develop, lead and sustain CS programs that achieve sufficient rigor to collect valid data, and meaningfully engage participants over the spatial and time scales needed. The first initiative could be realised through inviting CS programs that operate at scales larger than the local community (i.e., state, national or international programs) to join the larger scale initiatives (e.g., GEO BON), involving clear linking mechanisms (e.g., a GEO BON representative) providing guidance to this effect. For example, this representative could ensure that the data are standardized internationally, e.g. Darwin Core (GBIF 2012), and assist in identifying the most suitable national or international databases for storage. Such guidance would reduce the costs associated with developing web interfaces and web server costs associated 9 Involving Citizen Scientists in Biodiversity Observation 221 with housing online databases. The outcome would provide global biodiversity observatories and the broader scientific community with access to usable, standardised data and provide a mechanism that can be communicated to the general public. Institutions wishing to support global biodiversity observatories can also assist CS by designing and testing both existing and new methods of data collection, analysis and interpretation, as well as by scaling these to protocols of international standards. Protocols could then be disseminated to other agencies and thereby improve both the research and communication quality of CS programs globally. The second initiative requires global coordination and mobilisation of efforts. There has been a proliferation of CS programs in recent years (see list on scistarter.com and citsci.org), which means added competition for human resources. CS activities are often small scale and respond to local needs. The strength of CS is to develop and implement new research programs rapidly and can also expose challenges in linking to other programs with common interests. Often these programs are regional variations of the same basic theme (e.g., phenology programs such as Nature’s Calendar, ClimateWatch, Project Budburst). Scistarter and others are looking into how best to simplify and serve interested participants who may want to contribute to multiple projects without needing to navigate, sign up and learn how to interact with different interfaces, tools and systems (Azavea and Scistarter 2014). Moreover, there are clearly trade-offs between projects focused on the local level and the needs of larger scale monitoring. A more coordinated approach and global framework to CS, such as the Wiki model (e.g., Geo-Wiki), would better address global issues such as climate change, land use, or introduced pests. Such a global framework would also reduce program operating costs in each participating country significantly, while simultaneously increasing the value of these data and enhance educational benefits that link local actions to global consequences. Such a global framework would also benefit from the identification of gaps where existing and future CS programs could compliment or enhance global data sets. Danielsen et al. (2014) have made progress in this area by examining how different approaches can contribute to the monitoring of the CBD Aichi Targets and 11 other international environmental agreements, including community-based projects but a comprehensive gap analysis is still lacking. The third area is currently being addressed in part through the development of professional CS associations across the globe that are helping to coordinate and support training and capacity building around the creation and delivery of CS programs. Moreover, a number of CS toolkits (e.g., Roy et al. 2012; Tweddle et al. 2012; http://www.birds.cornell.edu/citscitoolkit/toolkit/steps; https://crowdsourcing-toolkit. sites.usa.gov/) are now available online to assist in the creation of new CS programs. GEO BON is also developing the BON-in-a-Box toolkit, which includes specifictools for CS projects aligned with BON efforts. The rest of this chapter deals with practical issues around implementing CS programs including data quality, recruitment and motivation of participants, and the role of emerging technologies. 222 M. Chandler et al.

9.3 Enhancing Data Reliability and Reuse

CS projects span a spectrum of citizen engagement, from education and raising awareness on one end, where data collection is not necessarily a key component, to rigorous CS, where the data collected by citizens will be used for scientiﬁc research. Below we discuss two key aspects for enhancing data reliability: data quality and data standards.

9.3.1 Data Quality and Control

Accurate species identification including the identification of species through secondary field signs, such as scat surveys, bird song recording, cetacean calls (Buesching et al. 2014), is one of the most common and essential components of many CS projects. Generally, citizen scientists are better at identifying higher taxonomic categories that show a higher difference in physical characteristics and can struggle with genera lacking simple distinguishing characteristics among species. Another tendency is for participants to misidentify rarer species with limited or highly localised distributions. While an increase in data quality has been associated with the length of time and confidence of the person participating in the project (and the more familiar they become with the species monitored; e.g., Buesching et al. 2014), it is often best to leave difficult species to taxonomists. This generalisation does not always hold: for some taxa and in some places, the most reliable identifier may be an experienced and passionate lay person. Some CS systems establish a hierarchy of observers, and use the more experienced and tested observers to assess and moderate data supplied by less experienced observers. On the other hand CS participants are often willing to try and make identifications to a finer level from photographs than taxonomists are. Part of this issue is that keys and identification tools are not necessarily geared to advances in technology (e.g., digital camera and sound recordings) so that CS initiatives may result in a rethink about how tools are constructed by taxonomists, e.g. the use of Bayesian keys for biological identification on mobile devices (Rosewell and Edwards 2009). In addition to issues of species identification, sources of bias may be present in the data, such as uneven recording intensity over time, uneven spatial coverage, uneven sampling effort per visit, uneven species detectability and variation in the types of data collected (i.e., presence-only versus presence-absence data; Bird et al. 2014; Isaac et al. 2014). Each source of variation has the potential to introduce substantial bias in trend estimates for individual species (Isaac et al. 2014). These concerns have encouraged CS practitioners to maximise data quality through improved sampling protocols and training, data standardisation and database management, and filtering or subsampling data to deal with error and uneven effort (Bird et al. 2014). For large projects or for broadly distributed databases it may be challenging to implement rigid protocols, or to effectively train volunteers or to 9 Involving Citizen Scientists in Biodiversity Observation 223 eliminate all sources of error and bias. In these situations new statistical and high-performance computing tools can help address data-quality issues such as sampling bias, detection, measurement error, identification, and spatial clustering (Bonney et al. 2014). Whilst there are a number of proposed methods in the literature based on filtering the data to remove bias, methods of statistical correction procedure to treat recorder activities are less frequent but have (according to Isaac et al. 2014) a greater variety of mechanisms to control for recorder activity (see Isaac et al. 2014; Bird et al. 2014) for a description of statistical methods). In order to maximise data quality in citizen science, basic principles of data collection, management and analysis need to be carefully planned, and collaborations with statisticians should be considered, potentially leading to the development of new statistical approaches and survey designs for CS (Bird et al. 2014). Training is essential and can be through online instruction and quizzes, training courses, workshops or field sessions. Face-to-face training is the most effective (e.g., Newman et al. 2003), but it is typically limited to smaller regional projects although larger scale projects can partner with local organisations to hold regional workshops. Videos are a particularly powerful way of training participants (e.g., http://masswoods.net/outsmart-workflow), as they bring a personal feel, and can also be re-watched when volunteers need a refresher. McShea et al. (2015) found that online training with videos was just as effective as in-person training while Newman et al. (2010) found that online training tools improved the quality of citizen observations in measuring percentage plant cover. Aside from introductory training, careful supervision is necessary to minimise observer error and to enhance volunteer performance (Newman et al. 2003; Buesching et al. 2014). This has proven to be particularly important in the initial training period, with follow-up spot-checks and intensive training sessions concentrating on any emergent issues to do with quality (Buesching et al. 2005, 2014). Online communities of support such as iSpot and iNaturalist can help citizen scientists to reduce errors in their identification by drawing on the experience of others—users upload photographs of a species with a suggested identification and the online community confirms the identification or suggests other possibilities. The development of online communities can take several forms including one where members of the online community can be awarded badges to reflect their individual abilities and for the taxonomic groupings they are best able to identify. The maintenance of these communities of practice through recognition and reward systems is one of the most promising avenues of growth for helping to identify the more challenging species when using crowdsourced CS projects. iSpot provides one of the best developed systems for supporting citizens scientists and uses a multi-dimensional reputation and reward system, which is also used to verify observations (Silvertown et al. 2015). In the process of submitting data, automated online forms can be used to highlight suspect species identification (i.e., species that are outside their known range) to both the observer as they enter these data and for data users after sub- mission—see eBird http://ebird.org/content/ebird/ and Project FeederWatch http:// feederwatch.org/ who use such systems (Bonter and Cooper 2012). Asking 224 M. Chandler et al. volunteers to upload photographs of the species recorded allows experts to carry out spot checks and address common identification issues. Innovative smartphone applications such as Leafsnap http://leafsnap.com/, which uses visual recognition software to help identify tree species from photographs of leaves, can further advance accurate species identification. Camera trap based surveys have the added advantage that all records can be verified by expert review (McShea et al. 2015)or through consensus identification by multiple crowdsourced volunteers (Swanson et al. 2015). Validation can be further enhanced at the data entry phase, with data being filtered as they are entered in a database, using specific criteria that generate an instantaneous automated evaluation of data submissions, achieved with a checklist of species for a certain area and/or species count limits for a given date and location. Any information added that is inconsistent with predicted values should then be reviewed by an expert, e.g. depending on the type of survey, verification of an observation by photo identification, supported by extra information about the observation from the volunteer including metadata. A subset of these data can also be requested, or a few participants may be accompanied and their measurements observed, thus providing another way to understand how they are following the project protocols. A number of papers have appeared on the quality of the data collected by citizens. Some suggest that volunteers are able to collect data of a quality similar to professionals (Brandon et al. 2003; Engel and Voshell 2002; Fore et al. 2001) while others showed variable performance; e.g. Gollan et al. (2012) found that volunteers were in less agreement with benchmark measurements compared to scientists but that this varied by individual and attribute while Kelling et al. (2015) examined data from eBird and showed variability in quality between participants. However, those with high quality submissions also tended to be the ones who contributed the most data. Techniques like those outlined above as well the big data approaches of Kelling et al. (2015) are needed to ensure that data quality is controlled for in CS projects. In addition to the quality of those primary data that are collected (e.g., species identification), the quality of ancillary data should also be considered, e.g. the accuracy of land cover/land use maps and other demographic and ecological data obtained. Mobile apps can be used to help volunteers verify this information or some data may be checked automatically, e.g. by electronic comparison of entries against existing map layers and checklists. Feedback to contributors is essential and can be a valuable component of training or follow-up/refresher training. Statistics on frequent contributors can contribute to detecting inconsistencies in definitions and differing interpretation of instructions.

9.3.2 Data Sharing and Standards

CS projects must adopt data uniform standards if these data are to be shared across multiple projects and networks, nationally or globally. The Darwin Core (DwC) is a 9 Involving Citizen Scientists in Biodiversity Observation 225 commonly used metadata standard for biodiversity applications, which consists of a vocabulary for taxa and their occurrence in nature. The DwC has been adopted by the Global Biodiversity Information Facility (GBIF). GBIF’s website (http://tools. gbif.org/) also provides links to a number of tools that can be adopted by CS projects to facilitate the publishing of biodiversity data for further scientiﬁc use. iNaturalist, for example, was an early adopter of data standards and they now share their data openly through the GBIF portal. Another site that promotes the sharing of species and ecosystem data is the NatureServe network (http://www.natureserve.org/) which has operated for almost 30 years. Using a set of standards and protocols referred to as the natural heritage methodology, more than 75 distributed databases have been linked successfully, searchable via a resource discovery tool on the site. NatureServe is also a data provider to GBIF and provides templates that may be of use to CS projects. DataONE is a distributed framework that links together 75 data centres, networks and organisations in order to openly share environmental data. The site includes a data management guide speciﬁcally written for the CS community that discusses the eight stages within the data management life-cycle including data discovery and sharing (Wiggins et al. 2013) while more information on standards can be found at https://www.dataone.org/all-best-practices.

9.4 Recruiting, Motivating and Retaining Participants

There are three key issues in developing a committed community of participants that will help CS projects collect reliable data successfully. These are the recruitment of contributors; the importance of considering participant motivation in the project design; and how participation can be retained and supported over the longer term, as well as ensuring that the experience is safe and well-managed. Much has been written on these topics and the reader is referred to a number of good guidance documents and articles (Dickinson et al. 2012; Roy et al. 2012; Pandya 2012; Tweddle et al. 2012; Van den Berg et al. 2012; Silvertown et al. 2013; Buesching et al. 2015). Searchable databases are available from sites such as CS Central (http://www. birds.cornell.edu/citscitoolkit), SciStarter (http://scistarter.com/) and CS Alliance (http://www.citizensciencealliance.org/) for finding scientists and other project partners. Not surprisingly, these sites are dominated by projects for participants in English. National portals for CS projects also afford important avenues to selecting projects (e.g., Artportalen.org (Sweden), Observation.org (Netherlands), Atlas of Living Australia). Most of these sites also provide many resources and best practice guidelines on CS projects in general. Emerging technologies (see Sect. 9.5) can also play a potentially powerful role in finding partners, developing virtual communities and appealing to those people with a particular interest in technology. Simplifying and enhancing how participants can choose and participate in the right 226 M. Chandler et al. project(s) is an active area of exploration, especially for larger networks such as SciStarter (Azavea and SciStarter 2014) or iNaturalist. Recruitment is necessary so that citizens become aware of a project’s existence. The starting point for recruitment is to determine who the target audience is (e.g., school children vs. bird watchers) and to then tailor the promotion and recruitment process towards this group (Tweddle et al. 2012). The creation of a safe and meaningful experience requires careful forethought about the nature of the participant’s experience, including where, what, when and how the data will be collected, any inherent risks that may arise and how to avoid, mitigate or manage those risks. This is especially important when participants may encounter challenging or hazardous conditions, such as observations which take place on or near waterbodies, from light aircraft, in remote or risky areas or involving dangerous or poisonous species. Addressing these considerations early on with careful planning and a response plan in case problems arise is essential to creating a sustainable CS program. Earthwatch has created templates for planning and managing risk on field-based CS projects as part of a broader approach to developing field-based CS projects (Earthwatch Institute 2013). For those individuals already engaged with these subjects, promotion and support via e-mail, newsletters, Facebook and Twitter may be sufficient. Other actions might, however, be necessary to recruit new participants, such as through the use of the national, local or regional press or utilizing different types of media (e.g., TV, radio, print, online) and specialist publications. Holding a launch event, or an event at an existing festival or fair, can provide valuable face-to-face contact that will inform potential volunteers about the aims of the project, why their help is important and what they will gain from the project. These types of events also allow citizens to interact directly with the scientists involved and establish close relationships. Word-of-mouth recruitment by existing participants is one of the most powerful means of growing the base of volunteers for a program (Prestopnik and Crowston 2012a, b; Tweddle et al. 2012). With respect to volunteer motivation, there are many studies (Bramston et al. 2011; Bruyere and Rappe 2007; Buesching et al. 2015; Raddick et al. 2013; Silvertown et al. 2013; Van den Berg et al. 2009) that have examined this aspect of CS projects. Understanding motivation is a critical prerequisite to developing successful CS projects. For example, Van den Berg et al. (2009) surveyed volunteers enrolled in a conservation program, and revealed a number of motivations including: the desire to learn more about the science behind the project; enjoyment of the outdoors; the feeling that they are helping the environment; getting to know other people with similar interests and as a way to make new friends; and having fun. The main motivation found by Raddick et al. (2013) in participating in the Galaxy Zoo CS project was the desire to contribute to science while other moti- vators included interest in the scientific subject and the possibility of making new scientific discoveries. Although this list of motivations is far from exhaustive, it highlights the need to recognise that individuals are motivated by a number of different drivers and that these may differ across communities and across different demographic groups. Some communities may feel excluded and identifying the 9 Involving Citizen Scientists in Biodiversity Observation 227 barriers to participation is important for finding solutions to widening the participation (Pandya 2012). Project design will inevitably involve trade-offs between achieving scientific goals, e.g. gathering comprehensive, high quality data according to rigorous scientific protocols, and the ease of data collection. If the data collection is too complex or too time consuming, volunteers often lose their desire to participate and thus understanding and adapting the program to the skills, expectations and interests of the volunteers is critical (Roy et al. 2012). Motivation is also clearly linked to maintaining participation in the longer term and data quality. Giving rapid feedback and providing regular communication about their contribution and the outcomes from the project are also excellent tools to motivate participants (Rotman et al. 2014). This can be done in different ways, such as through field events, email, phone, newsletters, blogs, discussion forums and various forms of social media. Volunteers like the idea of knowing that their work is important and that their contributions can help scientists make better and more comprehensive analyses (Rotman et al. 2014). Rewarding citizen scientists is therefore an effective way to encourage and support participation (Tweddle et al. 2012). A reward system can be implemented in several different ways, e.g. highlighting the identity of contributors with observations to acknowledge their contributions explicitly (e.g., in Observado, iSpot and iNaturalist); providing participants with certificates of recognition (Dickinson et al. 2012); thanking participants and acknowledging their role, e.g. through organisation of a closing event, which can also be used to solicit further inputs and present the project results (Tweddle et al. 2012); providing open access to all of the non-sensitive records in the database; holding a competition to encourage participation, e.g. a photography contest (Dickinson et al. 2012); and recognizing the degree of volunteer expertise (e.g., progressing from amateur to expert levels in iSpot). Websites should make an effort to provide easy access to scientific, institutional, managerial and/or legislative products produced from project data, and to summarise these in ways of interest to contributors. It may not be readily apparent to citizens what contributions a few species observations might make collectively, e.g. to alert authorities to the arrival of invasive/pest species that appear on a list published under a national or provincial law. Encouraging these types of outreach and communication activities with citizen scientists may help to increase motivation. Corporate engagement, fellowships and sponsorship (such as Earthwatch’s ‘Student Challenge Award Program/Ignite’ for teenagers, the Sustainability Leadership Program for senior corporate executives (e.g., HSBC Bank) and the African Fellows program to build capacity among conservation managers) help to fulfil cross-sector participation. Integrating volunteer service directly into educational programs is another effective way to recruit and motivate individuals (Van den Berg et al. 2009). Master Naturalist programs have been established in several states such as California, Virginia, Texas and Florida that partner universities with extension services and wildlife management agencies at the state level while the Conservation Stewards Program has been established in Michigan. These programs 228 M. Chandler et al. provide individuals with a certification and require a certain number of volunteer hours, both as part of the certification and to retain certification in the future. This type of approach caters towards educational motivations for participation in CS projects and encourages longer term engagement (Van den Berg et al. 2009). School children can become highly motivated contributors in the long term to BONs, becoming networks in and of themselves. The GLOBE (Global Learning and Research to Benefit the Environment) network is one very successful example of involving students aged 13–18 in CS (Bowser and Shanley 2013). Enabling features include the development of learning elements that align with relevant core curriculum standards. Partnerships between schools and BONs are likely to become much more important in the future.

9.5 New Tools and Technologies

CS has gained in popularity over the last decade due to the emergence of a number of new tools and technologies. Web 2.0 and the Internet of Things have radically changed the way that individuals interact, collaborate and share data online. Good overviews of the technology available for CS along with the strengths and weaknesses are provided in Roy et al. (2012) and Newman et al. (2012). Here we brieﬂy outline the potential of a range of new tools and technologies that can be used in CS projects.

9.5.1 Websites and Portals

Websites are now an established media for disseminating information, where many CS projects have online forms for data collection. Some projects also provide visualisation and analysis tools and facilities to download the data (see, for example, eBird http://ebird.org). In some countries, national level web portals exist, which provide the ability to customise local projects to suit the needs and interests of key stakeholders (i.e., project leads, participants) at the same time as feeding into larger databases using standardised data collection and curation protocols. Moreover, these web portals provide extensive training and support to prospective and ongoing programs. Examples include Artportalen in Sweden, the Norwegian Biodiversity Information Centre, Observation.org (Netherlands), National Biodiversity Network (UK), Atlas for Living Australia, India Biodiversity Portal among others. These portals create a bridge between the needs of large BONs and addressing local needs by reducing many of the barriers that would facilitate data ﬂow. Namely, these portals provide many of the tools, systems access to expertise, feedback and other resources that otherwise make connecting local projects to global programs challenging. 9 Involving Citizen Scientists in Biodiversity Observation 229

9.5.2 Mobile Devices

Smartphones and tablets have fundamentally altered CS. Through software applications or ‘apps’ developed specifically for these devices, training materials can be disseminated and data collection on the ground is now much easier. Since most of these mobile devices have an integrated GPS (Global Positioning System), these data can be spatially referenced automatically, with a specified degree of accuracy. Constant internet connectivity is not required as these data, collected while in the field, can be stored locally and then uploaded to a server once a wireless connection is available. With the high quality cameras that are now a common feature of many mobile devices, photographic evidence can readily accompany observations, which makes the verification of species possible. In the context of biodiversity monitoring, there are many different species identification apps available, e.g. the iNaturalist (http://www.inaturalist.org/) and iSpot (http://www.ispot.org.uk/) apps, which cover a broad geographical area, as well as more localised apps to address a specific issue, e.g. the US Department of Agriculture provides a list of apps for reporting invasive species locally (http://www.invasivespeciesinfo.gov/toolkit/monitoringsmart.shtml). Other apps include phenological information for key species (http://www. climatewatch.org.au; http://www.budburst.org).

9.5.3 Sensors

Mobile devices can also act as sensors for measuring environmental variables, e.g. the built-in microphone in these devices can be used to measure noise levels (e.g., the NoiseTube project; http://noisetube.net/) while new sensors have emerged that can measure environmental variables where the sensor communicates directly with the mobile devices using Bluetooth and other wireless technologies, e.g. SenseBox, which is a DIY sensor box for measuring environmental variables such as weather and air quality (http://www.sensebox.de/). Citizens can also wear or transport many of these new devices and take measurements as they move around in space during their daily routine. In the EU, a number of environmental citizen observatories have been developed to measure air quality, air pollution, water quality and flooding (http://www.citizen-obs.eu/). In the USA, Public Lab is a non-profit initiative to allow communities to develop and mobilise low cost, open source sensors for environmental monitoring (https://publiclab.org/). Their first project involved mapping the BP oil spill on the Gulf Coast using balloons, kites and digital cameras and they now have several ongoing community-led projects. As the Internet of Things continues to become more prevalent, sensors will become a common part of everyday citizen life. 230 M. Chandler et al.

9.5.4 Camera Traps

Camera traps are motion-sensitive sensors that record a photograph or video when an animal passes in front of it. The photographs can be verified by experts for accurate species identification. Used by scientists since the 1920s, recent developments in digital photography and the cost reduction resulting from mass commercial production have finally made them an appropriate tool for citizen use. Camera traps are used to record which species live where, to estimate their abundance, to establish rarity in the endangered species context, to capture interesting behaviours or rare events and to potentially put off poachers. Choosing a camera model can be complicated because they are constantly improving with better technology becoming available. The website http://trailcampro.com provides an annual test of commercial units in their ‘trail camera shootout’. Swann et al. (2011) provide a good overview of different types of cameras, the most frequent types of problems encountered and a framework for assessing needs, while other guides are available for Australian and Malaysian contexts (Ancrenaz et al. 2012; Meek et al. 2012). The eMammal project (http://emammal.si.edu/) has developed robust cyber-infrastructure and software to have volunteers process and upload pictures directly to a digital archive at the Smithsonian. In their first year, volunteers processed over 1.5 million pictures from 1200 camera locations (McShea et al. 2015). The Snapshot Serengeti project used scientists to set cameras in Africa, but recruited citizens to help them identify the animals in their 1.2 million pictures (Swanson et al. 2015). Live image transmission from cameras via phone networks is relatively expensive, but offers a powerful way to engage the public through the unpredictable flow of animal pictures to their screen. This has been used, for example, by the Instant Wild project (http://www.edgeofexistence.org/instantwild/), which asks volunteers to use a smartphone app or website to identify animals that have been photographed from camera traps in remote places such as Kenya, Sri Lanka and Indonesia. @Camtrap live is a similar Twitter feed that streams live images and commentary from two cameras in the USA.

9.5.5 Social Media and Social Networking

There has been considerable growth in social media and social networking sites. In 2015, Facebook was estimated to have 1.55 billion active monthly users worldwide, with 1.31 billion accessing the application through their mobile devices (Statista 2015), while Twitter was estimated to have more than 320 million monthly active users (Twitter 2015). Instagram, which is another popular social media site, had more than 182.5 million users who uploaded around 58 million photos per day based on statistics for September 2015 (Statistic Brain Research Institute 2015). There has been a recent trend away from smaller, local social platforms to these large global sites, which has implications for CS projects wanting to establish a 9 Involving Citizen Scientists in Biodiversity Observation 231 presence via social media. Social networking sites represent a very powerful way for building and maintaining CS communities and for providing virtual support mechanisms to a wide geographical audience. Many CS projects already provide integration via Facebook (e.g., iSpot), while Twitter is used to report sightings of invasive species in Ontario, (e.g., to @invspecies). Discussion forums and blog sites have been around for longer but also represent effective methods of virtual communication while Skype is now being used by teachers live from the ﬁeld to reach out to children in their schools.

9.5.6 Gaming

Another approach used in CS for generating participation is ‘gamification’, or the addition of game elements to existing applications (Deterding et al. 2011). This approach can help to improve volunteer motivation as a tangible form of recognition by linking their contributions to levels of achievement or badges of expertise. For example, the iSpot project allows individuals to progress to ‘expert’ status as they identify more species, as well as a quiz to test oneself (http://www.ispotnature. org/quiz/try). The Biotracker app, which is used to contribute phenology data to Project Budburst, and uses badges and a leader board, was shown to attract an additional user group referred to as Millennials, which is the younger, technolog- ically experienced generation (Bowser et al. 2013). Other examples of gamification in CS include Tiger Nation, which tracks the movements of tigers (Mason et al. 2012), and Happy Soft, which uses gamification in species identification (Prestopnik and Crowston 2012a, b).

9.5.7 Cyber-Infrastructure and Networked Databases

Cyber-infrastructure refers to the IT systems that support various data and system functions and ensures interoperable data exchange via networked databases. Functions include support for data storage and management, geospatial analysis tools, visualisation capability, social networking tools, quality control and training. Newman et al. (2011) provide a framework that advises CS project managers in developing and/or selecting data management systems based on the scope, scale, activities and the system approach taken within a given project. They have also developed the CitSci.org cyber-infrastructure system as a ﬂexible open source solution (Wang et al. 2015). Other available cyber-infrastructure systems are compared by system features in Newman et al. (2011), which may help guide the choice of a system to meet project needs. More recently, some CS projects have begun to provide the otherwise expensive cyber-infrastructure to help facilitate scaling up. For example, iNaturalist lets you create a group within their program, which allows use of their cyber-infrastructure to record the location and time of any 232 M. Chandler et al. sub-group of biodiversity desired, and they offer their code as open access. Zooniverse has developed a platform for setting up CS projects, which can then be showcased on the Zooniverse platform for tapping into the Zooniverse network of users. eMammal is providing the same service for camera traps. Finally, SciStarter is preparing to upgrade their system to serve as a better basic sign-up infrastructure for simpler projects.

9.6 Challenges and Opportunities for the Future

CS provides many opportunities for increased data collection and greater involvement of citizens in scientific research across many areas that are of relevance to BONs. Indeed every day, new CS programs are launched in every corner of the globe offering people new opportunities to monitor or track species or environmental events. While this proliferation of projects offers great opportunity, there are also a number of challenges that will need to be resolved. There are trade-offs between localised, customised projects focusing on a restricted taxonomic group or location where the advantages are more local buy-in, ownership and control, versus more interconnected or networked larger scale efforts, where there are economies of scale with data that are often more accessible and shared. How are participants to choose between similar sounding programs? How can localised programs feed into larger scale initiatives, and vice versa? Resolving questions around data standards, interoperability of systems, and attribution will be important in creating a more coherent ‘marketplace’ of CS opportunities. Two promising avenues are opening up. One explores how to simplify the choice of projects and reduce the barriers to learning new tools and systems for citizen scientists by improving the front end of engagement by participants (Azavea and Scistarter 2014). The other is the development of web portals that simplify much of the data management, processing and sharing across many projects. These web portals may be national in scope such as Artportalen (Sweden), the National Biodiversity Network (UK), Atlas of Living Australia and the India Biodiversity portal; taxonomic in scope (e.g., eBird), observation tool based (e.g., iNaturalist, iSpot); or EBV based (e.g., National Phenology Network). While many of these programs are mainly focused on species occurrence data, they bring together tools, processes and systems that link the local with the large scale databases. There are also trade-offs between the collection of rigorous or reliable data gathered in a systematised fashion, on the one hand, and the ease of use or accessibility of CS programs, on the other (Pocock et al. 2015). Easing data collection protocols and reducing the number of variables collected can reduce barriers and increase or broaden involvement. Environmental education and other engagement goals are important but they can simultaneously act to increase the volume of data collected. Yet, verifiable and reliable data are often seen as essential for management decision making and scientific research outcomes. Moreover, ensuring data quality is important in attracting more scientists to use and engage 9 Involving Citizen Scientists in Biodiversity Observation 233 with CS programs. More explicit statements about a CS program’s goals, whether they seek more rigorous science or a broader environmental education effort is an important step in avoiding confusion, in expectations and outcomes, among participants and scientists alike (Pocock et al. 2015). Secondly, the development and adoption of more robust statistical approaches can help programs reduce sampling error, allowing a better balance between quantity and quality of data collected (Isaac et al. 2014; van Strien et al. 2013). A key challenge in the next few decades is to extend the reach of CS into places where it has not had a prominent role in the past. Current CS networks are pre- dominantly active in Europe, North America and some former colonies, such as Australia, New Zealand and South Africa. Africa, Latin America and Asia are under-represented. Growing wealth and education in these areas, along with near-universal penetration of internet services and cell phones, creates an opportunity to extend CS into these biodiversity-rich regions. The motivations and social mechanisms to do so may differ from those found in ‘western’ societies, but there is nevertheless a rich vein of traditional knowledge and interest in biodiversity which can be tapped. CS is already playing an important role in ground-based monitoring, comple- menting and corroborating the global satellite-based observations and more focused government or institution led efforts. This chapter outlined some of the tools and opportunities for building on existing and developing new CS initiatives to help BON efforts increase our understanding of the status and trends of biodiversity. Perhaps most importantly, a growth of CS programs that engage a broader con- stituency of people collecting biodiversity information will build the essential social equity and foster the necessary dialogue that stimulates the political will to make the decisions necessary for a sustainable and biodiverse planet.

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